Futuristic Communication and Network Technologies: Select Proceedings of VICFCNT 2021, Volume 2 (Lecture Notes in Electrical Engineering, 995) 9811997470, 9789811997471

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Lecture Notes in Electrical Engineering 995

N. Subhashini Morris. A. G. Ezra Shien-Kuei Liaw   Editors

Futuristic Communication and Network Technologies Select Proceedings of VICFCNT 2021, Volume 2

Lecture Notes in Electrical Engineering Volume 995

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Yong Li, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering and Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Walter Zamboni, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering—quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning: • • • • • • • • • • • •

Communication Engineering, Information Theory and Networks Electronics Engineering and Microelectronics Signal, Image and Speech Processing Wireless and Mobile Communication Circuits and Systems Energy Systems, Power Electronics and Electrical Machines Electro-optical Engineering Instrumentation Engineering Avionics Engineering Control Systems Internet-of-Things and Cybersecurity Biomedical Devices, MEMS and NEMS

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N. Subhashini · Morris. A. G. Ezra · Shien-Kuei Liaw Editors

Futuristic Communication and Network Technologies Select Proceedings of VICFCNT 2021, Volume 2

Editors N. Subhashini School of Electronics Engineering Vellore Institute of Technology Chennai, Tamil Nadu, India

Morris. A. G. Ezra Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Petaling Jaya, Malaysia

Shien-Kuei Liaw Department of Electronic and Computer Engineering National Taiwan University of Science and Technology (NTUST) Taipei, Taiwan

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-19-9747-1 ISBN 978-981-19-9748-8 (eBook) https://doi.org/10.1007/978-981-19-9748-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Reviewers

M. Nalini, Sri Sairam Engineering College, [email protected] P. Kumar, Rajalakshmi Engineering College, [email protected] Dr. Rajesh Natarajan, University of Technology and Applied Science, Shinas, [email protected] V. Vijaylakshmi, Pondicherry Engineering College, [email protected] Dr. D. Harimurugan, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, [email protected] N. Prabhakaran, New Horizon College of Engineering, Bengaluru, [email protected] Dr. S. Sreejith, National Institute of Technology, Silchar, [email protected] Dr. Chandirasekaran Duraikannu, OQ Oman, [email protected] K. Kalaivani, Easwari Engineering College, [email protected] S. Sankara Gomathi, Adhi College of Engineering and Technology, [email protected] M. Geetha, Saveetha University, [email protected] B. Karthikeyan, VIT, Vellore, [email protected] B. V. Krishna, Rajalakshmi Engineering College, [email protected] B. Priya, Rajalakshmi Engineering College, [email protected] Satheesh Perepu, Ericsson, [email protected] K. Jayashree, Rajalakshmi Engineering College, [email protected] K. Kalaivani, [email protected], [email protected] Priya Vijay, Rajalakshmi Engineering College, [email protected] Dr. Gopalkrishna Hegde, Indian Institute of Science, Bengaluru-560012, Karnataka, India, [email protected] Massoudi Radhouene, University of Tunis El Manar, National Engineering School of Tunis Communications, [email protected] Dr. S. Robinson, Mount Zion College of Engineering and Technology, Pudukkottai-622507, Tamil Nadu, India, [email protected] Dr. Martin Leo Manickam, St. Joseph’s College of Engineering, [email protected]

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Reviewers

Dr. Selvakumar Raja, Arunai College of Engineering, [email protected] Dr. P. G. V. Ramesh, St. Joseph’s Institute of Technology, [email protected] A. M. Balamurugan, St. Joseph’s College of Engineering, [email protected] Dr. K. Esakki Muthu, Anna University Tuticorin, [email protected] Dr. Srikanth Itappu, Alliance University, Bengaluru, [email protected] Dr. I. B. Arun, CISCO, Chennai, [email protected] Ms. J. S. Vaishnavi, Samsung Research, Bangalore, [email protected] Dr. Ganesh, University of Southern California, [email protected] Sriram V. Iyer, Flipkart, [email protected] Dr. D. Bhasker, CMR Institute of Technology, [email protected] Dr. Rahul Pandya, IIT Dharwad, [email protected] Dr. Jayasri, Adhiparasakthi College of Engineering, [email protected] Dr. Suresh Gulivindala, GMR Institute of Technology, Rajam, [email protected]. in Dr. Gajendra Sharma, MITS, Madanapalle, [email protected] Dr. Shirley Selvan, St. Joseph’s College of Engineering, Chennai, [email protected] Dr. Sangeetha Nagarajan, Saveetha School of Engineering, sangeethana2016@ gmail.com Dr. G. V. Vijayalakshmi, BMSIT, Bengaluru, [email protected] Dr. K. Palani Thanaraj, St. Joseph’s College of Engineering, Chennai, [email protected] Dr. J. Sofia Priyadarshini, RGM College of Engineering and Technology, Nandyal, [email protected] Dr. V. N. V. Satya Prakash, RGM College of Engineering and Technology, Nandyal, [email protected] Dr. P. Manimaran, Vellore Institute of Technology, Vellore, [email protected] Dr. S. Rajkumar, Rajalakshmi Engineering College, [email protected] Dr. P. Gnanasivam, Jerusalem College of Engineering, Chennai, pgsivam@gmail. com Dr. R. Parthasarathy, St. Joseph’s College of Engineering, Chennai, [email protected] Dr. Manju Kandaswamy, Sona College of Technology, Salem, manjukandas@ sonatech.ac.in Dr. V. Rajinikanth, St. Joseph’s College of Engineering, Chennai, v.rajinikanth@ ieee.org Dr. M. E. Paramasivam, Sona College of Technology, Salem, [email protected] Dr. A. Chandrasekar, St. Joseph’s College of Engineering, Chennai, [email protected] Dr. P. G. V. Ramesh, St. Joseph’s College of Engineering, Chennai, ramsdr_76@ yahoo.co.in Dr. R. S. Sabeenian, Sona College of Technology, Salem, [email protected]. in Dr. Amudha Jeyaprakash, Dr. Mahalingam College of Engineering and Techonogy, [email protected]

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Dr. Morris Ezra, Universiti Tunku Abdul Rahman, Sungai Long Campus, Selangoor, Malaysia, [email protected] Dr. Stella Morris, Universiti Tunku Abdul Rahman, Sungai Long Campus, Selangoor, Malaysia, [email protected] Dr. Daniel Pu Chuan Hsian, University of Nottingham Malaysia, PuChuan.Hsian@ nottingham.edu.my Dr. Manickam Ramasamy, UCSI University, ManickamRamasamy@ ucsiuniversity.edu.my Dr. Mumtaj Begam, University of Nottingham Malaysia, Mumtaj.Begam@ nottingham.edu.my Dr. Joselin Retna Kumar, SRM Institute of Science and Technology, joselinr@ srmist.edu.in Dr. Sankar Ganesh, Vellore Institute of Technology, Vellore, s.sankarganesh@vit. ac.in Dr. Jenifer, Sri Venkateswara College of Engineering and Technology (SVCET), [email protected] Dr. Epsiba, Pallavi Engineering College, [email protected] Dr. G. Suresh, Sri Indu College of Engineering and Technology, geosuresh@gmail. com Dr. Thomas Joseph, NITPY, [email protected] Dr. K. Anusudha, Pondicherry University, Pondicherry, [email protected] Dr. M. S. Sudhakar, Vellore Institute of Technology, Vellore, [email protected] Dr. A. Bhagyalakshmi, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, [email protected] Dr. Lilly Sheeba (SRM), SRM Institute of Science and Technology, Ramapuram Campus, [email protected] Dr. Geetha Priya, CIIRC-Jyothy Institute of Technology, [email protected] Dr. Bharthi Raja, Srmist Ramapuram Campus Chennai, [email protected] Dr. I. B. Arun, CISCO, Chennai, [email protected] Vaishnavi, Samsung Research, Bangalore, [email protected] Sriram V. Iyer, Flipkart, [email protected] Dr. Bhasker Dappuri, CMR Engineering College, [email protected] Dr. Rakesh Misra, VM Ware, [email protected] Dr. B. Arunsundar, Saveetha School of Engineering, arunsundarb.sse@saveetha. com Dr. Abhilasha Sharma, CMR Institute of Technology, abhilashadadhich@gmail. com Ms. Vandana Jayaraj, Qualcomm, [email protected] Dr. G. Anitha, Saveetha School of Engineering, [email protected] Dr. R. Vidhyapriya, PSG College of Technology, Coimbatore, [email protected]. ac.in Dr. S. V. Manisekaran, Anna University Regional Campus, Coimbatore, [email protected] Dr. R. Venkateswari, PSG College of Technology, Coimbatore, rvi.ece@psgtech. ac.in

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Reviewers

Ms. H. G. Nanditha, Dayanandasagar College of Engineering, Bengaluru, nanditha. [email protected] Dr. R. Gandhiraj, Amrita Vishwa Vidyapeetham School of Engineering, Ettimadai, Coimbatore, [email protected] D. Thiripurasundari, VIT University, Vellore, [email protected] K. Karthick, Amphenol, [email protected] Dr. Jothilakshmi Prakash, Sri Venkateswara College of Engineering, jothi@svce. ac.in Dr. S. C. Ragavendra, Higher Colleges of Technology, Madinat Zayed, UAE, sc. [email protected] Dr. Srinivasa Rao Inabathini, VIT, Vellore, [email protected] Dr. Boopalan, VIT, Vellore, [email protected] Vijay Kumbhare, DSPM IIIT Naya Raipur, [email protected] Rajesh Kumar, VIT, Vellore, [email protected] Abhay Chaturvedi, GLA University Mathura, [email protected] Subhrakanta Behera, KIIT DU, [email protected] Sachin Gupta, Shri Mata Vaishno Devi University, Katra (J&K), India, sachin. [email protected] Dr. Gautam Makwana, GTU—Graduate School of Engineering and Technology, [email protected] Dr. G. Anitha, Saveetha School of Engineering, [email protected] Dr. Maruti Tamrakar, Intel Technologies India Pvt. Ltd., tamrakar.maruti@gmail. com Akshita Gupta, SMVDU, [email protected] Dr. Rajkumar Rengasamy, Vel Tech University, [email protected] Dr. V. Phani Kumar Kanaparthi, SRM Institute of Science and Technology, [email protected] Dr. Gaurav, NITP, [email protected] Dr. M. Bala Subramanian, Bigcat Wireless, [email protected] Dr. D. Indumathy, Rajalakshmi Engineering College, indumathy.d@rajalakshmi. edu.in Dr. S. Deepa, SRM Institute of Science and Technology, Ramapuram campus, [email protected] Dr. A. Umamageswari, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, [email protected] Dr. B. Ebenezer, VISTAS, [email protected] Dr. A. Elakkiya, Saveetha Engineering College, [email protected] Dr. Esther Florence, Sri Sivasubramaniya Nadar College of Engineering, [email protected] Dr. R. Gangothri, Jain University, [email protected] Dr. T. Ilavarasan, VIT, Vellore, [email protected] Dr. Jino Hans, Sri Sivasubramaniya Nadar College of Engineering, jinohansw@ ssn.edu.in Dr. C. Joshitha, KLEF, Vaddeswaram, Guntur, [email protected]

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Dr. Jyotirmayee Dash, TeraLumen Solutions, jyotirmayee.dash@ teralumensolutions.com Dr. V. Padmapriya, SASTRA, [email protected] Dr. M. Palanivelan, Rajalakshmi Engineering College, palanivelan.m@ rajalakshmi.edu.in Mr. Ramprakash Ramamoorthy, Zoho Corporation, ramprakash.ramamoorthy@ zohocorp.com Dr. M. Sathish, Rajalakshmi Engineering College, [email protected] Dr. Sherlin Solomi, Hindustan Institute of Technology and Science, vsherlins@ hindustanuniv.ac.in Dr. Stella Stanley, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, [email protected] Dr. S. Suchitra, SRM IST, KTR, [email protected] Dr. R. Sujarani, SASTRA Deemed University, [email protected] Dr. C. Tharini, Crescent Institute of Science and Technology, hodece@crescent. education Dr. M. Vanmathi, B. S. A. Crescent Institute of Technology, [email protected] Dr. Vimal Samsingh, Sri Sivasubramaniya Nadar College of Engineering, [email protected] Dr. A. Sathya, Rajalakshmi Engineering College, [email protected] Dr. B. Arthi, SRM Institute of Science and Technology, [email protected] Dr. M. Aruna, SRM Institute of Science and Technology, [email protected] Dr. R. Bharathi, University College of Engineering, Nagercoil, bharathiucen@ gmail.com Dr. B. P. Bhuvana, St. Joseph’s Institute of Technology, bhuvanabp1509@gmail. com Prof. G. Deepa, Kongu Engineering College, [email protected] Dr. V. S. Dharun, Immanuel Arasar JJ College of Engineering, dharunvs@yahoo. com Dr. D. C. Diana, Easwari Engineering College, [email protected] Dr. G. Lloyds Raja, NIT, Patna, [email protected] Dr. G. Anitha, Saveetha School of Engineering, [email protected] Dr. V. Jeyalakshmi, College of Engineering, Guindy, [email protected] Dr. K. Martin Sagayam, Karunya University, Coimbatore, martinsagayam.k@ gmail.com Dr. M. Edwin, University College of Engineering, Nagercoil, edwinme1980@ gmail.com Prof. M. Parisa Beham, Sethu Institute of Technology, [email protected] Dr. S. Praveen Chakkravarthy, CVR College of Engineering, [email protected] Dr. S. Sahaya Elsi, University College of Engineering, Nagercoil, ucen9628008@ gmail.com Prof. Senthilarasi Marimuthu, Dr. Mahalingam College of Engineering and Technology, [email protected] Dr. K. C. Sriharipriya, Vellore Institute of Technology, Vellore, sriharipriya.kc@ vit.ac.in

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Reviewers

Prof. Tamilselvi Rajendran, Sethu Institute of Technology, [email protected] Dr. Varun P. Gopi, National Institute of Technology, Tiruchirappalli, Tamil Nadu., [email protected] Dr. K. Prabu, National Institute of Technology, Surathkal, Karnataka, prabu@nitk. edu.in Dr. V. K. Jagadeesh, National Institute of Technology, Patna, Bihar, vkjagadeesh. [email protected] Dr. M. Surendar, National Institute of Technology, Karaikal, Pondicherry, surendar. [email protected] Dr. K. Adalarasu, SASTRA Deemed to be University, Thanjavur, adalbiotech@ gmail.com Dr. R. Helen, Thiagaraja College of Engineering, Madurai, [email protected] Dr. K. Kalimuthu, SRM Institute of Technology, Chennai, kalimuthu.k@ktr. srmuniv.ac.in Dr. S. Pravin Kumar, SSN College of Engineering, [email protected] Dr. V. Ganesan, Bharath Institute of Science and Technology, Chennai, [email protected] Mrs. Kirti Yadav, Luminite Electronics, UK, [email protected] Mrs. M. OmaMageswari, Sri Manakula Vinayagar College of Engineering, [email protected] Mrs. Payal Tayade, All India Shree Shivaji Memorial Society, College of Engineering (AISSMS COE), [email protected] Jayavignesh Thyagarajan, VIT Chennai, [email protected] Dr. Sasikumar Periyasamy, VIT Chennai, [email protected] V. Prakash, VIT Chennai, [email protected] Dr. G. Idayachandran, VIT Chennai, [email protected] R. Ramesh, VIT University, Chennai Campus, [email protected] Dr. Berlin Hency, Vellore Institute of Technology, Chennai, berlinhency.victor@ vit.ac.in Dr. A. Sasithradevi, Vellore Institute of Technology, Chennai, sasithradevi.a@vit. ac.in Dr. S. Shoba, Vellore Institute of Technology, Chennai, [email protected] Dr. R. Karthik, Vellore Institute of Technology, Chennai, [email protected]

About This Book

Every year, communication technologies break through new limits, and the rate of development is no secret. There is a lot of room for improvement, which allows us to discuss the newest developments and forecast future trends. This book aims at offering new ideas and an in-depth information on the research findings in the field of communication and networks and contains the original research work presented at the Virtual International Conference on Futuristic Communication and Network Technologies(VICFCNT 2021) held on December 10 and 11, 2021, in Vellore Institute of Technology, Chennai. Problems, challenges, prospects and research findings in communication and network technologies are the primary topics of discussion. The book is published in two volumes and covers the cutting-edge research in cyberphysical systems, optical communication and networks, signal processing, wireless communication, antennas, microwave engineering, RF technologies, Internet of Things, MEMS, NEMS, wearable technologies, as well as other contemporary technological advances. This book presents the state-of-the-art innovations in the fields of communication and offers promising solutions to many real-world problems. It will be a valuable resource for individuals to expand their knowledge and enhance their research ideas, as well as channelling them in the ideal direction for future research in these areas.

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Contents

Modern Approaches for the Human Activity Detection and Recognition Using Various Image Processing Methods: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaykumar S. Dhage, Avinash K. Gulve, and Praveen C. Shetiye A Study of COVID-19 and Its Detection Methods Using Imaging Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Bharatha Sreeja, T. M. Inbamalar, S. Kalaivani, T. D. Subha, Chettiyar Vani Vivekanand, A. Jasmine Vijithra, and Nithin L. Raja Numerical Evaluation of 3D Printable Patch Antenna for Wearable Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Priya, E. Manikandan, Manavalan Saravanan, Sarfraz Nawaz, and M. Mahalingam Ensuring Location Privacy in Crowdsensing System Using Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Sangeetha, K. Anitha Kumari, M. Shrinika, P. Sujaybharath, S. Muhil Varsini, and K. Ajith Kumar

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Energy-Efficient Arithmetic State Machine-Based Routing Algorithm in Cognitive Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . S. Kalpana, R. Gunasundari, and S. Sneha

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Design of Modified V-shaped Slot Loaded on Substrate-Integrated Waveguide Antenna for Smart Healthcare Applications . . . . . . . . . . . . . . . R. Muthu Krishnan and G. Kannan

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A Review on Deep Learning Algorithms for Diagnosis and Classification of Brain Tumor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tessy Annie Varghese and J. Roopa Jayasingh

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Fuzzy-Enhanced Optimization Algorithm for Blockchain Mining . . . . . . K. Lino Fathima Chinna Rani and M. P. Anuradha

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Power Quality Improvement Using Luo Converter-Coupled Multilevel Inverter-Based Unified Power Flow Controller for Optimized Time Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Ramya, P. Suresh, J. Madhav Ram, A. Sarath Kumar, and S. Sathish

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Performance Analysis of Fiber-Optic DWDM System . . . . . . . . . . . . . . . . . 113 K. Venkatesan, A. ChandraSekar, and P. G. V. Ramesh Comparison Energy Efficiency and Spectral Efficiency in Beamspace MIMO and Beamspace MIMO-NOMA System Model . . . 123 Haitham Al Fatli, Khairun Nidzam Ramli, and Elfarizanis Baharudin Design and Simulation of Flexible Antenna with a Defected Ground Structure for Wireless Applications . . . . . . . . . . . . . . . . . . . . . . . . . 137 C. G. Akalya, D. Sriram Kumar, and P. H. Rao Maturity Level Detection of Strawberries: A Deep Color Learning-Based Futuristic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 T. K. Ameetha Junaina, R. Kumudham, B. Ebenezer Abishek, and Shakir Mohammed Cross-Layer Energy Efficient Radio Resource Management Scheme with QoS Provision in LTE-Uplink Systems . . . . . . . . . . . . . . . . . . 165 Leeban Moses, Perarasi, Mano Raja Paul, and Kannan Fairness for All User in mmWave Massive MIMO-NOMA: Single-Beam Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 M. Vignesh Roshan, K. Shoukath Ali, T. Perarasi, V. Sugirdan, and S. Soundar A Tri-Band Frequency Reconfigurable Monopole Antenna for IEEE 802.11ax and Sub-6 GHz 5G NR Wi-Fi Applications . . . . . . . . . 187 P. Rajalakshmi and N. Gunavathi An Optical Switchable Bandwidth Reconfigurable UWB and Multiband Antenna for IoT Application . . . . . . . . . . . . . . . . . . . . . . . . . 199 Bhakkiyalakshmi Ramakrishnan and Vasanthi Murugiah Sivashanmugham Transmission Performance Analysis of Various Order of UWB Signals Through Single Mode Fiber Link . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 C. Rimmya, M. Ganesh Madhan, and K. Amalesh FANET Routing Survey: An Application Driven Perspective . . . . . . . . . . . 217 P. Krishna Srivathsav, Sai Abhishek, and Jayavignesh Thyagarajan Wearable Health Monitoring Glove for Peri and Post COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 S. Elango, S. Praveen Kumar, K. Gavaskar, J. Jayasurya, and G. Abilash

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Intelligent Smart Transport System Using Internet of Vehicular Things—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 M. Vinodhini and Sujatha Rajkumar LoRaWAN-Based Intelligent Home and Health Monitoring of Elderly People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 S. Elango, K. P. Sampoornam, S. Ilakkiya, N. Harshitha, and S. Janani A Hybrid Guided Filtering and Transform-Based Sparse Representation Framework for Fusion of Multimodal Medical Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 S. Sandhya, M. Senthil Kumar, and B. Chidhambararajan Design of Wearable Antenna for Biomedical Telemetry Application . . . . 275 A. L. Sharon Giftsy, K. Usha Kiran, and Ravi Prakash Dwivedi Hand-Off Selection Technique for Dynamic Wireless Network Scenario in D2D Multihop Communication . . . . . . . . . . . . . . . . . . . . . . . . . . 285 D. Shobana, B. Priya, and V. Samuthira Pandi Fabrication of Hexagonal Fractal Antenna for High-Frequency Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Perarasi, Leeban Moses, Rajkumar, and Gokula Chandar Study and Comparison of Various Metamaterial-Inspired Antennas . . . . 309 Sathyamoorthy Sellapillai, Rajkumar Rengasamy, and V. Praveen Naidu Wireless Sensor Network-Based Agriculture Field Monitoring Using Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Ashwini Bade and M. Suresh Kumar Cylindrical Dielectric Resonator Antenna with a Key-Shaped Microstrip Line for 2.4 GHz Wireless Applications . . . . . . . . . . . . . . . . . . . 331 B. Manikandan, D. Thiripurasundari, R. Athilingam, G. Karthikeyan, and T. Venish Kumar A Dual Mode Quad-Band Microstrip Filter for Wireless Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 P. Ponnammal and J. Manjula Integer and Fractional Order Chaotic Systems—A Review . . . . . . . . . . . . 349 G. Gugapriya and A. Akilandeswari Machine Learning-Based Binary Regression Task of 3D Objects in Digital Holography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 R. N. Uma Mahesh and Anith Nelleri Performance of the RoF Network with Multi-carrier Modulation Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Praveen Yadav and R. G. Sangeetha

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Performance Analysis of User Pairs in 5G Non-orthogonal Multiple Access Downlink Transmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 J. Arumiga and C. Hemanth Image Encryption Based on Watermarking and Chaotic Masks Using SVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 R. Girija, S. L. Jayalakshmi, and R. Vedhapriyavadhana Complementary Planar Inverted-L Antennas (PILAs) for Metal-Mountable Omnidirectional RFID Tag Design . . . . . . . . . . . . . . 401 Jiun-Ian Tan, Eng-Hock Lim, Yong-Hong Lee, and Kim-Yee Lee Interactive Chatbot for Space Science Using Augmented Reality—An Educational Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 N. Shivaanivarsha and S. Vigita Interference Power Reduction Algorithm for Massive MIMO Linear Processing ZF Receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Abdul Aleem Mohammad and A. Vijayalakshmi Currency Identifier for Visually Impaired People . . . . . . . . . . . . . . . . . . . . . 445 S. Rohit and N. Bhaskar Machine Learning Enabled Performance Prediction of Biomass-Derived Electrodes for Asymmetric Supercapacitor . . . . . . . . 453 Richa Dubey and Velmathi Guruviah Impact of High-K Material on the Short Channel Characteristics of GAA-Field Effect Transistor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Lucky Agarwal, Saurav Gupta, and Ranjeet Kumar Disaster Management Solution Based on Collaboration Between SAR Team and Multi-UAV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Amina Khan, Sumeet Gupta, and Sachin Kumar Gupta Photonic Crystal Drop Filter for DWDM Systems . . . . . . . . . . . . . . . . . . . . 483 V. R. Balaji, Shanmuga Sundar Dhanabalan, T. Sridarshini, S. Robinson, M. Murugan, and Gopalkrishna Hegde Evaluation of Modulation Methods for SOA-Based All-Optical Logic Structure at 40 Gbps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 V. Sasikala, K. Chitra, and A. Sivasubramanian Tunable U-Band Multiwavelength Brillouin-Raman Fiber Laser with Double Brillouin Frequency Spacing in a Full Open Cavity . . . . . . . 501 Salah Abdo, Amer Abdulghani, A. W. Al-Alimi, N. A. Cholan, M. A. Mahdi, and Y. G. Shee

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Correction to: Maturity Level Detection of Strawberries: A Deep Color Learning-Based Futuristic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . T. K. Ameetha Junaina, R. Kumudham, B. Ebenezer Abishek, and Shakir Mohammed

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About the Editors

N. Subhashini is an Associate Professor in the School of Electronics Engineering, Vellore Institute of Technology, Chennai. She has over 18 years of teaching and research experience. She received her B.E. degree from University of Madras, Master’s degree from College of Engineering, Guindy, India and Ph.D. from VIT University. She is a gold medalist in her Post graduation. She has several research papers published in reputed peer-reviewed journals and conferences. Her research interests include optical metro/access networks, FTTx technologies, Next Generation architectures and services, optical fiber technology, WDM systems, network, and information security. Morris. A. G. Ezra received his B.E. degree from Bharathiar University, his M.E. degree from Anna University, and his Ph.D. degree from Multimedia University, Malaysia. He started his career with the Karunya Institute of Technology as a lecturer in 1993 before moving to Malaysia in 1998. Professor Ezra has over 23 years of experience in the academic field. He has secured national and international research grants worth more than RM 1 million. He is actively involved in supervising undergraduate and postgraduate students. His research areas include digital signal processing, wireless ad-hoc networks, mobile communication, optimization using PSO, and GA/IGA. He has published over 40 papers in international journals, conferences, and co-authored book chapters. Shien-Kuei Liaw received double Ph.D. degrees from National Chiao-Tung University in photonics engineering and National Taiwan University in mechanical engineering. He joined the faculty of Taiwan Tech (also known as NTUST) in 2000. Currently, Prof. Liaw is Chairman of the Department of Electronics and Computer Engineering and the Graduate Institute of Electro-Optical Engineering, NTUST. Besides inventing 40 patents, he authored and co-authored more than 280 journal articles and international conference presentations in optical communication, fiber sensing, and optical devices. Professor Liaw was an academic visitor to the University of Oxford and the University of Cambridge in 2011 and 2018, respectively. He gave presentations as a keynote speaker or an invited speaker at many conferences. xix

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About the Editors

He also served as a guest editor for several textbooks. Professor Liaw was President of the Optical Society (OSA), Taiwan Section, and Secretary-General of the Taiwan Photonics Society. Professor Liaw is a senior member of IEEE and OSA.

Modern Approaches for the Human Activity Detection and Recognition Using Various Image Processing Methods: A Review Jaykumar S. Dhage, Avinash K. Gulve, and Praveen C. Shetiye

Abstract Today, the human activity detection technology is becoming very popular. It has scopes in various applications such as health care, security, surveillance, and entertainment. Human activity detection has a very important role as far as the human being is concerned. It is directly connected to the well-being of an individual. It is an important research area that deals with the healthcare mechanism of the people. Analyzing the data can be a big challenge. The actions or activities carried out by a person can be different from the other person. Despite so much of ongoing efforts and research on human activity recognition, still there are many challenges to overcome. In this paper, the details of various modern approaches which are adopted in order to detect human activity are discussed. A list of popular human activity databases and a comparison of various deep learning techniques are mentioned in this paper. It also includes the different sub-methods which are required for the collection of data, pre-processing approaches, feature extraction methods, and the different training approaches. Lastly, this paper also discusses various challenges for human activity recognition and gives suggestions for further work. The deep learning methods and the advancement in hardware devices are the elements for future research work. Keywords Human activity detection (HAD) · Machine learning · Deep learning

J. S. Dhage (B) Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India e-mail: [email protected] A. K. Gulve · P. C. Shetiye Government College of Engineering, Aurangabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_1

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1 Introduction From the last decade, human activity detection is becoming a very crucial research topic for new researchers. Despite the amount of research in this field, many unresolved issues such as challenges to identify exact activity, movement of sensors, background clutter, and the accuracy of the algorithms are still persistent. It also becomes more complex from person to person, as the involvement of body gestures relative to time and gravity is different. The tasks can become very much easy if it is correctly detected by HAD system. In HAD, the machine analyzes various human activities from the given input source. These systems are based on supervised and unsupervised learning algorithms. Different techniques such as the vision-based approach and sensor-based approach are available to detect human activities. Tracking daily activities in the healthcare field is crucial to check the progress of the patient and overall improvement in his health. HAD can also play an important role in military applications. The action, movement, and position of the soldiers at a given moment can be tracked. These are challenging issues for data collection. The action detection of a soldier may be complex to extract. There are no standardized techniques available for the data collection. The task becomes very complex due to the enormous data generated during the process. Figure 1 [1] shows the popular approaches along with the framework. Currently, the deep learning approach is becoming very much popular. It has achieved remarkable results in various topics such as object detection, classification, and natural language processing. The exact features can be extracted by the deep learning approach with unsupervised and reinforcement learning.

Fig. 1 An overview: Human action detection process

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1.1 Approaches To identify the human action, human activities such as gestures, atomic movements, human-to-object and human-to-human interaction, group action behaviors, and events are very much important. By identifying an exact class, the depth can be found [2]. These are all series of activities. Suppose a person is doing a set of k activities, from the given set of X, then the equation as, X = X0, . . . , Xk − 1

(1)

Based on the type of approaches, for the HAR an exact detection is done with the available dataset. HAD system takes input from the various devices. There are different methods to extract information from those devices. Sensors of various types such as gyroscope, accelerometer, heart rate sensor, and CCTV are used in the sensor and vision-based approach. These are the modern approaches. Table 1 [3–8] shows the comparison of various techniques. The different approaches and the models used for getting the results are also shown [9–11]. Generally, discriminative models, generative models, and template-based models are the various machine learning algorithms. In generative models, algorithms like Bayesian network, naïve Bayes, HMM, and k-means are available. In the discriminative model, C4.5, random forest, KNN, SVM, and CNN algorithms are available. In a template-based model, template matching with dynamic time warping is available.

2 Datasets There are various data sets available for human action detection and recognition purpose. The deep learning methods are applied on all the different types of data sets. The raw input is received from a resource. The dataset is classified into single view or multi-view based on the depth camera technologies. Multiple input devices Table 1 Comparison of various techniques S. No

Author

Technique/model

Features

Accuracy(%)

1

Bulbul et al.

SVM

Smartphone

99.4

2

Ullah et al.

CNN/LSTM

Surveillance

94.4

3

Nandy et al.

Decision tree

Smartphone

93.54

4

Tsai et al.

HMM

Vector quantization

95.64

5

Gatt et al.

LSTM/CNN

PoseNet model

93

6

Khokhlov et al.

J48/Random forest

Accel. and gyroscope

98.4/95

7

Chowdhury et al.

Ensemble

Smartphone

94.2

8

Gnouma et al.

CNN/AE

Video streaming

97.8

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Table 2 A list of datasets for the various approaches S. no

Year

Dataset

Modality

Approach based on

1

2004

KTH

I

Vision

2

2006

XMAS

RGB, A

Vision

3

2010

MSR Action-II

RGB

Vision

4

2012

WISDM

RGB

Sensor

5

2015

UCI Heterogeneity AR

RGB

Sensor

6

2015

ActivityNet

RGB

Vision

7

2016

NTU RGB + D

RGB, DS, IR

Vision

8

2017

UniMib SHAR

RGB

Sensor

9

2017

20BN-something

RGB

Vision

10

2018

Kinetics

RGB

Vision

11

2019

HACS

RGB

Vision

are required to keep track of the large crowd like film theaters. An ideal dataset for the HAR system should consider issues like still images/videos, resolution, RGB, depth, occlusions, critical backgrounds, and poses. There are large variations in the different representations, shown by the human being. So, it is quite a difficult task to build a universal dataset. Table 2 [2, 12] summarizes the available datasets with modalities such as Depth(D), Skeleton(S), Audio(A), Intensity(I), and Infra-red(IR). These datasets are useful for the different approaches [13]. These datasets are also used to check the effectiveness of deep learning-based models. The data of the RGB modality is present in abundance and is also inexpensive. This kind of data produces rich texture data. However, the approach to be used is also an important decision.

2.1 Data Preprocessing and Feature Engineering Before feeding the data to the training algorithms, pre-processing of data and feature extraction are necessary. Preprocessing of the data is an important step to prepare the data suitable for further analysis. It consists of various techniques such as sensor calibration, unit conversion, and normalization or cleaning of corrupted data. The corrupted data is generated, because of possible hardware failures or problems with data transfer. If the use of several sensor modalities is required, then matching and coordination of different sensor channels should be taken into consideration [7, 8, 14]. Sampling finds the correct segments with activity. At this stage, irrelevant data will be removed and it will not be part of the next phase for processing. In feature extraction, values are computed to provide the details needed to convert a segment into a representation that is relevant to the activity. Finding a high-level representation is critical for ensuring the HAR system’s ability to handle vast amounts

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Fig. 2 Data processing, feature extraction with an evaluation process

of data, which is required in the case of a vision-based approach. Feature extraction uses deep learning methods or handcrafted methods based on the given approach. Figure 2 [1] shows the data processing and feature extraction, and evaluation process. In classification, a classifier is constructed according to the requirement. It consists of features extracted from the process. The classifier gives a clear idea about the activities to be detected. The classification process establishes rules. The rules distinguish between the features computed on a segment with one class and the other segment class. In the vision-based approach HAR, local, global, or semantic method is used for the feature extraction [15].

3 Deep Learning Algorithms In the last few years, the data collection processes received very much uplift because of the latest sensors and other devices. As discussed in the earlier sections, the algorithms are divided into 3 main categories. They are generative, discriminative, and hybrid models. Generative models are a wide class of machine learning algorithms that make predictions by modeling joint distribution P(y, x,). An example is Naive Bayes. Discriminative models are a class of supervised machine learning models that make predictions by estimating conditional probability P(y|x). In hybrid models based on coupling parameters, the user specifies a joint probability model p(x; y). The discriminative model takes a shorter way: It simply estimates the conditional probability. An example is logistic regression. In hybrid models based on coupling parameters, the user specifies a joint probability model p(x; y). The multi-conditional likelihood is maximized as, L(x, y) = α. log p(y|x) + β. log p(x)

(2)

where α; β > 0 are hyper-parameters. When α = β = 1, there will be a generative model. When β = 0, there will be discriminative model. Through the integration of generative models and deep neural networks, a new family of methods known as deep generative models (DGMs) has emerged as a result of the advent of deep learning. The trick with DGMs is that the number of parameters in generative models is far

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fewer than the amount of data used to train them on, forcing the models to identify and effectively internalize the essence of the data to produce it. DGMs include variation auto-encoders (VAEs), generative adversarial networks (GANs), and auto-regressive models. In the research field, there is a tendency toward creating big deep generative models [2]. Figure 3 [1] shows the deep learning methods for human activity detection and recognition. Restricted Boltzmann Machine is a generative stochastic artificial neural network capable of learning a probability distribution across its inputs. RBMs were first developed in 1986 by Paul Smolensky under the moniker Harmonium. Instead of using deterministic distribution, Restricted Boltzmann Machine uses the stochastic units with a particular distribution. It trains the model to understand the association between the two sets of variables [16, 17]. Restricted Boltzmann machines are implemented with PyTorch, which is a highly advanced deep learning and AI platform. It just requires an installation of PyTorch on the machine and then a few simple steps to be done. A convolutional neural network [18] is suitable for spatial data such as images and so it is ideal for image and video processing. It is also more powerful than a recurrent neural network. Temporal data is very much needed for the recurrent neural network. It is also referred to as sequential data and can handle arbitrary input/output lengths. An example of an artificial recurrent neural network (RNN) is long short-term memory (LSTM). It is useful in the field of deep learning. LSTM [19, 20] can be applied for time series forecasting.

Fig. 3 Deep learning methods for human activity detection and recognition

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4 Conclusion The paper presented various methods, modern approaches, and techniques for human action detection and recognition. Depending on the target application, various approaches will be beneficial to get the result. The information about the various data sets available for the experimental purpose along with the accuracy of the various modern techniques is presented. Different deep learning approaches are also discussed. Irrespective of the various achievements toward activity recognition still, there are some challenges. Training deep networks on videos, handling occlusions, unlabeled data processing, dynamic backgrounds, person-to-person variations in the activities, and non-availability of the universal dataset are all the challenges. Bayesian network, HMM, K-means, C4.5, Random forest, KNN, SVM, and CNN algorithms are available to proceed into the depth of action detection. Due to the advancement in devices like sensors, electronic gadgets, and in parallel to the deep learning approaches, it is very much possible to improve the overall performance of human action detection and recognition system so as to locate the exact activity.

References 1. Wang J, Chen Y, Hao S, Peng X, Lisha H (2019) Deep learning for sensor based activity recognition: a survey. Pattern Recogn Lett 19:3–11 2. Dang LM, Min K, Wang H, Piran MJ, Lee CH, Moon H (2020) Sensor-based and vision based human activity recognition: a comprehensive survey. Pattern Recogn. 108:107561 3. Bulbul E, Cetin A, Dogru IA (2018) Human activity recognition using smartphone. In: 2nd international conference on multidisciplinary studies and innovative technologies, Ankara, pp 1–6. 4. Tsai AC, Ou YY, Sun CA, Wang JF (2017) VQ-HMM classifier for human activity recognition based on RGB-D sensor. In: International conference on orange technology, Singapore, pp. 201– 204 5. Gatt T, Seychell D, Dingli A (2019) Detecting human abnormal behavior through a video generated model. In: 11th international symposium on image and signal processing and analysis, Dubrovnik, Croatia, pp 264–270 6. Khokhlov I, Reznik L, Cappos J, Bhaskar R (2018) Design of activity recognition systems with wearable sensors. In: IEEE sensorts applications symposium, Seoul, pp 1–6 7. RoyChowdhury I, Saha J, Chowdhury C (2018) Detailed activity recognition with smartphones. In: Fifth international conference on emerging applications of information technology, Kolkata, pp 1–4 8. Nandy A, Saha J, Chowdhury C, Singh KP (2019) Detailed human activity recognition using wearable sensors and smartphones. In: International conference on opto-electronics and applied optics, Kolkata, India, pp 1–6 9. Ghazal S, Khan US (2018) Human posture classification using skeleton information: 2018 international conference on computing. In: Mathematics and engineering technologies (iCoMET), Sukkur, pp 1–4 10. Liu R, Chen T, Huang L (2010) Huang: research on human activity recognition based on active learning. In: 2010 international conference on machine learning and cybernetics, Qingdao, pp. 285–290

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11. Jagadeesh B, Patil CM (2019) Video based human activity detection, recognition and classification of actions using SVM, Trans Mach Learn Arti Intell 6(6):22 12. Boualia SN, Essoukri ben Amara N (2019) Pose based human activity recognition: a review. In: 2019 15th international wireless communications and mobile computing conference(IWCMC),Tangier, Morocco, pp 1468–1475 13. Karthickkumar S, Kumar K (2020) A survey on deep learning techniques for human action recognition. In: International conference on computer communication and informatics, Coimbatore, INDIA, 978-1-7281-4514-3, IEEE 14. Lara OD, Labrador MA (2012) A survey on human activity recognition using wearable sensors, IEEE Commun. Surv. Tutor 15(3):1192–1209 15. Khaire P, Kumar P, Imran J (2018) Combining CNN streams of RGB-D and skeletal data for human activity recognition. Pattern Recogn Lett 115:107–116 16. Mliki H, Bouhlel F, Hammami M (2019) Human activity recognition from UAV-captured video sequences. Pattern Recogn 100:107140 17. Guo J, Mu Y, Xiong M, Liu Y, Gu J 920190 Activity feature solving based on TF-IDF for activity recognition in smart homes. Complexity 2010 18. Singh R, Dhillon JK, Kushwaha AKS, Srivastava R (2019) Depth based enlarged temporal dimension of 3D deep convolutional network for activity recognition. Multimed Tools Appl 78(21):30599–30614 19. Li M, Zhou Z, Liu X (2010) Multi person pose estimation using bounding box constraint and LSTM. IEEE Trans Multimed 21(10):2653–2663 20. Kim K, Jalal A, Mahmood M (2019) Vision based human activity recognition system using depth silhouettes. A smart home system for monitoring the residents. J Electr Eng Technol 14(6):2567–2573 21. Gupta A, Gupta K, Gupta K, Gupta K (2020) A survey on human activity recognition and classification. In: International conference on communication and signal processing, India, 978-1-7281-4988-2, IEEE 2020 22. Boualia SN, Amara NEB (2019) Pose-based human activity recognition: A review. 97-1-53867747-6, IEEE 2019 23. Ullah A, Mohammad K, Del Ser J, Baik SW, Albuquerque V (2018) Activity recognition using temporal optical flow convolutional features and multi-layer LSTM. IEEE Trans Ind Electron 66(12):9692–9702 24. Gnouma M, Ladjailia A, Ejbali R, Zaied M (2019) Stacked sparse autoencoder and history of binary motion image for human activity recognition: multimed. Tools Appl. 78(2):2157–2179 25. Nweke HF, Teh YW, Al-Garadi MA, Alo UR (2018) Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Syst. Appl., 105:233–261

A Study of COVID-19 and Its Detection Methods Using Imaging Techniques G. Bharatha Sreeja, T. M. Inbamalar, S. Kalaivani, T. D. Subha, Chettiyar Vani Vivekanand, A. Jasmine Vijithra, and Nithin L. Raja

Abstract The coronavirus disease 2019 (COVID-19) has been spreading everywhere nowadays in the world. The affected people are having different symptoms including fever, cough, fatigue, and breathing problem. It is difficult to control the spreading of coronavirus. Artificial intelligence plays a vital role in detection of COVID-19. Various methodologies available for the detection of COVID-19 have been discussed. The efficiency of the different methods was also analysed. We made a summary of different methods in detection of coronavirus. The analysis proved that the artificial intelligence is strengthening and supporting to detect the virus. Methodbased AI can be developed for early detection of COVID-19, and it can save the time of medical experts. Keywords Artificial intelligence (AI) · Coronavirus disease 2019 (COVID-19) · Computed tomography (CT) · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) · Long short-term memory (LSTM) network

1 Introduction The coronavirus disease is spreading among people while they are speaking or sneezing or coughing. Small particles from sneezing or coughing may fall on floor and may spread while the affected person is travelling for long distance. Mostly, normal people are also affected by this while they are touching the virus-affected G. B. Sreeja (B) · T. M. Inbamalar · C. V. Vivekanand · A. J. Vijithra · N. L. Raja Electronics and Communication Engineering, R.M.K. College of Engineering and Technology, Thiruvallur, Tamil Nadu, India e-mail: [email protected] S. Kalaivani Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India T. D. Subha Electronics and Communication Engineering, R.M.K Engineering College, Chennai, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_2

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areas and then they are touching their mouth or nose. SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the major reason for the corona disease. The people affected by COVID-19 have the symptoms which include reduction in blood cell count, breathing problem, fever and pneumonia. There is no proper treatment and medicine for this disease [1].

2 Literature Review The great efforts are taken nowadays to find the solution for this virus, and it is a great challenge to medical specialists. There is a need to identify a very cheapest tool for detecting the COVID-19 efficiently. This virus can be controlled by taking proper measures like prevention awareness and not roaming outside for unnecessary reasons [2]. The big challenge in most of the countries nowadays is the required medical resources. Now it is an urgent situation to find the best device for detecting the coronavirus. Every doctor is trying to find the best and quick solution for finding the affected people in an initial stage itself [3]. The disease can be identified by using segmentation concept and feature extraction methods. For segmentation, specific algorithms are required to separate the regions which are affected by the corona disease. That segmented areas can be more useful to analyse the people whether they are affected by this or not. Segmentation is the important step in image processing which is used for dividing the regions of affected and unaffected areas, and this can increase the effectiveness of disease identification using various kinds of methods in image processing [5]. This location division or segmentation is more useful in different areas like image processing for satellite applications, object detection, remote sensing applications, etc. In image processing, lots of algorithms are available to segment the locations effectively like edge detection method, segmentation based on region, feature-based clustering, and segmentation by threshold method [6]. Threshold techniques are based on different approaches like parametric and non-parametric. The probability density function is the important method to categorize the image areas which is mostly used in parametric approach. Increasing the number of thresholds processing time is also increasing. Optimal threshold can be used by traditional techniques [7]. Image processing techniques are mostly used to find the coronavirus. These techniques are applied to lung image of patient [8]. After segmentation, the important features are extracted for identifying the coronavirus. Segmentation is the important step in this which is separating the image regions and computer vision is used to improve the accuracy of image processing techniques [9, 10]. Histogram-based feature extraction concept is applicable in different fields including the medical field like diagnosis, recognition, remote sensing, satellite image processing, and historical newspapers [11].

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3 Methodologies 3.1 COVID-19 Detection Using Hybrid AI Model The coronavirus can be predicted using hybrid artificial-intelligence (AI) model. Here, different models are used to treat the people affected by corona. If the people affected by coronavirus with same infection rate, then the traditional epidemic models can be used, and if people are having different infection rate, then an improved susceptible–infected model is used. The hybrid artificial intelligence model is used for finding the coronavirus, and the Improved Susceptible Infected model can use natural language processing (NLP) module and the long short-term memory (LSTM). The current scenario shows the spreading of coronavirus is in exponential growth [13]. While analysing the traditional epidemic models, the hybrid AI model can perform better and it can minimize the errors in the detected results.

3.2 Image Processing Techniques in Detection of COVID-19 Nowadays, the coronavirus disease 2019 (COVID-19) is spreading everywhere. Medical imaging like CT and X-ray is playing an important place to detect the COVID-19 [14–16]. Image acquisition is used for scanning the body and minimal contact is given to patient and provides good protection to the patient and technicians. Segmentation is used for the lung image for predicting COVID-19. The computer-aided method helps the radiologists to bring the clinical decisions such as disease diagnosis and tracking. While considering medical image for the analysis of COVID-19, more steps are used like collection of various images, segmenting image, feature extraction, and detection of disease. AI-based image acquisition method is proven that it is the most accurate method and also saves clinicians from infection due to COVID [14]. The image quality is the important thing to identify the disease so that AI-based automated ISO centring and scan range determination are used to increase the quality of the image. When the patient is affected by COVID-19, various parameters are evaluated and it is analysed using AI method. Here, low dosage radiation should be used for scanning the patient to measure the necessary parameters [15]. High-quality cameras are necessary to monitor the patient. So that X-ray and CT systems are used to give image with good quality [16, 17]. Those devices can be implemented by contactless scanning method. The patients are observed by the doctors or medical staff from nearest room through live video telecast from the camera fixed in the device. Scan range is the challenging parameter for the technician. Using AI can find the patient position and shape from the data collected by the sensors like scanning parameters [18–20].

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3.3 Multi-view Representation Learning in COVID-19 Detection Machine learning algorithm is urgently needed for predicting COVID-19. Number of patients is affected nowadays which is difficult to handle everyone by the medical staff. So that for diagnosis of disease Chest computed tomography (CT) can be used for giving image with clear quality and from the scanned image features are extracted with different view [21–24]. For analysis, various kinds of features are used and it is used for training the network to detect the disease accurately. Backward neural network is used with all separated features. This method can give better stable performances while changing the input training data [25–28].

3.4 COVID-19 Detection Algorithm and Ranking-Based Method All the countries are trying to give solution to COVID-19. There is a necessity to develop low-cost tool. Chest scanned image using X-ray is a useful information that can be analysed exactly and quickly to find the coronavirus. If the patient is having corona disease, it can be automatically detected by the ranking method [29]. Different features can be collected from various views of chest images and the smallest areas are detected and those locations may have the identified features which may be important for detection. It uses the algorithm for segmenting the X-Ray image and also uses the ranking-based method to improve the effectiveness of the method to give good solution within some steps. Ranking method is not able to give best result even with more iterations and still moving towards the good result so far.

4 Results and Discussion Medical resources are not enough in many countries due to the fast spreading of COVID-19. If we diagnose manually using CT images, it will need more medical staff and it will take more time to detect the disease. So that to reduce this problem artificial intelligence-based tools have been developed for quick detection. This AI tool can improve the diagnostic accuracy and avoids the mental pressure of medical staff [44]. Figure 1 shows the lung image, and Fig. 2 represents the sample histogram for the specific lung image for feature extraction [44]. Different methods are analysed in the table. The deep-learning model is used to identify the affected areas in the scanned image, and 3DResNet is used to detect the COVID-19. Before feature extraction, the affected areas were first segmented out using a 3D deep-learning model from the scanned image. After segmentation,

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13

Fig. 1 Image showing lungs

300 250 200 150 100 50 0

1 18 35 52 69 86 103 120 137 154 171 188 205

Number of Pixels

Fig. 2 Histogram of the lung image

Grey level

the result is detected as normal or abnormal cases. Table 1 shows the comparison of different methods using AI in detection of COVID-19. Here, various methods are compared with different samples. The performance is good while applying UNet and 2D CNN. Table 1 Comparison of different methods in detection of COVID-19 using AI Author

Reference number

Year

Method

Number of samples

Efficiency (%)

Wang

[32]

2020

Decision tree

44

78

Fang

[33]

2020

Radiomic feature, clustering

46

82.6

Xu

[34]

2020

3D CNN

224

86

Wan g

[35]

2020

FPN, Dense Net External

4106

87

Shi

[36]

2020

VBNet

1658

88

Shi

[37]

2020

Logistic regression

151

89

Jin

[38]

2020

ResNet50

723

92

Song

[39]

2020

DRE-Net

88

95

Chen

[40]

2020

UNet++

51

95

Li

[41]

2020

ResNet5 0

468

96

Jin

[42]

2020

2D CNN

496

98

Zheng

[43]

2020

UNet

313

98

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While using the decision tree method for 44 samples, the efficiency achieved is only 78%. Random feature and clustering method are used in [33] for 46 samples, and this method is giving 82.6%. The 3D Convolutional Neural Network is used for 224 samples, and the efficiency achieved is 86% [34]. But when 2D convolution neural network is used, the method provides 98%. Also, the number of sample usage is higher ie.496 samples. The inference obtained is difficult based on samples. In [39], the number of samples used is 88. Here, the segmentation is done with the lung itself to avoid noise caused by different lung contours. Then, used a Details Relation Extraction neural network (DRE-Net) to extract the top-K details in the CT images and got the image-level predictions. Also, the image-level predictions were aggregated to achieve patient-level diagnoses of coronavirus. The efficiency achieved is 95%. UNet + + is a modification of the UNet model, which is originally designed for biomedical image segmentation. For CT image slice, the UNet++ model can segment the areas with lesions. Afterwards, the bounding box of the segmented lesion is generated. While using the UNet++ model for 51 samples, 95% accuracy is achieved. For 4106 samples, DenseNet model is applied and the efficiency obtained is 87%. In overall analysis, the maximum samples applied for this DenseNet model. Similarly for VBnet model, the number of samples used is 1658 and it provides 88% efficiency. But in [38] by using same ResNet50 classification model to 723 samples 92% efficiency is achieved. While taking the overall analysis, the uniformity is not maintained for calculating the efficiency. But we can understand that AI can give better solution in detection of coronavirus.

5 Conclusion and Future Work The findings from the analysis indicate that the changes in the input chest images can be concentrated well in the detection and management of COVID-19. The AI methods are more applicable in detection of COVID-19 which is also analysed in this paper. More techniques using imaging tools were also discussed for the detection of COVID19. The methods to apply AI technique to the entire process of the imaging-based diagnosis of COVID-19. The analysis shows the importance of artificial intelligence which can be more useful in detecting the virus which can save the life of patient and save the time of medical staff. In future, the same work can be extended along with the help of AI. Also to reduce the radiation dosage consumed by patients and to improve the quality of the scanned image, artificial intelligence in-built applications will be added in image acquisition system.

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21. Siemens CT scanner SOMATOM Force, SOMATOM Drive or SOMATOM Edge Plus. Available https://www.siemens-healthineers.com/computed-tomography/technologies-and-innova tions/fast-integrated-workflow 22. Li J, Udayasankar UK, Toth TL, Seamans J, Small WC, Kalra MK (2007) Automatic patient centering for MDCT: effect on radiation dose. Am J Roentgenol 188:547–552 23. Martin CJ (2007) Optimisation in general radiography. Biomed. Imaging Intervention J. 3 24. Achilles F, Ichim AE, Coskun H, Tombari F, Noachtar S, Navab N (2016) Patient MoCap: Human pose estimation under blanket occlusion for hospital monitoring applications. In: Medical image computing and computer assisted intervention, pp 491–499 25. GE Xtream camera. Available: https://www.gehealthcare.com/products/computed-tomograph y/rev olution-maxima 26. https://new.siemens.com/global/en/company/stories/research-technologies/artificialintellig ence/artificial-intelligence-imaging-techni ques.html 27. Casas L, Navab N, Demirci S (2019) Patient 3D body pose estimation from pressure imaging. Int J Comput Assist Radiol Surg 14:517–524 28. Kang H, Xia L, Yan F, Wan Z, Shi F, Yuan H, Jiang H, Wu D, Sui H, Zhang C, Shen D (2020) Diagnosis of coronavirus disease 2019 (COVID-19) with structured latent multi-view representation learning. IEEE Trans. Med. Imaging 29. Bhagavathula AS, Shehab A (2020) The story of mysterious pneumonia and the response to deadly novel coronavirus (2019-nCoV), Bentham Sci J 1(2). https://doi.org/10.2174/025068 8202001010007, ISSN:0250-6882 30. https://www.worldometers.info/coronavirus/ 31. Abdel-Basset M, Mohamed R, Elhoseny M, Chakrabortty RK, Ryan M (2020) A hybrid COVID-19 detection model using an improved marine predators algorithm and a ranking-based diversity reduction strategy. IEEE 32. Wang S et al (2020) A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) medRxiv 33. Fang M et al (2020) CT radiomics can help screen the coronavirus disease 2019 (COVID-19): a preliminary study. Sci. China Inf. Sci 63(1674–733X):172103. https://doi.org/10.1007/s11 432-020-2849-3 34. Xu X et al (2020) Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv preprint arXiv:2002.09334 35. Wang S et al (2020) A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. medRxiv 36. Shi F et al (2020) Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:2003.09860, (2020). 37. Shi W et al. (2020) Deep learning-based quantitative computed tomography model in predicting the severity of COVID-19: a retrospective study in 196 patients. SSRN Electronic J https://doi. org/10.2139/ssrn.3546089 38. Jin S et al (2020) AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. medRxiv 39. Song Y et al (2020) Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv 40. Chen J et al (2020) Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. medRxiv 41. Li L et al (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology, p 200905 42. Jin C et al (2020) Development and Evaluation of an AI System for COVID-19 Diagnosis. medRxiv 43. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D (2020) Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID19,. IEEE Rev. Biomed. Eng. https://doi.org/10.1109/RBME.2020.2987975 44. Zheng C et al (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv

Numerical Evaluation of 3D Printable Patch Antenna for Wearable Applications A. Priya, E. Manikandan, Manavalan Saravanan, Sarfraz Nawaz, and M. Mahalingam

Abstract The existing conventional microstrip patch antennas are evolving everyday with its application widening from mobile communication, GPS systems and more. With these advancements, the 3D printed antennas show a promising future as it can provide enhanced performance and applications. 3D printing also helps in reducing the cost and time for fabrication of the antenna. Hence, in this paper, the 3D printed patch antenna for wearable application is proposed. This antenna is designed to radiate at 3.7 GHz frequency and has promising applications such as in smart watches. The simulated results yield a gain of about 4.74 dB for a substrate thickness of 1.5 mm. The proposed antenna is to be designed for two substrates–Polylactic Acid and Polyurethane. The use of these two substrates helps in reducing the cost of the antenna and also makes the antenna less fragile. Keywords Printed patch antenna · Polylactic acid · Polyurethane · Wearable applications · Smart watches

1 Introduction A conventional microstrip patch antenna is designed with a substrate material on the substrate which contains a radiating patch antenna on the top made up of any conducting material and a ground plane at the bottom of the substrate. Paper [1] briefs A. Priya (B) · S. Nawaz · M. Mahalingam Department of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India e-mail: [email protected] E. Manikandan School of Electronics Engineering, Vellore Institute of Technology, Chennai, India e-mail: [email protected] M. Saravanan Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_3

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about the different types of microstrip patch antennas vary from microstrip dipoles, printed slot antennas, microstrip traveling wave antennas, etc. Although, there are different microstrip patch antennas available, a basic inset feed patch antenna is studied and progressed in this paper. A single microstrip patch antenna with a rectangular patch containing inset feed is proposed in [2]. The antenna is designed with the substrate dimensions at 6.28 mm × 7.23 mm × 0.5 mm. Microstrip patch antennas are widely considered due to their light weight, compatibility and the ease with which it can be designed. These antennas can be designed with a printed strip line feed networks and active devices. The antenna proposed in [3] is designed using an FR4 substrate material and is designed in the shape of a small elliptical structure thereby resulting in high efficiency for 5G applications. Microstrip patch antennas radiate mainly due to the fringing fields between the edge of patch and the ground plane. A thick dielectric substratum with low dielectric constant is desirable for better antenna performance, since this provides better efficiency, greater bandwidth and better radiation. Jayakumar and Saranyakumari [4] demonstrated an E shaped patch and a double E shaped patch antenna that is designed by cutting two or four rectangular slots in the patch. This design results in the antenna inheriting a dual band characteristic that can radiate at multiple frequencies. Although paper [5] illustrates a wideband and lowprofile microstrip antenna for 5G communications designed with multiresonant structure to improve the impedance bandwidth, some of the major drawbacks experienced in the use of microstrip patch antenna can be Narrow bandwidth, Low efficiency, Low Gain, etc. In paper [6], multiple layers of the patch are stacked on top of one another in order to obtain multiple resonances. This method helps in improving the bandwidth of the antenna while maintaining the surface area occupied by the antenna. [7] demonstrates a printed antenna having planar microstrip configuration. The antenna is designed with printed arrays wherein the efficiency produced is low due to the antenna experiencing ohmic and dielectric losses in the feed network. On the contrary, a 3D printed patch antenna is fabricated layer by layer rather than cutting a piece of material from a bigger piece. 3D printing has become popular in recent times with its use expanding from vehicles to house components, instruments and even biological tissues. This vast use of 3D printing makes it even more promising for its use in the design of antenna. In [8], a circular patch antenna is fabricated using 3D printing technique. The antenna is made with a substrate material known as Acrylonitrile Butadiene Styrene (ABS) thereby helping in reducing the cost of the antenna. Also, the antenna seems to radiate at a frequency of 2.34 GHz expressing a promising advancement in the 3D printing technology. 3D printing has been much efficient in the recent times and its ease of making things has all the more added to its advantage. Although 3D printing mechanism has been successful in other industries, its efficiency is yet to be deeply explored in manufacturing of functional antennas for RF/MW frequency. Some of the major advantages of using 3D printing to fabricate the antenna include rapid processing of the design rather than carrying out processes such as machining and photolithography, ease of designing complex geographical design structures. The goal is to design a 3D printed antenna that operates at sub-6 GHz frequency for

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5G applications. To understand about sub-6 GHz frequency spectrum, we must first understand the definition of spectrum. Spectrum is the band of electromagnetic radio frequencies used for airborne transmission of sound and data. While consumers are using their mobiles, their devices do not transmit haphazardly over the entire spectrum of radio communications. Rather they are related through different bands of frequencies. These bands are like invisible channels or pipes which deliver information. Generally speaking, the bigger the pipe, the greater the capacity and the more information that can be carried. Millimeter Wave (MM wave or mm Wave) refers to a radio frequency range over 24 GHz, but 6 GHz can be used as a dividing line for MM wave in the sense of 5G: The frequency above 6 GHz is MM wave, while the band under 6 GHz is not MM wave. Lower frequency spectrum, like sub-6 GHz, can travel farther and better penetrate solid objects such as buildings than a higher frequency spectrum such as mm Wave. Sub-6 GHz spectrum has restricted bandwidth and so with mm Wave spectrum the velocities could theoretically be slower than normal.

2 Antenna Design 2.1 Substrate Material For the antennas to be 3D printed, the fabrication has to be done with a substrate material that is more flexible and less fragile. Therefore, the antennas are designed with two different types of substrate materials–Polylactic acid (PLA) with dielectric constant value of 3.2107 and Polyurethane (PU) having relative permittivity 3.5. Polylactic acid (PLA) is produced from renewable resources, such as maize starch or cane sugar. Polylactic acid is biodegradable and has similar properties to polypropylene (PP), polyethylene (PE) and polystyrene (PS). It can be made from already existing production equipment (those originally built and used for plastics in the petrochemical industry). This makes production fairly cost effective. Polylactic acid (PLA) is a bioplastic manufactured from lactic acid, used for packaging sensitive food items in the food industry. However, PLA is too fragile and is not compatible with many packaging manufacturing processes. Polyurethane (PU) is a polymer which consists of organic units joined by carbamate (urethane) connections. While most polyurethanes are thermosetting polymers, which do not melt when heated, there are also thermoplastic polyurethanes. Polyurethanes are formed in the presence of suitable catalysts and additives by reacting a polyol (an alcohol with more than two reactive hydroxyl groups per molecule) with a diisocyanate or a polymeric isocyanate. Since polyurethane can be manufactured with a variety of diisocyanates and a wide range of polyols, a broad spectrum of materials can be developed to meet the needs of different applications.

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2.2 Proposed Antenna Design Initially, a Rectangular Patch Antenna is designed with Inset Feed using both the substrates. The thickness of the substrate is varied from 0.5 mm, 1 mm and 1.5 mm while the dimension of the patch for PLA is 21.8 mm × 27.94 mm and the dimensions of the patch for PU is 21 mm × 27.02 mm. These above parametric constrains are followed for all the designs. This is done to study the variations of return loss and gain of the antenna. In advancement to this design, four L-slots are cut from the patch of the antennas in order to maintain the frequency at 3.7 GHz and to increase the return loss. Further, due to the lack of efficient gain produced by the antennas with respect to the return loss, a new method of cutting the corners of the patch of the antenna is proposed. This method initiates a discontinuity in the flow of wave of the antenna which helps the antenna maintain an efficient gain while having higher return losses. This method is first implemented in the Rectangular Patch Antenna with Inset Feed. After these results have been analyzed, the cutting of corners is further implemented in the antennas with L-slots to reach at a constructive outcome. The progression of these antenna designs can be referred from Fig. 1.

Fig. 1 Stages of designed rectangular patch antennas (RPA)

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2.3 Design Parameters The material properties are studied and considered during numerical evaluation of the antenna. In order to determine the dimensions of the antenna, certain parameters are brought into account. These parameters are studied and calculated with respect to the dielectric constant of the substrate in order to design the antenna. The following parameters briefed below are taken into consideration while designing the antennas.

2.3.1

Length

Firstly, as seen here, the duration of the patch “L” regulates the resonant frequency. In general, this is true even for more complicated microstrip antennas weaving around-the length of the longest path on the microstrip controls the lowest operating frequency. The following Eq. (1) gives the relation between the resonant frequency and the length of the patch. fc =

1 c √ = √ 2L εr 2L εr ε0 μ0

(1)

where εr is the dielectric constant of the substrate.

2.3.2

Width

Second, the “W” width influences impedance of the inputs and pattern of radiation. The larger the patch is, the lower the impedance on inputs. The Eq. (2) shows the formula for calculating the width of the microstrip patch antenna. W =

2.3.3

2 f0

c √ εr +1

(2)

2

Permittivity

Fringing fields are dominated by the r-lower permittivity of the substratum which has broader fringes and thus stronger radiation. Reducing the permittivity often increases antenna bandwidth. The permittivity efficiency is increased with a lower value as well. With greater permittivity the antennal impedance increases. Higher permittivity values make the patch antenna “shrink.”

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2.3.4

A. Priya et al.

Effective Dielectric Constant

The Eq. (3) shows the formula for finding the effective dielectric constant. εeff =

εr + 1 2

(3)

where εeff = Effective dielectric constant. εr = Dielectric constant of the substrate.

2.3.5

Extension Length

This is the length of the antenna which can be further adjusted in order to alter the output to obtain the desired result. Equation (4) shows the formula to calculate this length. w

 + 0.264   L = (εeff − 0.268) wh + 0.8 (εeff + 0.3)

h

(4)

εeff = Effective dielectric constant.

3 Results and Discussion In order to understand the simulated output about the return loss and gain of the different thicknesses of both the substrates and to compare their outputs with one another. The outputs yielded by both PLA and PU are studied and analyzed with respect to their specific gain and return losses at 3.7 GHz frequency.

3.1 Output—RPA with Inset Feed The comparative S-parameter output graph of PLA and PU for Rectangular Patch Antenna with Inset Feed for all three thicknesses is given in Figs. 2 and 3, respectively. For the Inset Feed Patch Antenna, it is observed from Fig. 2 that for PLA substrate, the return loss for 0.5 mm, 1 mm and 1.5 mm thickness are − 10.58 dB, − 10.69 dB and − 11.97 dB, respectively. Also from Fig. 3, for PU substrate, the return loss obtained for 0.5 mm, 1 mm and 1.5 mm thickness are − 18.18 dB, − 22.84 dB, − 21.78 dB, respectively. The comparative output of the gain obtained for inset feed antenna by both PLA and PU for all three thickness is simulated in Figs. 4 and 5, respectively. Therefore, the comparative readings of the simulation are noted in

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Fig. 2 Comparative result—S -parameter of RPA with inset feed (PLA)

Fig. 3 Comparative result—S -parameter of RPA with inset feed (PU)

Table 1 for analysis and it can be inferred that the Polyurethane substrate shows better return loss for all thickness compared to the Polylactic acid substrate. From Table 1, it can be noted that the gain obtained for inset feed patch antenna shows that the PLA substrate is capable of yielding a more positive gain when compared to PU. Although, the return loss of PLA is less, it is much efficient in terms of the gain of the antenna as the gain gradually increases from − 5.14 dB for 0.5 mm thickness to 4.83 dB thickness for 1.5 mm thickness. On the contrary, the gain for PU is only able to reach up to 2.04 dB for 1.5 mm thickness from − 10.6 dB for 0.5 mm thickness.

24

Fig. 4 Comparative result—Gain of RPA with inset feed (PLA)

Fig. 5 Comparative result—Gain of RPA with inset feed (PU)

A. Priya et al.

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Table 1 Comparative result—RPA with inset feed Parameters Thickness (mm) Frequency (GHz) Return Loss (dB) Gain (dB)

PLA 0.5 3.712

PU 1 3.72

1.5 3.728

− 10.58

− 10.69

− 11.97

− 5.14

2.77

4.83

0.5 3.724

1 3.724

1.5 3.744

− 18.18

− 22.84

− 21.78

− 10.6

− 1.82

2.04

3.2 Output—RPA with L-Slots For the Rectangular Patch Antenna with L-Slots, it is observed from Fig. 6 that for PLA substrate, the return loss for 0.5 mm, 1 mm and 1.5 mm thickness are − 10.10 dB, − 9.97 dB and -11.36 Db, respectively. Also from Fig. 7, for PU substrate, the return loss obtained for 0.5 mm, 1 mm and 1.5 mm thickness are − 17.95 dB, − 21.02 dB, − 19.48 dB, respectively. It can further be inferred that PU substrate has better return loss compared to PLA even for Rectangular Patch Antenna with L-Slots. The comparative output of the gain obtained for Rectangular Patch Antenna with L-Slots by both PLA and PU for all three thickness is simulated in Fig. 8 and 9, respectively. It can be inferred from Table 2 that the L-Shaped Edge Patch Antenna for PLA produces gain of − 5.55 dB, 2.5 dB and 4.72 dB for 0.5 mm, 1 mm and 1.5 mm thickness, respectively. Whereas, the PU on the other side yields − 10.5 dB, -2.49 dB and 15.6 dB gain for 0.5 mm, 1 mm and 1.5 mm thickness, respectively. Thus, it can be noted from Table 2 that there is a steady increase in the gain of the antenna both for PLA and PU substrate with increase in the thickness of the substrate. Also, the frequency of antenna for all three thicknesses is close to 3.7 GHz.

Fig. 6 Comparative result—S -parameter of RPA with L-slots (PLA)

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Fig. 7 Comparative result—S -parameter of RPA with L-slots (PU)

Fig. 8 Comparative result—gain of RPA with L-slots (PLA)

3.3 Output—RPA with Trimmed Corners For the Rectangular Patch Antenna with trimmed corners, it is observed in Fig. 10, that for PLA substrate, the return loss for 0.5 mm, 1 mm and 1.5 mm thickness are − 8.97 dB, − 8.71 dB and − 10.39 dB, respectively. Also from Fig. 11, for PU substrate, the return loss obtained for 0.5 mm, 1 mm and 1.5 mm thickness are − 17.31 dB, − 21.25 dB, − 18.09 dB, respectively. It can be inferred that there is a slight increase in the gain with respect to the previous design.

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Fig. 9 Comparative result—gain of RPA with L-slots (PU) Table 2 Comparative result—RPA with L-slots Parameters

PLA

PU

Thickness (mm)

0.5

1

1.5

0.5

1

1.5

Frequency (GHz)

3.696

3.692

3.704

3.7

3.692

3.704

Return Loss (dB) Gain (dB)

− 10.10

− 9.97

− 5.55

2.5

− 11.36 4.72

− 17.60

− 18.76

− 10.5

− 2.2

Fig. 10 Comparative result—S -parameter of RPA with trimmed corners (PLA)

− 19.82 1.71

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Fig. 11 Comparative result—S -parameter of RPA with trimmed corners (PU)

The comparative output of the gain obtained for Rectangular Patch Antenna without Corners and L-Slots by both PLA and PU for all three thicknesses is simulated in Figs. 12 and 13, respectively. It can be inferred from Table 3 that the Rectangular Patch Antenna without Corners and L-Slots for PLA produces gain of − 4.52 dB, 2.64 dB and 4.76 dB for 0.5 mm, 1 mm and 1.5 mm thickness, respectively. The PU on the other hand yields − 11.2 dB, − 1.82 dB and 1.78 dB gain for 0.5 mm, 1 mm and 1.5 mm thickness, respectively. Although, there is a slight increase in the gain with respect to previous design, it can be inferred that there is no further improvement in the return loss for both PLA and PU.

3.4 Output—RPA with L-slots and Trimmed Corners For the Rectangular Patch Antenna with L-Slots and trimmed Corners, it is observed from Fig. 14, that for PLA substrate, the return loss for 0.5 mm, 1 mm and 1.5 mm thickness are − 10.52 dB, − 9.87 dB and − 23.10 dB, respectively. Also from Fig. 15, the return loss obtained by PU for 0.5 mm, 1 mm and 1.5 mm thickness are − 17.44 dB, − 22.98 dB, − 20.04 dB, respectively. It can be inferred that there is a slight increase in the return loss with respect to the previous design. The comparative output of the gain obtained for Rectangular Patch Antenna with L-Slots and no Corners by both PLA and PU for all three thickness is simulated in Figs. 16 and 17, respectively.

Numerical Evaluation of 3D Printable Patch Antenna for Wearable …

Fig. 12 Comparative result—gain of RPA with trimmed corners (PLA)

Fig. 13 Comparative result—gain of RPA with trimmed corners (PU)

29

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Table 3 Comparative result—RPA with trimmed corners Parameters Thickness (mm)

PLA 0.5

PU 1

1.5

Frequency (GHz)

3.7

3.7

Return Loss (dB)

− 8.97

− 8.71

− 10.39

Gain (dB)

− 4.52

2.64

4.76

3.708

0.5 3.704

1 3.708

1.5 3.708

− 17.31

− 21.25

− 18.09

− 11.2

− 1.82

1.78

Fig. 14 Comparative result—S -parameter of RPA with L-slots and trimmed corners (PLA)

Fig. 15 Comparative result—S -parameter of RPA with L-Slots and trimmed corners (PU)

Numerical Evaluation of 3D Printable Patch Antenna for Wearable …

Fig. 16 Comparative result—gain of RPA with L-slots and trimmed corners (PLA)

Fig. 17 Comparative result—gain of RPA with L-slots and trimmed corners (PU)

31

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Table 4 Comparative result—RPA with L-slots and trimmed corners Parameters

PLA

Thickness (mm) Frequency (GHz) Return Loss(dB) Gain(dB)

0.5 3.752

PU 1 3.7

1.5 3.7

− 10.52

− 9.87

− 23.10

− 4.53

2.65

4.74

0.5 3.708

1 3.724

1.5 3.724

− 17.44

− 22.98

− 20.04

− 10.3

− 2.14

1.71

It can be inferred from Table 4 that the Rectangular Patch Antenna with L-Slots and no Corners for PLA produces gain of − 4.53 dB, 2.65 dB and 4.74 dB for 0.5 mm, 1 mm and 1.5 mm thickness, respectively. The PU on the other hand yields − 10.3 dB, − 2.14 dB and 1.71 dB gain for 0.5 mm, 1 mm and 1.5 mm thickness, respectively. Thus, it can be noted from Table 4 that the return loss produced by the PLA substrate for 1.5 mm thickness is much higher and the gain it yields makes the antenna much more efficient. It can be noted that with increasing thickness, the gain and return loss starts increasing, respectively.

4 Conclusion In this paper, four different antenna designs have been analyzed. The antennas are designed with two different polymer substrates namely Polylactic acid and Polyurethane. The design is carried out for three different thickness of 0.5, 1, and 1.5 mm in order to analyze the behavior of the antenna’s gain and its return loss with respect to substrate thickness. After all the simulation and comparative analysis of gain and return loss, it can be concluded that the gain obtained for Rectangular Patch Antenna with L-slots and no Corners with PLA substrate of thickness 1.5 mm is 4.74 dB and performs better in comparison with the rest of the antennas. Although, the return losses produced by the antenna designed with PU are much higher, the PLA substrate generates a much higher gain as compared to PU thereby increasing the efficiency of the antenna. It can also be concluded that the property of gain for these two substrates increases with increase in their thickness. Therefore, the PLA substrate is more suitable for its applications in the 3D printed antenna design.

References 1. Singh I, Tripathi VS (2011) Micro strip patch antenna and its applications: a survey. Int. J. Comput. Appl Technol 2(5):1595–1599 2. Darboe O, Konditi DB, Manene F (2019) A 28GHz rectangular microstrip patch antenna For 5G applications. ISSN 0974-3154 12(6):854–857 3. Ferdous N, Hock GC, Hamid SH, Raman MN, Kiong TS, Ismail M (2018) Design of a small patch antenna at 3.5GHz For 5G application. In: International conference on sustainable energy

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and green technology. https://doi.org/10.1088/1755-1315/268/1/012152 4. Jayakumar A, Saranyakumari A (2019) Design of E-shaped nano patch dual band antenna for 5G applications. Int J Eng Adv Technol (IJEAT) ISSN: 2249-8958, vol 8(5) 5. An W, Li Y, Fu H, Ma J, Chen W, Feng B (2018) Low-profile and wideband microstrip antenna with stable gain for 5g wireless applications. IEEE Antennas Propag Lett 17(4).https://doi.org/ 10.1109/LAWP.2018.2806369 6. Ershadi S, Keshtkar A, Abdelrahman AH, Xin H (2017) Wideband high gain antenna subarray for 5G applications. progress in electromagnetics research C 78:33–46 (2017) 7. Levine E, Malamud G, Shtrikman S, Treves D (1989) A study of microstrip array antennas with the feed network IEEE Trans Antennas Propag 31(4). https://doi.org/10.1109/8.24162 8. Ramly AM, Malek NA, Mohamad SY, Sukor MA (2016) Design of a circular patch antenna for 3D Printing. In: International conference on computer and communication engineering (2016).https://doi.org/10.1109/ICCCE.2016.92

Ensuring Location Privacy in Crowdsensing System Using Blockchain S. Sangeetha , K. Anitha Kumari , M. Shrinika, P. Sujaybharath, S. Muhil Varsini, and K. Ajith Kumar

Abstract In recent years, crowdsensing has gained prominence as it provides smart solution to a variety of real-world problems. Crowdsensing is an IoT-based data collection environment where workers collect the data from multiple locations for a reward. Such data collection from multiple user devices exposes the precise location of workers, which raises privacy concerns of crowd workers. Such privacy infringement restricts users from participating in the crowd. Hence, the success of crowdsensing is dependent on the security and privacy guarantee promised by such environments. This paper proposes a location privacy preserving technique in crowdsensing environment based on blockchain. Blockchain overcomes the issues in traditional crowdsensing, and the worker location is preserved with the proposed privacy preserving algorithm. The algorithm is implemented, and its performance is experimentally evaluated. Keywords Location privacy · Crowdsensing · Blockchain · Privacy · Security · Hyperledger

1 Introduction Crowdsensing also termed as mobile crowdsensing delivers a promising large-scale sensing [1, 2]. It relies on data collected from large number of participatory users’ devices like smart phones, wearable devices, and smart vehicles. Data collection takes place in these devices, and the aggregated information is sent to the server. Traditional crowdsensing systems depend on a trusted third party like upwork, freelancer. It

S. Sangeetha (B) · K. A. Kumari · M. Shrinika · P. Sujaybharath · S. M. Varsini · K. A. Kumar Department of Information Technology, PSG College of Technology, Coimbatore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_4

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follows a centralized architecture and has several drawbacks. The major drawbacks are single point of failure, vulnerable attacks like distributed denial of service attack (DDoS), Sybil attack, hefty processing, and maintenance fee from the centralized authority, privacy concerns. To solve these issues, blockchain-based mechanisms are proposed in the literature [3–6]. The crowdsensing platform consists of requesters and workers. The requesters usually post the task along with the reward for completion of the task. Workers move to the specific location and complete the task to get the reward. A variety of largescale applications uses these platforms for data collection and contains huge number of requesters and workers. Some of the applications include air quality monitoring [7], traffic detection [8], map generation [9], point of interest [10], environment monitoring [11]. In this job completion and submission, multiple communication happens between the server and worker. Usually, worker submits their precise location for task assignment within the range or bounds of the worker. In such scenario, privacy of the workers in crowdsensing platform is highly questionable. From literature, [12] worker privacy is violated in untrusted server in the following three ways: • For effective task assignment, workers submit their location to the server. • Once the worker accepts the task, their future location is known to the server. • Task completion by worker is rewarded by the server; hence, the server is aware of the task completed by the worker. A brief survey of the various privacy preserving mechanisms is provided by Sangeetha et al. [13]. Several solutions are proposed to protect location privacy like combining game theory and encryption [14], differential privacy, obfuscation [17], and k anonymity [18]. Most of the existing solutions are based on the assumption that the trusted server is involved in the crowdsensing. Also, they don’t consider the single point of failure and other issues in traditional crowdsensing. Blockchainbased crowdsensing overcomes the drawbacks in traditional crowdsensing. Since the blockchain is distributed, it is highly fault tolerant; processing fee is reduced, high security than traditional approach. Also, the users are associated with a public key, and their information is completely anonymous in blockchain environment. Hence, the issues like revealing the future location and task completion reward are addressed by blockchain design. A bigger challenge is the first issue of protecting the worker location. Literature survey suggests the usage of blockchain in federated learning [19], resource allocation, [20] and vehicular edge computing [21]. In this paper, an efficient algorithm is designed to protect worker privacy in blockchain environment. In existing work, private blockchain [12] with a trusted agent is designed. We observe several drawbacks with the work like managing synchronization between public and private blockchain is challenging, workers are forced to join multiple private networks to ensure privacy; it is not a holistic solution

Ensuring Location Privacy in Crowdsensing System Using Blockchain

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for untrusted server since the solution demands multiple trusted agents. These issues are addressed in this paper by proposing a simple and practical privacy preserving algorithm. The main contributions of this paper are summarized as follows: • Real identity of the worker along with their location information is protected from the untrusted sever. The solution removes the necessity for a trusted third-party server and provides high privacy to the workers. • The worker’s transaction is maintained in the transparent blockchain, and reidentification attack has to be prevented. Since the blocks added to the blockchain cannot be reversed or modified, it is important to prevent worker form attack. Hence, the location information is stored in encrypted form in the blocks to prevent reidentification. • Privacy preserving algorithm that works with such encrypted data is designed in order to achieve maximum privacy. • Further the implementation is evaluated for accuracy and time consumption. The paper is organized as follows: Sect. 2 introduces the preliminaries followed by proposed system in Sect. 3. In Sect. 4, the result analysis is presented followed by the conclusion in Sect. 5.

2 Preliminaries 2.1 Crowdsensing Crowdsensing involves data collection, data storage, and data upload. The data are collected by user with sensors and smartphones. The collected data from multiple users are aggregated to analyze a common task. Crowdsensing framework has a requester, worker, and server. The requester submits a task to the server which is assigned to a worker by the server. Further worker completes the sensing task for a reward. Server requires the precise location of worker for optimal task assignment. Hence, the worker location and task assignment are exposed to the server. This raises privacy concerns for the workers involved in data collection.

2.2 Blockchain Blockchain makes use of a public ledger to create a trust-dependent network [22]. Every transaction in the framework is added as a block to the public ledger which is viewable by every entity in the network [23, 24]. Every block addition is performed by miners who perform complex computations and verifies the transaction. Once

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a block is added to the chain, it cannot be retracted. The usage of blockchain for crowdsensing has the following advantages over the conventional mechanisms: • • • • •

Anonymity for users Eliminates single point of failure with decentralization Less processing charge with high integrity and there is no need for a Third party Blockchain is immutable.

Implementation of blockchain is accomplished with Hyperledger, and it is explained in the following section:

2.3 Hyperledger Storage of data in a non-tampered manner is of high importance in the current digital world to ensure transparency and non-repudiation. Blockchain, a distributed ledger technology, facilitates the secure transfer of records across the devices/nodes/ applications that ensures immutability. An advanced enterprise blockchain, namely Hyperledger, a distributed ledger blockchain software is used in this proposed work to record the transactions in the proposed work. The standard component/tool Hyperledger Sawtooth, a blockchain-as-a-service platform is applied in this work that is capable of running customized smart contracts by abstracting the underlying core system design. It aims to maintain the concept of truly distributed ledgers, thus making the smart contracts more secure and suitable for recording the transactions in the proposed work. After successful analysis, the results obtained are securely stored in the ledger. A performance study of Hyperledger Sawtooth is proposed by Zeshun Shi et al. [25]. In this paper, the performance of Sawtooth is applied and tested in the cloud environment, and the results are observed. By adjusting the Scheduler and maximum batches per block, performance is optimized in this work. A novel embedded architecture to run Hyperledger Sawtooth application is presented by Roland Kromes et al. [26]. This work employs a Hyperledger Sawtooth blockchain to design the primary IoT architecture to interact with other devise/nodes. A significant gain is obtained by this modification.

3 Proposed System Consider a set of workers {w1 , w 2 , . . . , w n }, each of person has location w i (x, y). The requesters {r 1 , r 2 , . . . ., r m } release a set of tasks {t 1 , t 2 , . . . ., t k }. A worker w i completes a task t j and gets the reward. The server is considered as untrusted, and the worker location w i (x, y) should be preserved to ensure privacy. Hence, the

Ensuring Location Privacy in Crowdsensing System Using Blockchain

39

objective of this paper is to protect the worker location from untrusted third-party server. Existing crowdsensing systems requires a trusted third-party server. The workers location and task assignment are revealed to the server. Such information revealed to the server creates a severe threat to user privacy. In this paper, an encryption-based algorithm is proposed to protect the location privacy of user using blockchain. The tasks are crowdsourced to worker without relying on trusted third-party server. Such privacy preservation is enforced with CrypTen and blockchain which is discussed in the following paragraphs: The proposed system is implemented in blockchain using Hyperledger. Since blockchain is transparent, its public ledger is viewable to all the entities in the network. Using our algorithm, the worker location is stored in the public ledger in encrypted form. Such encrypted storage preserves the location information of workers, and they cannot be identified. Hence, reidentification attack cannot be launched by untrusted server or other entities in the blockchain. The location information of the worker is stored in encrypted form using CrypTen [27]. CrypTen is a privacy preserving machine learning framework written in PyTorch. The usage of CrypTen helps in secure computation performed in encrypted form. Few mathematical computations can be conducted on the encrypted form itself with the help of CrypTen, and data do not need to be decrypted for computation. The user encrypts their location information in their device using CrypTen and send it to the server. The details of the location privacy preserving task assignment are elaborated in Algorithm 1. Algorithm 1: Privacy Preserving task assignment Input: Location of the worker E(wi (x, y)), Task T with Reward Budget R and location E(T j (x, y)) Output: Task assigned to the worker using encrypted location information 1. The server releases all the tasks {t1 , t2 , . . . ., tk } with encrypted location to the blockchain 2. Workers interested in the task uploads their encrypted location information to server E(wi (x, y)) 3. Server compares the encrypted task location E(T j (x, y)) and worker location E(wi (x, y)) 4. Based on the worker location the reward is allocated by the server 5. Worker verifies the reward and chooses the task based on the reward

In algorithm 1 three entities are involved in the system. Those entities are requester, worker, and miner. A requester initiates the crowdsensing by submitting tasks and encrypted location for the tasks to the server. Worker chooses the task assigned in the blockchain and completes the task for a reward. Miner authenticates every transaction that happens in the blockchain and they are also responsible for updating the ledger. In step 1 the server releases the task obtained from the requester with encrypted location to the blockchain. In order to protect privacy, the worker uploads the encrypted location information to the blockchain in step 2. The server compares encrypted worker location and task location with the help of crypten (step 3). Further

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the reward calculation is performed by the server and worker chooses the task based on the reward.

4 Privacy Analysis In this section, three privacy threats in conventional blockchain system are realized, and an analysis on solutions to handle these attacks is provided. • The worker submits their location to the server in order to obtain the tasks given by the requester. Such location information submission to server exploits worker privacy. In the proposed algorithm, the worker encrypts their location before submitting it to the server. Since the worker location is encrypted, it preserves the privacy of workers involved in the blockchain. • The future location of the worker is revealed in conventional blockchain. This privacy violation happens since the server knows tasks assigned to the worker. In the proposed system, the privacy is preserved with the help of encryption of both the task location and worker location. • Previous location of the worker is also vulnerable to the server in traditional blockchain environment. Through the payment process made for previous task, the server identifies the worker location. In the proposed system, all the worker locations are in encrypted form and does not reveal the worker location. Based on the above analysis, it is evident that the proposed algorithm preserves the privacy of workers in blockchain environment.

5 Implementation and Result Analysis It is possible to deploy more than one Sawtooth node with separate permissions. There is no centralized service that potentially leaks transaction patterns or other confidential data. The decentralization feature, on top of the cryptography, makes blockchain to offer higher security than other systems. Blockchain is employed in this work to record each and every transaction that happens across edge devices and its communication with cloud/storage server. All digital information is recorded across the devices completely in a decentralized manner that ensures immutability. In Hyperledger Sawtooth, blocks and batches of the clients/nodes are validated in a similar method by validator. The block validation process checks for on-chain transaction permissions to checks the entity who is allowed to issue blocks and batches. These batches are sent to the transaction scheduler. The network layer ensures the communication between the validators of a Sawtooth. It performs its activities by

Ensuring Location Privacy in Crowdsensing System Using Blockchain

41

Fig. 1 A sample committed transaction

interconnecting REST API, transaction processors, and clients/nodes. Both serial and parallel scheduling of transactions are supported by Sawtooth. Transaction handling and execution is performed efficiently by handling the transactions that alters the same state addresses and also transactions within the same block. Sawtooth has no block level restrictions. This helps improve the performance of transactions and prevents double spending. Once the transaction processor validates the request sent by the client, transaction is committed. A sample committed transaction output is shown in Fig. 1. Few hash values of committed transactions are listed in Table 1 and for better understanding pictorially represented in Fig. 2. Transaction per second (TPS) is considered as the standard evaluation metric to measure the performance of blockchain deployed in any sort of machines. In that connection, the transactions happened between edge device and cloud server are recorded and depicted in Fig. 3. Table 2 shows the number of transactions recorded in edge device and cloud server during its peak load. S. No

Machine type

Avg. transactions/minute

1

Cloud server

7

2

Edge server

4

11:33:21 AM

11:32:24 AM

{ “address”:"1d78d50140b244112641dd78dd4f93b6c9190dd46e0099194d5a44257b7efad6ef9ff4”, “data”: “eyJhcmRzIjoiTm8iLCJwYXRpZW50XelkIjoiMiJ9” }

{ “address”:"1d78d5014dff4ea340f0a823f15d3f4f01ab62eae0e5da579ccb851f8db9dfe84c58b2”, “data”:"eyJhcmRzIjoiWWVzIiwicGF0aWVudF9JZCI6IjEifQ = = “ }

{ “address”:"be369ik41o24i2b606kk0i0346398ae6617v00w3ke652331v2o27711626219752i9kzw”, “data”:” h8yV8DwSnVqEoLAuB2zTbbx7EGLz6URqE8gFPQ6HgTrK” }

{ “address”:"a139648f58184798049z6457f61e7592o9b2646eeb49804v3w49w3ab6476275l61a48k”, “data”:” Wb67Y3ycLQmtYODZY89ASKeOE22GEB5qIaUqB7RAomu” }

{ “address”:"v5b1192916b42e61522822w1l5vz6123k43473e20333b2652zwz453f1e426k531l1452”, “data”:” uGTWomt7jpjQ3IqaXOeCAV3Ksw0TlhXG9RIzGH33HtDV” }

2

3

4

5

6

11:36:14 AM

11:35:10 AM

11:34:32 AM

(continued)

Transaction Time 11:32:23 AM

Hashed Values of Results

{ “address”:"1d78d5013bafbf0888202d10133o93a1b8433f50563b93c14acd05b79028eb1b127990”, “data”:"eyJhcmRzIjoiWWVzIiwicGF0aWVudF9JZCI6IjMifQ = = “ }

ID

1

Table 1 Hash values of committed transactions

42 S. Sangeetha et al.

11:39:21 AM

11:40:22 AM

{ “address”:"20s98a1567rv66857788125y4d12402 × 5584c9270 × 67w58629yua66w5v93f348882559”, “data”:” GjnO4wdMxnZ2iBngFvIbjlk1fcsucM8oj9nrkX40fbNo” }

{ “address”:"757uq118176130g162ase8q74627cw137bu9c459s481eg39c22cf737q41rv23w5e55b5”, “data”:” l3o5goQLYocYEnZCyGDosBtynFvrVpmjyVnWI0PHcxLe” }

{ “address”:"bt51u60 × 7223v2gau67tx6q7cw013r8052vf3f63ufatr952b5035727908 × 0547x9861c”, “data”:” 3kPOftzprHumi52lxzhtmvdaqxTdamf9rnXVf4laGuie” }

9

10

11:42:26 AM

11:37:15 AM

8

Transaction Time

Hashed Values of Results

{ “address”:"585e46w63f361wz34ve48w64653e16oe34lee0e6w94322lf3fz3a5058b2332491063v4”, “data”:” r71ybUUqJubJgmdgmSjx3c2uvqnibXCvzZUiGwjqbedQ” }

ID

7

Table 1 (continued)

Ensuring Location Privacy in Crowdsensing System Using Blockchain 43

44

Fig. 2 Committed transaction versus transaction time in cloud server

Fig. 3 Transactions in edge device and cloud server

S. Sangeetha et al.

Ensuring Location Privacy in Crowdsensing System Using Blockchain Table 2 Recorded transactions

45

Time (hr:min)

No. of transactions Cloud server

Edge server

11:31 am

5

4

11:32 am

7

5

11:33 am

6

5

11:34 am

7

5

11:35 am

8

4

11:36 am

7

4

11:37 am

6

4

11:38 am

6

5

11:39 am

7

4

11:40 am

8

3

11:41 am

6

3

11:42 am

8

4

6 Conclusion This paper uses blockchain-based crowdsensing that overcomes the drawbacks in traditional sensing system. But, usage of blockchain exposes worker location since every block is transparent. Therefore, this paper proposes an encryption-based location storage to ensure privacy of workers. Even though the worker transaction history is available in the public ledger, the encrypted location information prevents worker from reidentification attacks. The experimental method confirms that the proposed solution performs well while protecting the privacy of workers.

References 1. Guo B, Wang Z, Yu Z, Wang Y, Yen NY, Huang R, Zhou X (2015) Mobile crowd sensing and computing. ACM Comput Surv 48(1):1–31 2. Tong Y, Zhou Z, Zeng Y, Chen L, Shahabi C (2019) Spatial crowdsourcing: a survey. VLDB J 29(1):217–250 3. Li M, Weng J, Yang A, Lu W, Zhang Y, Hou L, Liu JN, Xiang Y, Deng RH (2019) CrowdBC: a blockchain-based decentralized framework for crowdsourcing. IEEE Trans Parallel Distrib Syst 30:1251–1266 4. Berdik D, Otoum S, Schmidt N, Porter D, Jararweh Y (2021) A survey on blockchain for information systems management and security. Inf Process Manag 58:102397 5. Hewa T, Ylianttila M, Liyanage M (2021) Survey on blockchain based smart contracts: applications, opportunities and challenges. J Netw Comput Appl 177:102857 6. Belchior R, Vasconcelos A, Guerreiro S, Correia M (2021) A survey on blockchain interoperability: past, present, and future trends. ACM Comput Surv 54

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7. Mineraud J, Lancerin F, Balasubramaniam S, Conti M, Tarkoma S (2015) You are AIRing too much: assessing the privacy of users in crowdsourcing environmental data. In: Proceedings—14th IEEE international conference on trust, security and privacy in computing and communications. 1, pp 523–530. IEEE, Finland 8. Mohan, P., Padmanabhan, V. N., Ramjee, R. Nericell: Rich monitoring of road and traffic conditions using mobile smartphones. In: SenSys’08—proceedings of the 6th ACM conference on embedded networked sensor systems. pp 323–336, Association for Computing Machinery, Raleigh, NC, USA. https://doi.org/10.1145/1460412.1460444 9. Chen X, Wu X, Li XY, Ji X, He Y, Liu Y (2016) Privacy-aware high-quality map generation with participatory sensing. IEEE Trans Mob Comput 15:719–732 10. Chon Y, Lane ND, Li F, Cha H, Zhao F (2012) Automatically characterizing places with opportunistic crowdsensing using smartphones. In: UbiComp’12—proceedings of the 2012 ACM conference on ubiquitous computing, pp 481–490, Association for Computing Machinery, Pittsburgh, Pennsylvania. https://doi.org/10.1145/2370216.2370288 11. Rana RK, Chou CT, Kanhere SS, Bulusu N, Hu W (2010) Ear-phone: An end-to-end participatory urban noise mapping system. In: Proceedings of the 9th ACM/IEEE international conference on information processing in sensor networks, IPSN ’10, pp 105–116. Association for Computing Machinery, Stockholm, Sweden. https://doi.org/10.1145/1791212.1791226 12. Yang M, Zhu T, Liang K, Zhou W, Deng RH (2019) A blockchain-based location privacypreserving crowdsensing system. Futur Gener Comput Syst 94:408–418 13. Sangeetha S, Sudha Sadasivam G (2019) Privacy of big data: a review. In: Dehghantanha A, Choo KK (eds) Handbook of big data and IoT security. Springer, Cham, pp 5–23 14. Xiong J, Ma R, Chen L, Tian Y, Li Q, Liu X, Yao Z (2020) A Personalized privacy protection framework for mobile crowdsensing in IIoT. IEEE Trans Ind Inf. 16:4231–4241 15. Wang Z, Guo C, Liu J, Zhang J, Wang Y, Luo J, Yang X (2021) Accurate and privacy-preserving task allocation for edge computing assisted mobile crowdsensing. IEEE Trans Comput Soc Syst. https://doi.org/10.1109/TCSS.2021.3070220 16. Wang L, Yang D, Han X, Wang T, Zhang D, Ma X (2017) Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation. In: 26th Int. World Wide Web Conf. WWW 2017, pp.627–636. International World Wide Web Conferences Steering Committee, Perth, Australia. https://doi.org/10.1145/3038912.3052696 17. Ardagna CA, Cremonini M, di Vimercati SD, Samarati P (2011) An obfuscation-based approach for protecting location privacy. IEEE Trans Dependable Secur Comput 8:13–27 18. Xing L, Jia X, Gao J, Wu H (2021) A location privacy protection algorithm based on double K-anonymity in the social internet of vehicles. IEEE Commun Lett. https://doi.org/10.1109/ LCOMM.2021.3072671 19. Nguyen DC, Ding M, Pham QV, Pathirana PN, Le LB, Seneviratne A, Li J, Niyato D, Poor HV (2021) Federated learning meets blockchain in edge computing: opportunities and challenges. IEEE Internet Things J 8:12806–12825 20. Zhang L, Zou Y, Wang W, Jin Z, Su Y, Chen H (2021) Resource allocation and trust computing for blockchain-enabled edge computing system. Comput Secur 105:102249 21. Gawas M, Patil H, Govekar SS (2021) An integrative approach for secure data sharing in vehicular edge computing using blockchain. Peer-to-Peer Netw Appl 14:2840–2857 22. Blockchains: The great chain of being sure about things | The Economist. https://www. economist.com/briefing/2015/10/31/the-great-chain-of-being-sure-about-things, Accessed 20 Dec 2021 23. Kumari KA, Padmashani R, Varsha R, Upadhayay V (2020) Securing internet of medical things (IoMT) using private blockchain network. Intell Syst Ref Libr 174:305–326 24. Dharani D, Kumari KA, Aishwarya S, Sangavi GM, Lavanya N (2020) A robust blockchain framework for healthcare information system. Int J Eng Adv Technol 9:1149–1154 25. Shi Z, Zhou H, Hu Y, Jayachander S, de Laat C, Zhao Z (2019) Operating permissioned blockchain in clouds: a performance study of hyperledger sawtooth. In: Proceedings 2019 18th international symposium on parallel and distributed computing ISPDC, pp 50–57. Netherlands. https://doi.org/10.1109/ISPDC.2019.00010

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Energy-Efficient Arithmetic State Machine-Based Routing Algorithm in Cognitive Wireless Sensor Network S. Kalpana, R. Gunasundari, and S. Sneha

Abstract The sensor and network lifetimes extension is one of the most critical issues in the widespread usage of Cognitive Wireless Sensor Networks (CWSNs). The energy harvesting (EH) sensors are used to extend the sensor lifetime. The energy harvesting sensors can harvest their required energy from environment in different methods, resulting in longer lifetime of the sensor. The amount of its harvested energy is more than the consumption power, and then, EH sensor is allowed to transmit its data. The algorithmic state machine (ASM)-based routing algorithm has been proposed to extend the network lifetime and allocate optimal resource. The cognitive radio community is an answer for the spectrum shortage and unlicensed secondary users (SUs) take advantage of unoccupied spectrum bands on a case-by-case basis. Keywords Resource allocation · Energy efficiency · Wireless sensor networks

1 Introduction The green useful resource allocation like electricity and power harvesting era might expend sensors’ lifestyles time and community lifestyles time and play a main position in maximizing machine performance. Cognitive Radio Networks (CRNs) gift a promising answer for the spectrum shortage in Wi-Fi networks to deal with the ever-growing call for better bandwidth in cellular communications. In CRNs [1], unlicensed secondary users (SUs) take

S. Kalpana (B) IFET College of Engineering, Puducherry Technological University, Pillaichavady, India e-mail: [email protected] R. Gunasundari Puducherry Technological University, Pillaichavady, India S. Sneha IFET College of Engineering, Gangarampalaiyam, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_5

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advantage of unoccupied spectrum bands on a case-by-case basis without interfering with certified number one users (PUs). This guarantees a huge set of ability programs, given the shortage of the unlicensed Wi-Fi spectrum, along with allotted cell programs for high call for and highly crowded eventualities which include the Internet of Things, tremendous cell video, and catastrophe or emergency reaction settings. Hosein et al. in [2] proposed the power harvesting sensors to conquer the sensor lifetime trouble. Zhaohui et al. in [3] has taken into consideration a power green useful resource allocation for a device-to-device-enabled mobile community with nonlinear power harvesting, specifically that specialize in distinct a couple of get admission to strategies, particularly non-orthogonal a couple of get admission to (NOMA) and time department a couple of get admission to (TDMA). Sultana et al. in [4] has taken into consideration a cognitive D2D communique machine to enhance the useful resource allocation performance in addition to spectral performance. The Wi-Fi-powered cell community become studied wherein committed power beacons are deployed withinside the cell community to rate cellular terminals. Moreover, the Wi-Fi-powered cognitive radio community has been taken into consideration in [5], wherein lively number one customers are applied as strength transmitters for charging their close by secondary customers that are not allowed to transmit over the equal channel because of sturdy interference. Furthermore, considering that radio indicators bring power in addition to statistics on the identical time, a joint research of simultaneous Wi-Fi statistics and strength transfer (SWIPT) has currently drawn a great attention. To recall a power green ASM primarily based on a total routing set of rules in the cognitive Wi-Fi sensor community [6]. Our goal is to grow the community lifetime, triumph over the spectrum shortage, and overall power intake withinside the sensors. To observe, the ASM is primarily based on a total set of rules to locate the most excellent useful resource allocation within the community and develop the community lifestyles’ time. The cognitive radio community is an answer for the spectrum shortage and unlicensed secondary users (SUs) take advantage of unoccupied spectrum bands on a case-by-case basis without interfering with certified number one users (PUs).

2 Existing System This package contains the WEHSN One HAP (Hybrid Access Point), which is connected to an endless supply of electricity with M sensors, which can maximise the energy collection (see Fig. 1). The “harvest-and-then-transmit” protocol is proposed to selects an energy-efficient path based on multiple parameters. The TDMA is primarily based on totally Cognitive Energy Harvesting Sensor Network (CEHSN) wherein the time slot consists of two-time intervals; the primary one is applied to soak up strength while the second is used to transmit the sensors’ information. At first, sensors harvest strength in downlink (DL) from a Wireless Energy

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Fig. 1 Flow diagram of ASM algorithm

Transferring (WET), and then they transmit facts in uplink (UL) toward a Wireless Information Transmission (WIT). hi is denoted as UL channel benefit among sensor i and HAP, and gi is denoted as DL channel benefit among the HAP. The quantity of harvested strength at sensor i may be expressed as, E ih = (ηi P0gi − Pcri )τ 0 = f i τ 0∀i ∈ {1, 2, ....M}

(1)

The achievable throughput for sensor i can be expressed as, ri = τ i log 2(1 + pi hi σ 2)

(2)

wherein σ 2 is the additive white Gaussian noise power at the HAP, Therefore, the gadget throughput could be achieved as R=

M  i=1

ri =

  Pi hi σ i log 2 1 + σ2 i=1

M 

(3)

Then, the fed on strength of every sensor and overall strength intake with inside the network might be as follows,

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E iT = (P I + Pcti )τ i ET =

M  i=1

E iT

=

M 

( pi + Pcti )τ i

(4)

(5)

i=1

Also, the Energy Efficiency (EE) is described as E E = R/E T

(6)

To maximize EE, set C1 : E iT ≤ E in

C2 : τ 0 +





M

τi = Tmax C3 : 0 ≤ τ0 , 0 ≤ τi, 0 ≤ pi

(7)

i=1

wherein C1 constraint assures that the fed on electricity in WIT period is much less than the harvested electricity in every sensor. No records could be transmitted, if the primary constraint, C1, does now no longer keep for sensor i. . The hassle described in (7) is understood as fractional programming (FP). Thus, we may want to convert the optimization hassle given in (7) to parametric form as {R − λE T }.

3 Proposed System The cognitive Wi-Fi sensor community is used to enhance the electricity law on aid dealing with method. The Cognitive Wireless Sensor Network (CWSN) is proposed to avoid spectrum scarcity, improving quality of service and power saving. The cognitive radio community is an answer for the spectrum shortage, and unlicensed secondary users (SUs) take advantage of unoccupied spectrum bands on a case-bycase basis. The Algorithmic State Machine (ASM)-based routing algorithm has been proposed to extend the network lifetime and to allocate optimal resource. Cognitive Wireless Sensor Network (CWSN) is a brand-new paradigm in a WS community area that makes use of the spectrum aid successfully for bursty traffic. The device has the functionality of packet loss reduction, electricity waste reduction, excessive diploma of buffer management, and has higher conversation quality. The Cognitive Wireless Sensor Network (CWSN) is an answer to the spectrum shortage and unlicensed secondary users (SUs) opportunistically make use of vacant quantities of the spectrum. The Algorithmic State Machine (ASM)-based routing algorithm is easier to understand. It is used to improve the network lifetime and used to allocate optimized resource in the surrounding area. To find optimized proper value of λ, wherein λ is a non-negative parameter.

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Fig. 2 System model of cognitive wireless energy harvesting sensor network

The Algorithmic State Machine (ASM) algorithm is used to find optimized proper of λ (Fig. 2). To allocate an optimal resource using ASM algorithm. Wherein λ is found optimized manner in ASM algorithm. The red line to transmit the power and blue line is a transmission of information. The harvesting energy is more than energy consumption, then information is transmitted to the HAP. The cognitive radio community is an answer for the spectrum shortage and unlicensed secondary users (SUs) take advantage of unoccupied spectrum bands on a case-by-case basis without interfering with certified number one users (PUs).

4 Simulation Results Due to node density, the network lifetime is increased. The CMAC is improving the security in the transmission of information (Figs. 3, 4, 5 and 6). The Algorithmic State Machine (ASM)-based routing algorithm improves the Network lifetime in cognitive wireless sensor network. The energy efficiency is more compared to other methods. The energy efficiency is evaluated by using a number of sensors in each node. The dynamic environment is plotted using both throughput and node distance. Each and every node can have harvesting sensor. These sensors can harvest the energy from the rapid change environment. The ASM algorithm is used to more decrease a packet loss compared to other algorithms. The packet loss is plotted using node density and transmit power. The DCF is the data carrying factor. Two data carrying factors are used to transmit the information from one node to another node. When the information was transmitted from one node to another node, at that time packet loss will occur in nodes. The ASM algorithm minimizes a loss of packets (Fig. 7).

54 Fig. 3 Network lifetime

Fig. 4 Energy efficiency

Fig. 5 Dynamic environment

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Fig. 6 Packet loss

Fig. 7 Throughput versus transmit power

The ASM algorithm for maximizing the throughput, due to transmit power and node distance. In two-time intervals, the primary one is applied to soak up strength while the second is used to transmit the sensors’ information (Fig. 8). The overall energy consumption is plotted by using source to the destination node and overall energy consumed from HAP.

5 Conclusion To advocate a brand-new gadget version, the Wi-Fi sensors use the harvest-thentransmit protocol to attain the desired power of information transmission. Also, the sensors use TDMA within side the ultimate time c programming language to talk

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Fig. 8 Total energy consumption

with a Hybrid Access Point, and to derive the optimization trouble for strength performance because the gadget overall performance, making use of the limitations at the time agenda parameter and transmission strength for every sensor. Using ASM is primarily based totally on routing set of rules to resolve the trouble and to locate the optimized useful resource. The throughput is lower in evaluation of the alternative methods, and the spectrum shortage is triumph over on this method, ensuing within side the higher strength performance because of the overall performance of the network.

References 1. Pei L, Yang Z, Pan C, Huang W, Chen M, Elkashlan M, Nallanathan A (2018) Energyefficient d2d communications underlaying noma-based networks with energy harvesting. IEEE Commun Lett 22(5):914–917 2. Wang L, Jin H, Ji X, Li Y, Peng M (2013) Power allocation for cognitive D2D communication assisted by two-way relaying. IEEE Sym. on Microwave, Antenna, propagation and emc technologies for wireless communications (MAPE), pp 1–6 3. Ju H, Zhang R (2014) Throughput maximization in wireless powered communication networks. IEEE Trans Wireless Commun 13(1):418–428 4. Khoshkholgh MG, Zhang Y, Chen K-C, Shin KG, Gjessing S (2015) Connectivity of cognitive device-to-device communications underlying cellular networks. IEEE J Sel Areas Commun 33(1):81–99 5. Wang YE, Lin X, Adhikary A, Grovlen A, Sui Y, Blankenship Y, Bergman J, Razaghi HS (2017) A primer on 3g pp narrowband internet of things. IEEE Commun Mag 55(3):117–123 6. Huang J, Xing C, Wang C (2017) Simultaneous wireless information and power transfer: technologies, applications, and research challenges. IEEE Commun Mag 55(11):26–32 7. Kang K, Ye R, Pan Z, Liu J, Shimamoto S (2018) Full-duplex wireless powered Iot networks. IEEE Access vol 6, pp 53 546–53 556 8. Zhu Z, Zhou F, Chu Z, Hu RQ, Xiao P (2018) Wireless powered sensor networks for IoT: maximum throughput and optimal power allocation. IEEE Internet Things J 5(1):310–321

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9. Sultana A, Zhao L, Fernando X (2017) Efficient resource allocation in device-to-device communication using cognitive radio technology. IEEE Trans Veh Technol 66(11):10 024– 10 034 10. Nobar SK, Mehr KA, Niya JM (2016) Rf-powered green cognitive radio networks: architecture and performance analysis. IEEE Commun Lett 20(2):296–299 11. Nobar SK, Mehr KA, Niya JM, Tazehkand BM (2017) Cognitive radio sensor network with green power beacon. IEEE Sensors J PP(99): 1–1 12. Azarhava H, Niya JM (2020). Energy efficient resource allocation in wireless energy harvesting sensor networks. IEEE Wireless Commun Lett 1–1. https://doi.org/10.1109/lwc.2020.2978049 13. Yang Z, Xu W, Pan Y, Pan C, Chen M (2018) Energy efficient resource allocation in machineto-machine communications with multiple access and energy harvesting for iot. IEEE Internet Things J 5(1):229–245 14. Ali A, Hamouda W (2016) Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Commun Surveys Tutorials PP(99):1–29 15. Ding J, Jiang L, He C (2018) User-centric energy-efficient resource management for time switching wireless powered communications. IEEE Commun Lett 22(1):165–168

Design of Modified V-shaped Slot Loaded on Substrate-Integrated Waveguide Antenna for Smart Healthcare Applications R. Muthu Krishnan and G. Kannan

Abstract This paper employs a v-shaped slot as a radiator on substrate-integrated waveguide for smart healthcare applications. The two-slot arms are placed with an angle of 110° for radiation at the operating 5.8 GHz frequency. The proposed modified v-shaped slot substrate-integrated waveguide (MVSIW) antenna uses jeans as the dielectric substrate with a relative dielectric constant of 1.6 and a loss tangent of 0.025. The top and bottom layers of the substrate-integrated waveguide cavity are realized using electro textiles. The brass eyelets are deployed as metallic vias to create the perfect electric wall in the substrate-integrated waveguide structure. The proposed MVSIW antenna is constructed using materials that enable high flexibility for wearable applications. The proposed MVSIW antenna has been analyzed and investigated for the parameters such as reflection coefficient, Z-parameters, and radiation pattern. The proposed MVSIW antenna has a bandwidth of about 150 MHz (5.75 GHz to 5.9 GHz frequency). The proposed MVSIW antenna illustrates good co- and cross-polarization with 4 dB gain, which is suitable for short communication in smart healthcare applications. The MVSIW antenna prototype is fabricated using jeans material, and the measurement results of the hardware prototype are validated using a vector network analyzer (VNA). Keywords Substrate-integrated waveguide · Wearable antenna · Smart healthcare applications

R. M. Krishnan (B) · G. Kannan Department of ECE, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India e-mail: [email protected] G. Kannan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_6

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1 Introduction Smart health care is the new domain of research that facilitates the healthcare providers in improving the quality of the healthcare services served to society. Smart health care provides services such as telemedicine, patient health monitoring through flexible electronics, and communicating the patient’s significant health data to distant healthcare providers. Upgradation in health care is necessary, which helps and secures the healthcare providers in adverse conditions and natural calamities. Smart health care eases people from accessing quality health care from healthcare providers, which improves the quality of life and health of the people. Wireless communication devices have evolved from handheld devices to wearable devices in the last decade. Wearable devices enable seamless communication of health signals from the human body to healthcare providers in smart healthcare applications. The wearable systems demand flexibility and unobtrusive integration from wearable sensors and wearable antennas in smart healthcare applications for patient health monitoring purposes. In smart healthcare applications, wearable antenna plays a vital role in creating the wearable system’s flexible and unobtrusive integration. The wearable antenna should be constructed using flexible materials to meet the requirement of unobtrusive integration in patient health monitoring applications. The jeans fabric suits wearable antenna because of its thick and flexible nature [1, 2]. In wearable antenna design, specific absorption rate (SAR) plays a vital role, and it is considered to be the most important parameter in determining the amount of hazardous energy absorbed by the body. SAR gives a measure of the absorbed electrical energy in the vicinity of the wearable antenna by the human body and should be less than 1.6 W/Kg. The substrate-integrated waveguide technology can be deployed to reduce specific absorption rate metrics. Substrate-integrated waveguide cavity blocks the backward radiation and reduces the specific absorption rate at a considerable rate. In the proposed MVSIW wearable antenna, substrate-integrated waveguide cavity is deployed to mitigate the backward radiation which travels toward the human body during wireless health signal transmission to healthcare providers. In the recent literature, significant research contributions have been made on substrate-integrated waveguide technology implemented with the wearable antenna. In [3], a 5.8 GHz operating band substrate-integrated waveguide (SIW) cavity with a circular ring slot loaded on SIW is fabricated. A piece of conductive fabric is chosen to realize the top and bottom planes. To realize a perfect electric wall in the substrate-integrated waveguide cavity, conductive threads are knitted on the top and bottom planes. This antenna’s measured peak gain and bandwidth are reported as 3.12 dBi and 230 MHz, respectively [3]. Similarly, the MVSIW antenna proposed in this work chooses 5.8 GHz as the operating frequency for smart healthcare applications. A wearable antenna for on-body applications has been reported by Agneessens [4], and it uses a SIW of quarter mode, which is usually denoted by QMSIW. This antenna performs 4.2 dBi and 3.8 dBi in the human body’s free space and vicinity, respectively. This antenna employs conductive polyester taffeta for both planes in the cavity and brass eyelets for perfect electric wall realization. This antenna operates at

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2.4 GHz center frequency which is an industrial, scientific, and medical (ISM) band. It has loaded two-slot arms in an orthogonal position on a circular QMSIW cavity for radiation. The main drawback of this antenna is that it uses a 2.4 GHz frequency for operation, which is more crowded than the 5.8 GHz frequency in unlicensed industrial, scientific, and medical bands. In [5], the half mode of the SIW is employed for dual-band wearable antenna for on-body communication. It operates on both 2.4 and 5.8 GHz in ISM bands. Congestion in the 2.4 GHz can be reduced by switching to 5.8 GHz, which is also the unlicensed ISM band. It uses electro textiles, brass eyelets, and wearable clothes to create the SIW-backed wearable antenna with high flexibility and unobtrusive integration with the on-body communication systems. This antenna radiates with 4.1 and 5.8 dBi gain at 2.4 GHz and 5.8 GHz, respectively. In addition, measured SAR values at 2.4 GHz and 5.8 GHz are 0.55 and 0.90W/Kg, respectively. The drawback of this antenna is that it uses a coaxial feeding structure which inhibits unobtrusive integration of the wearable antenna with on-body systems. Another SIW antenna with miniaturization is reported in [6], which operates at 2.4 and 5.8 GHz. It achieves good miniaturization compared with the other reported papers by bifurcations on a perfect magnetic line in the cylindrical substrate-integrated waveguide cavity. It employs a slit in the top plane of the substrate-integrated waveguide cavity for radiation at 2.4 and 5.8 GHz. The main drawback of this antenna is that it measures 2.1 dBi gain at the lower operating frequency, which is not adequate for on-body communication applications. Kumar et al. [7] have reported broadband substrateintegrated waveguide antenna with dual circularly polarized characteristics. This antenna operates at 12.5 GHz and uses defected ground structure (DGS) for good isolation between two ports. The drawback of this antenna is that a complex structure has been employed in the ground plane to improve isolation between the two ports, leading to fabrication errors in the end antenna prototype. The possibilities and limitations of quarter mode substrate-integrated waveguide are thoroughly investigated [8], and circular polarization based on quarter SIW is fabricated and validated. In [9], Arvind Kumar reported a quarter SIW for quadruplexing applications. It operates at 3.5, 5.2, 5.5, and 5.8 GHz frequencies, and corresponding gains are 5.43, 4.10, 3.56, and 3.6 dBi, respectively. Four v-shaped slots are loaded on four-quarter mode substrate-integrated waveguides in the single antenna for quadruplexing applications, and by changing the slots’ width and adding metallic vias orthogonal to the slot, good isolation among the four ports is achieved [10]. Another quadruplexing antenna with modified cross-slot loading on the top conductor of the SIW is reported [11]. By modifying the shape of the slots, good isolation among the four ports has been realized in [11]. Even though there have been considerable research contributions on the SIW cavity, there is still a demand to create a less complex, compact slot antenna based on SIW cavity for the 5.8 GHz operating frequency explored in this paper.

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2 Antenna Design The proposed MVSIW wearable antenna employs a substrate-integrated waveguide as the cavity, and two-slot arms are loaded on the cavity with an angle of 1100 between them for the maximum radiation in the desired 5.8 GHz frequency. Compared with the 2.45 GHz ISM band, the 5.8 GHz ISM band is less crowded and less explored, so the 5.8 GHz ISM band is chosen as desired frequency. The proposed MVSIW antenna has used 0.57 mm thickened jeans fabric as the dielectric substrate. The dielectric constant of the selected jeans fabric is 1.6, and the loss tangent value is 0.025. In order to realize an effective substrate-integrated waveguide structure, guidelines are given in [12 and 13] followed. Below expressions are considered for selecting metallic vias parameters to create an effective perfect electric conductor wall in the substrate-integrated waveguide cavity. d ≤ 0.1λg

(1)

p ≤ 2d

(2)

d stands for the diameter of the cylindrical vias (Brass eyelets) in the SIW cavity, and p stands for the distance between two located cylindrical vias. λg is the guided wavelength of the operating 5.8 GHz frequency. The diameter of the brass eyelets in this paper is selected as 4 mm, and spacing between two brass eyelets is chosen as 7 mm for effective confinement of electromagnetic energy inside the substrateintegrated waveguide for 5.8 GHz operating frequency. f mn0 =

1 √

2π με

/( mπ )2 a

+

( nπ )2 2b

(3)

The above equation is used to calculate the initial value for the length and width of the MVSIW antenna and then optimized for good antenna performance. The width and length of the ground structure are selected as 36 mm, which is approximately equal to the 0.87λg of the operating 5.8 GHz frequency. The MVSIW antenna optimized the top plane structure dimensions to achieve desired antenna performance with the help of Ansys HFSS. The layout of the top plane of the MVSIW antenna is drawn in Fig. 1. The length (L) and width (W) of the MVSIW antenna are optimized to 22 mm and 30 mm, respectively. Two-slot arms are placed at a distance of approximately half-guided wavelength from the microstrip feed. Two-slot arms are located with a v-shape for antenna radiation. The v-shaped slot radiator possesses a good radiation pattern along the broadside direction compared with the other radiators in the SIW slot antenna, so the v-shaped radiator is selected in the proposed antenna. The length of each slot arm (A1 and A2 ) is selected as 13.79 mm to operate at 5.8 GHz. The dark circle in the MVSIW antenna layout in Fig. 1 represents the cylindrical vias of the MVSIW antenna. The microstrip feed is employed for the excitation of the MVSIW antenna, and its dimensions are shown in Table 1.

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63

Fig. 1 Layout of the MVSIW top conductor

Table 1 MVSIW antenna dimensions

Dimensions

Value (mm)

L

22

W

30

A1

13.79

A2

13.79

L1

25

p

7

d

4

m

7

n

11

v

2.13

3 Results and Discussion The simulated S11 reflection coefficient of the MVSIW antenna is—25 dB at operating 5.8 GHz frequency. The vector network analyzer is employed for S11 reflection coefficient measurement. The simulated and measured reflection coefficient is presented in Fig. 2. The discrepancies in the S11 value of the simulated and measured antenna are attributed to fabrication errors. The bandwidth of the MVSIW antenna is 150 MHz which is 5.75 to 5.9 GHz. In the MVSIW antenna, the v-shaped slot

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is modified by increasing the angle between the two-slot arms from 90° to 110° to improve the input impedance of the MVSIW antenna. The input impedance of the MVSIW antenna is given in Fig. 3. The input impedance of the MVSIW antenna consists of resistance and reactance. The magnitude of the input impedance is referred to as resistance which should be equal to 50 ohms for good impedance matching. The reactance of the input impedance could be either inductance or capacitance that stores in the SIW structure should be equal to null. In [10], 90° v-shaped slots are implemented, and more cylindrical vias have been patterned to tune the reactive elements of the antenna, but in the MVSIW antenna, this problem is addressed by altering the angle between the slots into 110°. By implementing an 110° v-shaped slot, the number of cylindrical vias can be reduced, and the input impedance of the antenna has been improved. It is noted that altering the angle of more than 110° between the slot arms leads to deterioration in impedance

Fig. 2 S11 response of the simulated and fabricated MVSIW antenna

Fig. 3 Resistive and reactance parameters of the MVSIW antenna

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Fig. 4 Simulated radiation pattern at a phi = 0° and b 90° for 5.8 GHz

matching and worsens the radiation pattern of the MVSIW antenna so that the 110° angle is optimized between the two-slot arms in the MVSIW antenna. The co-polarization and cross-polarization characteristics of the MVSIW antenna for the E-plane and H-plane are revealed in Fig. 4. Both the E and H-plane pattern in Fig. 4 shows that the radiation pattern is linear, and hence, it can be said that the MVSIW radiates linearly polarized electromagnetic waves. The frequency of the MVSIW antenna changes concerning the length of the slot arms. The length of the slot arm is optimized to 13.79 mm for the operating 5.8 GHz frequency. For the unequal slot arm length, the co- and cross-polarization of the MVSIW antenna results in degradation of pattern result. The MVSIW antenna radiates a simulated gain of 4 dB. The position and angle of the slot arms plays an important role in the efficiency of the antenna radiation, which can be understood by the vector electric field pattern at 5.8 GHz, which is shown in Fig. 5. The distance from the microstrip feed to the center point of the modified v-shaped slot should be approximately equal to the quarter guided wavelength of the operating frequency, and it determines the center frequency of the MVSIW. After the v-shaped slot position on the MVSIW antenna, varying the angle between the slot arms from 90° to 110° leads to better antenna gain. The electric current flow is strengthened in the perimeter of the slot arms when the angle is changed into an 110° angle which is demonstrated in Fig. 6. Comparing with the 90° v-shaped arms, the aperture area in the MVSIW antenna is widened by the 110° angle which leads to strong current flow. The simulated SAR metric of the MVSIW antenna is 0.97 W/Kg. During simulation using a human phantom in HFSS, there is a negligible effect from the body on the antenna observed due to the SIW cavity. The MVSIW antenna is compared with the existing designs in Table 2, and it can be noted that the proposed design has a compact size and better performance compared with the existing designs. The shieldit electro textiles [14] are preferred to realize the top and ground conductors of the MVSIW antenna. The MVSIW antenna is fabricated with a CO2 laser

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Fig. 5 Vector electric field at 5.8 GHz

Fig. 6 Vector current flow pattern at 5.8 GHz

Table 2 Comparison of MVSIW with the existing textile-based SIW antennas References

Frequency (GHz)

Bandwidth (MHz)

Size (mm3 )

Textile material

Gain

[3]

5.8

354

75 * 42 * 1

Shieldex (zell)

2.8 dBi

[4]

2.45

117

59.9 * 59.9 * 3.7

Copper plated polyester

3.8 dBi

[5]

2.4 and 5.8

83 and 150

64.6 * 53.7 * 3.94

Copper plated polyester

4.4 dBi and 5.7 dBi

This work

5.8

150

33 * 30 * 0.57

Shieldit

4 dB

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Fig. 7 Fabricated top and ground plane of the MVSIW antenna

cutting machine with high precision, as shown in Fig. 7. The patterned top and ground layer is adhered with jeans material by manual ironing on the structure. The MVSIW antenna is fabricated with high-precision technology with cost-effectiveness.

4 Conclusion The MVSIW antenna is fabricated using textile materials for smart healthcare applications with significant antenna performance at 5.8 GHz. The MVSIW antenna can be integrated unobtrusively with the patient-monitoring wearable systems, and patient-important health data recorded using wearable sensors can be transmitted to the nearby healthcare providers in the smart healthcare systems. Instead of using a conventional v-shaped structure, the v-shaped structure is subjected to modification, and its potential for antenna radiation was explored in this paper, which brings novelty to this MVSIW antenna design. Furthermore, the bandwidth of the MVSIW antenna can be widened by introducing another TE mode. The fabricated MVSIW antenna gain has to be measured using an anechoic chamber to validate the antenna design further. Acknowledgements The researchers would like to acknowledge that this work is supported by the crescent seed money (CSM) scheme under grant Lr. No. 1254/Dean (R)/2019, B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.

References 1. Moro R, Agneessens S, Rogier H, Dierck A, Bozzi M (2015) Textile microwave components in substrate integrated waveguide technology. IEEE Trans Microw Theory Tech 63(2):422–432 2. Krishnan RM, Kannan G (2016) Polygon shaped 3G mobile band antennas for high tech military uniforms. Adv Electromagnet 5(3):7–13

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3. Hong Y, Tak J, Choi J (2016) An all-textile SIW cavity-backed circular ring-slot antenna for WBAN applications. IEEE Antennas Wirel Propag Lett 15:1995–1999 4. Agneessens S, Lemey S, Vervust T, Rogier H (2015) Wearable, small, and robust: the circular quarter-mode textile antenna. IEEE Antennas Wirel Propag Lett 14:1482–1485 5. Agneessens S, Rogier H (2014) Compact half diamond dual-band textile HMSIW on-body antenna. IEEE Trans Antennas Propag 62(5):2374–2381 6. Zhu X, Guo Y, Wu W (2016) A compact dual-band antenna for wireless body-area network applications. IEEE Antennas Wirel Propag Lett 15:98–101 7. Kumar K, Dwari S, Mandal MK (2017) Broadband dual circularly polarized substrate integrated waveguide antenna. IEEE Antennas Wirel Propag Lett 16:2971–2974 8. Jin C, Li R, Alphones A, Bao X (2013) Quarter-mode substrate integrated waveguide and its application to antennas design. IEEE Trans Antennas Propag 61(6):2921–2928 9. Kumar A (2019) Design of self-quadruplexing antenna using substrate integrated waveguide technique. Microw Opt Technol Lett, pp.1–3, 2019. 10. Priya S, Dwari S, Kumar K, Mandal MK (2019) Compact self-quadruplexing SIW cavitybacked slot antenna. IEEE Trans Antennas Propag 67(10):6656–6660 11. Singh AB, Paras (2021) Compact self-quadruplexing antenna based on SIW cavity-backed with enhanced isolation. Progress Electromagnet Res C, Vol. 110:243–252 12. Xu F, Wu K (2005) Guided-wave and leakage characteristics of substrate integrated waveguide. IEEE Trans Microwave Theory Tech 53(1):66–73 13. Deslandes D, Wu K (2006) Accurate modeling, wave mechanisms, and design considerations of a substrate integrated waveguide. IEEE Trans Microwave Theory Tech 54(6):2516–2526 14. Shieldit electro textiles Homepage, https://www.lessemf.com/fabric4.html, Accessed 11 Nov 2021

A Review on Deep Learning Algorithms for Diagnosis and Classification of Brain Tumor Tessy Annie Varghese and J. Roopa Jayasingh

Abstract Brain is the utmost vital organ in the human body. The tumor affecting the brain can prove fatal to a person. The diagnosis of tumor in the early stage itself is very important. Over these years, many researches have been carried out in this area. Deep learning (DL) is an emerging area in which many researches are being carried out. DL finds many applications in medical imaging, especially in the timely determination of cancer. In this review paper, we have presented an investigation of certain techniques employed in diagnosing and classifying brain tumor. The different algorithms used in each stage are also discussed. The challenges posed by the various techniques have been included in the end to open further researches in this emerging area. Keywords Brain tumor · Machine learning · Deep learning · MRI · Classification

1 Introduction Tumor is the abnormal and uncontrolled multiplication of tissues in an organ. Brain tumors represent around 80–90% of all tumors affecting the central nervous system [1]. According to a study conducted in India, the occurrence of tumor in the central nervous system varies from 5 to 10 cases per 1,00,000 population. Although a serious illness, sufficient information and convenient treatment can save many lives. There are various kinds of brain tumors. They are primarily classified into primary tumors and secondary tumors. Primary tumors originate inside the brain itself. Secondary tumors originate elsewhere in the human body and later extend to the brain. Primary tumors are classified into two types: benign and malignant. Secondary tumors are normally malignant. Early discovery of tumor can forestall casualty yet this isn’t T. A. Varghese (B) · J. R. Jayasingh Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India e-mail: [email protected] J. R. Jayasingh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_7

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generally conceivable. Treatment of brain tumor depends upon appropriate diagnosis and various factors such as the type of growth, area, size, and condition of advancement. The most common diagnostic tools for the detection of brain tumor involve CT scan and MRI images. MRI images give a precise imaging of the tumor. It uses non-ionizing radiation to create the diagnostic images. MRI imaging techniques are noninvasive, present little hazard, and can be utilized on babies and in utero, giving a reliable method of imaging. One limitation of this technique is that the patient needs to keep still for significant period of time in a confined space while the imaging is performed. Machine learning (ML) is a field of artificial intelligence (AI). It is a computeraided approach used for identifying patterns and has applications in medical image processing. Diagnosis and detection using machine learning algorithms can significantly help the radiologists diagnose the patients more accurately in less time. Deep learning is a class of the broader family of machine learning methods which imitates the way human brain acquire certain type of information. Despite the fact that many researches have been done to find an effective technique for brain tumor segmentation, no ideal algorithm has been found. Moreover, most depend on ordinary AI strategies or segmentation techniques for different constructions [2]. Several have been recommended for the diagnosis of brain tumor which includes semi-supervised and supervised methods. Supervised methods require a separate phase of training with a large set of input data whereas semi-supervised methods require supervision during the segmentation process [3]. There are various models for deep learning. Convolutional neural networks (CNNs) are one of the architectures in deep learning. CNN architecture implements a feed forward network with convolutional filters and pooling layers. After the last layer, 2D feature map is converted to 1D vector for the classification. Deep neural network (DNN) is another architecture that is extensively used for classification and regression. DNN is also a feed forward network which implements a number of hidden layers [4]. The primary motive of this paper is to highlight the algorithms used for the classification of brain tumor by analyzing MRI images.

2 Literature Survey Many researches have been carried out for detecting and classifying brain tumors. Seed growing method and unsupervised SSAE model is used proposed in this paper for segmentation and feature extraction, respectively [5]. For tumor segmentation, generative method is used [6, 7]. The GrabCut method and VGG-19 is used for the segmentation process and to extract the features. These extracted features are then combined with hand-crafted features. These features are then optimized for classification of tumor [8].

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A fully automatic segmentation and classification method using artificial neural networks (ANNs) is used to identify the ROI [9]. A method which uses multi-level features extraction and feature concatenation is proposed for identification of brain tumor. Then, for classifying the tumor, these features are then passed through a SoftMax classifier [10].

3 Overview of the System The system for identifying and categorizing the brain tumor using deep learning has the following structure (Fig. 1). Fig. 1 System model for the detection of brain tumor

Input Image

Image Preprocessing

Image Segmentation

Feature Extraction

Classifier

Classified Output

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3.1 Image Preprocessing Image preprocessing methods include various techniques such as the removal of noise, edge enhancement, and histogram enhancements. These techniques are done before the MRI images are processed in the later stages. These are done to enhance the images for further processing. Wavelets are used for removing noise from the image regardless of the frequency component. Edge enhancement is performed used Laplacian techniques. These enhance the high-frequency components. Histogram equalization is used for image enhancement. Multi-histogram equalization divides the image into sub-images, equalizes each histogram, and then combines them to form the equalized output image [11].

3.2 Image Segmentation Segmentation of the affected region from the MRI image is an important step. It involves extracting the affected region from the normal tissues for further classification and prediction. The typical brain region includes white matter, gray matter, cerebrospinal fluid, and skull. The segmented tumor tissue will be used in the further steps. Several techniques are being used for the segmentation of tumor tissues. Two types of data are used for training the network for segmentation—fully annotated data and weakly annotated data. A method implementing deep neural network has been presented in [12]. This approach is applicable to low-grade and high-grade glioblastomas featured in MRI images. Another method which employs deep convolutional neural network (DCNN) is suggested. Two patches are retrieved from the input image for training the network by using patch-based approach and inception module [13]. One main challenge in segmentation is the variation in intensities in the MRI images. A new method which includes confidence region with contour detection determines the intensity difference in the input image, and the level set algorithm is employed to differentiate the inner and outer regions in the segmented output [14].

3.3 Feature Extraction Feature extraction algorithms extract the most relevant features that represent the image. These features are then given to classifiers which use these features to classify the tumors. The major challenge in feature extraction is to identify the relevant features, which can result in a very good performance. The various features used for classification are shape features, intensity features, and texture features. These include area, perimeter, irregularity, shape, mean, variance, skewness, contrast, correlation, entropy, energy etc.

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3.4 Classification Deep learning is one of the most widely used method for the analysis of brain MRI in applications such as tumor classification, Alzheimer disease diagnosis, and stroke lesion segmentation. Convolutional neural network (CNN) is a widely used model for the analysis of medical images [15]. The basic layers of a CNN network are as given below: Input image. The input image is considered as an array of pixels with the pixel value depending on the intensity and resolution of the image. A colored image is represented by 3 * m * n array of pixels. Each pixel value will range from 0–255 in terms of red, green, and blue colors. Grayscale image is represented by a 2D array with pixel value ranging from 0–255. Convolution Layer. This is the first layer in the CNN architecture. This layer extracts features from the input image. Convolution operation is done between the image and a filter of a particular size. The output of this layer is called the feature map. This is given as input to the succeeding layers to learn various features. CNN learns and updates the filters during the training phase. Pooling Layer. The layer which follows the convolution layer is the pooling layer. This layer decreases the size of the feature map still containing the important feature information. This is done to reduce the computational cost. There are various types of pooling operations depending on the method used. MaxPooling takes the largest element from the feature map. In average pooling, the average of all elements in a predefined region is calculated. In sum pooling, the sum of all the elements in a predefined section is calculated. Fully connected Layer. This layer converts the 2D layers into a 1D feature vector. The classification process takes place in this layer. The output of this layer is multiple output classes. Only one class among the various output classes is chosen by using probabilistic methods such as Softmax. Dropout. When the entire features from the pooling layer are forwarded to the fully connected layer, it can result in overfitting. To reduce overfitting, a dropout layer is used where a few features are dropped in the training phase reducing the size of the model. Activation Function. It imparts nonlinearity to a network. The widely used activation functions are ReLU, Softmax, Sigmoid functions, tanH, etc. Sigmoid and Softmax functions can be used for binary classification. For multi-class classification, Softmax function can be used (Fig. 2).

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Fig. 2 CNN architecture

4 Challenges The deep learning algorithms implemented in the diagnosis of brain tumor possess various challenges. One main challenge in the deep learning methods is the unavailability of large training datasets. The training of deep learning methods from a limited amount of input data is a major limitation in deep learning algorithms. Many researchers have used only 2D images to train their 3D segmentation models. But, it has been observed that the number of publicly available datasets has increased largely. Another challenge encountered during deep learning methods is class-imbalance. Many researchers use data augmentation techniques like scaling and rotation to enhance the database. But, this may result in class-imbalance [16].

5 Conclusion This paper has focused on how the various algorithms are applied in the detection and classification of brain tumor. Various algorithms used in various stages were discussed. The challenges involved in the classification of brain tumor using these algorithms are also discussed. The different algorithms which we have discussed here give different efficiencies each with its own advantages and limitations. So, it is relevant to develop a new model by combining different deep learning models which will result in improved efficiency than the existing models.

References 1. https://www.cancer.net/ 2. Zhao L, Jia K (2016) Multiscale CNNs for brain tumor segmentation and diagnosis. Comput Math Methods Med 2016. https://doi.org/10.1155/2016/8356294 3. Amin J, Sharif M, Yasmin M, Saba T, Raza M (2020) Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions. Multimed.

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Tools Appl. 79(15–16):10955–10973. https://doi.org/10.1007/s11042-019-7324-y 4. Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM (2018) Classification using deep learning neural networks for brain tumors. Futur Comput Inf J 3(1):68–71. https://doi. org/10.1016/j.fcij.2017.12.001 5. Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Futur Gener Comput Syst 87:290–297 6. Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S Entropy based segmentation oftumor from brain MR images–a study with teaching learning based optimization. Pattern Rec. 7. Rajinikanth V, Thanaraj KP, Satapathy SC, Fernandes SL, Dey N (2019) Shannon’s entropy and watershed algorithm based technique to inspect Ischemic stroke wound. In: Smart intelligent computing and applications, ed. Springer, pp 23–31 8. Saba T, Sameh Mohamed A, El-Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 59:221–230. https:// doi.org/10.1016/j.cogsys.2019.09.007 9. Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJPC, de Albuquerque VHC (2020) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurr Comput 32(1). https://doi.org/10.1002/ cpe.4962 10. Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M, Shoaib M (2020) A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8:55135– 55144. https://doi.org/10.1109/ACCESS.2020.2978629 11. Tahir B et al (2019) Feature enhancement framework for brain tumor segmentation and classification. Microsc Res Tech 82(6):803–811. https://doi.org/10.1002/jemt.23224 12. Havaei M et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31. https://doi.org/10.1016/j.media.2016.05.004 13. Hussain S, Anwar SM, Majid M (2018) Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 282:248–261. https://doi.org/10.1016/j.neu com.2017.12.032 14. Ejaz K, Rahim MSM, Bajwa UI, Chaudhry H, Rehman A, Ejaz F (2021) Hybrid segmentation method with confidence region detection for tumor identification. IEEE Access 9:35256–35278. https://doi.org/10.1109/ACCESS.2020.3016627 15. Tandel GS, Biswas M, Kakde OG, Tiwari A, Suri HS, Turk M, Laird JR, Asare CK, Ankrah AA, Khanna NN, Madhusudhan BK, Saba L, Suri JS (2019) A review on a deep learning perspective in brain cancer classification. Cancers 11(1):111. https://doi.org/10.3390/cancer s11010111 16. Ge C, Gu IYH, Jakola AS, Yang J (2020) Enlarged training dataset by Pairwise GANs for molecular-based brain tumor classification. IEEE Access 8:22560–22570. https://doi.org/10. 1109/ACCESS.2020.2969805

Fuzzy-Enhanced Optimization Algorithm for Blockchain Mining K. Lino Fathima Chinna Rani and M. P. Anuradha

Abstract In recent years, blockchain performance plays a major role in many applications. Blockchain maintains a public ledger which contains the authenticated transactions, and also, the completed transactions were hooked up; here, the mining process is mandatory for verifying and placing the transaction in the chain. The approval time for each transaction is rising because of limited mining process capability. Several frameworks and methodologies were proposed to alleviate this problem yet they all have come up with their own limitations. In this paper, a new fuzzy logic methodology has been introduced which can regulate the mining capacity dynamically based on the congestion issues. Here, a novel procedure does not need the block size or block mining duration. The proposed methodology can efficiently handle the block when the transaction congestions were occurred. This paper describes the upgrading of TPS and how to handle the difficulty levels with consideration of CPU, memory, and nodes utilization. In addition, this paper analyzes the average active nodes and average transaction latency for significant process. This paper projects the simulation results that are comparing the execution of traditional single or binary blockchain performance. Keywords Blockchain Mining · Vertical mining · Scalability · Throughput

K. L. F. Chinna Rani Department of Computer Science, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli, TamilNadu, India e-mail: [email protected] M. P. Anuradha (B) Department of Computer Science, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli, TamilNadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_8

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1 Introduction Blockchain is a trending technology where all the applications are merging and to produce a secure framework. In this innovative technology, the authorized transactions are placed in the distributed ledger which in turn to achieve the distributed transaction management. Distributed transaction management (DTM) is defined as any system in the blockchain network can insert the block and add the details into the ledger equally based on the predefined protocols; therefore, the transactions are not required to be handled through mediator [1]. All the authorized transactions are approved by the miners and converted in the form of blocks; subsequently, they are inserted into the chain in a linear order. Further, these undergone transactions are an immutable and transparent to all the nodes in the network. These are the key points that show the blockchain technology has been significantly stand-alone from other traditional centralized conviction entities and grows into substantial promoter for all application like banking, education, etc. [2]. One of the drawbacks of blockchain is, it does not depend entailed scalability [3]. The transactions of blockchain are enduring via the sequence of one transaction by another per second, but in real time, the competency must be increased when the sensor data are tied up with blockchain solutions, it must be increased at least 1000 times a second. Blockchain is designed to be act as a peer-to-peer network. When the new block is received by a node, it should be transmitted to all the rest of the nodes in the network while the rest of the nodes pay attention to the blockchain network and transmit the respective block. The main observation is that only the miner node can attach the authorized block into chain [4]. The selection of miner can be achieved through implementing the significant blockchain algorithms like PoW, PoS, etc. Each algorithm has its unique procedure to choose the miners for processing the transactions. PoW is used high system resources in order to find the miners. Due to the creation of frequent forks, a leader must be selected often randomly by this technique [5]. To overcome this unavoidable issue, PoS has been used commonly; here, the miners are selected by solving the hash puzzle which is generated by the network, such leaders can authorize the transactions and create the new block. Fortunate miners are benefited through agreeing them to reward themselves or maintain some transaction output amount as transaction fee. In our day today lives, the extensive espousal of blockchain has getting entailed everywhere which in turn the blockchain users also rapidly rises. In the public chain architecture, scalability problems have raised monotonously that would generate issues against its growth. The performance of blockchain is measured by transaction throughput (number of transactions per second) and transaction confirmation latency that has been encompassed with the blockchain [6]. Throughput and latency are considered to be a research issue of blockchain. These two metrics are not getting the adequate standard in recent proposed commonly used blockchain systems; regrettably, users are not satisfied with the results. When the centralized system’s performance related with blockchain distributed system, these remarkable measures are likely to be upgraded simply. Blockchain is otherwise called as self-regulating or

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Fig. 1 Horizontal scalability versus vertical scalability

automated system which requires more attention for decentralization sustainment [7] (Fig. 1). Bitcoin mining usually depends on two Parameters: Block Time-It defines how frequently a new block is attached to the existing chain. Block Size-It states the volume of data that can be appended to every Block. Normally, the block size of bitcoin is 1 MB whereas the interval time for each block is about to ten minutes [8]. These measures are limited to maximum seven transactions per second and the confirmation of block takes 10 min on average approval time. Contrast, in real time, an average of about 24,000 transactions are supposed to be processed. Therefore, these are the challenges still exists in the blockchain apart from the virtuous operation of its framework [9].

1.1 Vertical and Horizontal Mining When dealing with the scalability issues of blockchain, it is noteworthy to expand the transaction capability of a specified network [10]. Here, vertical scaling describes the enlargement of systems in the network through accumulating more processing

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power and storage capacity to system’s core execution. Horizontal scaling comprises of more than one number of nodes to existing system’s blockchain network [11]. Vertical scaling relates to traditional client server architecture which implies the upgradation of system’s existing hardware resources for energizing more processing as well as storage control. Similarly, horizontal scaling is interrelated to altering the main framework of the platform for launching a large number of groups of system’s servers so that we can easily handle a mass number of transaction requirements [12].

2 Literature Survey Peram and Premamayudu [9] have demonstrated the twining technology framework of blockchain-based IoT model. They describe the blockchain unique characteristics as well as provide the efficient block mining and communication algorithms. This algorithm guarantees the user’s privacy when illegal transactions encountered, therefore, improves the searchable encryption techniques that permits examine the keywords knotted with the encoded data. This model shows the improved transaction efficiency when it is compared with previous model. Zhou et al. [11] have given a detailed survey on solutions of blockchain scalability. This paper gives the details about prevailing scalability solutions. They also compare different methodologies as well as list out several efficient paths for improving the scalability issues of blockchain. They explain that the blockchain efficiency issue caused by the scalability issue; in addition, they pay attention to categorize the present mainstream resolutions into each layer of blockchain framework. Hazari and Mahmoud [5] have described a technique for enhancing the performance and scalability of permission-less blockchain which are driven by the proof-of-work consensus algorithms. The technique proposed in this paper introduces the novel parallel proof-of-work algorithm; here, all the miners can solve the puzzle which is taking part on the competition. They provide the results for implementing and evaluated the proposed methods in an efficient manner. The proposed method results show that substantial results for parallel proof-of-work when the exertion stage at the same time number of miners are increased. Gervais et al. [2] have proposed a new experimental architecture that relates the PoW blockchains provided current Web influences along with blockchain margins. This architecture allows the user to estimate the network-layer parameters. In this paper, the proposed framework permits the users to remove the PoW limitations of blockchain by throughput, concurrently, the security observations show that it gives the solutions for optimality for selfish mining and double spending attack. Baheti et al. have [4] demonstrated the DiPETrans (Distributed Parallel Execution of Transactions) techniques for examining the parallel execution of blocks depending on the shards’ statistical analysis. They have also implemented this mechanism within a Blockchain server; here, Leader shard the blocks concurrently while followers execute the mining. The transaction of statistical analysis and distributed mining were also discussed.

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Murugathas et al. [13] have proposed a new approach enhances the cost, resource power, coverage, and fuzzy inference conditions. The novel methodologies effectively handle the number of active nodes, heterogeneity among the nodes. The placement sites derived from the results fulfilled the maximum level of required coverage, cost, and traffic demands; the automation process of cell shrinking and resource accessibility investigation is regulated. Chen et al. [14] have presented the fuzzy logic control algorithm for cloud computing resource dynamic allocation. The main aim of this strategy is to improve the cloud computing quality of service. The dynamic along with friendly resource allocation is obtained. It efficiently expands the user satisfactory cloud computing. Cong et al. [15] have design a new blockchain model named as CHECO. In this design, each node maintains a personal hash chain of records which stores that node particular transactions. They make a known to validation protocol; here, any node in the network can checked the authenticity of any transaction. The results prove the strong sign of horizontal scalability. Fritsch et al. [16] have described those digital commons authority architecture is a novel model of which supports the scalability of organization communities based on the mutual goals along with maximum resources size, difficulty, and the level of partitions. They highlighted the power of distributed ledger technologies and their significances. Hafid et al. [17] have elaborated the sharding concept which is to partition the blockchain network to more than one groups; here, each processing contains a specific transactions set. Precisely, they propose a classification-based committee formation and consensus among the nodes. They compare the current sharding-based blockchain protocols. They also listed the performance-based analysis of merits and demerits of current scalability solutions. Shae et al. [18] have provided a vision and proposed a mechanism for the conversion of duplicated blockchain computing into distributed parallel computing through smart contract. This novel computing architecture can be applied to construct a massive dataset from distributed medical data. The novel approach is elaborated to create existence evidence of medical record by providing precision medicine. Figure 2 explains the proposed method of FEOABM workflow model. The goal of our proposed method is to overcome the problem raised in solo mining. In solo mining, all the transactions were solved by a single miner; if they go for either offline or any single point of failure occurs, the entire transactions will not be validated [19]. In order to overcome this problem, fuzzy optimization blockchain mining can be used. In this mechanism, the fuzzy logic has been implemented for checking the condition whether the network has been upgraded for vertical mining or the system has added in the blockchain network for horizontal mining. The proposed method routines the fuzzy logic transactional computing for selecting the transactions for mining. If transaction count exceeds the threshold limit (0.67), then the network gets alarmed to adopt the higher power resources for mining the flooded transactions. Generally, vertical scaling strengthens the core components of the node in the network. According to the distributed Ledger system, Blockchain has maintained its records in highly resourced trained nodes in the network [20].

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Fig. 2 Proposed workflow model of FEOABM

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On the flip of the side, the horizontal mining is selected when the median is crossed the highest mean using fuzzy logic. The horizontal mining is achieved through the addition of system in the overall network. Horizontal scaling is always used to improve the overall throughput, but it took time to construct the network whereas vertical mining is otherwise reaching its effectiveness easily. Here, the chance has been given to all the transactional miners [5]. Here, the 64-bit random number created; on the other hand, the miners produce the block hash value along with previous block hash value, private key, nonce, and time stamp. At the end, if the generated hash block is less than the 64-bit value, then the miner has been successfully selected [21].

3 Proposed Work 3.1 Algorithm

Input: Number of Transactions Output: Based on the Fuzzy Logic System Resource allocation occurs for Block Mining Set of Possible Transactions Set of Goals: Gi (I ∈ Xn); Gi (i∈Xn) Set of Conditions: Cj(j∈Xm) Cj(∈Xm) Goals &Conditions e Fuzzy Sets A = Number of Maximum Transactions Gi(a) Gi(a) = composition [Gi(a)[Gi(a)] = G1i (Gi(a)) Gi1 (Gi(a)) with G1i Gi1 Cj(a)cj(a) = composition [Cj (a)Cj (a)] = C1j (Cj (a)) Cj1 (Cj (a)) with C1j Cji for a eA aeA Fuzzy Decision FD = min(i∈Xinn fGi(a), j∈Xinm fCj (a)] Void FuzzyDesi () LoadDefaultFuzzySet (Resource_opti); { do { Inthres = outthres; Outthres = FineTuneThreshold(inthres); } While (ExecutePilotProcess ()); Fdes = GetFuzzyResult(outthres); Cases are created; Based on the threshold value cases created; Horizontal (or) vertical mining adopted’ End

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3.2 The Flow of Fuzzy Optimization Mining Algorithm The basic mining process of fuzzy dynamic decision algorithm executed according to the transaction execution demand and resource availability process as follows: In this algorithm, set of goals are initialized as Gi (I e Xn); Gi (I e Xn) for each transaction in the current machine. Set of Conditions: Cj(j e Xm); Cj(j e Xm) are initialized for making the dynamic mining decision. The threshold values are initialized as inthres and outthres. Step 1: Initialize the predefined blockchain network machines. Based on the threshold value of resources user’s requirement, predetermine the quantity of machine’s resources. Step 2: Create the fuzzy control-rule base. Step 3: Join the transactions that each user requested for mining; then, the miner gets the consistent request. Step 4: Make fuzzy classification for those resource demands, according to various resource demands of the selected transactions (inthres). Step 5: Based on the current node resource requirement level and the availability of resources, implement fuzzy logic to conclude whether the current machine can mine the resource requirements of the transactions or not. If the requirements can be met, then execute the transactions using the current machine. If not, transfer to Step 6. Step 6: Use fuzzy control system to take decision dynamically whether horizontal and vertical mining adopted based on the resource demand level. Then, the goal that not only meets the task requirement decision of vertical mining but it also alarm the need of horizontal mining (outthres). Step 7: Confirm that all the transactions have been authenticated. If there are unmined transactions, repeat Steps 3–6.

4 Experimental Setup The proposed work setup involves two computational platforms: (i) The embark blockchain estimated platform is connected with cloud server which offered SaaS to analyze the prevailing as well as proposed method. (ii) It is a remote desktop which is connected with cloud server using Common Cloud Gateway Interface (CCGI). It is used to estimate the performance metrics which can be accumulated in the remote system deprived of physical accessible to the server. Amazon server is rented by I2K2 cloud server which act like an intermediate setup. Server is heartened with 2 virtual executional cores recognized through 2 GB RAM, and 100 GB high IOPS solid-state hard disk (SSD) gives the 99.99% guaranteed uptime. The software is licensed by I2K2 service for windows OS and remote desktop server (RDS) and client access license (CAL). KNIME usually provides the I2K@

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infrastructure for enormous transactional processing. An efficient user interface is developed for using Visual Studio which is linked with the server; the performance metrics of both existing and proposed methods are evaluated. The proposed method is executed in the visual C++ environment inscribed with CLR libraries. A system with core i5-7200 processor running at 2.7 GHz and embedded with 8 GB RAM is used for execution (Figs. 3, 4, 5 and 6) and (Tables 1, 2, 3, 4, 5, and 6).

Fig. 3 User interface for mining

Fig. 4 Common gateway interface

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Fig. 5 Connection establishment to embark

Fig. 6 Connecting CGI to hypervisor server

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Table 1 Computation of number of transactions/second Time (S)

LBMCA

BSPPW

DiPETrans

SLIMCHAIN

FEOABM

6

255

335

299

369

427

12

273

326

290

390

423

18

254

337

281

371

438

24

255

350

289

367

417

30

248

347

303

374

417

36

254

327

270

388

427

42

257

348

296

385

438

48

265

349

279

371

429

54

243

336

278

397

427

60

253

322

285

377

430

SLIMCHAIN

FEOABM

Table 2 Analysis of average active nodes in the blockchain Time (S)

LBMCA

BSPPW

DiPETrans

6

165

241

211

284

337

12

176

239

207

304

337

18

163

245

202

294

342

24

172

252

207

288

329

30

158

250

213

287

334

36

163

241

193

305

341

42

164

251

210

298

347

48

174

257

192

293

341

54

159

250

193

311

337

60

162

235

197

292

344

Table 3 Computation of average transaction latency (ms) Time (S)

LBMCA

BSPPW

DiPETrans

SLIMCHAIN

FEOABM

6

162

135

155

105

86

12

157

141

156

105

89

18

159

135

155

102

81

24

159

134

158

103

89

30

161

143

154

102

89

36

157

134

155

104

81

42

158

135

156

103

86

48

159

143

154

96

87

54

156

135

159

98

82

60

161

136

163

101

88

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Table 4 CPU utilization measured in terms of percentage for transaction mining Time (S)

LBMCA

BSPPW

DiPETrans

SLIMCHAIN

FEOABM

6

69.68

75.58

73

76.56

78.4

12

69.25

74.54

73.39

77.69

78.67

18

70.94

74.24

73.89

76.25

79.49

24

69.52

74.23

73.23

77.3

79.93

30

70.59

74.85

74.91

76.91

78.48

36

69.79

74.77

73.72

76.45

79.93

42

70.92

74.07

74.93

77.55

78.07

48

69.91

75.8

73.4

76.99

79.2

54

70.1

75.19

73.63

76.23

79.66

60

69.67

74.12

73.45

76.3

79.74

Table 5 Memory utilization measured in terms of percentage for transaction mining Time(S)

LBMCA

BSPPW

DiPETrans

SLIMCHAIN

FEOABM

6

65.11

72.3

74.62

77.83

78.73

12

65.75

73.83

73.65

77.09

78.71

18

65.94

73.3

74.02

78.88

78.25

24

66.21

72.4

74.98

77.86

78.35

30

65.4

73.16

74.52

77.45

77.23

36

65.21

72.81

73.85

78.06

77.49

42

65.29

72.67

73.26

77.69

78.53

48

65.35

73.29

74.93

78.81

77.85

54

66.27

73.67

73.12

77.09

77.3

60

66.03

72.65

73.49

77.67

77.16

Table 6 Node utilization measured in terms of percentage for transaction mining Time(S)

LBMCA

BSPPW

DiPETrans

SLIMCHAIN

FEOABM

6

69.4

73.94

74.81

80.19

80.57

12

68.5

77.18

74.52

79.39

81.69

18

71.44

76.77

73.96

77.57

79.87

24

70.86

74.32

77.11

80.58

82.14

30

68.99

75.01

76.71

80.18

79.86

36

68.5

76.79

75.79

80.25

80.71

42

71.1

74.37

76.1

80.62

79.3

48

70.63

75.54

77.17

78.9

78.52

54

68.18

75.43

75.38

78.66

81.48

60

70.85

74.39

75.47

77.99

79.45

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5 Results and Discussion Overall Performance Enhancement: Fig. 7 shows the number of nodes utilized to execute the transactions against the time. The FEOABM proposed work engages all the nodes in the network for transaction processing at maximum, so that, no system in the blockchain network becomes idle which achieves the horizontal scaling. The memory consumption and CPU process has depicted in the Figs. 8 and 9. Both the limitations, node and memory utilization of our proposed work has compared with all the other existing methods and yields the improvement up to 82%. Figures 9 and 10 report the topmost throughput and the respective transactions commit latency for different transactions under the default system conditional parameters. As shown in Fig. 9, FEOABM improves the throughput by 430 per 60 s. In certain, the proposed work attains the best performance under the concurrent computation of transactions. Figures 11 and 12 display that the CPU utilization time and average active nodes on the time of mining process. The graphs deliberately show that the proposed method (Fuzzy Enhanced Optimization Algorithm for Blockchain Mining) increases the performance efficiency of Blockchain mining more than the other existing methods like LBMCA, Slim Chain, DIE Trans, etc. the proposed method FEOABM achieves the greater performance than the other existing methods like LBMCA, Slim Chain, DIE Trans etc. The proposed method achieves 82% of CPU Utilization and more than 50% of resource utilization than the others. Fig. 7 Node utilization versus time

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Fig. 8 Memory utilization versus time

Fig. 9 Total no. of transactions versus time

6 Conclusion In this paper, we have a designed the new fuzzy-enhanced optimization algorithm for blockchain transaction mining. The proposed method tested by extracted transactions from Ethereum blockchain. Transaction scaling is decided through fuzzy logic whether the network requires horizontal or vertical scaling. To improve the

Fuzzy-Enhanced Optimization Algorithm for Blockchain Mining Fig. 10 Average transaction latency versus time

Fig. 11 CPU utilization versus time

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Fig. 12 Average active nodes versus time

performance, the novel method FEOABM has been proposed and executed under heavy computational environment. Extensive experiments show that the proposed method improves the throughput at the same time reduces the transactional latency. The proposed method has been evaluated over the existing methods and significantly proves that the betterment of resources utilization as well as the memory management.

References 1. Xu C, Zhang C, Xu J, Pei J (2021) SlimChain: scaling blockchain transactions through off-chain storage and parallel processing 2. Gervais A, Karame GO, Wüst K, Glykantzis V, Ritzdorf H, Capkun S (2016) On the security and performance of proof of work blockchains. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp 3–16 3. Baheti S, Anjana PS, Peri S, Simmhan Y (2019) DiPETrans: a framework for distributed parallel execution of transactions of blocks in blockchain. arXiv preprint arXiv:1906.11721 4. Baheti S, Anjana PS, Peri S, Simmhan Y (2018) DiPETrans a framework for distributed parallel execution of transactions of blocks in blockchain. arXiv preprint arXiv:1906.1172 5. Hazari SS, Mahmoud QH (2019) A parallel proof of work to improve transaction speed and scalability in blockchain systems. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), IEEE, pp 0916–0921 6. Kan J, Chen S, Huang X (2018) Improve blockchain performance using graph data structure and parallel mining. In: 1st IEEE international conference on hot information-centric networking (HotICN), IEEE, pp 173–178

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7. Gopalan A, Sankararaman A, Walid A, Vishwanath S (2020) Stability and scalability of blockchain systems. In: Proceedings of the ACM on measurement and analysis of computing systems, vol 4(2). pp 1–35 8. Nagar A (2019) Privacy-preserving blockchain based federated learning with differential data sharing. arXiv preprint arXiv:1912.04859 9. Peram SR, Premamayudu B (2020) Blockchains: improve the scalability and efficiency of conventional blockchain by providing a lightweight block mining and communication algorithm. Ingénierie des Systèmes d Inf 25(6):737–745 10. Kim S, Kwon Y, Cho S (2018) A survey of scalability solutions on blockchain. In: 2018 International conference on information and communication technology convergence (ICTC), IEEE, pp 1204–1207 11. Zhou Q, Huang H, Zheng Z, Bian J (2020) Solutions to scalability of blockchain: a survey. IEEE Access 8:16440–16455 12. Biswas S, Sharif K, Li F, Nour B, Wang Y (2018) A scalable blockchain framework for secure transactions in IoT. IEEE Internet Things J 6(3):4650–4659 13. Chen Z, Zhu Y, Di Y, Feng S (2015) A dynamic resource scheduling method based on fuzzy control theory in cloud environment. J. Cont. Sci. Eng. 14. Murugadass A, Pachiyappan A (2017) Fuzzy logic based coverage and cost effective placement of serving nodes for 4G and beyond cellular networks. Wirel Commun Mob Comput 15. Cong K, Ren Z, Pouwelse J (2018) A blockchain consensus protocol with horizontal scalability. In: 2018 IFIP networking conference (IFIP networking) and workshops, IEEE, pp 1–9 16. Fritsch F, Emmett J, Friedman E, Kranjc R, Manski S, Zargham M, Bauwens M (2021) Challenges and approaches to scaling the global commons. Front. Blockchain 4 17. Hafid A, Hafid AS, Samih M (2020) Scaling blockchains: A comprehensive survey. IEEE Access 8:125244–125262 18. Shae Z, Tsai JJ (2017, June). On the design of a blockchain platform for clinical trial and precision medicine. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS), pp 1972–1980. IEEE 19. Herrera-Joancomartí J, Pérez-Solà C (2016) Privacy in bitcoin transactions: new challenges from blockchain scalability solutions. In: International conference on modeling decisions for artificial intelligence, Springer, Cham, pp 26–44 20. Kaur G, Gandhi C (2020) Scalability in blockchain: challenges and solutions. In: Handbook of research on blockchain technology, Academic Press, pp 373–406 21. Shahriar Hazari S, Mahmoud QH (2018) Improving transaction speed and scalability of blockchain systems via parallel proof of work. Future Internet 12(8)

Power Quality Improvement Using Luo Converter-Coupled Multilevel Inverter-Based Unified Power Flow Controller for Optimized Time Response G. Ramya, P. Suresh, J. Madhav Ram, A. Sarath Kumar, and S. Sathish

Abstract This paper deals with the power quality improvement using Luo convertercoupled closed loop multilevel inverter-based unified power flow controller (CLMLIUPFC). UPFC is a combination of series and shunt converter, capable of enhancing transient stability limit in a power system. Luo converter is a modified buck boost converter with additional filter element used to produce ripple-free and stable output, so which is applicable for DC chargers and electric vehicle controller and so on. The circuit model of CLMLIUPFC employs Luo converter-coupled multilevel inverterbased UPFC system. PI and PID-controlled closed loop analysis is done to CLMLIUPFC system to enhance the stability to find the better faster response, and it is simulated using MATLAB Simulink. Keywords Power quality · Multilevel inverter (MIL) · Static series synchronous compensator (SSSC) · PI control (PIC) · PID control (PIDC) · Total harmonic distortion (THD) · Closed loop multilevel inverter-based unified power flow controller (CLMLIUPFC)

1 Introduction Flexible AC transmission system plays an important role in improving the transmission line, which is uncontrolled and under-utilized and also can able to improve the power flow in the transmission line. The receiving and sending end voltages are determined by impedances in the line, and transmitted electrical power over a line is determined by phase angle of the system. Uncontrolled conditions like bus voltage violations, line overloadings can be avoided by analyzing the system G. Ramya (B) · J. M. Ram · A. S. Kumar · S. Sathish SRM Institute of Science and Technology, Ramapuram, Chennai, India e-mail: [email protected] P. Suresh St. Joseph College of Engineering, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_9

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security [1–3]. FACTS controllers are classified into different types based on configuration, switches, and voltage source converter. The one port controllers are static synchronous compensator (STATCOM), unified power flow controller (UPFC), static synchronous series compensator (SSSC), and interline power flow controller (IPFC) are superior two-port controllers help to enhance the system stability [4–6]. The power system employs UPFC system to improve the maximum transmission capacity by adjusting the parallel transmission lines [7–9]. The different applications such as modal control method for placing the pole in multimachine can be done using UPFC system. The different optimization problem like optimal sizing, siting is solved to support many applications based on UPFC system [10–12]. The damping of a SMIB power system in low frequency includes a UPFC system is presented in [13, 14]. The load flow model of UPFC system has a capacity to control magnitude of voltages, real power, and reactive power simultaneously are given in [15]. The voltage magnitudes, system losses, and flow of power in transmission lines are enhanced and controlled by FACTS controller. UPFC can control the reactive and active power flow in line and voltage magnitudes in transmission system [16–18]. The above literature does not deal with Luo converter-coupled multilevel inverter-based UPFC system, and also, closed analysis is not done to optimize the time response. Section 2 deals with system description; simulation results and discussion are given in Sect. 3, and the research work is concluded in Sect. 4.

2 System Description UPFC is a combination of both shunt and series device used for reactive power compensation. A totally transformer-less UPFC dependent on a creative arrangement of Luo converter-coupled MLI has been proposed. The new UPFC offers a few favorable circumstances over the customary innovation, for example, transformerless, light weight, high effectiveness, minimal effort, and quick powerful reaction. The proposed CLMLIUPFC block diagram is depicted in Fig. 1. In vast power plants, more than one PI controller are utilized. Customarily, these HC controllers are tuned with steady parameters which are not sufficiently strong when there is change in plan parameters. So, as to conquer this issue, distinctive strategies are proposed. A closed loop analysis of an UPFC system associated to an infinite bus via a transmission line is delineated in Fig. 1. This work recommends UPFC for the power quality enhancement in the multilevel inverter system. Luo converter is a modified buck boost converter with filter to obtain the constant output voltage. The Luo converter circuit diagram is depicted in Fig. 2.

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Fig. 1 Proposed CLMLIUPFC block diagram

Fig. 2 Circuit diagram of Luo converter

3 Simulation Results and Discussion This section describes the simulation results of open loop and closed loop system UPFC system. In open loop system, two-bus system is considered, and voltage sag occurs in the two-bus system by the addition of load-2. The same system is replaced with two-level inverter and nine-level inverter-based UPFC system to analyze and compare the performance of the system.

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3.1 Open Loop Simulation Results Figure 3 depicts the simulation diagram of two-bus system with an additional load. Load-1 and Load-2 voltages across the two-bus UPFC system are 5000 V and 4000 V, and it is shown in Fig. 4. Figures 5 and 6 depict the real and reactive powers of the two-bus system, respectively. The two-bus system with two-level UPFC Simulink circuit diagram is shown in Fig. 7, and the UPFC circuit diagram is depicted in Fig. 8. The output voltage of two-level inverter is 550 V, and it is depicted in Fig. 9. Load-2 and Load-1 voltages across two-level inverter-based UPFC system are shown in Fig. 10. 0.235 MW real power and 0.124 MVAR reactive power are obtained using two-level inverter-based UPFC system are depicted in Figs. 11 and 12, respectively. Figure 13 shows the frequency spectrum of two-level inverter-based UPFC is 7.95%. The two-bus system with multilevel UPFC Simulink circuit diagram is shown in Fig. 14. Figure 15 shows the voltage across Load-2 and Load-1 of multilevel inverter-based UPFC system.

Fig. 3 Two-bus system with an additional load

Fig. 4 Load-2 and load-1 voltages across two-bus system

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Fig. 5 Real power of two-bus system

Fig. 6 Reactive power of two-bus system

Fig. 7 Two-bus system with two-level inverter-based UPFC

Figure 16 shows the circuit diagram of Luo converter with nine-level inverter model, and the output voltage of nine-level inverter is shown in Fig. 17. 0.275 MW real power and 0.142 MVAR reactive power are obtained using multilevel inverterbased UPFC system are depicted in Figs. 18 and 19, respectively. Figure 20 shows the frequency spectrum of multilevel inverter-based UPFC is 3.00%, which comes under the acceptable level of IEEE standard. The comparison of voltages, real and reactive power of UPFC system is shown in Table 1. The graphical comparison of real and reactive powers and the output voltage of UPFC system are shown in Figs. 21 and 22, respectively. The summary of THDs of UPFC system is shown in Table 2.

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Fig. 8 UPFC circuit diagram

Fig. 9 Output voltage of the two-level inverter

Fig. 10 Voltage across Load-2 and Load-1 of two-level inverter-based UPFC

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Fig. 11 Real power of two-level inverter-based UPFC

Fig. 12 Reactive power of two-level inverter-based UPFC

Fig. 13 Frequency spectrum of two-level inverter-based UPFC

3.2 Closed Loop Simulation Results The PI-controlled UPFC Simulink diagram is shown in Fig. 23. The receiving end voltage is compared with the reference voltage, and the corresponding difference value is given to the optimized PI controller. The comparator output updates the

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Fig. 14 Two-bus system with multilevel UPFC

Fig. 15 Voltage across Load-2 and Load-1 of multilevel UPFC system

pulse width applied to UPFC system. Multilevel inverter-based UPFC system is employed to generate output voltage from several levels of DC voltage. The output voltage across RL load of PI-controlled UPFC system is depicted in Fig. 24, and its value is 5000 V. Figure 25 shows the RMS load voltage of PIcontrolled UPFC system, and its value is 3900 V. The RMS load voltage decreases from t = 0.3 s due to the addition of second load at that time, and it is then compensated by MLI-based UPFC system to reach the normal value. The PI-controlled UPFC real power and PI-controlled UPFC reactive powers are shown in Figs. 26 and 27, and their values are 0.225 MW and 0.15 MVAR, respectively. The output current THD of PI-controlled UPFC system is shown in Fig. 28, and its value is 2.76%. Figure 29 depicts the simulation diagram of PID-controlled UPFC system. The reference voltage and receiving end voltage is compared, and the corresponding difference value is given to the optimized PID controller.

Power Quality Improvement Using Luo Converter-Coupled Multilevel …

Fig. 16 Circuit diagram of luo converter with nine-level inverter model

Fig. 17 Output voltage of the inverter

Fig. 18 Real power of multilevel UPFC system

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Fig. 19 Reactive power of multilevel UPFC system

Fig. 20 Frequency spectrum output voltage of multilevel UPFC system

Table 1 Comparison of voltages, real and reactive power of UPFC system

UPFC

Voltage (V)

Real power (MW)

Reactive power (MVAR)

Line model

4000

0.201

0.091

Two-level inverter

4500

0.235

0.124

Nine-level inverter

4950

0.275

0.142

The output voltage across RL load of the transformer-less UPFC is depicted in Fig. 30, and its value is 5000 V. The nine-level output voltage of MLI is depicted in Fig. 31. The closed loop RMS load voltage of UPFC system is shown in Fig. 32, and it is approximately 3800 V. The RMS output voltage decreases from t = 0.34 s due to the addition of Load-2, and then, it reaches the normal value by UPFC system.

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Fig. 21 Bar chart comparison of real and reactive power of UPFC system

Fig. 22 Bar chart comparison of output voltage of UPFC system

Table 2 Summary of THDs of UPFC system

UPFC

Output current THD (%)

Two-level inverter

7.95

Nine-level inverter

3.00

The PID-controlled UPFC real power and PID-controlled UPFC reactive power are shown in Figs. 33 and 34, and their values are 0.2356 MW and 0.2292 MVAR, respectively. Figure 35 depicts the output current THD of PID-controlled UPFC. The time domain parameters comparison is given in Table 3. The rise time of PI controller is 0.34 s, and it is reduced to 0.32 s for PID controller; the peak time is reduced to 0.33 s from 0.37 s for PID controller in comparison to the PI controller. The PID controller has an improved dynamic response than PI controller. Figure 36 shows the bar chart comparison of time domain parameters of PI and PID controllers. The comparison of output current THD is given in Table 4, and

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Fig. 23 Closed loop MLI-based UPFC with optimized PI controller

Fig. 24 Voltage across RL load of PI-controlled UPFC

Fig. 25 RMS load voltage of PI-controlled UPFC system

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Fig. 26 PI-controlled UPFC real power

Fig. 27 PI-controlled UPFC reactive power

Fig. 28 Output current THD of PI-controlled UPFC system

the output current THD of PID controller is reduced to 2.38% in comparison with PI controller. The graphical representation of comparison of output current THD is shown in Fig. 37.

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Fig. 29 Closed loop MLI-based UPFC with optimized PID controller

Fig. 30 Voltage across RL load of PID-controlled UPFC

Fig. 31 12 MLI output voltage

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Fig. 32 RMS load voltage of PID-controlled UPFC

Fig. 33 PID-controlled UPFC real power

Fig. 34 PID-controlled UPFC reactive power

Fig. 35 Output current THD of PID-controlled UPFC system

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Table 3 Time domain parameters comparison S. No.

Controllers

t r (s)

t s (s)

t p (s)

ess (V)

1

PI

0.34

0.40

0.37

1.9

2

PID

0.32

0.34

0.33

0.8

Fig. 36 PI and PID controllers time domain parameters comparison chart

Table 4 Comparison of current THD

S. No.

Controller

THD (%)

1

PI

2.76

2

PID

2.38

Fig. 37 Bar chart comparison of output current THD

4 Conclusion The line model of UPFC system is analyzed with two level and multilevel to analyze and compare the performance in terms of ripple voltage, output voltage, real power, and reactive power. Closed loop transformer-less UPFC with optimized PI controller and closed loop transformer-less UPFC with optimized PID controller are modeled and simulated. The steady-state error of PID controller is reduced to 0.6 V from 1.9 V of PID controller. Hence, closed loop transformer-less UPFC with optimized PID controller has better closed loop responses. The present work deals with simulation

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of optimized PI and optimized PI-controlled CLMLIUPFC systems. The simulation of slide mode-controlled CLMLIUPFC system will be done in future.

References 1. Hingorani NG, Gyugyi L (2000) Understanding facts: concept and technology of flexible AC transmission systems. Piscataway, IEEE Press, NJ, USA 2. L.Gyugyi CD, Schauder S, Williams L, Rietman TR, Torgerson DR, Edris A (1995) The unified power flowcontroller: Anewapproach to power transmission control. IEEE Trans Power Del 10(2):1085–1097 3. Rajabi-Ghahnavieh A, Fotuhi-Firuzabad M, Shahidehpour M, Feuillet R (2010) UPFC for enhancing power system reliability. IEEE Trans Power Del 25(4):2881–2890 4. Fujita H, Watanabe Y, Akagi H (1999) Control and analysis of a unified power flow controller. IEEE Trans Power Electron 14(6):1021–1027 5. Sayed MA, Takeshita T (2014) Line loss minimization in isolated substations and multiple loop distribution systems using the UPFC. IEEE Trans Power Electron 29(11):5813–5822 6. Fujita H, Watanable Y, Akagi H (2001) “Transient analysis of a unified power flow controller and its application to design of dc-link capacitor. IEEE Trans Power Electron 16(5):735–740 7. Fujita H, Akagi H, Watanable Y (2006) Dynamic control and performance of a unified power flow controller for stabilizing an AC transmission system. IEEE Trans Power Electron 21(4):1013–1020 8. Ramya G, Ganapathy V, Suresh P (2019) Comprehensive analysis of interleaved boost converter with simplified H-bridge multilevel inverter based static synchronous compensator system. Electric Power Syst Res 176:105936 9. Kanna S, Jayaram S, Salama MMA (2004) Real and reactive power coordination for a unified power flowcontroller. IEEE Trans Power Syst 19(3):1454–1461 10. Ramya G, Ganapathy V, Suresh P (2017) Power quality improvement using multi-level inverter based DVR and DSTATCOM using neuro-fuzzy controller. Int J Power Electron Drive Syst 8(1):316 11. Bebic JZ, Lehn PW, Iravani MR (2003) P-/\ characteristics for the unified power flow controller - analysis inclusive of equipment ratings and line limits. IEEE Trans Power Del 18(3):1066– 1072 12. Liu L, Zhu P, Kang Y, Chen J (2007) “Power-flow control performance analysis of a unified power-flow controller in a novel control scheme. IEEE Trans Power Del 22(3):1613–1619 13. Rahman MM, Ahmed A, Galib MMH, Moniruzzaman M (2021) Optimal damping for generalized unified power flow controller equipped single machine infinite bus system for addressing low frequency oscillation. ISA Transactions 14. Ramya G, Suresh P, Venkatasubramani K, Srinivasan JD, Pemila M (2018) Power quality improvement using thermo-electric transducer powered multilevel inverter. Int J Pure Appl Math 119(16):4241–4249 15. Singh P, Senroy N (2021) Steady-state models of STATCOM and UPFC using flexible holomorphic embedding. Electric Power Syst Res 199:107390 16. Ramya G, Ganapathy V (2016) Comparison of five level and seven level inverter based static compensator system. Indonesian J Electr Eng Comput Sci 3(3):706–713 17. Ghaedi S, Abazari S, Markadeh GA (2021) Transient stability improvement of power system with UPFC control by using transient energy function and sliding mode observer based on locally measurable information. Measurement 183:109842 18. Suresh P, Baskaran B, Ramya G (2017) Fuzzy logic controller based IDVR in IEEE 30 bus system for voltage sag compensation. Indian J Sci Technol 10(29)

Performance Analysis of Fiber-Optic DWDM System K. Venkatesan, A. ChandraSekar, and P. G. V. Ramesh

Abstract Lightwave system using wavelength division multiplexing (WDM) meets the demand over larger data rates, higher capacity, and improved network throughput. In this paper, we discuss the multi-channel WDM system’s performance using a single-stage erbium-doped fiber amplifier (EDFA) and compares BER, Q-factor, and eye height for both co-channel and counter-channel propagation. The proposed WDM system identifies the optimal EDFA length, pump power, and input power to achieve a high Q-factor, proper eye-opening characteristic, and low bit error rate (BER). The proposed WDM system is simulated using OptiSystem, and results are compared for 16, 32, and 64-channel WDM systems. The proposed system’s performance is evaluated by achieving low BER, high Q-factor, and higher gain with excellent eye characteristics, which enhance the signal quality at the receiver end. In this work, a 64-channel WDM system achieves min BER in the range of 10–15–10–19 in cochannel propagation and 10–16 from counter-channel propagation. Furthermore, the proposed system achieves a low noise figure (NF) around < 9 dB and flatten gain of 39.77 ± 0.7 dB from 1530 to 1562 nm operating bandwidth for 16, 32 and 64-channel WDM system using single-stage EDFA. Keywords WDM · EDFA length · Pump power

1 Introduction Modern world upgrades with 5G technology, which requires a higher data rate and ultra-speed Internet connectivity [1]. Copper wires devices fail to meet the end-user requirements such as high-level network connectivity and wider bandwidth [2]. In the last few years, increasing demand for data transmission and bandwidth capacity has led to fiber-optic communication. Wavelength division multiplexing (WDM) technique deploys bandwidth capacity enhancement, large data rate, and high-speed K. Venkatesan (B) · A. ChandraSekar · P. G. V. Ramesh Department of ECE, SJCE, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_10

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network in long-haul optical communications [3]. In WDM schemes, more number of wavelengths are combined and propagated into a single fiber. Channel spacing in WDM system design avoids inter-channel interference. WDM forms an orthogonal set of carriers, which separates, routs, and switches the wavelength without intrusive with one another, which provides low optical power intensity and avoids nonlinear effects that degrade the system performance [4]. Fiber-optic technology enables ultra-speed, long haul, and wider bandwidth capacity around the globe. In fiber optic, EDFA performs amplification of multiple lightwave signals, meets bandwidthhungry requirements, network traffic rate, and increases the transmission channel capacity [5]. Increasing more channels and the transmission distance leads to arise fiber nonlinearities and dispersion effects that limit DWDM performance [6]. WDM design techniques are developed to meet the increasing bandwidth requirements, larger data rate, and low network latency in fiber-optic communications. The implementation of any modulation scheme in WDM realizes the entire wavelength [7]. However, the input signal’s spectral width must not exceed the channel spacing limitation [8]. A single-stage erbium-doped fiber amplifier (EDFA) in a WDM system leads to multi-channel amplification and does not provide any interference [9]. EDFA encounters problems such as scattering, attenuation, distortion, and improves the signal quality [10]. EDFA has a flatten gain for wider bandwidth, low loss at 1550 nm, which plays a vital role in optical WDM data transmission [11]. Single-mode fiber (SMF) is widely used in WDM design techniques. For WDM design, a typical identification of EDFA length, pump power, and input power shows their impact on the signal’s quality at the receiver end [12]. Furthermore, passing an optical pulse in fiber-optic cables causes pulsebroadening problems, drastically affecting DWDM performance [13]. The fiber-optic network designer must confirm that the network’s experimental design phase anticipates the end-user’s channel capacity and bandwidth requirements [14]. However, these network designs help the field teams understand the design’s scope, develop the new network to meet users’ requirements, and effective network management. This makes the operator to identify network vulnerabilities effectively and address the issues well in advance [15]. This paper proposes a WDM optical network with optimal EDFA length, input power, and pump power to achieve signal quality. The effects of different pump configurations are also discussed to identify the suitable range of pump power to obtain optimized BER, Q-factor, and distortion-free eye-opening at the receiver end. Furthermore, this paper investigates the dependence of BER on fiber length and pump power. It also provides a discussion over the minimal range of transmitting power to meet the minimum BER as per end-user requirements. The impact of pump power and fiber length on BER, Q-factor, and eye-opening, from the aspect of noise figure, has been discussed. However, this paper introduces new constraints on defining an allowable range of pump power and link length for inter-channel interference and crosstalk and related factors such as OSNR, BER, Q-factor, and spectral characteristics at the receiver end. The concept behind this research paper

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is to maintain low BER and find the limit of the fiber span length before using it for amplification purposes. However, the proposed system is also analyzed signal quality in terms of minimum BER, eye height, Q-factor, gain, and noise figure for both co-channel and counter-channel propagation. The rest of the research paper is organized as follows: In Sect. 2, enabling technologies of WDM system is discussed. DWDM system modeling is presented in Sect. 3. Section 4 presents result and discussion, and finally, Sect. 5 discussed conclusion of our work.

2 Enabling Technologies WDM’s effective implementation requires active and passive components to perform various processes such as amplification, combining, distributing, and isolating the input power for different wavelengths [16]. The multiplexer combines of input light signal with different wavelengths or frequencies into a single fiber. Active devices such as optical filters, laser input sources, and amplifiers are distinctly valuable in WDM applications, which provides higher network flexibility. Passive devices perform combining, splitting, and tapping off the optical signal, and it does not require external control for their operations [17]. The narrow channel spacing in the WDM system introduces crosstalk, which degrades system performance. The optical signal travels in a dielectric medium give rise to nonlinear effects such as intra- and inter-channel crosstalk [18]. Intra-channel cross-talk, which arises due to introduction signals, comes from an adjacent channel. Inter-channel cross-talk occurs due to the wavelength that differs from their desired signal’s wavelength that introduces nonlinearities and affects the parameters such as gain, noise power, and Q-factor. Some other performance limitation factors in the WDM system are amplified spontaneous emission (ASE) noise, inelastic scattering process, which increases SNR and introduces more fiber nonlinearities that degrade system performance [19]. In a WDM system, nonlinearities such as FWM depend on channel spacing, higher input power, and fiber dispersion characteristics, which degrade system performance [20]. In single-mode fiber (SMF), propagation of different input signal generates a new signal known as FWM, which disturbs the phase matching and reduces signal efficiency. WDM system design with increasing channel spacing and higher group velocity that lowers FWM. Negative dispersion compensation is another approach that reduces the accumulation of dispersions in optical fiber transmission and lowers FWM [21]. Phase mismatch in wavelength channels leads to nonzero dispersion, which reduces the possibilities of FWM. The fiber transmission with low positive dispersion and DCF with high negative dispersion will result in zero-dispersion effects.

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3 WDM System Modeling Figure 1 presents the proposed WDM design. Input channels from WDM transmitters are designed in the bandwidth range of 1530 to 1562 nm. Each channel in Wdm design is assigned with a 10 Gbps data rate, 100 GHz channel spacing, and −10 dBm as input power. Er3+ ions attain higher energy level at 980 nm pump along with the presence of ASE. The effects of ASE noise are solved through an isolator, which has been deployed after WDM multiplexer. In addition, it also avoids the backward signal propagation, which reduces population inversion that happens due to the reflection of ASE [13]. In this paper, both forward (co-channel) propagation and backward (counter-channel) propagation methods are used. In co-channel propagation, pump configuration and input signals are traveled in a similar direction. WDM combines these signals, and the multiplexed signals will reach the isolator, which avoids the backward signal propagation. EDFA performs amplification through a 980 nm pump, which is applied to the received signal. Finally, the amplified signal reaches the isolator, which performs signal propagation in one direction that is placed near the WDM demultiplexer. In counter-channel propagation (Backward pumping), input signal and pump configuration are traveled in the opposite direction. In WDM data transmission, the design challenge is to provide a lossless path for each optical source that is passed into multiplexer output. Multiplexed output signal fails to attain sufficient energy and introduces intra-channel cross-talk. At earlier stages, channel spectral width is not considered as much important for intra-channel cross-talk. An optical isolator is used to avoid the generation of amplified spontaneous emission (ASE) noises and signals that process in opposite directions [14]. In a WDM optical network, EDFA efficiently transmits modulated signals in a long-haul optical WDM system whose operating bandwidth is 1530–1560 nm. EDFA provides flatten gain over the wider bandwidth (1520–1562 nm), amplifying the signals from WDM transmitter channels. The pump lasers are used as light sources to excite the erbium ions inside the EDFA. The EDFAs are placed at equal intervals to amplify weak input signals and act as a repeater device. Co-propagation and counter-propagation

Fig. 1 WDM system design

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techniques are used at different pump power values to identify the optimal power and propagation techniques and achieve maximum Q-factor, MIN.BER, and distortionless eye characteristics. At receiver’s end, WDM demultiplexer receives an amplified signal from the optic fiber and separates into individual frequency components. Furthermore, each channel’s signal is passed into PIN photodetector, which converts the optical signal into an electrical signal. Linear low-pass Bessel filter, which protects the shape of the passband-filtered signal [10]. The filtered signal is influenced by various factors like dispersion, nonlinear effects, crosstalk on the optical fiber transmission system, which degrades WDM performance. To overcome the above problems, filtered electrical signal is passed into 3R generator. Finally, the 3R generator signal is traveled into BER analyzer, which provides distortion-less eye-diagram characteristic and calculates Q-factor, BER, eye-height characteristics from its spectral diagram.

4 Result and Discussion In DWDM system design, fiber length is considered an essential factor that determines the crosstalk phenomenon. Increasing fiber length accumulates more crosstalk and increases fiber attenuation, which deteriorates the transmission, link performance, and receiver power at the end of the transmission. Therefore, it is necessary to set a constraint over the maximum length of optical fiber before introducing an in-line amplifier. This paper considers the optimal length as 10 m, and no further fiber length is allowed without compromising the DWDM system performance. The reference pump power is set to 120 mW, and optimal length of the optical fiber is considered as 10 m from previous simulation analysis [11]. By varying the pump power from 100 to 500 mw and analyzing BER, Q-factor and eye height will understand WDM system performance. The channel spacing allocations of the DWDM system are clearly based on WDM system capacity, available in the transmission window. The channel spacing considered for this simulation setup is 0.8 nm and 1552.534 GHz as central frequency. In these aspects, telecommunication services use these features in the low loss spectral range from 1300 to 1600 nm (C to L band) spectral region of optical fibers. Based on this analysis, the operating bandwidth for different channels is chosen. Simulations are performed for 16, 32, and 64 channels, and their output characteristics are analyzed through optical spectrum analyzer. From results, 16-channel achieves flat gain characteristic over 1550–1565 nm bandwidth for varying pump power up to 500 mw. Q-factor for the proposed 16 channels has narrow deviations up to 0.8 for the same bandwidth. Furthermore, the 32-channel WDM system obtains increased gain flatten characteristics and its deviations over slope value of 0.7 for the same operating bandwidth and pump power variations. 64-channel WDM system provides an increasing gain flatten the curve and shows variations in Q-factor over wider bandwidth of 1550–1610 nm. For 400mw pump, 32 and 64-channel WDM system provides the same Q-factor value of 8.6. Q-factor analysis for the proposed

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Fig. 2 a Co-channel propagation and b counter-channel propagation for pump power versus Qfactor

WDM configuration is shown in Fig. 2. (a) and (b). Simulations are performed using forward and backward pump propagation. In forward propagation, 64-channel WDM with a 500 mw pump achieves 8.80105 Q-factor value that is higher than other channeled WDM system designs. Moreover, Q-factor of 64-channel is varying from 7.84854 to 8.80105 for the various pump configurations. Q-factor of 16 channels is 8.63468 is low, compared to 16 and 32-channel configuration in the proposed WDM system. Increasing pump power will result in more noise power and noise figure, which affects OSNR and Q-factor and provides distorted eye characteristics. In counterchannel propagation, 16-channel WDM configuration achieves a high Q-factor compared to 32 and 64 channels. High channel (64-channel) WDM system obtains low Q-factor, which validates that increasing more channel number with narrow channel spacing result higher inter-channel crosstalk, which lowers Q-factor [11]. Figure 3. (a) and (b) provides the eye-height characteristics for proposed WDM channel configurations using co-channel and counter-channel propagation. In this simulation setup, pump power varies from 100 to 500mW; the optimal length of EDFA is considered 10 m, and the remaining parameters are considered the same as the previous simulation analysis. In co-channel propagation, 16 channels have the maximum value and minimum value of eye height around 0.0163797 for a pump power of 500mW and 0.00355675 for a pump power of 150 mW, respectively. Eye-height characteristics for 32 and 64 channels will closely travel and lie on the same range, which clearly shows eyeopening with low signal transmission loss. From simulation results, it is observed that 16-channel WDM system with counter-channel propagation also attains the maximum value eye-height characteristics as 0.0169773, which achieves a proper eye-opening; this will results in their corresponding Q-factor and BER values that discussed in Figs. 2 and 4. Low eye-height characteristic of 0.00370394 is obtained for a pump power of 150 mW. For 32 channels, when pumping power travel from

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Fig. 3 a Co-channel propagation and b counter-channel propagation for pump power versus eye height

Fig. 4 a Co-channel propagation and b counter-channel propagation for pump power versus Min. BER

150 to 200 mW, the eye-height value crosses the 64-channel eye height equivalent and maintains a gradual increasing ratio to those corresponding channels. In Fig. 4, we observe BER of the co-channel and counter-channel propagation as a function of pump power. Here, the allowable range of BER is chosen as < 10−9 for the DWDM standard performance. The figure shows that the threshold pump power for the DWDM system is chosen to reduce noise figure, increase gain, and provide flexibility to fiber length and channel spacing. DWDM system with high pump power introduces more crosstalk power products for higher data rate system and enhances more nonlinearities. Lower pump configurations may achieve determined BER < 10−9 and reduce various noises, reducing fiber nonlinearities. As pump configuration increases, the difference between co- and counter-channel propagation curves decreases and induces more crosstalk products and thermal noises (dependent on pump power). Figure 4 (a) and (b) illustrate the BER characteristics for 16, 32, and 64-channel WDM systems using co-channel and counter-channel propagation. In co-channel (forward) propagation, the BER of 16 channels varies in the range of 10–16 –10–18 for different pump power values. The BER of 32 channels is

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the lowest in the range of 10–18 . The 64 channels have the highest BER in the range of 10–19 . In counter-channel propagation, the 64 channels have the lowest BER in the range of 10–16 , and maximum BER characteristic is achieved for 16-channel WDM channel in the range of 10–18 –10–20 . It is concluded from simulation results that 16-channel WDM system with 100 GHz channel spacing, −10 dBm as input power and optimal pump power achieves maximum Q-factor, Min BER, and very good eye characteristics. The BER of 32 channels is in the range of 10–17. Figure 5 discusses the graphical analysis of noise figure and gain characteristics of the 16-channel WDM system. Noise figure varies dynamically at high input signal power because of the fiber’s low population inversion. In the WDM system design, increasing input power reduces noise figure and reaches a minimum [12]. This is because of degradations in clamping laser, noise figure reaches the maximum for the low input power. As the input signal’s power increases, it competes with the clamping laser for population inversion, which reduces the degradation that occurs due to the clamping process. However, at low input signal power, EDFA fails to attain sufficient energy for population inversion to maintain the amplification, which leads a high noise figure. From simulation results, it is observed that higher gain can obtain at high pump power, which shows their correlations of these factors [15] whereas obtained relationship is inverse for the noise figure characteristic. The maximum gain can be achieved is 40.403 dB in 16-channel WDM simulation with a low noise figure of 7.65324 dB. The gain and the noise figure deviations occur due to the variations in pump power with different channels configurations of the simulation setup. For the three different channels configuration, the maximum gain is obtained at the frequency of 192.52 THz with the noise figure values less than 8 dB. Increasing the number of channels results in low channel spacing, increasing the channel’s inter-channel crosstalk, and fiber nonlinearities that degrade WDM channel performance. For 32 channels, the maximum gain obtained is 39.832757 dB, with the noise figure value of 7.69855. The gain value of 39.776576 dB is obtained with a noise figure of 7.71047 for 64 channels.

5 Conclusion This paper performed a detailed comparative analysis for various factors such as the gain flatness, Q-factor, Min.BER, and eye height for multi-channel WDM design using single-stage EDFA. The typical identification of optimized input power, EDFA length, and pump power is obtained for different configurations of the WDM system using co-channel and counter-channel propagations. It is observed that gain and noise figure variations clearly depend on pumping configuration and optical fiber length, which shows a better result during co-channel propagation. Variations in pump power and increasing EDFA’s length will introduce more noises that affect the gain flatness. Furthermore, the pump power variations can also influence the BER, Q-factor, and eye height of the 16, 32, and 64-channel WDM system. From simulation results, the EDFA’s optimum length is obtained as 10 m, and WDM performance is evaluated for

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Fig. 5 a and c Gain characteristic and b and d noise figure characteristics versus frequency of 16, 32 channel DWDM system

BER, Q-factor, gain, and eye-height characteristics. WDM system with co-channel propagation achieves better BER values, which lies in 10–15 to 10–20, 9.2466 as a Qfactor and proper eye-diagram characteristics. The gain values of 39.77 ± 0.7 dB and the noise figure values of h 2 > · · · > h |h=Sn | . After using the successive interference cancellation (SIC) by using the Eqs. (1) and (2), the beam can be removed from the inter-intra-beam interferences. The residual signal received at nth beam is progressed as: |S j | k−1 E EE / / / H H /\ H Wn Pi,n Si,n + h k,n W j Pi, j Si, j + vk,n yk,n = h k,n w Pk,n Sk,n + h k,n j/=1 i=1

i=1

(9) / H • The desired signal at nth beam is as follows: h m,n W Pm,n Sm,n m−1 E/ H • Intra-beam interferences are as follows: h m,n Wn Pi,n Sm,n i=1

• Inter-beam interferences are as follows:

H h m,n

• Noise is as follows: υm,n .

j EE

i/=n i=1

Wj

/

Pi, j Si, j

The signal-to-interference-plus-noise ratio (SINR) for the mth user in nth intrabeam can be expressed as follows: γm,n =

|| || H ||h Wn ||2 Pm,n m,n 2 ξm,n

(10)

The sum of rate for user mth at nth beam will be presented as: ( ) Rm,n = log2 1 + γm,n

(11)

The sum of rate, which can be achieved is as follows: Rsum =

|Sn | NRF E E n=1 m=1

Rm,n

(12)

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2 Simulation Results The design of a Massive MIMO system in this paper as per the parameters mentioned in Table 1, using varying SNR levels in MATLAB simulation tools. We present the performance of the simulation between three methods in this section, such as: • Fully digital MIMO: In this case, one antenna can connect with one RF chain. • BS-MIMO: In this method, each beam can connect only to one user. N RF ≤ K, where MIMO is performed for inconsistent users, and the same beam is allocated with OFDM resources. • BS-MIMO-NOMA: In this case, each beam supports multiple users. N RF ≤ K that integrates the beamspace NOMA with beamspace MIMO. Therefore, the normalized process design in this research is to overcome the challenges of previous techniques. Figure 5 demonstrates the EE increase by increasing the SNR level in both BSMIMO and BS-MIMO-NOMA compared to the fully digital MIMO system. As the results show, the fully digital MIMO system has the worst performance for EE compared to other methods. This is because it needs one RF chain for each antenna. From this comparison, we want to demonstrate the EE and SE beamspace MIMONOMA system model has better results than the beamspace MIMO, and also, the result of EE by using beamspace MIMO-NOMA is better than the result of EE by using beamspace MIMO same as the result of EE and SE in other studies. Therefore, the normalized technique designed in this research is BS-MIMONOMA that shows the highest EE compared to other methods. The EE achieves this by introducing the NOMA with the BS-MIMO technique to serve multiple users in the same beam. Figure 6 shows the SE performance, and we observed that the normalized fully digital MIMO method achieves better SE performance than the BS-MIMO method and BS-MIMO-NOMA method. Therefore, there is no case of beam selection in the fully digital MIMO method and the NRF = N , the number of the required allocating Table 1 Design parameters for varying SNR values

SNR level

0:2:40

Iterations number

50

Number of transmit antennas

300

Maximum transmitter power (P)

32 mW

PRF

300 mW

PSW

5 mW

PBB

200 mW

Number of users

50

Lambda

½

Number of paths per user

3

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Fig. 5 EE against varying SNR values

channels for all users. Thus, it shows a higher SE in the fully digital MIMO method than the BS-MIMO and BS-MIMO-NOMA techniques. However, it has the very worst EE. Figures 5 and 6 show that the BS-MIMO-NOMA technique achieves the trade-off between EE and SE performances, and that is the target of this research to find a relationship to increase EE and SE in the same mode. Therefore, there is no beam selection in the fully digital MIMO with N RF = N RF chains used to serve all users. This favorable outcome is attributed to a strong connection in the beamspace channels of the same beam. The practical result indicates MIMO can decrease the RF chains required on the mmWave MIMO system without loss of achievement. The main problem of the current beamspace is that it can support only a limited number of users at the same time and frequency resources. It means the number of users that supported in one beam is not more than the number of RF chains. Therefore, the proposed transmission system can give a better result in SE and EE that integrates the NOMA beamspace with the MIMO in the mmWave wireless communication system. By using NOMA on the system, the number of users in the frequency resources will be greater than the number of RF chains. Specifically, we compare the performance gain of achievable sum-rate when using BS-MIMO-NOMA to BS-MIMO. The simulation results show that BS-MIMO-NOMA achieves higher EE and SE compared to BSMIMO. The BS-MIMO-NOMA has 3 dB SNR gain compared to BS-MIMO, which benefits sought of utilizing the NOMA to provide multiple users at any beam. In the simulation results, we can see the number of user increases in the case of BSMIMO-NOMA. Therefore, the performance difference between the proposed BSMIMO and the proposed BS-MIMO-NOMA grows. This is because as the number of user increases, the probability that the same beam will be selected for different user increases. As a result, the available MIMO beamspace will suffer obvious problems

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such as performance loss. The recommended MIMO-NOMA beam area can still function properly due to the use of NOMA. The convergence of the solution has been evaluated for the proposal iterative power allocation. The user is set to be k = 50, and the SNR value is set to be 10 dB. It can be observed from Fig. 7 that the number of iteration R shows stable values after 15 times of iteration, which proves convergence from the iterative power allocation.

Fig. 6 SE against varying SNR values

Fig. 7 R number for iterations for power provision

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3 Conclusion This study has focused of EE maximization problem under the QoS, which is necessity on the beamspace MIMO-NOMA when using multi-user in wireless communication system. The proposed power allocation (PA) strategy is used to solve the problem of EE maximization in the BS-MIMO. Moreover, the low complexity user protocol suggested, which could provide the necessary power for any user by ascending order to conciliate the QoS requirement. Therefore, the numerical results show that the proposed PA strategies in NOMA are better than orthogonal multiple access (OMA). Moreover, the EE in the NOMA system can chiefly rely upon the channel situation of the first user, and it is requisite to stratify EE and PA strategy, particularly at high transmit power. Therefore, by increasing the number of users, the users can get a difference of channel gain and higher EE. Also, from the simulation results in the Massive MIMO with varying SNR levels, the EE increases as the SNR level increases for both BS-MIMO and BS-MIMO-NOMA. The fully digital MIMO system shows the worst performance for EE as compared to the other methods. In addition, the BSMIMO-NOMA method achieves better SE performance compared to the BS-MIMO method. It means BS-MIMO-NOMA technique achieves the trade-off between the EE and SE performances, and it is the best. Acknowledgements The authors would like to give acknowledgment to the Universiti Tun Hussein Onn Malaysia for financial support.

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8. Tabassum H, Ali MS, Hossain E, Hossain M, Kim DI (2016) Non-orthogonal multiple access (NOMA) in cellular uplink and downlink: challenges and enabling techniques. arXiv preprint arXiv:1608.05783 9. Gao X, Dai L, Chen Z, Wang Z, Zhang Z (2016) Near-optimal beam selection for beamspace mm wave massive MIMO systems. IEEE Commun Lett 1054–1057 10. Cui J, Ding Z, Fan P (2017) Beamforming design for MISO non-orthogonal multiple access systems. IET Commun 720–725 11. Ali S, Hossain E, Kim DI (2016) Non-orthogonal multiple access (NOMA) for downlink multiuser MIMO systems: user clustering, beamforming, and power allocation. IEEE Access 565–577 12. Nimmagadda SM (2021) A new HBS model in millimeter-wave beamspace MIMO-NOMA systems using alternative grey wolf with beetle swarm optimization. Wirel Pers Commun 2135–2159 13. Nguyen HV, Kim HM, Kang GM, Nguyen KH, Bui VP, Shin OS (2020) A survey on nonorthogonal multiple access: from the perspective of spectral efficiency and energy efficiency. Energies 4106 14. Brady J, Behdad N, Sayeed AM (2013) Beamspace MIMO for millimeter-wave communications: system architecture, modeling, analysis, and measurements. IEEE Trans Antennas Propag 3814–3827 15. Hodge JA, Mishra KV, Zaghloul AI (2020) Coded intelligent surface design for generalized beamspace modulation in massive MIMO communications systems. In: IEEE international symposium on antennas and propagation and North American radio science meeting, pp 1819– 1820 16. Ding T, Zhao Y, Li L, Hu D, Zhang L (2019) Energy-efficient hybrid precoding for beamspace MIMO systems with lens array. In: IEEE 19th International conference on communication technology (ICCT), pp 628–632 17. Karabacak M, Afeef L, Arslan H (2020) Dynamic sidelobe multiplexing in beamspace MIMO systems. IEEE Wirel Commun Lett 2216–2219 18. Sheng H, Chen X, Shen K, Zhai X, Liu A, Zhao MJ (2020) Energy efficiency optimization for beamspace massive MIMO systems with low-resolution ADCs. IEEE Wirel Commun Networking Conf 1–7 19. Fatli HA, Katiran N, Shah SM (2019) Normalized user allocation technique for beam-space massive MIMO communications. In AIP Conf Proc 2173(1):020007 20. Ilango P, Sudha V (2021) Energy-efficient transmit antenna selection scheme for correlation relied beamspace mmWave MIMO system. AEU-Int J Electron Commun 137:153783 21. Ashraf M, Shahid A, Jang JW, Lee KG (2017) Energy harvesting non-orthogonal multiple access systems with multi-antenna relay and base station. IEEE Access 17660–17670 22. Li A, Lan Y, Chen X, Jiang H (2015) Non-orthogonal multiple access (NOMA) for future downlink radio access of 5G. China Commun 12(Supplement):28–37 23. Brady J, Behdad N, Sayeed AM (2013) Beamspace MIMO for millimeter-wave communications: system architecture, modeling, analysis, and measurements. IEEE Trans Antennas Propag 61(7):3814–3827 24. Zhang H, Yang N, Long K, Pan M, Karagiannidis GK, Leung VC (2018) Secure communications in NOMA system: subcarrier assignment and power allocation. IEEE J Sel Areas Commun 1441–1452 25. Ye N, Wang A, Li X, Liu W, Hou X, Yu H (2017) On constellation rotation of NOMA with SIC receiver. IEEE Commun Lett 22(3):514–517

Design and Simulation of Flexible Antenna with a Defected Ground Structure for Wireless Applications C. G. Akalya, D. Sriram Kumar, and P. H. Rao

Abstract In this paper, three configurations of an antenna are to be discussed. These configurations are simple flexible antenna, flexible antenna without DGS, and flexible antenna with DGS. The return loss of simple flexible antenna, flexible antenna without DGS, and flexible antenna with DGS is − 18.0133, − 26.283 and − 19.1454 dB, respectively. The bandwidth percentage of simple flexible antenna, flexible antenna without DGS, and flexible antenna with DGS is 5.7318%, 28.57% and 42.38%, respectively. The gain of simple flexible antenna, flexible antenna without DGS, and flexible antenna with DGS is 2.822 dB, 1.2671 dB and 5.2223 dB, respectively. The proposed antenna with DGS has bandwidth of 42.38% with the bands from 2.0769–3.1268 GHz, 4.9151–6.0558 GHz, 11.289–11.74 GHz and 18.183–19.7889 GHz, respectively. The optimized gain is obtained at 5.2223 dB. Keywords Scattering parameter · Flexible antenna · Bandwidth · Gain · DGS

C. G. Akalya (B) · D. S. Kumar Department of Electronics & Communication Engineering, National Institute of Technology, Tiruchirappalli, India e-mail: [email protected] D. S. Kumar e-mail: [email protected] P. H. Rao Microwave Communication and Antenna Division, SAMEER-Centre for Electromagnetics, Chennai, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_12

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1 Introduction Wireless communication technology and its industry are growing between access points generic without using wires. Many areas like automated organizations and industries, remote telemedicine, smart home, appliance, etc., are developed from research-oriented idea to practical availability. Wireless network attracts the researchers and media as well as normal pubic. Wireless network was revolutionized including the developments in microwave system which directed to compact size and less cost system. The wireless network industry is increasing quickly and their product like mobiles are now need of life. Correspondence frameworks need a wide recurrence transmission capacity to transmit and get mixed media data at high information rates. Versatile remote system items must be effectively convenient and shabby to make them alluring to modem individuals. Since microstrip nourished reception devices have a wide impedance transfer speed and basic structure that is effectively made with ease, are exceptionally reasonable for correspondence items, for example, WLAN or blue-tooth applications. All of a sudden, it appears everything from cell phone to MP3 players, printers to GPS collectors, instruments in medical clinics, pathology research facilities, and even the science and material science labs have Bluetooth worked for remote activity cutting the typical wired lines. Antenna “The eyes and ears in space” is encountering a versatile change from earliest patching type for radio impart, correspondence associated with the protection applications, aircraft, radars, rockets and space applications. This circumstance is brisk changing with the headway of cellular flexible individual correspondence as CDMA and GSM and so forth. The broadband portable individual correspondence with versatile top-notch video is the popular expression today. 3G GSM, WCDMA, Wireless Fidelity (Wi-Fi), 4G WiMAX and WLAN are all towards this course. The conventional structure of microstrip patch antenna (MSA) consists of patch and substrate [1, 2]. Different types of patch structures available are rectangular, circular, triangular and annular ring [3]. The commonly used feeding techniques are microstrip feed, coaxial feed and aperture-coupled feed [4, 5]. The dimension of patch is defined with width (W ) and length (L). The fringing fields radiate at the patch edges as shown in Fig. 1. Fringing is a function of patch and substrate height (h). Due to the effective dielectric constant getting introduced, fringing effect of the microstrip line seems to be a bit electrically wider compared to its physical dimensions.

1.1 Advantages of MSA . MSA operates where conventional antennas are not able to be designed at microwave frequencies. . MSA has brought down manufacturing cost, and thus, they could be mass made.

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Fig. 1 MSA with fringing effects

. MSA supports various frequency bands. . MSA is light in weight. . MSA can undoubtedly be intended to have distinctive polarizations, by asymmetric patch structures with a single feed point or various feed points. This makes them a preferable choice for the current wireless system. Some disadvantages of the MSA are mentioned below.

1.2 Disadvantages of MSA . Spurious radiations exist in various MSA such as microstrip slot antenna, printed dipole antenna and other configurations. . MSA is reported to have low radiation efficiency due to dielectric and conductor losses. . MSA have a better radiation level of cross-polarization. . MSA have capability of handling low power. Various issues with MSA are overcome by using Dielectric Resonator Antenna (DRA). The next subsection presents DRA with certain advantages over MSA [6, 7]. The paper is organized in the following way, Sect. 1 is following the general introduction of microstrip antenna. In Sect. 2, Flexible antenna design configuration and their result are to be discussed. In Sect. 3, Flexible antenna design without DGS configuration and their result is to be discussed. Similarly, in Sect. 4, proposed antenna design with DGS configuration and their result is to be discussed. Comparative Analysis of different configuration is discussed in Sect. 5. At last, Sect. 6, conclude the overall paper.

2 Flexible Antenna In this section, flexible antenna configuration and results are to be discussed.

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2.1 Antenna Configuration The substrate of the antenna is made up of FR4 epoxy material. The antenna is designed with dimension 71 × 13 × 1.5 mm (Fig. 2).

Fig. 2 Simple flexible antenna configuration a top view b back view

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2.2 Result Analysis In this section, performance is analysed by various metrics. The field distribution is one of the parameters for antenna performance. The scattering parameter of flexible structure is exposed in Fig. 3. The graph appears that antenna work for single-band operations. The return loss was calculated as − 18.0133 dB with 820 kHz bandwidth. The antenna is operated below − 10 dB with a band from 1.4265 to 1.4349 GHz. The antenna is operated with 1.4306 GHz resonant frequency. Similarly, VSWR of flexible antenna is analysed with a range 1.424–1.437 GHz as displayed in Fig. 4. The results are analysed in form of radiation pattern for gain. Figure 5 shows the radiation pattern of simple antenna structure, and simulated gain is 2.8239 dB.

Fig. 3 Scattering parameter of simple flexible antenna

Fig. 4 Voltage standing wave ratio of simple flexible antenna

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Fig. 5 H & E plane representation of simple flexible antenna

The field distribution was analysed with electric, magnetic and current field parameters and shown in Fig. 6. The electric field is distributed on overall antenna and calculated as 8.83372 × 104 V/m. The magnetic field is calculated as 8.5847 × 101 A/m. The current distribution of flexible antenna is 1.4454 × 102 A/m.

3 Flexible Antenna Without DGS In this section, flexible antenna design without DGS configuration and results are to be discussed.

3.1 Antenna Configuration The antenna substrate material is FR4 epoxy with dielectric constant 3.9. The antenna design with substrate dimension is 50 × 26 × 1.6 mm (Fig. 7).

3.2 Result Analysis In this part, return loss, bandwidth and gain results are analysed. The field distribution is one of the parameters for antenna performance. The scattering parameter of flexible antenna without DGS is shown in Fig. 8. The graph resembles that antenna work for dual-band operations. The return loss was calculated as − 26.2833 dB and − 25.4777 dB, respectively. The antenna is operated

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Fig. 6 Field distribution of flexible antenna a current b electric field c magnetic field

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Fig. 7 Flexible antenna without DGS configuration a top view b back view

below − 10 dB with a band from 4.999–6.63 GHz and 18.2620–19.3667 GHz, respectively. The antenna is operated with 5.8556 and 18.9444 GHz resonant frequencies. The bandwidth of flexible antenna without DGS is 1.664 and 1.10467 GHz, respectively. The result is analysed in form of radiation pattern for gain. The radiation pattern of simple structure without DGS is shown in Fig. 9, and simulated gain is 1.2671 dB. The field distribution was analysed with electric, magnetic and current field parameters and shown in Fig. 10. The electric field is distributed on overall antenna and calculated as 1.3391 × 104 V/m. The magnetic field is calculated as 9.6352 × 101 A/m. The current distribution of flexible antenna is 1.1929 × 102 A/m.

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Fig. 8 Scattering parameter of proposed antenna without DGS

Fig. 9 E & H plane radiation pattern without DGS antenna

4 Flexible Antenna with DGS In this section, flexible antenna design with DGS configuration and results is to be discussed.

4.1 Antenna Configuration The antenna is designed with dimension 60 × 36 × 11.6 mm with ground 62 × 20 mm with x- and y-axis 12 × 1 mm (Fig. 11).

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Fig. 10 Field distribution of flexible antenna a electric field b magnetic field c current field

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Fig. 11 a Top view of DGS configuration b back view of DGS configuration

4.2 Result Analysis In this part, return loss, bandwidth and gain results are analysed. The field distribution is one of the parameters for antenna performance. Figure 12 presents the scattering parameter of flexible antenna with DGS. The graph illustrates that antenna work for multiband operations. The return loss calculated as − 19.11464 dB, − 15.2348 dB, − 11.4150 dB and − 15.2348 dB, respectively. The antenna is operated below − 10 dB with a band from 2.0769–3.1268 GHz, 4.9151–6.0558 GHz, 11.289–11.74 GHz and 18.183–19.7889 GHz, respectively. The antenna is operated with 2.4778, 5.6444, 11.555 and 18.9444 GHz resonant frequencies. The bandwidth of flexible antenna without DGS is 1.0499, 1.1407, 0.451 and

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1.6009 GHz, respectively. The result is analysed in form of radiation pattern for gain. The radiation pattern of simple structure with DGS is indicated in Fig. 13, and simulated gain is 5.2223 dB. The field distribution analysed with electric, magnetic and current field parameters and shown in Fig. 14. The electric field is distributed on overall antenna and calculated as 1.8324 × 104 V/m. The magnetic field is calculated as 9.6978 × 101 A/m. The current distribution of flexible antenna is 1.3133 × 102 A/m.

Fig. 12 Scattering parameter of proposed antenna with DGS

Fig. 13 E & H plane representation of DGS antenna

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Fig. 14 Field distribution of flexible antenna a E-field b H-field c current field distribution

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Fig. 15 Comparative analysis of scattering parameter of antenna

Table 1 Comparative analysis of different configurations SI. No.

Antenna configuration

Return loss (dB)

% B.W

Gain (dB)

1

Simple flexible antenna

− 18.0133

5.73186

2.822

2

Flexible antenna without DGS

− 26.283

28.57

1.2671

3

Flexible antenna with DGS

− 19.1454

42.38

5.2223

5 Comparative Analysis This section analyses the comparison of all the configuration of antenna. Different configurations of antenna are simple flexible antenna and flexible antenna without DGS and with DGS (Fig. 15 and Table 1).

6 Conclusion The proposed antenna is a good candidate for high gain and bandwidth. The impedance matching is proper as indicated in the result. The proposed antenna has bandwidth 42.38% with the bands from 2.0769–3.1268 GHz, 4.9151–6.0558 GHz, 11.289–11.74 GHz, 18.183–19.7889 GHz, respectively. The optimized gain is obtained 5.2223 dB. The proposed antenna operated for WiMAX, Bluetooth and other wireless applications. Acknowledgements This work is done under a part of project “Development of Dense Deployable Massive MIMO antenna Systems for 5G Wireless Communications with reduced Correlation/Mutual Coupling” under Grant DST-SERB/IMPRINT II (sanction no: IMP/2018/001011 dt.21/02/2019) from Smart Antenna Design Laboratory, Dept. of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli.

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References 1. Martínez ÀO, De Carvalho E, Nielsen J (2014) Towards very large aperture massive MIMO: a measurement based study. In: 2014 IEEE GLOBECOM workshops (GC Wkshps), December 8–12, 2014; Austin, TX, pp 281–286 2. de Spinadel VW (1999) The metallic means family and multifractal spectra. Nonlinear Anal 36:721–745 3. Ghyka MC (1977) The geometry of art and life. Dover Publications, New York, NY 4. Garg R, Ittipiboon A, Bahl IJ, Bhartia P (2001) Microstrip antenna design handbook. ARTech Publication 5. Blanch S, Romeu J, Corbella I (2003) Exact representation of antenna system diversity performance from input parameter description. Electron Lett 39(9):705–707 6. Sarkar D, Srivastava KV (2018) Modified cross correlation Green’s function with FDTD for characterization of MIMO antennas in nonuniform propagation environment. IEEE Trans Antennas Propag 66(7):3798–3803 7. Dattatreya G, Kousalya K, Jyothirmayi Y, Krishna MV, Harsha D, Sri PAV, Naik KK (2017) Analysis of complementary split ring resonators on rectangular patch with inset feed for X-band application. In: 2017 International conference of electronics, communication and aerospace technology (ICECA), IEEE, pp 248–250 8. Ma R, Gao Y, Cuthbert L, Zeng Q (2014) Antipodal linearly tapered slot antenna array for millimeter-wave base station in massive MIMO systems. In: 2014 IEEE Antennas and propagation society international symposium (APSURSI), July 6–11, 2014, Memphis, TN, 1121–1122 9. Zhang Q, Chen Z, Gao Y, Parini C, Ying Z (2014) Miniaturized antenna array with Co2Z hexaferrite substrate for massive MIMO. In: 2014 IEEE Antennas and propagation society international symposium (APSURSI), July 6–11, 2014, Memphis, TN, pp 1803–1804 10. Shepard C, Yu H, Anand N et al. (2012) Argos: practical many-antenna base stations. In: Proceedings of the ACM international conference on mobile computing and networking (MobiCom), August 22–26, 2012, Istanbul, Turkey 11. Suzuki H, Collings IB, Hayman D, Pathikulangara J, Chen Z, Kendall R (2012) Large-scale multiple antenna fixed wireless systems for rural areas. In: 2012 IEEE 23rd International symposium on personal, indoor and mobile radio communications—(PIMRC), September 9–12, 2012, Syndey, Australia, NSW, 1600–1605 12. Vieira J, Malkowsky S, Nieman K et al. (2014) A flexible 100-antenna testbed for massive MIMO. In: 2014 IEEE GLOBECOM workshops (GC Wkshps), December 8–12, 2014, Austin, TX, pp 287–293 13. Saha TK, Goodbody C, Karacolak T, Sekhar PK (2019) A compact monopole antenna for ultra wideband applications. Microwave Opt Technol Lett 61(1):182–186 14. Azim R, Islam MT, Misran N (2011) Compact tapered-shape slot antenna for UWB applications. IEEE Antennas Wirel Propag Lett 10:1190–1193 15. Kaushal D, Shanmuganantham T, Sajith K (2017) Dual band characteristics in a microstrip rectangular patch antenna using novel slotting technique. In: 2017 International conference on intelligent computing, instrumentation and control technologies (ICICICT), IEEE, pp 957–960 16. Gharakhili FG, Fardis M, Dadashzadeh GR, Ahmadi AKA, Hojjat N (2007) Circular slot with a novel circular microstrip open ended microstrip feed for UWB applications. Prog Electromagn Res 68:161–167 17. Liang J, Chiau C, Chen X, Parini C (2004) Printed circular disc monopole antenna for ultrawideband applications. Electron Lett 40(20):1246–1247 18. Abedian M, Rahim S, Danesh S, Khalily M, Noghabaei S (2013) Ultrawideband dielectric resonator antenna with WLAN band rejection at 5.8 GHz. IEEE Antennas Wirel Propag Lett 12:1523–1526

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Maturity Level Detection of Strawberries: A Deep Color Learning-Based Futuristic Approach T. K. Ameetha Junaina , R. Kumudham , B. Ebenezer Abishek , and Shakir Mohammed

Abstract The significance of including futuristic technologies in the field of agriculture is very crucial these days. In this fast-moving world, bringing automation at all levels of agro-supply chain will be beneficial to the supply chain management in many ways. Conventional manual method of detecting the ripeness level based on the appearance of strawberries involves workers sitting and sorting each fruit with the aid of their naked eye and bare hands. This is a tedious and time-consuming task. This work proposes and describes a technique to automatically sort strawberries into three main categories, namely RIPE, PARTIALLY RIPE, and UNRIPE depending on their color. Also, based on the color and freshness detection of strawberries by using deep learning-based image processing techniques, the ripe strawberries can be further graded to good and bad quality ones which can be done as a future work. This computer vision-based deep learning model in strawberry maturity level detection including the novel dataset of strawberry images was able to classify strawberries into three categories with an accuracy level of 91.38% by using the features extracted from the final layer of the ResNet-18, a CNN-based pre-trained network. The image dataset used for this classification was also acquired with the help of an image studio

The original version of the chapter was revised: The sequence of the second and third authors’ names were incorrectly published. It has now been corrected with thier respective ORCIDs. The correction to this chapter is available at https://doi.org/10.1007/978-981-19-9748-8_48 T. K. Ameetha Junaina Department of Electronics and Communications Engineering, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, Tamil Nadu, India e-mail: [email protected] R. Kumudham (B) Department of Electronics and Communications Engineering, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, Tamil Nadu, India B. Ebenezer Abishek Department of Electronics and Communication Engineering, VelTech MultiTech Dr. Rangarajan and Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India S. Mohammed WhiteBox Analytics, Sydney, Australia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_13

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setup. A multiclass SVM classifier was used for classification of strawberries into three main categories based on its maturity ripeness. Keywords Strawberry maturity level detection · Image processing · Deep learning techniques · ResNet-18 · Multiclass SVM classifier

1 Introduction India is a country where majority of its income comes from agriculture [1]. Agriculture is one of the fields where computer vision-based automation needs to be implemented at all stages of agro-supply chain, from farming to the distribution of fresh produces in the market. In a report that was launched by the World Government Summit in association with Oliver Wyman at the 2018 edition of an international event, ‘Agriculture 4.0-The Future of Farming Technology’, they had addressed few important developments, urging agriculture to meet four demands of the future, namely demographics, scarcity of natural resources, climate change, and food waste [2]. In order to meet these challenges, concerted effort will be needed to be made by governments, innovative agricultural technologists as well as investors. One of the issues mentioned above is the wastage of food (fresh produces) which can be dealt with by bringing automation in agriculture at the gradation process of fruit quality check. This work is an attempt to a small contribution toward this, focusing on the futuristic techniques in bringing automation at the sorting of strawberries based on its ripeness. Ripe strawberries will have bright red color and sweetness, whereas unripe strawberries will be white or partially ripe with less sweetness. Thus, color has a major role in the detection of ripeness in fruits, especially strawberries [3]. As compared to the conventional process of manual sorting of fresh produces, automation can bring many benefits to the agro-supply chain like it improves the efficiency in sorting of fresh produces, minimizes the wastage happening in fresh produce market, attains better customer satisfaction, and thus increases the profitability of farmers and all stakeholders in the supply chain. As a result of the many attempts that are being made in bringing automation in the field of agriculture [4], there are several related works which deal with implementation of automation in the process of fresh produce sorting and grading using innovative machine vision and machine learning techniques. Some of them will be discussed here in Sect. 1.1. This work is focusing on automatic sorting of the fruit (strawberries) based on its ripeness level depending on its color and quality using image processing and deep learning techniques as DL is a subclass of ML which has this ability to learn robust representations from images [5].

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1.1 Automatic Fruit Grading Techniques in Literature There are many works in literature related to automation in agricultural fields using image processing and computer vision techniques. Many are related to identification of fruits based on their features [6], in detection of quality of fruits and vegetables using computer vision [7], in detection of diseases on fruits and leaves [8], computer vision methods for counting fruits and yield estimation [9], prediction of harvesting time of fruits [10–12], etc. Fatma M. A. Mazen and Ahmed A. Nashat had done a work related to ripeness classification of bananas using ANN, in 2019 [13]. A computer vision-based automatic system to detect the banana ripening stages was developed. To detect banana ripening stages, an ANN-based framework that uses color features, Tamura statistical texture features, and the development of brown spots on the peel were used as inputs and the classification was done. Various classifiers like support vector machine, Naive Bayes classifier, decision tree, k-nearest neighbor classifier, and discriminant analysis classifiers were also used for classification, and the results were compared. Among all these techniques, it was found that the overall recognition rate was attained for the system proposed by them. In 2019, Xiaoyang Liu, Dean Zhao et al., had developed an algorithm for the detection of apple fruit based on shape and color characteristics [14]. As the fully grown apple skin is not completely red and contains light yellowish colors in them, it is difficult in detecting the maturity level using computer vision techniques. Apple detection method using color and shape features was proposed. From orchards, apple fruit images were acquired and segmented into super-pixel blocks after segmentation using simple linear iterative clustering (SLIC) techniques. Then by extracting the color features from blocks, candidate regions were identified, and by filtering out the non-fruit blocks, the precession in detection was improved. Candidate regions can be selected depending on the color characteristics effectively using support vector machine classifier with Gaussian kernel. The shape features were determined using the histogram of oriented gradient (HOG) to identify the fruit in the candidate regions. Testing was done using images taken under various illuminations. A linear support vector machine classifier was used to perform training and testing of these features. The results of performance of this method were compared with some existing methods like pedestrian detection method and faster RCNN and found that this algorithm showed better performance in fruit identification in terms of precision. The drawbacks of this method were the less robustness to noise and slower detection rate than RCNN.

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2 Deep Color Learning and Maturity Level Detection of Strawberries The proposed system is executed in three main stages: image acquisition, feature extraction using deep learning techniques, and classification of strawberries based on color into white, turning, and red which indicates the ripeness level. The block diagram of the proposed system is given below (see Fig. 1). White strawberries indicate unripe ones, turning strawberries indicates the partially ripe ones, and red strawberries are the ripe ones. The image dataset is acquired using a home studio setup with the help of a Logitech C920 HD camera. Each of the blocks is explained in the following sections: first the studio setup and image acquisition techniques, followed by feature extraction techniques using deep learning method and finally training and classification of test images using a multiclass SVM classification into ripe, partially ripe, and unripe strawberries.

2.1 Image Acquisition Image acquisition is a major task in creating an image dataset for the classification purpose. Mahabaleshwar strawberries from fresh markets were collected during the seasonal months of availability to create a dataset. Good quality images of strawberries were acquired with the help of a Logitech C920 HD cam and a studio setup (see Fig. 2a). Strawberries were placed at the center of the studio with white background at a fixed position from the camera. The strawberry images from various angles and Fig. 1 Block diagram representation of automatic strawberry ripeness level detection method

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Fig. 2 a Image acquisition: studio setup, b sample image views from a single strawberry

positions were captured, and a strip of LED lights was also attached to the studio setup to get a well-illuminated background; few of the acquired image samples are shown below (see Fig. 2b). The internal cross-sectional image was also acquired by cutting the strawberries using a sharp knife. The acquired images of each strawberry were stored individually in named folders.

2.2 Preprocessing After the acquisition of image data, certain preprocessing steps like creating image data store, splitting of image data into training and testing sets, loading of ResNet-18, a pre-trained CNN-based network and installation of its supporting package, image resizing, etc., were done. The preprocessing steps done here are explained below. Load dataset to image data store. After acquisition of images of strawberries which are at various stages of maturity, the images are loaded to an image data store in three different folders, namely RIPE, TURNING, and WHITE. Images are labeled

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Fig. 3 Random sample images from training set of image data store

according to their folder names in an image data store. Data in image data store will be stored as an object of the image data store. Splitting of dataset to training and testing set of images. A total of 195 image samples had been saved in the image data store in three different folders based on their maturity color. Out of which, 137 images (70%) had been used for training and 58 images (30%) had been used for testing purposes. A few samples from the training set are displayed below (see Fig. 3) which consists of strawberries from all three categories, namely WHITE, TURNING, and RED. Loading of ResNet-18, a CNN-based Network. After the acquisition of image dataset, ResNet-18, a CNN-based pre-trained network [15] was loaded to the system—MATLAB R 2019b version from the ImageNet database so as to do the feature extraction step. Installation of deep learning toolbox model for ResNet-18 network support package was also required. Resizing of images as required by ResNet-18 input layer. The network architecture (see Fig. 4) has to be analyzed before loading the images. As a preprocessing step, the input images should be resized to [224 * 224 * 3] as required by the input layer of the architecture, since the images stored in image data store will be of various sizes. For this, AUGMENTED IMAGE DATA STORE is created where the required size of the images is specified.

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Fig. 4 ResNet-18 architecture: a basic building block of residual learning and b filter information of different convolutional layer

2.3 Feature Extraction Using Deep Learning Techniques Feature extraction is a crucial step in the process of image classification. Deep learning techniques are employed for feature extraction. Here, features have been extracted from two different layers of the network: shallower layer features and deeper layer features. Each feature sets was given to the classifier independently for training and testing, and the accuracy of classification was computed separately. Initially, features were extracted from an earlier layer ‘res3b_relu’ layer in the network, and the classifier was trained on those features. A total of 137*128 training features were extracted from this layer and 58*128 testing features. Later, deeper layer features were also extracted from the global pooling layer, ‘pool5’, which is the final layer of the network. The global pooling layer gives 512 features in total. After the extraction of feature sets, the classifier was trained on these extracted features and then the test images were used for validation.

2.4 Image Classification A multiclass SVM classifier [16] was used for classification of strawberries into three different categories UNRIPE, PARTIALLTY RIPE, and RIPE based on their maturity (ripeness) color. Block diagram of overall classification is given below (see Fig. 5). For this, class labels had to be extracted from all images of training and test data samples. The extracted features from the training samples were used to predict classification and were given to the multiclass SVM classifier. The trained classifier was then used to classify the test samples into either of the three categories based on

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Fig. 5 Block diagram of proposed image classification algorithm

their ripeness color. The below figure (see Fig. 6) displays the classification results of a few randomly selected sample test images with their predicted labels.

2.5 Classification Results As two different sets of feature sets were extracted from the different layers of ResNet-18 and classification was done using the same multiclass SVM classifier using both feature sets separately, the classification accuracy of both the methods was computed. Also, confusion matrix was plotted (see Fig. 7) in both the cases to get the number of true/false predictions.

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Fig. 6 Maturity level classification output of strawberries with predicted labels

(a) Shallower Layer Feature plot

(b) Deeper Layer Feature plot

Fig. 7 Confusion matrix plot

The classification accuracy obtained by using the feature set obtained from the activations on the shallower layer, ‘res3b_relu’ layer, was computed and was found to be 79.31%. This ‘res3b_relu’ layer is at an intermediate layer of the network, giving 128 features. The classification accuracy was improved by using the deeper features to train the classifier. The accuracy was checked and was found to be 91.38%. This is the accuracy obtained by using activations on the global pooling layer, ‘pool5’, which gives a total of 512 features. It is observed that the classification accuracy has improved than using the features from shallower layer to train the classifier. A comparison of the obtained results is also done with an existing system in literature

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Table 1 Comparison table of existing method vs proposed method Parameters

Existing method

Proposed method

No. of images used

192 image samples

195 image samples

Feature set used

20-dimensional histogram (Hue: Feature vector from the global 10bins, saturation: 10bins) pooling layer of ResNet-18, 512 feature vectors features

Classification method

Multilayer perceptron neural network

Overall accuracy obtained 87.25%

Multiclass SVM classifier 91.38% from deeper layer features

where Wanhyun Cho et al. [17] had used 134 strawberry images as a learning data to derive a function that can classify strawberry images into three maturity stages by season and also used 58 image data to verify derived classification functions. They had used MLP neural network as a classification method, and the overall classification rate obtained was 87.25%. Table 1 gives a comparison between the two systems.

3 Conclusion Supply chain management has a major role in ensuring the reliability of produces in terms of quantity, quality, and cost. This work can be considered as a stepping stone in ensuring those factors. This work was aimed to develop an algorithm in detecting the maturity stages of strawberries based on ripeness level using image processing and machine learning technique. This can be used as an initial phase in detecting the quality of strawberries as the ripeness level can be detected based on its color. Classification of strawberry images into white (UNRIPE), turning (PARTIALLY RIPE), and red (RIPE) was done using ResNet-18, a CNN-based pre-trained network with an accuracy of 91.38%. Here two feature sets were considered; one from a shallower layer of ResNet-18 and the other from the deeper layer. Classification accuracy was considerably high when the features from the deeper layer were chosen. Thus, this work can be taken as the primary step in bringing automaton in quality detection of strawberries. Thus, by adopting such futuristic automation techniques in the field of agriculture, the speed of product to market can be increased by delivering good, consumable, reliable, and quality strawberries.

References 1. Arjun, K.M.: Int. J. Agric. Food Sci. Technol. 4(4), 343–346 (2013). http://www.ripublication. com/ijafst.htm ISSN 2249-3050, Research India Publications 2. Agriculture 4.0–The Future of Farming Technology. https://www.oliverwyman.com/our-exp ertise/insights/2018/feb/agriculture-4-0--the-future-of-farming-technology.html

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3. Mohamed, I., Williams, D., Stevens, R., Dudley, R.: Strawberry ripeness calibrated 2D colour lookup table for field-deployable computer vision. IOP Conf. Ser.: Earth Environ. Sci. 275, 012003 (2019). https://doi.org/10.1088/1755-1315/275/1/012003 4. Itsupplychain homepage. https://itsupplychain.com/smart-farming-how-automation-is-sha ping-the-future-of-agriculture/ 5. Naranjo-Torres J, Mora M, Hernández-García R, Barrientos RJ, Fredes C, Valenzuela A (2020) A review of convolutional neural network applied to fruit image processing. Appl Sci 10(10):3443. https://doi.org/10.3390/app10103443 6. Fiona, R., Thomas, S., Isabel Maria, J., Hannah, B.: Identification of ripe and unripe citrus fruits using artificial neural network. In: International Conference on Physics and Photonics Processes in Nano Sciences, 2019. https://doi.org/10.1088/1742-6596/1362/1/012033 7. Bhargava, A., Bansal, A.: Fruits and vegetables quality evaluation using computer vision: a review. J. King Saud Univ.—Comput. Inf. Sci. (2018). https://doi.org/10.1016/j.jksuci.2018. 06.002 8. Mim, T.T., Sheikh, M.H., Shampa, R.A., Reza M.S., Islam. M.S.: Leaves Diseases Detection of Tomato Using Image Processing. In: 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India, pp. 244–249 (2019). https:// doi.org/10.1109/SMART46866.2019.9117437 9. Behera, S.K., Mishra, N., Sethy P.K., Rath. A.K.: On-Tree Detection and Counting of Apple Using Color Thresholding and CHT. In: 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 0224–0228 (2018). https://doi.org/10. 1109/ICCSP.2018.8524363 10. Zhao, J., Chen, J:. Detecting maturity in fresh Lycium barbarum L. fruit using color information. Horticulturae 7, 108 (2021). https://doi.org/10.3390/horticulturae7050108 11. Gayathri Devi, T., Neelamegam, P., Sudha, S.: Image processing system for automatic segmentation and yield prediction of fruits using Open CV. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pp. 758–762. https://doi.org/10.1109/CTCEEC.2017.8455137 (2017). 12. Chen Y, Lee WS, Gan H, Peres N, Fraisse C, Zhang Y, He Y (2019) Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sens 11(13):1584. https://doi.org/10.3390/rs11131584 13. Mazen FMA, Nashat AA (2019) Ripeness classification of bananas using an artificial neural network. Arab J Sci Eng. https://doi.org/10.1007/s13369-018-03695-5 14. Liu, X., Zhao, D., Jia, W., Ji, W., Sun, Y.: A detection method for apple fruits based on color and shape features. IEEE Access 7, 67923–67933. https://doi.org/10.1109/access.2019.291831 3(2019). 15. Math Works Homepage. https://in.mathworks.com/help/deeplearning/ref/resnet18.html 16. Wikipedia Multiclass SVM. https://en.wikipedia.org/wiki/Support-vector_machine 17. Cho, W., Na, M., Kim, S., Jeon, W.: Automatic prediction of brix and acidity in stages of ripeness of strawberries using image processing techniques. In: 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). https:// doi.org/10.1109/itc-cscc.2019.8793349

Cross-Layer Energy Efficient Radio Resource Management Scheme with QoS Provision in LTE-Uplink Systems Leeban Moses, Perarasi, Mano Raja Paul, and Kannan

Abstract With the Internet of things and other network devices demanding faster and more reliable connectivity, combined with exponential data growth, LTEAdvanced system provides high data rate and low latency with increased mobility for multimedia applications and improved spectral efficiency. LTE-A also provides support to machine type communication (MTC) devices which describes the communication with machines without the engrossment of a humanoid. These MTC devices with the application of IoT provide small amount of sensing and monitoring data with low data rate requirement. In order to improve the performance of the LTE-A system, the radio resources for every user in the network should be efficiently managed by providing QoS requirements for every user. The radio resource management algorithm in LTE-A network provides cross-layer resource allocation between the user equipment (UE) and MTC devices. Since all the MTC devices are sensor nodes and battery powered equipment, they should consume very little amount of power. In this work, we propose a cross-layer energy efficient radio resource management scheme for allocating the physical resources to UE and MTC devices. Rate adaptive (RA) principle is utilized in this work to improve the energy efficiency and to upsurge the capacity of the channels. The performance of the system is evaluated by calculating the data rate of each user and allocating the resource to each user, packet loss ratio, fairness index, packet delay, peak signal-to-noise ratio, and time consumption. With the comparison of the existing algorithms, the simulated results obtained from the proposed algorithm guarantees QoS service to the user by consuming less energy for UE and MTC devices with increased fairness index and decreased packet loss. Keywords Machine type communication · Low latency · LTE-advanced system · Radio resource management algorithm · Rate adaptive principle · Packet loss ratio · Fairness index · Packet delay · Peak signal-to-noise ratio L. Moses (B) · Perarasi Bannari Amman Institute of Technology, Erode, India e-mail: [email protected] M. R. Paul · Kannan Nehru Institute of Engineering and Technology, Coimbatore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_14

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1 Introduction With the advent growth of IoT, LTE-A shall be most suitable cellular network to cater the IoT traffic. However, traditional LTE networks are designed to handle human traffic conditions such as Web browsing, file transfer, and video streaming. Further, IoT devices are expected to generate machine type traffic which will be quite different from the human type traffic. Machine type traffic is usually small in data size, and the devices are mostly inactive and they actually come into existence or transmit only when there is data to transmit. These patterns will create a new culture of data traffic in future, wherein a large amount of traffic will be generated by the control plane which in turn will cause huge congestion in evolved node B (eNB) and to mobility management entity (MME). This in turn shall increase the cost and overhead of the network service providers because the signal traffic shall considerably increase. There are two radio ON/OFF states in LTE—RRC_Connected and RRC_idle. In all the above-mentioned traffic patterns, the traffic is generated only in the RRC_Connected state. This will save huge power. However, in the case of IoT traffic, the patterns are going to be completely different, wherein the LTE has to switch between the RRC_Connected and the RRC_idle state. This is because the devices will automatically generate huge bursts in traffic which will cause excessive signaling load on the LTE networks. Hence, it is imperative that we carefully manifest the ON/OFF states in LTE to suit the IoT traffic. As the traffic patterns of the IoT devices are completely different, traditional scheduling algorithms will not be able to effectively manage the traffic for two primary reasons. Let us assume that the scheduling algorithm is always in the RRC_Connected mode, then the pointer in the scheduler has to keep switching between the RRC_Connected and the RRC_idle mode which can be reduced. However, this will considerably increase the power consumption or drain the power of the UE devices. Conversely if the scheduling pointer is set to RRC_idle mode, then the traffic patterns of the IoT devices will require frequent channel setups as the devices shall transmit short bursts frequently, and this in turn will increase the delay, thereby degrading the performance. In this case, delay-sensitive IoT traffic like medical health monitoring, surveillance, and vehicular communication will be disrupted. Hence, it is imperative to design a scheduling algorithm that will cater to all types of IoT traffic, namely delay-sensitive and delay-insensitive traffic and at the same time manage the bandwidth, delay, and power consumption of the system. In order to provide rigorous QoS chucks to all the users in the network, the mobile networks should adapt certain advancements to improve the coverage and provide cross-layer energy efficient radio resource management scheme. The MTC devices are classified according to the transfer rate of data and urgency of the packet. The sensor data obtained from the agricultural field and monitoring information field fall under massive-MTC networks, and the sensor data obtained from the medical field and traffic monitoring field are comes under critical MTC networks. The release 15 in 3GPP satisfies this constraint by increased uplink access in MAC protocol layer, reducing the time interval of packet and reducing the processing time of data.

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Generally, the allocation of resources to UE and MTC devices should be a different procedure because the transmission rate, peak signal-to-noise ratio, latency, QoS requirements, and size of the packet is different. Scheduling the resources to users from eNB and allocating the physical resource block to the user is a challenging task in radio resource management scheme. In this work, we provide an optimization algorithm which can efficiently allocate the resources to the UE and MTC devices.

2 Related Work In order to include the low data rate MTC devices with home type communication (HTC) network and efficiently allocate the resource to the users, many resource allocation techniques have been proposed. The authors in [1] have allocated the resources for both UE and MTC devices by considering a smart city application scenario. In this work, the scheduling problem is assumed to be NP-hard, and this issue is solved by creating three objective function. Two models proposed in this work are based on three optimization criteria modeled through mathematical assessment in CPLEX. The authors in [2] have proposed a semi-persistent scheduling algorithm for allocating resource to high traffic LTE-A MTC devices which is manipulated by their periodicity property and aims at reduction of overhead-signaling necessary for connection initiation and scheduling. The authors in [3] have proposed a whale optimization algorithm which optimizes the resource allocation problem by considering a multi-objective function and thereby allocating the resources efficiently to home users and MTC devices. The resource management is considered to be mixed-integer non-linear programming problem and considered to be NP-hard. The authors in [4] have proposed an An LTE-based optimal resource allocation scheme for delay-sensitive M2M deployments coexistent with H2H users. The computational complexity of the algorithm is reduced by scheduling the resources as a bank of M/D/1 queues and combining the resources of UE and MTC devices. The authors in [5] have proposed a delay optimal multiclass packet scheduler for general M2M uplink network. In this work, the MTC devices are classified based upon the variety of QoS constraints, and the packet delay of the concerned MTC devices is estimated by using sigmoid function. The complexity of the algorithm is reduced by implementing the delay aware priority-based scheduler at the application server that increases the fairness index. The authors in [6] have proposed weighted proportional fair scheduling method to reduce the interference between the adjacent cells. In order to increase the QoS of the users, the PF scheduler metrics considered must provide a proper delivery of data to the users associated with UE and MTC devices. The authors in [7] have proposed a QoS-aware uplink scheduler for LTE-A networks. The MTC devices are classified based on the urgency of the packet delivery. Round Robin scheduling algorithm proposed in this work in order to allocate the urgent packets of MTC device data, and bound Round Robin scheduling algorithm

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proposed in this work in order hold the transmission of MTC device data, since it has low data rate and very low priority factor. The authors in [8] have proposed an energy efficient scheduling algorithm in order to allocate the resources between home users and MTC devices. FDPS carrier by carrier algorithm is designed in this work in order to allocate the resource efficiently between home users and MTC devices. Recursive maximum expansion algorithm is proposed in this technique in order to classify the MTC devices depending upon the priority of data packets. The authors in [9] have proposed a joint QoS-aware and energy efficient scheduling algorithm in order to allocate the resources among the users. A memeticbased algorithm is discussed in the work which is based on both time, and frequency domain provides optimal resource allocation by considering the latency and QoS requirements. The energy efficiency is made possible by DRX switching algorithm. A multi-objective cooperative swarm algorithm implemented in [10] has taken three objectives and solved it using firefly and particle swarm optimization algorithm. ACB for delay-sensitive MTC devices is proposed in [11] in order to schedule the resources to users. Random access channel is created in this work to cluster the MTC devices based on the delay requirements, and the resources are allocated to the MTC devices based on the QoS classes of the clustered model. Power allocation scheme is proposed along with the scheduling strategies in [12] to provide high QoS and PDBV. The resources are shared among the users of heterogeneous networks by eliminating the interference. In order to increase the spectral efficiency among the users in the network, the SINR gets eliminated by selecting the optimal power. Remote radio head (RRH) and radio resource management in LTE-A networks are accomplished at antenna of eNodeB which is proposed in [13] in order to provide a soft computing-based scheduling algorithm. HORA algorithm and CRBA planning process are proposed in [14] in order to allocate the resource efficiently to the users in heterogeneous network. The number of physical resources from each user and energy efficiency is considered as optimal problem and solved using HORA and CRBA algorithm.

3 System Model and Problem Formulation LTE-A utilizes SC-FDMA for uplink data transmission process due to the reduction in peak to average power ratio. Each slot contains N resource element depending upon the number of subcarrier, and group of resource element forms a resource block. Figure 1 represents a single cell scenario which consists of a single eNB with multiple UE and MTC devices. The spectrum of every physical resource block would be of 180 kHz. The scheduling of every PRB to the user can be done in one transit time interval by providing stringent QoS service to the user. Let us assume that in this single cell structure, we have n UE and m MTC devices with service provider as A. Let us assume that there is vary less probability of occurrence of interference between UE and MTC devices. The user throughput for ith PRB to the ath service provider can be given as

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Fig. 1 LTE-A single cell scenario with coexistence of UE and MTC devices



a, j

γUE (a, i, j ) = B ∗ log2 1 +

Pi,a j ηUE,i



β2

(1)

where B represents the bandwidth of the PRB, P represents the transmission power of the UE device available, and β represents the variance of the noise. The throughput estimation of the kth MTC device when connected with the jth PB can be estimated as   Pk, j ηk, j (2) γMTC (k, j ) = B ∗ log2 1 + β2 where Pk,j represents transmission power of the MTC device available and represents the channel gain between eNB and MTC devices. The total throughput of the entire system would be the combination of the throughput estimation of UE and MTC device. In order to achieve higher throughput for UE compared with MTC devices, the energy efficiency must be taken into account. The energy efficiency is considered to be the optimization problem and can be defined as Tk  M

Max(Energy Efficency) =

A m=1 a=1 χa Tk  M  A T  X  n=1 m=1 a=1 t=1 x=1 δm,n,a,t,x n=1

 ∗ Pm,n,a,t,x (3)

where χa is the optimization variable represented in terms of a binary value and is equal to one if the PRB is assigned to the data, otherwise it assigns zero value. Pm,n,a,t,x represents the power of the data transmission from eNB. The curtailment is represented as Pm,n,a,t,x ≤ Pmax , ∀ m ∈ [1, M], n ∈ [1, N ], a ∈ [1, A]

(3a)

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The above curtailment (3a) represents the energy efficiency parameter in which the total power transmitted by MTC devices will never surpass the determined power Pmax . δm,n,a1,t,x ∗ Pm,n,a1,t,x = δm,n,a2,t,x ∗ Pm,n,a2,t,x δm,n,a1,t,x = δm,n,a2,t,x ∀ m ∈ [1, M], n ∈ [1, N ],

(3b)

a1 ∈ [1, A], t ∈ [1, T ], x ∈ [1, X ], a2 ∈ [1, A] The above curtailment (3b) represents the procedure for transmission of data in uplink LTE-A network, such that all the transmission power for MTC device data transmission must be equal. X    δm,n,a,t,x ∗ D Ym,n,a,t,x ≥ χa x=1

∀ m ∈ [1, M], n ∈ [1, N ], a ∈ [1, A] , t ∈ [1, T ]

(3c)

The above curtailment (3c) represents a condition in which the transmitted data size should be always a reduced amount of data rate that is obtained by the MTC data transmission and Y represents an optimization parameter. Pm,n,a,t,x + 10 ∗ log

 X

 δm,n,a,t,x

+ (μ − 1)

x=1

  ∗ path loss − ψ ≥ SINR Ym,n,a,t,x

(3d)

The above curtailment (3d) represents a condition for energy efficiency in a mixed environment in which the value of μ can be fixes as 0 in general and can be varied from 0.3 to 0.9 when the iteration gets increased. The curtailment in 3e represents the condition for allocating the PRB to the user in a mixed environment, such that the resource allocation to the users in the uplink channel should provide only 1 resources to the user, and the remaining data should be adjacent to each other. ⎧ ⎫ X    ⎪ max min ⎪ ⎪ δm,n,a,t,x − 1 ⎪ ⎨ δm,n,a,t,x − δm,n,a,t,x = ⎬ x=1   max ⎪ ⎪ = max x /δm,n,a,t,x = 1 δm,n,a,t,x ⎪ ⎪   ⎩ ⎭ min δm,n,a,t,x = min x /δm,n,a,t,x = 1

(3e)

The curtailment represented in Eqs. (3a–3e) allocates power to the UE and MTC devices, and the conditions describe the energy efficiency of resources allocated to the users from eNB. This problem of resource allocation is considered to be NP-hard.

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4 Energy Efficient Radio Resource Management Scheme Meta-heuristic deals with high-level problem independent algorithmic framework where this framework is used it is used to develop optimization algorithms. These meta-heuristic algorithms find the best solution out of all the possible solution of an optimization gray wolf optimization (GWO) algorithm and are inspired by the social hierarchy in the hunting technique used by gray wolf. One important thing that is important for the survival is the ability to work together in a pack that increase the chance of success during hunt, and in a pack of wolf, they have dominant leaders so it is done that they live in a highly organized back in the pack sizes five to twelve wolf for different ranks of the wolf in a pack are alpha by beta wolf, delta both, and omega wolf (Fig. 2). Fig. 2 Algorithm for representing resource allocation using GWO and GSO algorithm

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In this procedure, 3 wolves such as α, β, and δ could be considered as the stalking behavior. GWO mimics the leadership and hunting mechanism of the triple so we have certain staff that are followed by great wolf for hunting first searching for the prey track tracking chasing approaching, then they will encircle it and harass the prey until it stopped moving and the last step is attack. Group search optimization (GSO) is the populace-based calculation, which embraces the looking through conduct of creatures in a specific gathering. In the mixture calculation, the GSO calculation is forced into the GWO calculation. Here, the position α in GWO is refreshed by the GSO-based looking. It works creating and searching scattering. ω = ω+ + z 1 θmax

(4)

where θ max represents the concentrated rotating perspective, z represents the arbitrary value ranging from 0 and 1, ω + represents the preceding head perspective.   x j (1) = x +jλ(m) + z 2 ∗ x +j λ(m) − x +jλ(n)

(5)

The location update is given in the above equation, where x +j λ(m) , x +jλ(n) represents the preceding location of the creator and borrower correspondingly. x j (2) = x j (β) − C2 β ; x j (3) = x j (δ) − C2 δ ; C = (2h ∗ z 1 ) − h     β =  D2 ∗ x j (β) − x j ; δ =  D3 ∗ x j (β) − x j ; D = 2z 2

(6)

The above equation represents the updation procedure of the location of the UE and MTC devices in eNB, where the value of C and D represents the update position vector, z1 and z2 represent the arbitrary value ranging from 0 and 1. x ∗j =

x j (1) + x j (2) + x j (3) 3

(7)

The above equation represents the final updation that narrates the optimal scheduling to the users both for UE and MTC devices.

5 Results and Discussion The resource utilization maximization, interference minimization, and energy efficiency are the three parameters considered for allocating the resources efficiently in a mixed environment consisting of UE and MTC devices. A Vienna LTE-A simulator in MATLAB is used to obtain the performance results for the proposed work. In this section, we perform simulation by considering 100 resource block allocation between the users with multiple MTC devices ranging from 50 to 100. Here, we

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Fig. 3 Consumption of energy and packet loss ratio by increasing number of users

consider 50 UE and 8 variable MTC devices with the bandwidth of 5 MHz, subcarrier frequency of 2 GHz, and the time interval of scheduling is considered as 1 ms (Fig. 3). We consider a single cell scenario with interfering channels and compare the proposed energy efficient scheduling algorithm with semi-persistent scheduling algorithm (SPSA), whale optimization algorithm (WOA) [3], QoS-aware memeticbased scheduling (QAMBS) [9], multi-objective resource allocation methodology with collaborative swarm intelligence (MCSIA-RA) [10], and remote radio head scheduling (RRHS) [11]. The algorithm provides energy efficiency of 3.6% greater than QAMBS, 5.8% greater than RRHS, 6.3% greater than MCSIA-RA, 6.74% greater than WOA, and 19.3% greater than SPSA. The algorithm provides fairness index of 2.4% greater than QAMBS, 3.5% greater than RRHS, 4.8% greater than MCSIA-RA, 9.4% greater than WOA, and 24.9% greater than SPSA (Fig. 4). The throughput variation will be high and provides better fairness index, when we increase the number of UE and MTC devices. The algorithm provides throughput variation of 8.4% greater than QAMBS, 9.8% greater than RRHS, 11.5% greater

Fig. 4 Mean throughput and fairness index calculation by increasing number of users

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Fig. 5 Percentage of unserved nodes and satisfied nodes by increasing number of users

than MCSIA-RA, 14.6% greater than WOA, and 22.3% greater than SPSA. The algorithm provides fairness index of 3.4% greater than QAMBS, 4.8% greater than RRHS, 7.54% greater than MCSIA-RA, 8.48% greater than WOA, and 15.9% greater than SPSA (Fig. 5). The proposed percentage of unserved nodes in delay will be very less, and the percentage of satisfied nodes gets increased, when we increase the number of UE and MTC devices. The percentage of unserved nodes in our proposed algorithm provides 2.4% greater than QAMBS, 3.8% greater than RRHS, 5.6% greater than MCSIA-RA, 8.94% greater than WOA, and 43.3% greater than SPSA. The percentage of satisfied nodes in our proposed algorithm provides 6.8% greater than QAMBS, 9.4% greater than RRHS, 11.3% greater than MCSIA-RA, 11.8% greater than WOA, and 22.9% greater than SPSA. The results of extensive simulations that we performed show the ability of the proposed GSO-GWO optimization algorithm to satisfy the strict QoS requirements of critical MTC with no impact on those of HTC.

References 1. Abrignani MD, Giupponi L, Lodi A, Verdone R (2018) Scheduling M2M traffic over LTE uplink of a dense small cell network. EURASIP J Wirel Commun Netw 1:2018. https://doi. org/10.1186/s13638-018-1206-2 2. Karadag G, Gul R, Sadi Y, Coleri Ergen S (2019) QoS-constrained semi-persistent scheduling of machine-type communications in cellular networks. IEEE Trans Wirel Commun 18(5):2737– 2750. https://doi.org/10.1109/TWC.2019.2907625 3. Pham QV, Mirjalili S, Kumar N, Alazab M, Hwang WJ (2020) Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Trans Veh Technol 69(4):4285–4297. https://doi.org/10.1109/TVT.2020.2973294 4. Abdelsadek MY, Ahmed MH, Gadallah Y (2020) Cross-layer resource allocation for critical MTC coexistent with human-type communications in LTE: a two-sided matching approach. IET Commun 14(18):3223–3230. https://doi.org/10.1049/iet-com.2019.0944 5. Kumar A, Abdelhadi A, Clancy C (2019) A delay optimal multiclass packet scheduler for general M2M uplink. IEEE Syst J 13(4):3815–3826. https://doi.org/10.1109/JSYST.2019.290 1001

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6. Hojeij MR, Abdel Nour C, Farah J, Douillard C (2018) Weighted proportional fair scheduling for downlink nonorthogonal multiple access. Wirel Commun Mob Comput 2018. https://doi. org/10.1155/2018/5642765 7. Zhang J, Wu Y, Yu W, Lu C (2020) A QoS aware uplink scheduler for IoT in emergency over LTE/LTE-A networks, vol 845. Springer International Publishing 8. Ali A, Shah GA, Arshad J (2019) Energy efficient resource allocation for M2M devices in 5G. Sensors (Switzerland) 19(8). https://doi.org/10.3390/s19081830 9. Dawaliby S, Bradai A, Pousset Y, Chatellier C (2019) Joint energy and QoS-aware memeticbased scheduling for M2M communications in LTE-M. IEEE Trans Emerg Top Comput Intell 3(3):217–229. https://doi.org/10.1109/TETCI.2018.2883450 10. Moses ML, Kaarthick B (2019) Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE-A networks. Trans Emerg Telecommun Technol 30(12). https://doi.org/10.1002/ett.3748 11. Li N, Cao C, Wang C (2017) Dynamic resource allocation and access class barring scheme for delay-sensitive devices in machine to machin (M2M) communications. Sensors (Switzerland) 17(6). https://doi.org/10.3390/s17061407 12. Kurda R (2021) Heterogeneous networks: fair power allocation in LTE-A uplink scenarios. PLoS ONE 16(6):2021. https://doi.org/10.1371/journal.pone.0252421 13. Ramdev MS, Bajaj R, Sidhu J (2021) Remote radio head scheduling in LTE-advanced networks. Wirel Pers Commun. https://doi.org/10.1007/s11277-021-08916-z 14. Kukade S, Sutaone M, Patil R (2021) Optimal performance of resource allocation in LTE-A for heterogeneous cellular network. Wireless Netw 27(5):3329–3344. https://doi.org/10.1007/ s11276-021-02635-w

Fairness for All User in mmWave Massive MIMO-NOMA: Single-Beam Case M. Vignesh Roshan, K. Shoukath Ali, T. Perarasi, V. Sugirdan, and S. Soundar

Abstract Non-orthogonal multiple access (NOMA) for frequency selective millimeter wave (mmWave) massive multiple input multiple output (MIMO) channel with hybrid precoder/combiner architecture is considered to standardize the sum rate and spectral efficiency. Frequency selective mmWave channel is used for massive MIMONOMA system. NOMA with massive mmWave MIMO hybrid architecture for the single beam is used to enhance the sum rate of the weak users. The optimum power allocation method is used for the NOMA system to improve the sum rate of the weak users and the fairness of the users. In the NOMA systems, non-convex power allocation problems persist due to intra-beam and inter-beam interference. To overcome the problem, the max–min value of the NOMA system is designed for the single beam to enhance the achievable rate for the weak users. The proposed NOMA mmWave massive MIMO system achieves the maximum achievable rate compared to the orthogonal matching access (OMA) mmWave massive MIMO system. Keywords Non-orthogonal multiple access · Orthogonal multiple access · Fairness · Multiple input multiple output

1 Introduction The future wireless technologies have been defined by the mmWave technology, which increases the data rate due to the higher frequency range. mmWave has small wavelength, and these deployed the maximum number of antennas at the limited space. The maximum numbers of antenna give large beamforming gain [1–4]. Generally, we cannot use traditional systems, because every antenna in the transmission is fixed with radio-frequency (RF) chain in full-digital architecture. Fully digital architecture causes high-energy consumption due to large RF chain [5–7]. Many M. Vignesh Roshan · K. Shoukath Ali (B) · T. Perarasi · V. Sugirdan · S. Soundar Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_15

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solutions are identified such as hybrid precoder and beam to the MIMO, to attain high-energy efficient and low cost. But reduced numbers of RF chain provide good results compared to the other technique. In the traditional mmWave systems, one RF chain is beam to the one particular user that is limited by RF chain [8–10]. To overcome this problem, NOMA method is used to connect multi-users. NOMA is known technology for next generation communications, and NOMA has more advantages over OMA method with respect to the sum rate and using a greater number of users. NOMA uses same resources to superimpose the multiple users in the power domain and to detect the multi-users signal, used successive interference cancelation (SIC). The NOMA allocates a greater number of users than RF chains. So that, the mmWave MIMO with NOMA system is attracted extensively in future communication [11–16].

1.1 Prior Works Previously, the NOMA with frequency flat mmWave channel for single beam is considered. In that the base station (BS) uses a single path with RF chain. NOMA with hybrid architecture for mmWave system is achieving maximum sum rate via power allocation method and beam to the users. In few papers, the jointly power allocation and beamspace channel vector are carried out to improvise the NOMA scheme sum rate. Single-beam and multi-beam cases are explored for MIMONOMA system, where each beam supports multiple users. Multiple beams are equipped with multiple RF chains in NOMA scheme. Different algorithms in the NOMA scheme are used to optimize by the power allocation method. In the prior work, frequency flat mmWave channel for single beam is considered and focused only on improved sum rate, which leads more loss to the weak users. In that more power is allocated to primary users, so that the sum rate is increased. User fairness scheme guarantees the achievable rates for secondary users [17–22].

1.2 Contributions In this paper, frequency selective (wideband) millimeter wave channel for massive MIMONOMA system is presented. Proposed system achieves max–min rate of users with single-beam case. Proposed NOMA model is compared with OMA model. The proposed system model achieves max–min fairness to all users during single-beam case. Organization of the work: Sect. 2 with system model of massive mmWave MIMONOMA. Analysis of MAX–MIN fairness for massive MIMONOMA is focused on Sect. 3. Section 4 discussed the simulation results. A conclusion of wok with future work is presented in Sect. 5.

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2 System Model In this paper, multiuser massive mmWave MIMO hybrid system is considered. One base station serves K single antenna in hybrid architecture, and it is given in Fig. 1. BS is deployed with M number of antennas as a linear array element under uniform principle and has number of RF chains MRF. In BS, mmWave hybrid architecture has baseband digital beamformer F B B ∈ C M R F ×M R F , followed by an analog beamformer F R F ∈ C M×M R F and baseband digital combiner W B B ∈ C M R F ×M R F , followed by an analog combiner F R F ∈ C M×M R F .

2.1 Downlink Transmission Model In the traditional mmWave MIMO hybrid systems, in case of K > MRF , the multiple user signal cannot be separated. In this paper, NOMA scheme is used to transmit multiple user data simultaneously. Where Sm , m = 1, 2, . . . , MRF denotes user MRF Sm = {1, 2, . . . , K }. Single user schedule on mth beam to perform NOMA, ∪m=1 single beam is assumed, namely Sm ∩ Sn = ∅, m = n. Hence, user u m k specifies kth user in the m th beam. Digital baseband and analog precoder for the mth beam are denoted as fm = [F R F F B B ]:,m , and the digital baseband and analog combiner for the mth beam are denoted as wm = [W R F W B B ]:,m . The mmWave massive MIMO NOMA system of the received signal of u m k is expressed as ykm = hkm H

|Si | MR F  

wiH fi

√

p j,i s j,i + n k

i=1 j=1

Fig. 1 Hybrid architecture for mmWave massive MIMO NOMA system

(1)

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|Si | |Sm | MR F    √ √ √ H H = hkm H wm fm pk,m sk,m + hkm H wm fm p j,m s j,m + hkm H wiH fi p j,i s j,i    j=k i=m j=1 Desired signal       Intra-beam interferences

+

Inter-beam interference

nm k

(2)

where hkm M × 1 denotes the beamspace channel vector between the transmitter and power, and s j,i denotes the received receiver antennas. p j,i denotes the total  transmit  m 2 power of the system. u ij , n m k ∼ CN 0, σn is AWGN of kth user for the m beam u k , 2 and σn denotes the unit covariance noise. From Eq. (1), signal-to-interference-plus-noise ratio (SINR) of kth user for m beam u m k is written as SINRm k =

H 2 fm pk,m hkm H wm ξ + σn2

(3)

|S | |Si | H 2 where ξ = hkm H wm fm j=mk p j,m +hkm H iM=RmF wiH fi2 j=1 p j,i . The achievable rate is given by of kth terminal form beam u m k   Rkm = log2 1 + SINRm k

(4)

Finally, the overall achievable rate of mmWave massive MIMO NOMA system is expressed by R=

|Si | MRF   i=1 j=1

R ij =

|Si | MRF  

  log2 1 + SINRij

(5)

i=1 j=1

2.2 Channel Model In mmWave communications, the path loss is high due to the higher frequency components. So that the limited dominant paths are available in mmWave communications. In this paper, frequency selective mmWave channel for massive MIMO NOMA system is considered, and it is expressed as L  m   m m m hkm = a1,k a1,k θ1,k + al,k al,k θl,k   l=2  LOS path    NOLS paths

(6)

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m where L denotes number of propagation path components, al,k as complex path gain   m m in l th channel link of u k . Moreover, al,k θl,k represents steering vector of l th path of u m k , denoted bys

 m 1

m m T = √ 1, e j2π d f sinθl,k , . . . , e j2πd f (M−1)sinθl,k (7) al,k θl,k M

 m where f is the carrier frequency, θl,k ∈ − π2 , π2 is the DOA of the l th path of u m k . The value l = 1 is line of sight (LOS) component, and 2 ≤ l ≤ L is L −1 non-line of m m = sinθl,k ∈ [−1, 1), the steering vector sight (NLOS) components. By defining ωl,k  m al,k θl,k can be equivalently expressed as  m 1

m m T = √ 1, e j2π d f ωl,k , . . . , e j2π d f (M−1)ωl,k al,k ωl,k M

(8)

3 Analysis of Max–Min Fairness for Massive MIMO NOMA 3.1 Problem Formulation Increased minimal rate for frequency selective mmWave massive MIMO NOMA system is discussed for single beam in this paper. Max–min fairness for all NOMA users is enhanced by sorting the power allocation problem. The single-beam case has intra-beam interference with frequency selective channel hˆ k for k-th user. The ith user decode from the nth users and the SINR is given as

γk,i

2 ˆ h i pk = K −1 2 2 ˆ k=1 h i pk + σ

(9)

where pk denotes k-th user power allocation. In the single-beam case, the order 2 2 2 of channel vector simplifies to hˆ 1 > hˆ 2 · · · > hˆ K . The same procedure of channel vector ordered is follow for the k-th user γk,1 > γk,2 > · · · > γk,k . The achievable rate Rk is written as Rk =

min

i=1,2,...,k

Rk,i

  min log2 1 + γk,i   = log2 1 + γk,k

=

i=1,2,...,k

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= Rk,k.

(10)

In order to enhanced the minimal rate for single-beam case, the power allocation problem is formulated as

 max min Rk,k { pk }

(11)

k

The two following constraint is given pk ≥ 0, ∀1 ≤ k ≤ K

K 

pk ≤ Pmax

(12)

k=1

Let P1 , single-beam power allocation problem be expressed as P1 :

 max min Rk,k { pk }

k

C1 : pk ≥ 0, ∀1 ≤ k ≤ K

s.t.

C2:

K 

(13)

pk ≤ Pmax

k=1

Due to presence of intra-beam interference, the achievable rate is non-convex. This arises the problem in power allocation. To overcome the above problem, bisectionbased power allocation method is introduced.

3.2 Minimal Rate Maximization Bisection power allocation procedure is used to attain maximized max–min rate r ∗ . This bisection power allocation method solves the problem of non-convex objective function. Here, variable t to be auxiliary is required to evenly transfer single-beam power allocation P1 into a newer problem P1 , and it is expressed as P1 :

max t { pk }

s.t. C1 : pk ≥ 0, ∀1 ≤ n ≤ K K C2 : pk ≤ Pmax

(14)

k=1

C3 : Rk,k ≥ t, ∀1 ≤ k ≤ K P1 matches power allocation problem using bisection-based power allocation method for single-beam case.

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4 Simulation Results In the simulation results, the proposed frequency selective mmWave channel for massive MIMONOMA system is compared with the massive MIMO OMA system in the single-beam scheme. Single RF with single-beam case is deployed at the base station. In each time slot, only one user can use that specifies number of user and slots which are related. Max–min rate in each time slot is attained by user that allocates full power. The proposed system and existing system use single beam with multiple users. The proposed system uses the number of transceiver antennas as N T = N R = 64 and the count of RF chains N RF = 1. The pulse shaping filter is used with roll factor = 0.6 value. In Fig. 2, SNR versus max–min rate for proposed mmWave massive MIMONOMA system is shown and compared to existing system model. Whenever SNR value increases, due to the optimal power allocation algorithm, the max– min value also gets increased. The proposed system achieves higher max–min rate compared to the existing model, because the proposed system effectively uses the communication resources. Figure 3 shows the number of user versus max–min rate for the proposed system by fixing SNR value as 20 dB. Max–min rate decreases at the transmitter side due to limited power availability. But this rate for proposed model is achieved higher compared to the exiting OMA model.

Fig. 2 SNR versus maximized minimal rate (bits/s/Hz)

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Fig. 3 Number of served users versus maximized minimal rate (bits/s/Hz) for proposed system

5 Conclusion In this paper, frequency selective mmWave massive MIMO channel with NOMA scheme is presented. The proposed system model achieves the max–min rate for multiple users with single-beam case. The proposed mmWave massive MIMO NOMA system model is compared with mmWave massive MIMOOMA model. The proposed system model achieves max–min fairness to all users during single-beam case. In single beam, the value of SNR grows rapidly while multi-beam the region of SNR decreases due to interference limitations. The paper focuses both the advantages and limitations of NOMA system. In future, usage of multi-beam for mmWave massive MIMONOMA system model is discussed.

References 1. Jiao R, Dai L, Wang W, Lyu F, Cheng N, Shen X (2019) Power allocation for multibeam max-min fairness in millimeter-wave beamspace MIMO-NOMA. In: 2019 IEEE global communications conference (GLOBECOM), 2019. IEEE, pp 1–6 2. Pi Z, Khan F (2011) An introduction to millimeter-wave mobile broadband systems. IEEE Commun Mag 49(6):101–107 3. Wu W, Cheng N, Zhang N, Yang P, Aldubaikhy K, Shen X (2020) Performance analysis and enhancement of beamforming training in 802.11 ad. IEEE Trans Veh Technol 69(5):5293–5306 4. Aldubaikhy K, Wu W, Ye Q, Shen X (2020) Low-complexity user selection algorithms for multiuser transmissions in mmWave WLANs. IEEE Trans Wireless Commun 19(4):2397–2410

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5. Mumtaz S, Rodriguez J, Dai L (2016) MmWave massive MIMO: a paradigm for 5G. Academic Press 6. Rusek F, Persson D, Lau BK, Larsson EG, Marzetta TL, Edfors O, Tufvesson F (2012) Scaling up MIMO: OPPORTUNITIES and challenges with very large arrays. IEEE Signal Process Mag 30(1):40–60 7. Swindlehurst AL, Ayanoglu E, Heydari P, Capolino F (2014) Millimeter-wave massive MIMO: the next wireless revolution? IEEE Commun Mag 52(9):56–62 8. Heath RW, Gonzalez-Prelcic N, Rangan S, Roh W, Sayeed AM (2016) An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J Sel Top Signal Process 10(3):436–453 9. Han S, Chih-Lin I, Xu Z, Rowell C (2015) Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE Commun Mag 53(1):186–194 10. Alkhateeb A, Leus G, Heath RW (2015) Limited feedback hybrid precoding for multi-user millimeter wave systems. IEEE Trans Wireless Commun 14(11):6481–6494 11. Zhu L, Xiao Z, Xia XG, Wu DO (2019) Millimeter-wave communications with non-orthogonal multiple access for B5G/6G. IEEE Access 7:116123–116132 12. Dai L, Wang B, Peng M, Chen S (2018) Hybrid precoding-based millimeter-wave massive MIMO-NOMA with simultaneous wireless information and power transfer. IEEE J Sel Areas Commun 37(1):131–141 13. Qian L, Wu Y, Yu N, Jiang F, Zhou H, Quek TQ (2021) Learning driven NOMA assisted vehicular edge computing via underlay spectrum sharing. IEEE Trans Veh Technol 70(1):977– 992 14. Qian L, Wu Y, Jiang F, Yu N, Lu W, Lin B (2020) NOMA assisted multi-task multi-access mobile edge computing via deep reinforcement learning for industrial Internet of things. IEEE Trans Industr Inf 17(8):5688–5698 15. Wu Y, Qian LP, Mao H, Yang X, Zhou H, Shen X (2018) Optimal power allocation and scheduling for non-orthogonal multiple access relay-assisted networks. IEEE Trans Mob Comput 17(11):2591–2606 16. He H, Shan H, Huang A, Ye Q, Zhuang W (2019) Partial NOMA-based resource allocation for fairness in LTE-U system. In: 2019 IEEE global communications conference (GLOBECOM,2019). IEEE, pp 1–6 17. Xiao Z, Zhu L, Choi J, Xia P, Xia XG (2018) Joint power allocation and beamforming for non-orthogonal multiple access (NOMA) in 5G millimeter wave communications. IEEE Trans Wireless Commun 17(5):2961–2974 18. Zhu L, Zhang J, Xiao Z, Cao X, Wu DO, Xia XG (2019) Joint Tx-Rx beamforming and power allocation for 5G millimeter-wave non-orthogonal multiple access networks. IEEE Trans Commun 67(7):5114–5125 19. Wei Z, Zhao L, Guo J, Ng DWK, Yuan J (2018) Multi-beam NOMA for hybrid mmWave systems. IEEE Trans Commun 67(2):1705–1719 20. Ali KS, Sampath P (2021) Sparse Bayesian learning Kalman filter-based channel estimation for hybrid millimeter wave MIMO systems: a frequency domain approach. IETE J Res, pp 1–11 21. Ali KS, Sampath P (2021) Time domain channel estimation for time and frequency selective millimeter wave MIMO hybrid architectures: sparse Bayesian learning-based Kalman filter. Wireless Pers Commun 117(3):2453–2473 22. Jiao R, Dai L, Wang W, Lyu F, Cheng N, Shen X (2021) Max-Min fairness for beamspace MIMO-NOMA: from single-beam to multi-beam. IEEE Trans Wirel Commun

A Tri-Band Frequency Reconfigurable Monopole Antenna for IEEE 802.11ax and Sub-6 GHz 5G NR Wi-Fi Applications P. Rajalakshmi and N. Gunavathi

Abstract In this article, a frequency reconfigurable tri-band monopole antenna is presented for 5G Wi-Fi applications. The proposed antenna has a 50 y microstrip feed, a U-shaped radiating element, and three PIN diodes. The changing electrical length of the radiating element using PIN diodes is used to attain the reconfigurable frequency performance. The antenna is designed to resonate at 2.37 GHz, 3.56 GHz, and 5.19 GHz. The simulations are done by Ansys HFSS15. The proposed antenna is fabricated on an FR4 substrate with compact dimensions of 30.0 × 23.4 × 1.6 mm3 and tested. The measured reflection coefficients (dB), gain, and radiation patterns are agreed with the simulated results. Because of its compactness, simplicity, and radiation performance at all three operating frequencies, the proposed antenna is an excellent choice for recently announced sub-6 GHz 5G (3.4–3.6 GHz) and IEEE 802.11ax (2.4/5.0 GHz) Wi-Fi wireless devices. Keywords Antenna · FR4 · IEEE 802.11ax · PIN diode · Reconfigurable · Sub-6 GHz · Wi-Fi

1 Introduction In the emerging era of wireless communication, more operating bands are indeed being integrated into portable electronic devices. The reconfigurable multi-band frequency antennas are the requirement of 5G wireless communication devices. The various types of reconfigurable antennas are frequency reconfigurable, polarization diversity, radiation pattern, and combined antennas [1]. P. Rajalakshmi (B) · N. Gunavathi Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015, India e-mail: [email protected] N. Gunavathi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_16

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The antenna based on the 6 × 6 parasitic pixel layers is used to tune an antenna’s resonant frequency and polarization [2]. The PIN diode-based wide-band and multiband antenna using C slots and dual patch elements with a high volume of 50 × 50 × 1.7 mm3 are presented in [3]. The metamaterial-inspired antenna is reconfigured for 2.18 GHz and 2.64 GHz by placing an ON and OFF switch on the metamaterial unit cells [4]. The large size of 80 × 30 × 1.6 mm3 frequency reconfigurable antenna is presented, which covers the 2.45 GHz (Wi-Fi), 3.5 GHz (Wi-Max), and 5.8 GHz (WLAN) [5]. In [6], the dual-band antenna has a compact dimension of 27 × 16 × 1.6 mm3 and is only suitable for WLAN applications. A frequency reconfigurable with a differentially fed antenna is reported for sub-6 GHz 5G and WLAN applications with large size of 100 × 100 × 2.5 mm3 in [7]. In [8], circular polarization is achieved using the SRR metamaterial in the antenna, which covers 2.45/5 GHz (WLAN) with a size of 40 × 40 × 1.6 mm3 but operates at three bands. Another one proposed in [9] has a large extent of 53 × 35 × 1.6 mm3 . Similarly, in [10], the triple-band antenna is offered with a large volume of 39 × 37 × 1.6 mm3 . Moreover, the hexa-band reconfigurable antenna by PIN diodes is probed in [11, 12] with a large volume of 40 × 35 × 1.6 mm3 . By fine-tuning the varactor diode and PIN diode, the microcontroller-based EBN was able to achieve wide-band and multi-band operation. However, it has large dimensions of 47 × 120 mm2 [13]. The compact frequency reconfigurable coplanar strip line-like metamaterial antenna is proposed for WLAN and GSM applications [14]. Consequently, the frequency reconfigurable monopole antenna based on the fluidic channel is proposed in [15]. In [16], the frequency and pattern reconfigurability operation are accomplished using PIN diodes. The current distribution of the radiating patch is affected by the analogous ON and OFF conditions of the PIN diode, resulting in reconfigurable characteristics. In [17], slots are introduced in patch and ground planes for miniaturization, and PIN diodes are used to reconfigure the specific frequencies for WLAN and Wi-Max applications. A flexible spiral-shaped reconfigurable antenna is discussed in [18], which is compact in size. For WLAN/Wi-Max and LTE systems, a small frequency diversity antenna with DGS and SRR mode switching is reported [19]. A frequency reconfigurable antenna [20] is being researched for wireless communications. For Bluetooth, Wi-Max, and WLAN applications, a frequency reconfigurable bow-tie antenna is provided [21]. Frequency reconfigurability is achieved by MEMS-based metamaterials [22]. A triangle shaped monopole reconfigurable antenna is designed to cover the wide band (1.8–4.5 GHz) [23]. In the above literature, antennas are reconfigured using parasitic layers, metamaterials, PIN diodes, RF MEMS, and varactor diodes. PIN diodes are exceptionally secure, stable, low cost, and small when compared to any other reconfigurable ideas [24]. There have been numerous past studies which looked at the issues like bulkiness, fabrication complexity, etc. However, all these works could cover only 3.5/5.5 GHz Wi-Max and 2.4/5.2/5.8 GHz WLAN bands, respectively, which are unsuitable for recently introduced sub-6 GHz 5G (3.4–3.6 GHz) and IEEE 802.11ax (2.4/5 GHz) Wi-Fi applications. The proposed antenna addresses each of these issues, analyzed and validated with the help of PIN diodes. As a result, we

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propose a simple, compact, and low-cost tri-band reconfigurable antenna using PIN diodes.

2 Antenna Design Methodology Figure 1 shows the proposed frequency reconfigurable antenna’s geometry. The antenna comprises of a U-shaped radiating element, a 50 y microstrip feed, three PIN diodes, and a partial ground plane. The designed antenna is excited by a 4 mm wide microstrip line with a 50-y characteristic impedance. The U-shaped radiating element comprises two rectangular stubs (A1 & A2 ) and two L-shaped stubs. The two inverted L-shaped stubs are symmetrically connected, which results in good radiation pattern by PIN diode BAR 50-02 V into the rectangular stub 2 (A2 ). The stub 2 (A2 ) is connected to stub 1 (A1 ) via PIN Diode BAR 50-02 V. The large rectangular stub 1 (A1 ) is designed to resonate for 5 GHz. Next, a small rectangular stub 2 (A2 ) is designed to cover the 3.5 GHz band. And also, two inverted L-shaped stubs are used to control the resonance band of the antenna at 2.4 GHz. In the radiating element of the antenna, a slot of 1 mm × 2 mm is designated to accommodate the diode switch in proper position. Ansys HFSS analyzes the proposed reconfigurable antenna. The proposed antenna dimensions are shown in Table 1. The resonant length of a designed antenna is calculated using transmission line theory for f 5GHz, f 3.5 GHz, and f 2.4 GHz . By changing the switching states, the desired frequency band is achieved. L 5 GHz =

c = 9.0 mm √ 4 f 5 GHz εeff c

(1)

L 3.5 GHz =

= 13.0 mm √ 4 f 3.5 GHz εeff

(2)

L 2.4 GHz =

c = 19.01 mm √ 4 f 2.4 GHz εeff

(3)

| | 1 h −2 εr + 1 εr − 1 + 1 + 12 = 2 2 w

(4)

εeff

3 Equivalent Circuit Models and DC Biasing of PIN Diode To perform the reconfigurable frequency operation, Infineon BAR 50-02 V (PIN diode) is employed as a switching element. In RF frequency, a PIN diode operates as a variable resistor. The diode is forward biased when the switch is turned ON; it is reverse biased when the switch is turned OFF. Two series lumped RLC boundary conditions are used in HFSS software to represent the PIN diodes equivalent circuits.

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Top View

Bottom View

Fig. 1 Proposed antenna design

Table 1 Optimized dimensions of the proposed antenna Parameter

L1

W1

L2

L3

W3

Lg

Ls

Ws

Dimensions(mm)

10

4

9.5

7

2

5.5

23.4

30

The PIN diode equivalent circuit and DC biasing circuit are given in Figs. 2 and 3. To keep the RF signal out of the DC bias lines, a 10-H inductor is utilized as an RF choke. The DC bias current to the PIN diode is controlled by a resistor 1 y placed on the built antenna. The PIN diodes ON/OFF states are controlled by three push-button switches. The circuit failure is shown by three LEDs powered by a 9 V battery.

Diode ON

Diode OFF

Fig. 2 Equivalent circuit model of the PIN diode

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Fig. 3 DC biasing circuit of the PIN diode

4 Result and Discussion: The designed antenna structure has an entire dimension of 30 × 23.4 × 1.6 mm3 and simulated with the support of Ansys HFSS15 software. It is made on a FR4 substrate by dimensions of εr = 4.4, h = 1.6 mm, and tan δ = 0.01. Figure 4 depicts the fabricated antenna. The proposed antenna has three operating modes. In mode 1(D1 to D3 = OFF), a large rectangular stub (A1 ) is not attached with a small rectangular stub (A2 ). Thus, the model resembles like monopole antenna and resonates at 5.05 GHz. For mode 2 (D1 = ON, D2 , D3 = OFF), the stub (A1 ) is connected with A2 . Due to the increment of electrical length, the first band is shifted to a new resonance frequency of 3.55 GHz. When operating at mode 3 (D1, D2 , D3 = ON), the stub (A1 ) is connected with stub A2 and two inverted L-shaped stubs. Due to the further increment in the electrical length of the antenna, the frequency band is shifted to a new resonance frequency of 2.41 GHz. All the resonating bands reveal good impedance matching. Figure 5 shows the measured and simulated reflection co-efficient (dB) characteristics. Table 2 also shows the results of simulated switching states in terms of

Fig. 4 Fabricated antenna

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bandwidth, resonant frequency, gain, and efficiency. The antenna has been fabricated, and measurements have been taken. The reflection co-efficient (dB) was measured using vector keysight field fox microwave analyzer N99917A. When all diodes (D1 to D3 ) are OFF, the presented antenna works in Mode 1, resonating at 5.19 GHz with a reflection co-efficient of − 36 dB and measured bandwidth of 660 MHz. The designed antenna resonates at 3.56 GHz in Mode 2 (when D1 is ON), with a reflection co-efficient of − 23.1 dB and a measured bandwidth of 500 MHz. In Mode 3, when all diode switches are ON condition, the presented antenna operates at 2.37 GHz with a reflection co-efficient of − 20.12 dB and measured bandwidth of 390 MHz. From Tables 2 and 3, it is noted that the measured bandwidth, gain, and efficiency are enough to meet the necessities of recently announced IEEE 802.11ax and sub-6 GHz 5G applications. 10

Reflection Co-efficient(dB)

0

-10

-20

-30

Simulated PIN diode State - Mode1 Measured PIN diode State - Mode1 Simulated PIN diode State - Mode2 Measured PIN diode State - Mode2 Simulated PIN diode State -Mode3 Measured PIN diode State - Mode3

-40

-50 1

2

3

4

5

6

7

Frequency(GHz)

Fig. 5 Simulated and measured reflection co-efficient (dB) characteristics of various operating modes of the proposed antenna

Table 2 Simulated switching states results in term of the resonant frequency, bandwidth, gain, and efficiency Modes

PIN diode (D1 )

PIN diode (D2 )

PIN diode (D3 )

Resonant frequency (GHz)

Bandwidth (MHz)

Gain (dB)

Efficiency (%)

Mode 1 (000)

OFF

OFF

OFF

5.05

690

1.28

86.17

Mode 2 (100)

ON

OFF

OFF

3.55

570

0.74

73.04

Mode 3 (111)

ON

ON

ON

2.41

450

0.41

65.37

A Tri-Band Frequency Reconfigurable Monopole Antenna for IEEE …

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Table 3 Measured switching states results in terms of the resonant frequency, bandwidth, and gain Modes

PIN diode (D1 )

PIN diode (D2 )

PIN diode (D3 )

Resonant frequency (GHz)

Bandwidth (MHz)

Gain (dB)

Mode 1 (000)

OFF

OFF

OFF

5.19

660

1.04

Mode 2 (100)

ON

OFF

OFF

3.56

500

0.60

Mode 3 (111)

ON

ON

ON

2.37

390

0.45

The antenna’s radiation patterns were evaluated in 2D radiation patterns on both the E (Phi = 0) and H-planes (Phi = 90) for each resonant frequency of the three switching states. Figure 6 depicts the simulated and measured radiation pattern of the antenna for the XZ and YZ planes. The suggested antenna features an omnidirectional radiation pattern for a lower operational frequency range at Phi = 90 in all feasible operating modes. The antenna exhibits the same radiation pattern as a figure of eight at Phi = 0 for all conceivable switching states. Figure 7 shows the surface current distribution over the U-shaped radiating element and rectangular stub (A1 and A2 ) structure along with the PIN diode condition. When all diodes are in the OFF state, the surface current is only distributed in a rectangular stub (A1 ). This is because the remaining part of the antenna has a minimum amount of current. When diode D1 is ON, D2 and D3 are OFF, and the current is only distributed in the A2 stub. When diode D1 to D3 are ON, and the current is concentrated in the inverted L-shaped stubs. Table 4 compares the proposed antenna to existing antennas and shows that it is small in size and also provides the three operating bands with good impedance bandwidth of 390 MHz at 2.37 GHz, 500 MHz at 3.56 GHz, and 660 MHz at 5.19 GHz, respectively. Also, it is simple in design and low in cost for easy fabrication.

5 Conclusion In this paper, a simple structured and low contour (30 × 23.4 × 1.6 mm3 ) triband monopole antenna is presented. The antenna is operating in three different frequency bands of operations subject to the ON and OFF states of the PIN diodes. By changing the electrical length of the antenna, the proposed antenna is operated at 2.37 GHz (D1 to D3 = ON condition), 3.56 GHz (D1 = ON, D2 and D3 = OFF), and 5.19 GHz (D1 , D2, and D3 = OFF). The antenna has covered a single unique frequency band in each switching state for the recently introduced sub-6 GHz 5G and IEEE 802.11ax applications which is the newness of the proposed antenna. The antenna has achieved a good impedance bandwidth (390–660 MHz), reasonable gain (0.45–1.04 dB), and efficiency (65.37–86.17%) in each mode of switching state. For

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P. Rajalakshmi and N. Gunavathi 0 330

0

5

30

5

330

0

-5

-10

300

60

-10

300

-15

-15

-20

-20

-25 270 0

90 -5

0

270

-30 -5 5 -10 -15 -20 -25 -30 -25 -20 -15 -10 -25

0

5

90 -5

0

5

-20

-20

-15

-15 240

120

-10

120

-10 -5

-5

0

0 210

60

-25

-30 -5 5 -10 -15 -20 -25 -30 -25 -20 -15 -10 -25

240

30

0

-5

210

150

5

150

5 180

180Diode state 000 at 5.05 GHz Simulated H-Plane PIN Simulated E-Plane PIN Diode state 000 at 5.05 GHz Measured H-Plane PIN Diode state 000 at 5.05 GHz Measured E-Plane PIN Diode state 000 at 5.05 GHz

Simulated H-Plane PIN Diode state 100 at 3.55 GHz Simulated E-Plane PIN Diode state 100 at 3.55 GHz Measured H-Plane PIN Diode state 100 at 3.55 GHz Measured E-Plane PIN Diode state 100 at 3.55 GHz

(a)Mode 1(000) at 5.05 GHz

(b) Mode 2(100) at 3.55 GHz 0 330

5

30

0 -5 -10

300

60

-15 -20 -25 270 0

-30 -5 5 -10 -15 -20 -25 -30 -25 -20 -15 -10 -25

90 -5

0

5

-20 -15 240

120

-10 -5 0 210

150

5 180

Simulated H-Plane PIN Diode state 111 at 2.41 GHz Simulated E-Plane PIN Diode state 111at 2. 41 GHz Measured H-Plane PIN Diode state 111 at 2.41GHz Measured E-Plane PIN Diode state 111 at 2.41 GHz

(c) Mode 3(111) at 2.41 GHz

Fig. 6 Simulated and measured radiation pattern of the proposed antenna

all unique frequencies, the expected radiation characteristics are achieved. And also, the antenna is developed on a low-cost FR4 dielectric substrate for simple integration with wireless terminal devices.

A Tri-Band Frequency Reconfigurable Monopole Antenna for IEEE …

(a) Mode 1(000) at 5.05 GHz

(b) Mode 2 (100) at 3.55 GHz

(c) Mode 3(111) at 2.41 GHz Fig. 7 Surface current distribution for all resonant frequencies

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Table 4 Comparison table Ref. No

Antenna physical size (mm3 )

No of diodes used

No of bands

Frequency (GHz)

Impedance bandwidth (MHz)

Peak gain (dB)

Efficiency %

3

50 × 50 × 1.6

02

01

5.96

2000

4.92

60–70

4

25 × 30 × 1.6

02

02

2.18/2.64

90/230

–

–

5

80 × 30 × 1.6

02

03

2.4/3.5/5.8

100/330/1040

5.85

–

7

100 × 100 × 1.6

04

02

2.45/3.5

300/230

6.82

64.5–69.5

This work

30 × 23.4 × 1.6

03

03

2.37/3.56/5.19

390/500/660

1.04

65.37–86.17

References 1. Parihar MS, Basu A, Koul SK (2013) Reconfigurable printed antennas. IETE J Res 59(4):383– 391 2. Rodrigo, D., Cetiner, B.A., Jofre, L.: Frequency, radiation pattern and polarization reconfigurable antenna using a parasitic pixel layer. IEEE Trans. Antennas Propag. 62(6), 3422–3427 (2014) 3. Abutarboush HF, Nilavalan R, Cheung SW et al (2012) A Reconfigurable wide-band and multiband antenna using dual patch elements for compact wireless devices. IEEE Trans Antennas Propag 60(1):36–43 4. Razak A, Rahim MKA, Majid HA, Murad NA (2017) Frequency reconfigurable Epsilon negative metamaterial antenna. Int. J. Electr. Comput. Eng. 7(3):1473–1479 5. Mansoul A, Seddiki ML (2018) Multiband reconfigurable bow-tie slot antenna using switchable slot extensions for Wi-Fi, WiMax, and WLAN applications. Microw. Optical Technol. Lett. 60(2):413–418 6. Ali T, Biradar RC (2017) A compact hexagonal slot dual-band frequency reconfigurable antenna for WLAN applications. Microw Opt Technol Lett 59(4):958–964 7. Jin GP, Deng CH, Yang J, Xu YC, Liao SW (2019) A new differentially fed frequency reconfigurable antenna for WLAN and Sub 6GHz 5G applications. IEEE Access 7:56539–56456 8. Azadeh, P., Mohammed, N.-M., Zarrabiferdows, B.: Design of compact slot antenna based on split ring resonator for 2.45/5GHz WLAN applications with circular polarization. Microw. Opt. Technol. Lett. 58(1), 12–16 (2016) 9. Ullah S, Hayat S, Umar A, Ali U et al (2017) Design, fabrication, and measurement of triple-band frequency reconfigurable antennas for portable wireless applications. AEU—Int. J. Electron. Commun. 81:236–242 10. Iqbal, A., Saraereh, O.: A compact frequency reconfigurable monopole antenna for WiFi/WLAN applications. Prog. Electromag. Res. Lett. 68, 79–84 (2017) 11. Ullah, S., Ahamed, S., Khan, B.A., Flint, J.A.: A multi-band switchable antenna for Wi-Fi, 3G advanced, Wi-Max, and WLAN wireless applications. Int. J. Microw. Wirel. Technol. 10(8), 991–997 (2018). 12. Ullah, S., Ahmad, S., Khan, B., Ali, U., et al.: Design and analysis of a hexa-band frequency reconfigurable monopole antenna. IETE J. Res. 64, 59–66 (2018)

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13. Romputtal A, Phongcharoenpanich C (2017) Frequency reconfigurable multiband antenna embedded biasing network. IET Microw. Antennas Propag. 11(10):1369–1378 14. Nasir, U., Afzal, A.S., Ljaz, B., Alimgeer, K.S., et al.: A Compact frequency reconfigurable CPS like Metamaterial Inspired antenna. Microw. Opt. Technol. Lett. 59(3), 596–601 (2017) 15. Singh, A., Goode, I., Saavedra, C.E.: A multi-state frequency reconfigurable monopole antenna using fluidic channels. IEEE Antennas Wirel. Propag. Lett 18(5), 856–860 (2019) 16. Iqbal A, Smida A, Mallat NK, Ghayoula R, Elfergani I et al (2019) Frequency and pattern reconfigurable antenna for emerging wireless applications. MDPI Electron. 8:407 17. Abdulraheem YI, Oguntala GA, Abdullah AS et al (2017) Design of frequency reconfigurable multi-band compact antenna using two PIN diodes for WLAN/WiMAX applications. IET Microw. Antennas Propag. 11(8):1098–1105 18. Ahmad, A., Arshad, F., Naqvi, S., Amin, Y., et al.: Flexible and compact spiral-shaped frequency reconfigurable antenna for wireless applications. IETE J. Res. 66(1), 22–29 (2020) 19. Dave, T.P., Rathod, J.M.: A compact frequency diversity antenna with DGS and SRR mode switching for LTE and WLAN/WiMAX wireless systems. Wirel. Pers. Commun. 112, 411–420 (2020) 20. Boufrioua A (2020) Frequency reconfigurable antenna designs using PIN Diode for wireless communication applications. Wirel Pers Commun 110:1879–1885 21. Li, T., Zhai, H., Wang, X., Li, L., Liang, C.: Frequency reconfigurable bow-tie antenna for Bluetooth, WiMAX, and WLAN applications. IEEE Antennas Wirel. Propag. Lett. 14, 171–174 (2015) 22. Debogovic, T.: Perruisseau – carrier J. MEMS reconfigurable metamaterials and antenna applications. Int. J. Antennas Propag. 1–8 (2014) 23. Ghaffar, A., Li, X.J., Awan, W.A.: Design and realization of a frequency reconfigurable multimode antenna for ISM, 5G-Sub-6-GHz, and S-band applications. MDPI Appl. Sci. 11, 1635 (2021) 24. Parchin, N.O., Basherlou, H.J., Al-Yasir, YI.A., Abdulkhaleq, A.M., Abd-Alhameed, R.A.: Reconfigurable antennas: switching techniques—a survey. MDPI Electron. 9, 336 (2020)

An Optical Switchable Bandwidth Reconfigurable UWB and Multiband Antenna for IoT Application Bhakkiyalakshmi Ramakrishnan and Vasanthi Murugiah Sivashanmugham

Abstract This paper presents an octagonal antenna that can reconfigure between ultrawideband (UWB) and multiband. The multiband includes a narrow band 2.4 GHz and UWB band. An optical switch is used to switch between UWB and multiband. The antenna operates from 3–14 GHz, accommodating UWB band 3.1–10.6 GHz during the optical switch OFF state. The proposed antenna works in multiband 2.36– 2.64 GHz and 2.78–13.9 GHz when optical photodiode switches ON. The designed antenna provides a good peak gain of 5.4 dBi, good radiation efficiency of 86.5%, and a nearly omnidirectional radiation pattern. The designed antenna is suitable for IoT applications. Keywords Reconfigurable · IoT · Optical diode · UWB · Antenna

1 Introduction The growing modern communication Internet of things (IoT) market needs many types of electronic and communication devices with narrow and wideband operations, leading to many applications like vehicular, smart cities, automation in homes, smart cities, and industry. IoT devices need a high data rate and low-power consumption. Traditional IoT devices operate on the 2.4 GHz ISM band. The modern IoT UWB antenna can utilize the advantages of the UWB spectrum like high data rate, low power consumption, and low cost. The antenna operates in both the 2.4 GHz IoT band and UWB band which is suitable for IoT applications. Each frequency band requires an antenna to work on it dedicatedly. A single reconfigurable antenna eliminates the need for many antennas to serve on multiple frequency bands. B. Ramakrishnan (B) · V. M. Sivashanmugham Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, Tamil Nadu, India e-mail: [email protected] V. M. Sivashanmugham e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_17

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A multiband antenna for 2.3–2.69, 3.4–3.7, and 5.15–5.85 GHz for IoT applications is presented in [1]. A dual operating band antenna covering 2355–5000 and 5112–7000 MHz IoT application for 4G, 5 g, V2X, and DSRC is discussed in [2]. A reconfigurable antenna switchable among WiMAX, WLAN, and C bands is discussed in [3]. In [4], a slotted fractal patch antenna with EBG in the ground operating at 1.59– 13.31 GHz bandwidth for IoT application is discussed. An integrated Bluetooth and UWB antenna for automotive application are illustrated in [5]. A slot-based reconfigurable antenna in [6] operates in narrow bands Wi-Fi, WiMAX, and WLAN. In [7], a tunable monopole antenna that works in WiMAX, WLAN, LTE bands, and X-band is presented. Reconfiguration can be achieved by electrical, optical, mechanical, and material-based reconfiguration techniques. An antenna constructed in [8] reconfigures over four frequencies in WiMAX, WLAN, and C-Band. A U-shaped antenna reconfigures in WiMAX and WLAN bands which is described in [9]. A varactor diode-based continuously tunable antenna in C and S-band is depicted in [10]. The electrical and optical reconfiguration techniques have an advantage over the other two by low cost and no bias lines. Most of the reported works concentrate on multiband reconfigurations for narrow bands using electrical PIN diodes. But they were not focused on reconfiguration on both narrow and UWB bands of operation using optical switched. Hence, in this work, a reconfigurable antenna switchable between UWB and multiband for modern IoT applications is presented utilizing an optical switch. PIN diode needs complex biasing circuitry, whereas optical switch does not use any biasing circuit. The designed antenna works in both ISM band 2.4 GHz and UWB band utilizing the advantages of UWB like high data rate, low power, and low cost, which are required for modern IoT applications. This paper is organized as UWB antenna configuration in Sect. 2, reconfigurable antenna design in Sect. 3, and analysis of results in Sect. 4.

2 Antenna Configuration 2.1 UWB Antenna Configuration An octagon-shaped microstrip is placed over a low-cost economic FR4 substrate having the dimension of 32 × 32 × 1.6 mm3 and fed by a 50 y microstrip line. The proposed antenna is designed with a square patch width W, as shown in Eq. (1). The edges of the square patch are removed to get a wide operating frequency range. The partial ground is employed to get the operating frequency range 3–13.87 GHz which includes 3.1–10.6 GHz. Microstrip feed is used at the edge to aid the UWB operating range with good impedance matching. The dimensions of the proposed antenna configuration are shown in Fig. 1. / C W = 2f

2 εr + 1

(1)

An Optical Switchable Bandwidth Reconfigurable UWB and Multiband …

(a)

201

(b)

Fig. 1 Dimension of the UWB antenna: a top view and b bottom view

Fig. 2 Reflection coefficient of the UWB antenna

where C is the velocity of light, f is the working frequency of the antenna, and εr is the relative permittivity of the substrate material. The designed antenna construction and simulations are carried out in the commercial high-frequency tool CST microwave studio. The reflection coefficient of the partial ground edge feed octagonal antenna shows − 10 dB impedance bandwidth from 3 to 13.87 GHz, as shown in Fig. 2.

2.2 Reconfigurable Antenna Design A quarter-wave stub of 20 mm is connected to the UWB antenna through an optical switch to get 2.4 GHz, as shown in Fig. 3. The stub length is calculated using Eq. (2).

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Fig. 3 Proposed reconfigurable antenna

f =

C √ 4L εeff

(2)

where C is the velocity of light, L is the length of stub, and εeff is the effective dielectric constant. εeff =

εr + 1 2

(3)

The stub is added to the octagonal patch to get a lower IoT band 2.4 GHz in addition to the UWB band. The optimized width of stub is 0.5 mm. The cadmium sulfidebased optical switch is used to control the antenna bandwidth between ultrawideband and multiband. The antenna’s electrical length is varied by controlling the switching states of the antenna to get different operating frequency ranges. The ON and OFF state of the switch approximate high conductivity and high resistivity, respectively. The change in electrical length leads to different operating frequency ranges.

3 Results and Discussion The optical switch is ON to connect the stub with the octagonal patch. The stub responsible for 2.4 GHz is connected to the UWB patch via an optical switch. During the ON state of the optical switch, the antenna works on two bands, 2.36–2.64 GHz and 3–14 GHz, as shown in Fig. 4. The antenna operates in the UWB range from 2.78–13.9 GHz when the optical switch is OFF, as shown in Fig. 5.

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203

Fig. 4 Reflection coefficient of reconfigurable antenna for diode ON state

Fig. 5 Reflection coefficient of reconfigurable antenna for diode OFF state

Table 1 Switching state of diode

Diode state

Frequency band (GHz)

Application

ON

2.36–2.64 and 3–14

ISM and UWB

OFF

2.78–13.9

UWB

The designed antenna operates at two frequency bands when optical switch ON and works at UWB band when it OFF as given in Table 1.

3.1 Surface Current Distribution The surface current distributions for the antenna at both ON and OFF state are shown in Fig. 6. At ON state 2.45 GHz, the surface current is distributed maximum at the

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(a)

(b)

Fig. 6 Surface current distribution of reconfigurable antenna: a ON state 2.45 GHz and b OFF state 7.5 GHz

stub. Maximum surface current is distributed on the octagonal patch at 7.5 GHz for the OFF state.

3.2 Gain and Radiation Efficiency The designed reconfigurable antenna attained a peak gain of 5.35 and 5.4 dBi at the ON and OFF state of the switch. The realized gain of the antenna is varied from 1.5–5.4 dBi, as shown in Fig. 7. The antenna’s radiation efficiency is above 70% for both the state of the diode until 13.5 GHz, as illustrated in Fig. 8. The peak gain achieved for both ON and OFF states is 86.5% and 86%, respectively.

3.3 Far-Field Radiation Pattern The far-field broadband radiation pattern of the reconfigurable antenna for both the states has been obtained by simulation, as given in Fig. 9. It has been observed that the radiation pattern is approximately omnidirectional and is an attractive feature for most wireless communication devices. The results of the proposed work are compared with other reported research works in Table 2. The proposed work is good in gain, switching type, and operating bands. The designed antenna operates at both narrow and wideband. It takes the advantages of UWB spectrum in 2.78–13.9 and 3–14 GHz for IoT applications. It uses optical switching, which does not need biasing circuits like PIN diodes.

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205

(a)

(b) Fig. 7 Realized gain of reconfigurable antenna: a ON state and b OFF state

4 Conclusion This paper presented a bandwidth switchable narrowband 2.4 GHz and UWB antenna used for IoT applications. The designed antenna has a simple structure and uses an optical switch to switch between wideband and multiband mode of operation. The antenna achieved a peak gain of 5.4 dBi, an approximately omnidirectional radiation pattern, and appreciable radiation efficiency of 86.5%. The appreciable parameters and no biasing circuitry make the proposed bandwidth switchable antenna suitable for modern IoT applications.

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(a)

(b)

Fig. 8 Radiation efficiency of reconfigurable antenna: a ON state and b OFF state

(a)

(b)

Fig. 9 Far-field radiation pattern of reconfigurable antenna: a ON state and b OFF state

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207

Table 2 Comparison of proposed design with the previous research works References

Substrate

Size (mm3 )

Switch type

Operating frequency (GHz)

Maximum gain (dBi)

[10]

FR4

33.9 × 38 × 1.6

Varactor diode

3.29–4.09, 5.35–7

4.50

[9]

Rogers RO4350B

30 × 25 × 1.524

PIN

3.2/3.5, 5.2/5.8

3.9

[8]

FR4

25 × 25 × 1.6

PIN

4.94,5.35,6.25,6.83

3.91

[3]

FR4

25 × 25 × 1.6

PIN

3.82, 4.11, 4.48, 4.90, 4.42 6.04

This work

FR4

32 × 32 × 1.6

Optical switch

2.36–2.64, 2.78–13.9, 5.4 3–14

References 1. Chung M-A (2018) A miniaturized triple band monopole antenna with a coupled branch strip for bandwidth enhancement for IoT applications. Microw Opt Technol Lett 60(9):2336–2342 2. Chung M-A, Chang W-H (2020) Low-cost, low-profile and miniaturized single-plane antenna design for an Internet of Thing device applications operating in 5G, 4G, V2X, DSRC, WiFi 6 band, WLAN, and WiMAX communication systems. Microw Opt Technol Lett 62(4):1765– 1773 3. Singh PP, Goswami PK, Sharma SK, Goswami G (2020) Frequency reconfigurable multiband antenna for IoT applications in WLAN, Wi-Max, and C-band. Prog Electromagnet Res C 102:149–162 4. Goswami PK, Goswami G (2019) Trident shape ultra-large band fractal slot EBG antenna for multipurpose IoT applications. Prog Electromagnet Res C 96:73–85 5. Yang B, Qu S (2017) A compact integrated Bluetooth UWB dual-band notch antenna for automotive communications. AEU-Int J Electron Commun 80:104–113 6. Mansoul A (2017) Switchable multiband slot antenna for 2.4, 3.5, and 5.2 GHz applications. Microw Opt Technol Lett 59(11):2903–2907 7. Fertas F, Challal M, Fertas K (2020) A compact slot-antenna with tunable-frequency for WLAN, WiMAX, LTE, and X-band applications. Prog Electromagnet Res C 102:203–212 8. Singh PP, Sharma SK, Goswami PK (2020) A compact frequency reconfigurable printed antenna for WLAN, WiMax multiple applications. Prog Electromagnet Res C 106:151–161 9. Chaouche YB, Bouttout F, Nedil M, Messaoudene I, Mabrouk IB (2018) A frequency reconfigurable U-shaped antenna for dual-band WIMAX/WLAN systems. Prog Electromagnet Res C 87:63–71 10. Guo C, Deng L, Dong J, Yi T, Liao C, Huang S, Luo H (2020) Variode enabled frequencyreconfigurable microstrip patch antenna with operation band covering S and C bands. Prog Electromagnet Res M 88:159–167

Transmission Performance Analysis of Various Order of UWB Signals Through Single Mode Fiber Link C. Rimmya, M. Ganesh Madhan, and K. Amalesh

Abstract UWB over fiber refers to distribution of UWB signals over fiber for increasing the coverage range and to incorporate the existing UWB environment with fixed wired networks. Studies show that the lower order UWB signals are more prone to interference due to the strong power spectrum density. Hence, low-power spectrum density generated by higher order UWB signals is focused intensively. The transmission performance of four orders of UWB pulses, which are generated photonically, for a 20 km SMF link is investigated in this paper. The system is evaluated with eye diagram and BER for the respective UWB orders using simulation. It is observed that UWB monopulse, doublet, triplet, and quintuple pulses can be transmitted to a distance of 9 km,7 km, 7.5 km, and 3 km, respectively for a BER of 10–9 . From simulation, it is identified the higher order UWB pulses are restricted to shorter length of fiber for a given BER. Keywords UWB over fiber · Dual-drive Mach–Zehnder modulator (DDMZM) · Radio over fiber (ROF)

1 Introduction The ultrawide band (UWB) radio signal occupies the band of frequency ranging from 3.1 to 10.6 GHz with a power spectral density of − 41.3 dBm/MHz as in [1]. FCC interprets UWB as any wireless signal that occupies a fractional bandwidth of more than 20% along with an absolute bandwidth of more than 500 MHz.

C. Rimmya (B) · M. Ganesh Madhan · K. Amalesh Department of Electronics Engineering, Anna University-MIT Campus, Chennai, India e-mail: [email protected] M. Ganesh Madhan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_18

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In UWB over fiber scheme, UWB signals are generated and encoded in the central office and distributed over optical fiber to the access points where they are converted to the electrical domain and then radiated into free space. As reported in [2], an electrical UWB signal is generated and converted in to optical domain using direct modulation technique of a laser source. To avoid using additional electrical to optical conversion, UWB signals can also be generated directly in the optical domain. As reported in [3, 4] based on the principle of spectral shaping and dispersion-induced frequency-to-time mapping, an UWB signal is generated in the optical domain. In [5], the authors have proposed a method to photonically generate UWB signals with a system which comprises of semi-optical amplifier and band pass filter. Authors in [6] have analyzed the techniques for generation of UWB signals in optical domain based on three different techniques, namely optical spectral shaping and dispersion-induced frequency-to-time mapping, a photonic microwave delayline filter, and phase-modulation-to-intensity modulation conversion. UWB triplet pulses generated based on four-wave mixing and phase-to-intensity modulation conversion were investigated in [7]. Reference [8] has experimentally demonstrated a reflective semiconductor optical amplifier (RSOA)-based Mach–Zehnder interferometer structure for generating high-order UWB signals. In [9], the authors proposed a 3D UWB signal generation using a single dual-drive Mach–Zehnder modulator (DDMZM). In this approach, two different signals are fed into the two RF ports of DDMZM. By carefully adjusting the amplitude and bias voltage, 3D UWB signal can be obtained. But the generated UWB signal has higher power spectral density below 3 GHz, which may interfere with the GSM communications. The optically generated UWB signals are to be transmitted through the fiber link for wireless remote access point. This paper focuses on the transmission performance of various orders of UWB signal through SMF link by evaluating received eye diagram and BER.

2 Photonic UWB Generation UWB generation considered in this work is based on a dual-drive Mach–Zehnder modulator (DDMZM) as in [10]. DDMZM has a two phase modulators (PMs) which are individually modulated. Two different signals through the RF electrodes can be applied to the two phase modulators which generates the phase-modulated signals. By varying the signal magnitude, the time delay between the input signals applied to RF electrodes and the DC bias voltage provided to DDMZM, UWB pulses of four different order are generated. At DDMZM output, the two phase-modulated signals combine and result in the various orders of UWB signal. The modulator is operated at the quadrature operating point of the transfer characteristic for maximum efficiency.

Transmission Performance Analysis of Various Order of UWB Signals …

211

Light from a laser diode fed to the DDMZM can be described by its electric field. The optical power from laser source ∝ |E in |2 where E in is the input optical field.

Fig. 1 Block diagram for UWB pulse generation and transmission

V1 (t) and V2 (t) represent A1 S(t) and A2 S(t − τ ), where S(t) represents the normalized Gaussian pulse signal, τ is the relative time delay between the input signals, A1 and A2 denote the amplitude of the two Gaussian signals. The electric field at the output is expressed as E out =

     V2 (t) V1 (t) + Vdc E in + exp j π exp j π 2 Vπ Vπ

(1)

where V dc and V π indicate the bias voltage of the DDMZM and half-wave voltage, respectively. A simple receiver consisting of integrator and data recovery unit is used in the detection of electrical UWB signals. The block diagram for OOK UWB generation and detection is shown in Fig. 1. After photodetection of the optical UWB pulses, the electrical demodulation of UWB signals is done. The output signal can be written as    |E in |2 π cos i AC ∝ [V1 (t) + Vdc − V2 (t)] 2 Vπ    |E in |2 π (2) cos = [A1 S(t) + Vdc − A2 S(t − τ )] 2 Vπ The electrical UWB pulses are transmitted through a band pass filter for filtering out the UWB spectral components as per FCC specification. The noise components, which are out of band in the FCC mask of UWB, are removed. The pulse to binary converter consists of an electrical integrator which integrates the electrical UWB signal for over the bit period. A data recovery component is used to recover data from the integrated signal.

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3 Simulation The UWB link is implemented using OptiSystem simulation tool. The Gaussian pulse stream is generated by a pulse generator and is split into two, using a power splitter. The divided half of the input signal is fed to the RF port1 of DDMZM via an electrical gain unit to adjust the signal amplitude and electrical delay line to adjust the time delay between the input signals. By varying the bias voltage of DDMZM, the relative phase shift between the two optical signals is adjusted. Other part of the pulse amplitude is adjusted and fed to RF port2 of the DDMZM (Fig. 2). A CW laser light is fed to the input of the DDMZM. Table 1 gives the parameter values used in the simulation.

Fig. 2 OptiSystem layout for photonic UWB signal generation and measurement

Table 1 Simulation parameters values for the layout

S. No.

Parameter

Value

1

Bit rate

10 Gbps

2

Laser line width

10 MHz

3

Operating wavelength

1550 nm/193.41 THz

4

Laser source power

5 dBm

5

Insertion loss

0 dBm

6

Extinction ratio

20

7

Attenuation

0.2 dB/Km

8

Dispersion

16.75 ps/nm/Km

9

Nonlinear coefficient

2.6 × 10–20 m2 /W

10

PIN diode responsivity

1 A/W

Transmission Performance Analysis of Various Order of UWB Signals … Table 2 Integration period for various orders of UWB signals

S. No.

UWB pulses

Integration period

1

Monocycle

1/bit rate

2

Doublet

2/bit rate

3

Triplet

2/bit rate

4

Quintuple

2/bit rate

213

The received OOK UWB signal is decoded after transmitting over the fiber. A data recovery component is used to recover data from the integrated signal. The decision threshold, decision instant, and delay compensation are varied to recover the transmitted bit sequence. The integration period is varied for demodulation of different orders as shown in Table 2.

4 Results and Discussion A pulse pattern generator operating at 10 Gbps generates the Gaussian pulse stream. The operating point of the DDMZM is fixed at the quadrature point by setting the bias voltage and RF voltages of the DDMZM. The photonic UWB signal from DDMZM output is transmitted over the single mode fiber and fed to the photo diode for conversion to electrical UWB pulse. The various orders of UWB generated from the DDMZM output are shown in Fig. 3a, c, e, g. The generated UWB signals have the spectrum which satisfies FCC-specified spectral mask within 3.1–10.6 GHz. It is seen that the UWB signals are subjected to attenuation and dispersion after transmission over the fiber link of distance 20 km. It is observed that the power level decreases with distance as expected. The higher order UWB pulses are prone to more distortion when compared to the lower order UWB pulse as seen in Fig. 3b, d, f, h. The performance of a digital light wave system is described through the bit error rate (BER). Most light wave systems require a BER of 10–9 , and some even require a BER as small as 10–14 . For various orders of UWB signals, the performance analysis of BER with various link distances is analyzed. The optical fiber link length is varied by fixing the optical source power to 5 dBm. Eye diagram for all generated UWB orders, transmitted and received through single mode fiber of distance 20 km., is shown in Fig. 4. The eye diagram of the received signals indicates an error free operation with acceptable eye height, representing the successful generation and detection of the UWB signals. The simulation performance result of BER graph for OOK UWB signals of various orders after electrical demodulation is shown in the Fig. 5a. It is noted that the UWB monocycle provides better performance compared to higher order UWB signals. Due to the fiber dispersion, it becomes difficult for the demodulation of higher order of UWB signals over long distances. Error free detection of UWB monocycle can be obtained over the link distance about 10 km with BER of 10–8. .The received optical power and BER characteristics are analyzed for various

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Fig. 3 Waveforms of the generated UWB pulses and its corresponding waveform after transmission for 20 km a, b UWB monocycle pulse, c, d UWB doublet pulse, e, f UWB triplet pulse, g, h UWB quintuple pulse

Transmission Performance Analysis of Various Order of UWB Signals …

(a)

(b)

(c)

(d)

215

Fig. 4 Eye diagram for various orders of UWB a UWB monocycle, b UWB doublet, c UWB triplet, d UWB quintuple

orders of UWB, and the results are provided in Fig. 5b. The simulation indicates that higher order UWB pulses are affected by dispersion.

5 Conclusion An optical UWB pulse generation scheme capable of generating different order is used for transmission of UWB signals over single mode fiber. The effect of fiber dispersion on various order is analyzed. The performance of various UWB orders is

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Fig. 5 Comparison of RF power for various orders of UWB at different lengths of SMF

investigated by evaluating the BER for a 10 Gbps data transmission. It is observed that UWB monopulse, doublet, triplet, and quintuple pulses can be transmitted to a distance of 9 km, 7 km, 7.5 km, and 3 km, respectively, for a BER of 10–9 . It is evident that the transmission distance is limited for higher order pulses.

References 1. U.S. Federal Communications Commission (2002) Revision of part 15 of the commission’s rules regarding ultra-wideband transmission systems. In: First report and order, ET Docket 98-153, FCC 02-48 2. Le QT, Briggmann D, Kueppers F (2013) Generation of UWB pulses using direct modulation of semiconductor laser and optical filtering. Electron Lett 49(18):1171–1173 3. Wang C, Zeng F, Yao JP (2007) All-fiber ultrawideband pulse generation based on spectralshaping and dispersion-induced frequency-to-time conversion. IEEE Photon Technol Lett 19(3):137–139 4. Abtahi M, Mirshafiei M, LaRochelle S, Rusch LA (2008) All optical 500-Mb/s UWB transceiver: an experimental demonstration. J Lightwave Technol 26(15):2795–2802 5. Dong J, Zhang X, Xu J, Huang D, Fu S, Shum (2007) Ultra wideband monocycle generation using cross-phase modulation in a semiconductor optical amplifier. Opt Lett 32(10):1223–1225 6. Yao J, Zeng F, Wang Q (2007) Photonic generation of ultrawideband signals. J Lightwave Technol 25(11):3219–3235 7. Li W, Wang LX, Hofmann W, Zhu NH, Bimberg D (2012) Generation of ultra-wideband triplet pulses based on four-wave mixing and phase-to-intensity modulation conversion. Opt Exp 20(18):20222–20227 8. Feng H, Fok MP, Xiao S, Ge J, Zhou Q, Locke M, Toole R, Hu W (2014) A reconfigurable highorder UWB signal generation scheme using RSOA-MZI structure. IEEE Photon J 6(2):7900307 9. Cao P, Hu X, Wu J, Zhang L, Jiang X, Su Y (2014) Photonic generation of 3-D UWB signal using a dual-drive Mach–Zehnder modulator. IEEE Photon Technol Lett 26(14):1434–1437 10. Cao P, Hu X, Wu J, Zhang L, Jiang X, Su Y (2014) Reconfigurable UWB pulse generation based on a dual-drive Mach–Zehnder modulator. IEEE Photon J 6(5):7903206

FANET Routing Survey: An Application Driven Perspective P. Krishna Srivathsav, Sai Abhishek, and Jayavignesh Thyagarajan

Abstract With the need to design systems with higher efficiency, the applications for FANETs and the need to improve this technology have risen. The amount of expenses saved and the ease with which services are performed due to FANETs makes it a booming market for the future. With UAVs becoming lighter and faster, the need for better implementation and routing protocols also increases. Higher rate of mobility and increased freedom of movement are some of its advantages of FANETs over MANETs and VANETs. Many processes such as geographic routing where drones depend on GPS locations to proceed toward their destination can be implemented efficiently using FANETs. FANETs have been found to be useful for many fields such as agricultural tasks and military activities. In this work, the authors have analyzed and suggested the most efficient protocol and models for each application. There are several existing routing protocols which have their own attributes. A single protocol cannot be used for all applications. FANETs mainly use position-based protocols. Every application will have different obstacles; hence to counter each obstacle, a different routing protocol will be useful in making the power consumption and message delivery much simpler and hence enabling a much more efficient and easier completion of a given task. The work deals with the analysis of FANETs, the obstacles that come up with it and the most suitable protocols and their mobility models to achieve different tasks. Keywords FANETs · UAV ad hoc networks · Communication technologies

P. Krishna Srivathsav (B) · S. Abhishek · J. Thyagarajan School of Electronics Engineering, Vellore Institute of Technology, Chennai, India e-mail: [email protected] S. Abhishek e-mail: [email protected] J. Thyagarajan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_19

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1 Introduction Flying ad hoc network [1] (FANET) is a specialized form of mobile ad hoc network (MANET), and the nodes in a FANET move around in 3D without any restriction of movement. This is the main function that distinguishes a MANET where the nodes move around in 2D. Using FANETs can be very useful especially in military, agriculture, and geographical applications such as locating natural resources, evacuation operations, and rapid topology scanning. Recently amidst the COVID-19 pandemic, FANET network was used to identify people who were not following proper COVID precautions such as wearing a mask during the Kumbh Mela festival. This was achieved using autonomous drones. FANETs are highly flexible and can be used in very complex scenarios. The main advantage of FANET is that it is highly efficient, has better scalability, and is highly accurate. However due to the high mobility of the nodes, the maintenance of the communication system between the nodes and the base station takes a lot of power which makes it logistically difficult to deploy these systems for a long period, and sometimes, this might lead to lack of communication between nodes causing reliability issues. Since FANETs are used in different situation usually requiring high mobility in complex situations, there are a variety of communication protocols [2] and mobility models available to be used. This work mainly focuses on the use of UAV-based flying ad hoc networks in real-world applications. The recent technological advancement in the field of networked embedded systems makes unmanned aerial vehicles (UAVs), a great choice to pair them to form a flying ad hoc network. UAVs are specifically used in many cases because they are multifaceted, easy to install and operate, and also have a low initial cost. Such unique properties of UAVs make FANET implementation possible.

2 Literature Survey “A comprehensive survey on FANET: challenges and advancements” by Shashank Kumar Singh [3]. This paper deals with the challenges and obstacles in FANETs but with a special emphasis on the challenges faced by FANETs when being used for military purposes. The paper deals with the problems on how to use the set of protocols appropriately to counter problems which may suddenly pop up during a mission and how to integrate every node efficiently and move in an organized manner so as to not get detected by radars and other anti-drone systems. The paper also deals with a refueling approach which is required when FANETs are used for military operations. “FANET: communication, mobility models, and security issues” by Amira Chriki et al. [4]. This paper covers a huge area with respect to FANETs such as the business side of it with respect to the modifications in airspaces and such. The paper also has

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explained about the pros and cons of many proactive and reactive protocols using several mobility models which help stimulate the military scenarios and such. “A reliable, delay bounded, and less complex communication protocol for multicluster FANETs” by Wajiya Zafar et al. [5]. A network model which includes multicluster formation, cluster head selection, and a propagation model with the use of IEEE 802.15.4 has been used to explain the implementation of the said protocol which would make the communication between nodes much better and improved. However, the drawback in this paper is that it has used free space propagation which does not exactly match a real-life scenario but it has provided a better implementation to improve the communication much better than existing ones. “On the routing in flying ad hoc networks” by Md Hasan Tareque et al. [6], this paper deals with the survey of finding out which protocol would be suitable for which scenario depending on the task provided and analyzing the obstacles and challenges, FANETs could possibly face. The paper also does a comparison between MANETs, VANETs, and FANETs and lists out which network is suitable for which purpose depending on the situation. “Topology construction for flying ad hoc networks (FANETs)” by Kim and Lee [7] deals with the study, analysis, and construction of topologies that provides an end-to-end communication between the ground station and to each of the UAVs in the network. The UAVs in the network perform tasks given by the ground control by optimizing the location of the relay UAVs. Toward the end, the authors have proposed a topology construction algorithm based on the particle swarm ptimization algorithm. Their analysis shows that the proposed algorithm is superior to the existing randombased construction topology, making it more efficient with a higher performance gain. “FANETs: current trends and challenges” by Raj et al. [8] deal with the recent advancement in the field of ad hoc networks with special emphasis on FANETs. The authors of this paper have discussed the advantages of FANETs over other networks such as MANETs and VANETs. This paper also deals with the working of communication between each of the nodes of the FANET.

3 FANET Architecture and Communication Protocols FANETs can be configured broadly into three different variations depending on a number of factors such as communication range, data and information flow, and structure of the nodes. To represent the different types of architectures, we have used a specific flying ad hoc network with the use of unmanned aerial vehicles (UAV). These networks are also referred to as UAV ad hoc networks [9]. Figure 1 represents the simplest form of a flying ad hoc network. Such a network consists of a single backbone UAV. The backbone UAV acts as the only link between the base and the rest of the network. In such a network, flight controls such as the elevation and speed of the UAVs should be similar. To ensure good connectivity, all the drones must have a similar flight pattern (Fig. 2).

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Fig. 1 UAV ad hoc network

Fig. 2 Multigroup UAV ad hoc network

In a multigroup network, there are two or more backbone UAVs. Each backbone UAV has a link to the base station. Each sub-UAVs connected to the backbone UAV form a group; hence, this type of architecture is called multigroup network. In a multigroup network, each group can have different flight control patterns. This particular architecture is most useful when there are a large number of UAVs and each of them have different flight and communication characteristics. Figure 3 shows a multilayer type architecture. Here, there are two or more groups of UAV networks, but each of the groups is connected to the backbone network of its immediate lower-level backbone UAV. The difference between multigroup and multilayer is that, in multilayer, there is only one link between the base station and the UAV network. The main advantage of such a network architecture is that the

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Fig. 3 Multilayer UAV ad hoc network

base station need not be involved in communication between multiple groups. This greatly reduced the communication load on the network. These types of networks are decentralized. Communication protocols is classified into two broad groups based on their routing methods [9]. They are position-based routing protocols [10] and topologybased routing protocols [11]. In this section, the authors have analyzed each type of routing protocols with a special emphasis on position-based routing protocols and also the specific algorithms used by them. In the later sections, the authors will use this data to analyze the best routing protocols for different applications. Topology-based protocols are based on the network topology, and they maintain routing tables so as to specify the path to send a packet from one node to another. There are three strategies used in these protocols using topology information: proactive, reactive, and hybrid. An example of such a protocol is destination sequenced distance vector (DSDV) [12] and ad hoc on-demand distance vector (AODV) [13]. Hybrid protocols are a combination of the better features of proactive and reactive protocols [14]. Position-based protocols are based on the position of each node in the network to send the information from one node to another. There are several position-based routing protocols which are applicable to FANETs, and these algorithms use either a single path strategy or a multipath strategy. Some of those protocols are greedy algorithm [15] and compass algorithm [16] (Fig. 4).

4 FANET Applications The previous works on FANET survey analysis have identified the different fields where such a network would be helpful. This work focuses on real-world application

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Fig. 4 Summary of FANET routing protocols

and implementation of FANETs in the field of agriculture, military operations, and disaster management.

4.1 Agriculture FANETs can be highly useful in agricultural activities such as land surveying, crop monitoring, and fencing. UAV networks designed for these purposes should have high dynamicity and should be highly mobile. Sometimes to make FANETs highly mobile, the UAV networks end up consuming energy at faster rates which leads to higher maintenance costs. Looking at the requirements of such a network, UAV or a drone would be very useful in this field and would make farming efficient. Several activities can be performed using an autonomous swarm of UAVs such as crop scouting, crop surveying and mapping, crop insurance, cultivation planning and management, application of chemicals, and geofencing. Comprehensive analysis of the existing algorithms along with their mobility models has been done keeping in mind the requirements of each individual activities and has been carefully documented. Table 1 shows table which routing protocols would be suitable for these processes. We can see that the most used mobility model for agricultural application is the paparazzi model. The specific tasks require a different routing mechanism. From the above analysis, the RGR algorithm is suitable for map surveying and application of chemicals, the GLSR algorithm is best suitable for geofencing and security purposes, and APRAM algorithm works best for crop scouting activities.

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Table 1 Agricultural applications Agriculture application

Objective

Crop scouting

Mobility model

Real time

Most suitable routing protocol

To provide continuous Paparazzi information of the surroundings

Yes

Ad hoc routing protocol for aeronautical MANETs (ARPAM)

Crop surveying, and mapping

To provide a complete Paparazzi assessment of the conditions of crops

Yes

Reactive- greedyreactive (RGR)

Chemical application

Filter out weeds and destroy them

Paparazzi

Yes

Reactive- greedyreactive (RGR)

Geofencing

Keeping a watch on unusual movements

Distributed pheromone

No

Geographic load share routing (GLSR)

4.2 Military FANETs are of high importance to the security of a nation and it saves the governments around the world a huge amount of capital, but most importantly saves a greater number of human lives as UAVs are unmanned. Due to dynamic nature of military operations, it would not be easy to relay the new information to the UAV networks if the network was not within the range of a ground system. This can potentially lead to failure of the mission. When a complex mission is involved and which might require the use of more drones would mean a bigger network and more instructions. This could lead to problems between the links and could reduce the performance of the networks. FANET capabilities such as staying under the radar could be another problem when managing such networks since there is a huge amount of data and bandwidth involved; it can lead to a high amount of unnecessary power consumption, thereby increasing the need for refueling. UAVs can be used for many specific military operations, and several positionbased networks help in countering most of the problems they face such as highpower consumption, rapid topology change, and dynamicity of the operation. Since a FANET communication network would depend on several parameters such as number of UAVs, energy capacity, and power consumption capacity of the UAVs (Table 2). For military applications, from the final analysis, it is evident that the Gauss Markov model is the most suitable for activities such as rescue operations and surveillance. The most appropriate protocols are mentioned above in the table with their distinct advantage. For example, in the case of rescue operations in mountainous region, the most suitable algorithm would be DSR. Due to its more adaptable nature to

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Table 2 Military applications Protocol

Mobility model

Real time

Advantage

XLingo

Gauss Markov

Yes

Efficient QoS reduced packet delay

Topology broadcast reverse forwarding (TBRF)

Gauss Markov

Yes

Reduce overhead

Dynamic source routing (DSR)

Gauss Markov

Yes

More adaptable to dynamic topology of FANET

GPSR

Gauss Markov

Yes

Outperforms many existing non position-based

the rapid change in topology and since the distance between the UAV and the ground keeps varying due to rugged terrain, this algorithm would be the most efficient.

4.3 Disaster Management FANETs can also be used to perform various tasks and operation in the field of disaster management such as geographical surveillance, relief package drops, and also evacuation operation. FANETs designed specifically for these purposes should have high mobility, should be reliable, and should be able to handle rapid topology changes. UAV nodes travel at high speeds. Establishing communication through common methods like Mobile Internet Protocol will not be effective at such high speeds. Hence, while deciding the communication protocols, UAV speeds should be taken into account along with quality of service and efficiency of routing algorithms. These factors can cause increased overhead routing, low packet delivery ratio (PDR), and communication delays. Disaster management is a very sensitive area; hence, the networks designed to tackle them must be highly reliable. Any communication loss, delays, or miss calculated data can be very dangerous as it directly deals with people’s lives. Considering the above challenges, the most suitable mobility model is the reference point group mobility model (RPGM). The two main protocols used in the RPGM model are ad hoc on-demand distance vector (AODV) protocol and the ad hoc on-demand multipath distance vector (AOMDV) protocol. AODV and AOMDV are reactive routing protocols. Reactive protocols are generally preferred as they are highly efficient and reliable and well as the low-power consumption rate. To prove that RPGM-based AODV/AOMDV is more efficient than static AODV/AOMDV, the authors have compared the two using various performance metrics. The performance metrics taken into account to arrive at the conclusion are given below (Table 3).

FANET Routing Survey: An Application Driven Perspective Table 3 Disaster management applications

225

Parameters

Static AODV/AOMDV

RPGM AODV/AOMDV

Average throughput

Low

High

Energy consumption

High

Low

Average end-to-end delay

High

Low

As we can see, RPGM model-based AODV/AOMDV protocols have higher throughput, low-energy consumption, and low average end-to-end delay, making it a better suitable protocol for disaster management activities compared to static AODV/AOMDV models. High throughput and low delay make this network highly reliable, and it does not affect the performance metrics at high speeds.

5 Practical Implementation UAV technology is highly used in the agricultural industry. Activities such as crop monitoring can be performed by autonomous UAV networks with high precision and accuracy. UAV technology combined with data analysis can help to increase the farm produce output. A practical example of this is the development of AggieAir [17]. AggieAir is a low-cost remote sensing UAV operated system. It is implemented using the Paparazzi mobility model. To be able to control the UAV network, the team specifically used paparazzi open-source autopilot. This system is most useful in activities such as intrusion detection, maintaining crop security, and for crop surveying. UAVs can be used as remote sensing devices with the modules such as infrared cameras and air pollution sensors integrated with the system. Orchid [18], a project funded by the United King, aims to reduce response time of the first responders with the help of UAVs. The UAV system used by Orchid is able to gather the best possible information of the affected area such as infrared images, thermal imaging, CCTV feeds, and crowd generated content and pass it on to the first respondents decision-making efficient. Skyports recently developed a drone delivery system that was backed by the National Health Service. This system was first put to use in Scotland to deliver essential medicines and COVID-19 test kits to people through drones. Using delivery-drones, remote places in the country could be made accessible, also limit the human interaction, and hence prevent the spread of COVID-19. The work done in deep learning-based real-time object recognition for security in air defense is a practical implementation of UAV network in defense and security field [19]. This implementation comes under flying deep learning integrated drone (FIDLD) class. The key focus here is to keep constant surveillance without any disturbances and at the same time find out malicious points using the deep learning

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algorithms implemented in the system. The UAV comes under the FDILD class and is based on Gauss Markov mobility model and the standard DSR routing protocol.

6 Conclusion In this paper, we identified the fields where FANETs can be used to provide more and efficient solutions to existing problems. We have also explored the most common communication protocols used in flying ad hoc network communications. The authors have identified the three main applications of FANETs as agriculture, military operations, and disaster management. Further, these applications were used as an example to analyze the different types of routing protocols and most effective mobility models for specific and individual activities. This paper takes into account several performance metrics such as throughput, packet delivery ratio, and end-toend delay to arrive at the final results. This work contains comprehensive tabulation of different applications in a specific field and their implementation through a suitable model. The unique novelty of this paper is that it offers a complex analysis of different applications of FANETs along with the best possible mode of implementation. In future, further research on mobility models can be done making the system and network even more efficient. Perhaps applications of FANETs can be expanded to other fields using new routing protocols and mobility model designs.

References 1. Bekmezci ˙I, Sahingoz OK, Temel S¸ (2013) Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw 11(3) 2. Khan MF, Yau K-LA, Noor RM, Imran MA (2020) Routing schemes in FANETs: a survey. Sensors 20(1):38 3. Singh SK (2015) A comprehensive survey on Fanet: challenges and advancements. Int J Comput Sci Inf Technol 6 4. Chriki A, Touati H, Snoussi H, Kamoun F (2019) FANET: communication, mobility models and security issues. Comput Netw 163:106877. https://doi.org/10.1016/j.comnet.2019.106877 5. Zafar W, Khan B (2016) A reliable, delay bounded and less complex communication protocol for multicluster FANETs. Dig Commun Netw 3. https://doi.org/10.1016/j.dcan.2016.06.001 6. Tareque H, Hossain MS Atiquzzaman M (2015) On the routing in flying ad hoc networks 1–9. https://doi.org/10.15439/2015F002 7. Kim D-Y, Lee J-W (2018) Integrated topology management in flying ad hoc networks: topology construction and adjustment. IEEE Access, p 1. https://doi.org/10.1109/ACCESS.2018.287 5679 8. Raj S, Panchal VK, Vashist PC, Chopra R (2019) FANETs: current trends and challenges. In: 2019 2nd International conference on power energy, environment and intelligent control (PEEIC), 2019, pp 472–475. https://doi.org/10.1109/PEEIC47157.2019.8976730 9. Sang Q, Wu H, Xing L, Xie P (2020) Review and comparison of emerging routing protocols in flying ad hoc networks. Symmetry 12(6):971 10. Stojmenovic I (2002) Position-based routing in ad hoc networks. IEEE Commun Mag 40(7):128–134. https://doi.org/10.1109/MCOM.2002.1018018

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11. Khan MA, Khan IU, Safi A, Quershi IM (2018) Dynamic routing in flying ad-hoc networks using topology-based routing protocols. Drones 2(3):27 12. He G, Destination-sequenced distance vector (DSDV) protocol. Networking Laboratory, Helsinki University of Technology 13. Perkins CE, Royer EM (1999) Ad-hoc on-demand distance vector routing. In: Proceedings WMCSA’99. second IEEE workshop on mobile computing systems and applications, 1999, pp 90–100. https://doi.org/10.1109/MCSA.1999.749281 14. Boppana Rajendra V, Marrina Mahesh K, Konduru Satyadeva P (1999) An analysis of routing techniques for mobile and ad hoc networks 15. Wang F, Chen Z, Zhang J, Zhou C, Yue W (2019) Greedy forwarding and limited flooding based routing protocol for UAV flying ad-hoc networks. In: 2019 IEEE 9th International conference on electronics information and emergency communication (ICEIEC), 2019, pp 1–4. https:// doi.org/10.1109/ICEIEC.2019.8784505 16. mheidy Al-sofy K, Al-Talib SA (2020) IOP Conf Ser: Mater Sci Eng 928:022002 17. Coopmans C, Han Y (2009) AggieAir: an integrated and effective small multi-uav command, control and data collection architecture 18. Orchid. http://www.orchid.ac.uk/index.html 19. Pradeep S, Sharma YK (2019) Deep learning based real time object recognition for security in air defense. In: 2019 6th International conference on computing for sustainable global development (INDIACom), 2019, pp 295–298

Wearable Health Monitoring Glove for Peri and Post COVID-19 Pandemic S. Elango, S. Praveen Kumar, K. Gavaskar, J. Jayasurya, and G. Abilash

Abstract Health is the prime importance in our day-to-day life. Moreover, as the corona virus disease (COVID-19) becomes pandemic worldwide, it is essential to monitor our health regularly. Today, wearable technology is widespread and is rapidly becoming a trend and promising to offer key benefits in managing chronic diseases, especially at home. This article proposes two methods for real-time health monitoring systems using affordable wearable gloves. The first method collects data of patients’ health parameters like heart rate, oxygen level, and temperature using various sensors which are embedded in the glove. Along with that, a flexible sensor is used to measure arthritis. Collected data will be sent to the cloud and processed the data. Then, the data can be made available for doctors to alert and monitor through multiple means of communication. The second method is offline, the controller will process the data in the glove, and the results will be indicated in the organic light-emitting diode (OLED). This monitoring system will be helpful for the patients during COVID-19 as well as post-pandemic. Keywords Online health monitoring · Data glove · Wearable health monitoring device · Offline health monitoring

S. Elango (B) · S. Praveen Kumar · G. Abilash Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu 638401, India e-mail: [email protected] J. Jayasurya Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu 638401, India K. Gavaskar Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Tamil Nadu 638060, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_20

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1 Introduction Health is a state of complete physical, mental, and social well-being of a human being, not just physical. The concept of health is always the fundamental key to the betterment of living. But in many places, especially in developing countries, the quality of health services is not up to expectations. The quality includes inadequate services, improper healthcare, costly diagnostics, etc. But due to some technological advancements in domains like the Internet of things (IoT), embedded systems, etc., the situation is slowly being reversed. Many sensors have been invented and produced worldwide. Medical sensors have played essential roles in the early diagnosis of illness, such as body temperature measurement, blood pressure measurement, and much more [1]. IoT is a rapidly growing platform that connects sensors and other devices to the software and Internet [2, 3]. It has already found applications in intelligent health monitoring systems [4], intelligent home management, parking systems, smart city projects [5], agricultural sectors[6], industry automation [7, 8], and more [1, 9]. IoT is nothing but connecting and communicating between devices and the Internet [10]. IoT is known for its high accuracy, lower cost, and ease of accessibility. Sensors sense the data in different ways and send it to the controller as electronic messages. The controller processes the data received and handles the output in the manner desired to the requirement of the application. It may include displaying a featured visual design in a monitor integrated with the physical setup of the circuit. It communicates the output data with another device via communication protocols such as Bluetooth, ZigBee, or IEEE 802.11 (Wi-Fi) to use the output as input in another circuit setup [11]. It may even include sending the output data to the cloud storage via the Internet using communication protocols, most commonly IEEE 802.11. IoT has played a tremendous role in diagnosing abnormalities more accurately to treat the patients more effectively in healthcare. Many sensors are being invented and made available in the market, either in embedded devices or wearable in the human body [2, 3]. Sensors find their application in collecting physiological information [11, 12] from the patient’s body. In addition to that, some factors such as room temperature, humidity, date, and time information are recorded and stored. The data stored can be processed in various ways and made available to consulting doctors [13], close relatives, caregivers, or someone the patient can trust [14, 15]. IoT enables a wide variety of practical applications, especially in healthcare. Wearable technologies like smartwatches earn market attention nowadays, and more and more innovations are taking place in the research sector of IoT [16]. As a result, more investments are attracted in this sector, and many technology companies are shifting their focus to wearable technology. This paper proposes a wearable healthcare system that measures critical parameters such as heart rate, oxygen level, body

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temperature, and pain in joints through sensors and processes the data. This paper also proposes two methods of using the processed output. The first method includes sending the collected data to the cloud and processing the data. The data can be made available to a medical practitioner as an alert and for monitoring by multiple means of communication. The second method includes using a cloud service to process the data, the controller will process in the glove compartment itself, and the result will be shown in the OLED display embedded in the glove.

2 Related Works Many research works have been done across the globe on IoT and medical health monitoring systems. Some of them are discussed in this paper. A health monitoring system that can monitor heart rate, percentage of oxygen saturation, body temperature, and eye movement through IoT technology is developed [17]. However, although the developed system was deployed, there are no defined performance measures for any patients [17]. A fingerprint integrated IoT-based E-health monitoring system is designed to monitor blood oxygen level, body temperature, and heartbeat rate using sensors. ESP 8266 module is used to update the data in the cloud, and results are obtained through the mobile application. However, the device is too bulky [11]. A wearable IoT-cloud-based health monitoring system is framed for real-time personal health monitoring. This method uses a body area sensor network framework to offer real-time health monitoring [18].

3 Proposed Wearable Health Monitoring System In this work, two methodologies are proposed to monitor the health of humans. The first method measures the temperature and oxygen level of the patients. In addition to that, a flexible sensor is also embedded to monitor the conditions of the arthritis patient. The data collected through the sensor are sent to the cloud for further processing. Finally, the information reaches the doctor through mobile applications. The medicine reminder feature is also enabled in this method via the RTC module. The block diagram of the proposed work I is given in Fig. 1.

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Flexible Sensor

Temperature Sensor

ESP 8266 MCU Cloud

Pulse Oximeter

RTC Module

Buzzer

Fig. 1 Block diagram of the proposed I

3.1 Block Diagram (Proposed I) The major hardware components are a temperature sensor, digital infrared temperature sensor, flexible sensor, Arduino LilyPad, real-time clock (RTC), and OLED. The description of each element is discussed briefly in this paper. Temperature Sensor DHT11 sensor, also known as a temperature sensor, is used to sense the temperature. In addition to this, it can also sense the humidity. It is designed to be easily interfaceable to microcontrollers like Arduino and Raspberry Pi. It has a thermistor for sensing temperature and a humidity sensing capacitor for sensing humidity. Arduino LilyPad Arduino LilyPad is a microcontroller board that holds ATMEGA 168/328 microcontroller. It is unique for its design that is specially done for e-textiles and wearable applications. The microcontroller is where the uploaded programme’s processing is done, and results are produced. This paper has an Arduino LilyPad as the controlling and processing unit. ESP Module ESP 8266 is a low-cost microcontroller integrated with Wi-Fi chips. Through this, the data from the sensors can be uploaded to the cloud for further processing. OLED Display OLED is a flat light emitting technology made by placing a series of thin films between two conductors. Compared to LCDs, OLED is better in improved image

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quality, better durability, and low-power consumption. In addition, OLEDs are at the edge of finding applications in flexible technologies like rollable displays. Pulse Oximeter Max30102 is a wearable module capable of measuring heart rate as well as blood oxygen level. It is a non-chest-based ultra-low-power device. Real-Time Clock RTC module, DS1307, is a time tracking module that gives the current time and date. It is used in most devices we use to see the time. It works on the I2C protocol and can be easily interfaced in microcontrollers. Flexible Sensor The flex sensor is an analogue resistive sensor device that produces a resistance output correlated to the bend radius. The relation between the bend radius and resistance is inverse. Therefore, the resistance increases to 30–40 ky towards 90° bend.

3.2 Block Diagram (Proposed II) The second method deals with the measurement of temperature as well as the heartbeat of the patients. RTC module is used to remind the medicine time of the patients. All the data will be displayed in OLED in real-time. Based on the data shown in the OLED, the patients can react. The block diagram of the proposed work II is given in Fig. 2. Power Supply

Flexible Sensor

Temperature Sensor

Arduino Lilypad

Hearbeat Sensor

RTC Module

Fig. 2 Block diagram of the Proposed II

Buzzer

OLED

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Fig. 3 Circuit connection diagram of the proposed work II

The circuit connections of the proposed II are given in Fig. 3.

4 Implementation of the Proposed Methodologies The proposed I is implemented as hardware for real-time data acquisition. Figure 4 shows the dashboard of the data received from the respective sensor. ThingSpeak cloud is used to store the sensor data, and it will be processed. The dashboard shows the data in four-channel, channel 1 shows the temperature data, and heart rate is monitored in channel 2. Channels 3 and 4 show the flexible sensor data and oxygen level data. Figure 5 shows the proposed II’s working model which consists of a hand glove embedded with the sensors and displays. This working model is tested with humans to get real-time data, which is displayed in OLEDs.

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Fig. 4 Dashboard of the proposed work I

4.1 Measured Results Health parameters and their standard range value are given in Table 1. Table 2 shows the measured data from the glove for the six different patients/humans, and the corresponding status is indicated to the doctor and the patient as a message.

5 Conclusion In this work, two health monitoring methods are proposed. First is online health monitoring, and second is offline health monitoring. In an online health monitoring system, the data from the glove sent to the cloud. The data are monitored, and doctors can provide medical advice remotely. In the offline model, the sensors are embedded with the glove, and humans can see the data in the display and act accordingly. Both the systems are implemented as a prototype model. This system can be beneficial for the patients during COVID-19 and after the pandemic.

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Fig. 5 Implementation of the proposed work II

Table 1 Health parameters and normal range

Parameters

Normal range

Temperature

36.5–37.5 °C

Oxygen level

96% and more

Heart rate

60–100 beats/minute

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Table 2 Real-time data measurement from the glove Patient

Temperature (°F)

Blood oxygen level (%)

Pulse rate (beats/minute)

Message

1

101

96

85

Abnormal value of temperature

2

99.1

99

65

All values are normal

3

99.9

80

50

All values are normal

4

99

90

70

Abnormal value of blood oxygen level

5

100

98

80

Abnormal value of temperature

6

98

96

70

All values are normal

References 1. Pradhan B, Bhattacharyya S, Pal K (2021) IoT-based applications in healthcare devices. J Healthc Eng 2021. https://doi.org/10.1155/2021/6632599 2. Hasan M, Islam MM, Zarif MII, Hashem MMA (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things (Netherlands). 7:100059. https://doi.org/10.1016/j.iot.2019.100059 3. Nooruddin S, Milon Islam M, Sharna FA (2020) An IoT based device-type invariant fall detection system. Internet of Things (Netherlands). 9:100130. https://doi.org/10.1016/j.iot.2019. 100130 4. Mathew PS, Pillai AS, Palade V (2018) Applications of IoT in healthcare. Lect Notes Data Eng Commun Technol 14:263–288. https://doi.org/10.1007/978-3-319-70688-7_11 5. Froiz-Míguez I, Fernández-Caramés TM, Fraga-Lamas P, Castedo L (2018) Design, implementation and practical evaluation of an IoT home automation system for fog computing applications based on MQTT and ZigBee-WiFi sensor nodes. Sensors (Switzerland). 18. https://doi. org/10.3390/s18082660 6. Porkodi V, Yuvaraj D, Mohammed AS, Sivaram M, Manikandan V (2018) IoT in agriculture. J Adv Res Dyn Control Syst 10:1986–1991. https://doi.org/10.48175/ijarsct-1351 7. Menon VG, Jacob S, Joseph S, Sehdev P, Khosravi MR, Al-Turjman F (2020) An IoT-enabled intelligent automobile system for smart cities. Internet of Things 100213. https://doi.org/10. 1016/j.iot.2020.100213 8. Qin E, Long Y, Zhang C, Huang L (2013) Cloud computing and the internet of things: Technology innovation in automobile service. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 8017 LNCS, 173–180. https://doi.org/10.1007/978-3642-39215-3_21 9. Islam MM, Rahaman A, Islam MR (2020) Development of smart healthcare monitoring system in IoT environment. SN Comput Sci 1:1–11. https://doi.org/10.1007/s42979-020-00195-y 10. Khan M, Han K, Karthik S (2018) Designing smart control systems based on Internet of Things and Big Data analytics. Wirel Pers Commun 99:1683–1697. https://doi.org/10.1007/s11277018-5336-y 11. Keerthana N, Lokeshwaran K, Kavinkumar PS, Kaviyan T, Manimaran M, Elango S (2021) Fingerprint integrated E-health monitoring system using IoT. Springer Singapore. https://doi. org/10.1007/978-981-16-5048-2_12 12. Peng H, Tian Y, Kurths J, Li L, Yang Y, Wang D (2017) Secure and energy-efficient data transmission system based on chaotic compressive sensing in body-to-body networks. IEEE Trans Biomed Circuits Syst 11:558–573. https://doi.org/10.1109/TBCAS.2017.2665659

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13. Shaikh S, Waghole D, Kumbhar P, Kotkar V, Awaghade P (2018) Patient monitoring system using IoT. In: 2017 International conference on Big Data, IoT and data science BID 2017. 2018-January, pp 177–181. https://doi.org/10.1109/BID.2017.8336594 14. Patil S, Pardeshi S (2018) Human Health Monitoring System using IOT. Int J Recent Trends Eng Res 4:425–432. https://doi.org/10.23883/ijrter.2018.4256.fsht9 15. Valsalan P, Baomar TAB, Baabood AHO (2020) IoT based health monitoring system. J Crit Rev 7:739–743. https://doi.org/10.31838/jcr.07.04.137 16. Gatouillat A, Badr Y, Massot B, Sejdic E (2018) Internet of Medical Things: a review of recent contributions dealing with cyber-physical systems in medicine. IEEE Internet Things J 5:3810–3822. https://doi.org/10.1109/JIOT.2018.2849014 17. Tamilselvi V, Sribalaji S, Vigneshwaran P, Vinu P, Geetharamani J (2020) IoT based health monitoring system. In: 2020 6th International conference on advanced computing & communication systems ICACCS 2020, pp 386–389. https://doi.org/10.1109/ICACCS48705.2020.907 4192 18. Wan J, Al-awlaqi MAAH, Li MS, O’Grady M, Gu X, Wang J, Cao N (2018) Wearable IoT enabled real-time health monitoring system. Eurasip J Wirel Commun Netw 2018. https://doi. org/10.1186/s13638-018-1308-x

Intelligent Smart Transport System Using Internet of Vehicular Things—A Review M. Vinodhini and Sujatha Rajkumar

Abstract Recently, the population has increased tremendously in the way of each user has a private vehicle for their transportation. This scenario has raised heavy traffic congestion and road accidents. The paper spots the outlook of an accident detection, monitoring the driver’s behavior at various points, focusing on vehicle movement in broad hands. The work surveys the parameters under various technologies in the previous work also lightened the communication between one vehicle to another vehicle and communication from vehicle to environments such as terrain and roadside high buildings based on wireless technology. In addition, also analysis the crucial things and overcome the hazards by the latest technology LoRa and NB-IoT, and its class on LPWAN technology. It also highlights the pros and cons of both ML/DL techniques along with parameters such as energy efficiency, power consumption, bandwidth, and latency. The work also concentrates on the upcoming combination of LoRa and cellular things; it provides the system as efficient and fulfills their requirements of reducing accidents in urban and suburban places instantaneously. Keywords Vehicle-to-vehicle communication · Vehicular parameters · Vehicular sensors · Deep learning · Machine learning

M. Vinodhini Research Scholar, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India e-mail: [email protected] S. Rajkumar (B) Associate Professor, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_21

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1 Introduction The percentage of road accidents in several modes as without wearing a helmet, usage of mobile while driving, consumption of alcohol, driver sickness, high speed, or collision head-on has risen nowadays in both urban and rural places. Several safety measures are flooding in the region today. The paper fulfills the notice of the driver’s behavior, tracking the accident spot, chasing the movement of the vehicle in any location, and challenges involved in learning techniques. In the trend world, vehicleto-vehicle communication and vehicle-to-everything communication are required some particular specifications such as synchronization, physical layer for communication, transmission power, and bandwidth which are all discussed here. Figure 1 shows the survey of road accidents in several modes like head-on, usage of mobile while driving, and others. Death Rate in Several Parameters of Road Accidents

No of Deaths due to Road Accident

250000

200000

150000

100000

50000

0 2020

2019

2018

2017

2016

Total High Speed Head on

Overtaking

Alcohol Consumption

Mobile Phone Usage

Sickness of Driver

Not Wearing Helmet

Others

Total

Fig. 1 Death rate in several parameters of road accidents

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Table 1 Vehicular things involved in accident detection S. No.

Types of sensors used in accident detection

Purpose

Specification

1

Collision sensor

For sense obstacles in highway and it arises audio or alarm or arises vehicle brake

Current: 2 A Voltage: 125 VAC

2

Crash sensor

Detect the high-speed moving vehicle

Current: 2 A Voltage: 125 VAC

3

Ultrasonic sensor

For monitoring the accident in way of reducing the speed

Current: 8 mA Voltage: 3.3 V/5 V

4

Accelerometer

To detect crash by measuring the Voltage: 5 V vibration of a vehicle Current: 0.6 mA

5

Alcohol sensor

To detect the alcohol consumption

Voltage: 5 V Current: 150 mA

6

Heartbeat sensor

Monitor the driver’s pulse rate

Voltage: 3.3 V

7

Piezo electric sensor

To measure the temperature, pressure, strain

Voltage: ≤ 30 Vp-p

8

Shock sensor

ON or OFF the ignition system in a vehicle

Voltage: 12 V Current: ≤ 6 mA

9

Distance sensor

To measure the obstacle at certain distances

Voltage: 5 V Current: 15 mA

10

IR

Obstacle avoidance

Voltage: 5 V Current: 0.06 mA

In 2020, WHO surveys submitted the statement 3.6%, i.e., close to 1.35 million people per year die in a road accident and some are road accident injuries. A twowheeler accident is one of the major things of a caused accident, it will be monitoring the two-wheeler riders-based wearing of helmet [1–3]. Table 1 presents type of sensors which involves in accident detection and its specification. Sensors are used in the vehicle for sensing the parameters such as obstacles at certain distances, speed of a vehicle, pulse rate of a person, and consumption of alcohol level. The sensors that are all attached to a vehicle can make the smart vehicle. The following scenario explains the mode of accident that occurs in roadway transportation.

2 Monitor Driver’s Activity In the environment, half of the hazards occurred by the driver being out of focus while traveling. Based on the various method of an accident detection, observing the driver’s activity is one of the major consequences. It involves several sectors which are as follows:

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2.1 Using Camera The previous work was starting with observing the driver’s behavior in the way of the facial expression of the driver and sensing the driver’s eye blink by capturing the snaps and videos using a higher pixel camera, and outcomes can be drawn in a gradient map. The setting up to take a snap every 5 s, after picking the picture, alert the people who are inside the vehicle [4–6]. The next step is tracking the eye blink and taking a snap after analysis of the figure by computer digital video [7–9]. Some limitations also occurred in the work are the poor quality of images. Other objects show reflection as an obstacle is a major issue in the work.

2.2 Based on the Bayesian Network The work not only monitors the drowsiness but also detects the facial expression, closing, and opening of the eye under the state of unconscious, sleeper mode based on the Bayesian network which puts the driver’s state in an analog graph and also gives caution to the driver through buzzer or seat belt. The rider may be wakeup or bring to a normal state [10, 11]. The driver’s response can be measured accurately in Eq. (1), En CRR =

1(Ri ) n

i=1

(1)

where CRR—Correct response rate, N—No. of responses, 1(Ri )—It contains if Ri has the value of 1, then the driver is incorrect position. If Ri has a value of 0, it denotes drivers which are under fatigue or other sicknesses.

2.3 Based on ZigBee Enabled accelerometer sensor for detection of speed, equipped IR sensor for monitor the fatigue of rider, and using alcohol sensor for sensing level of alcohol consumed by people inside the vehicle and eye blink sensor, RFID tag and heartbeat sensor are used in the parameters. Those multiple sensors are attached to the ZigBee transceiver to alert the other vehicles on the road to prevent accidents furthermore track the accident location using GPS [12, 13].

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2.4 Monitoring Physical Parameters of Driver The author spots a peculiar concept that exposed the level of driver’s stress in different modes. The electro dermal activity (EDA) is equipped with a male driver, which collects the buster level based on road types, traffic collision, and weather conditions. After analysis of the work, when a driver is traveling on the density of urban places and peak hours, the system decides the driver which is under pressure mode. EEG sensor senses the driver brain signal, it automatically warns the driver in sleep or eye closing mode, and LED in the back of the car would be equipped to pass the message to other vehicles regarding moving down slowly [14, 15]. The flow chart Fig. 2 shows the section involved in accident detection. Three following steps are involved in computing the calibrated speed value using a camera [16]. (1) Based on all-weather conditions such as fog, smoke, and dark, the speed of the vehicle can keep track of the video frame that depends on area search method (ASM). (2) Road surface can be analyzed for vehicle speed intensity. (3) Depending on the calibrated image, internal and external camera video can be virtualized in the area.

3 Tracking Vehicle Location The work outlooks the tracking of vehicle and accident spots accurately in different ways. Those methods are based on IoT technology; it consumes low-power transmission. Table 2 shows the vehicle tracking using GPS and communication through GSM based on the Internet of things. Limitations in the above work

Fig. 2 Sections involved in accident detection

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Table 2 Vehicle tracking systems Study

Methodology

[17]

Onboard diagnostics for OBD-II technique tracking the accident location that developed by smartphone, vaguely get a call from the spot and prevails the medical services automatically

[18]

Tracking the spot of a Raspberry Pi 3 module B is It tracks the only location vehicle that needs utilized here for the and does not provide the emergency condition. prototype emergency services to spot Furthermore, pass the message to medical centers about emergency issues

[19]

Map form shows the GPS and GSM are enabled Speedy can be increased at outcomes of monitoring in Arduino UNO to trace time precision and the vehicle location using the location accuracy go down freeboard and ThingSpeak

[20, 21] Developing mobile apps using Google map, to detect the vehicle spot

Techniques

GPS antenna and GSM for latitude and longitude service

Gaps Pass the information gets delayed and fake messages also pass

It tested only on mobile applications and not brief also

[22]

Not only focusing on track Open-source GPS and but also attached GSM are used to track the speedometer, temperature position sensor, and odometer for capturing the other parameters

In real-time, arises of coverage issues are not brief here

[23]

Developed wreck watch Android application for tracking the accident through smartphones spot and forwarding messages to the concerned person

It gets damaged easily

[24]

Onboard sensors in vehicles gather the speed of a vehicle, and the number of vehicles indulges in spot for computing the accident severity

Used positioning device for Several connections are e-notify communication involved, and making the system is not simple

1. Using the Pi camera [4–6], poor quality images and recording of the video should be changed during the brightness of the sun and night time. 2. With multiple sensors attached to the board make the design complex and has many possibilities to create fake values [12, 13]. 3. In paper [16, 17], monitor the driver’s stress using EDA only applicable to male drivers.

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4. The work [25–32] can track the vehicle location using GPS technology but the accident/abnormality occurs. 5. With help of RFID tag provide ambulance services to spot [33, 34] but it consumes much time and needs efficient communication.

4 Rescue System for Accident Spot The theme of work is based on a rescue system in accident spots by providing ambulances instantly. By using GPRS 3G technology, the ambulance would reach the emergency spot in a way of saving people’s life and mislead of emergency services can be identified with help of an RFID tag. It only accesses the respective doctor side further it termed as emergency response and disasters management, using a phone to pass the message to the nearby medical center through a Web developer and also check the driving license [25, 35, 36]. DR =

no. of detected incident cases × 100% total no. of an incident case in data set

(2)

no. of false detected incident cases × 100% total no. of input instances

(3)

FAR =

MTTD =

n 1E (tdetected − ton-set ) × 100% n i=1

(4)

where t detected and t on-set represent no. of incident cases detected with time interval within around 10 m of distance, the accident spot would be determined by a satellite advance system and pass information to rescue centers with the attachment of rescuers and provide ambulance service from respective hospitals nearby spot with help of IoT technology [26–28].

4.1 Preventive Measures of an Abnormal Incident Using the Mobile Application The use of mobile-based accident detection and people safety measures is shown in Table 3. Using GPS technology, gather the data relate to accident spots, vehicle tracking, and information exchange to the respective person.

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Table 3 Accident detection apps S. No.

Detection apps

User review

Features

1.

Crash guard

4.5

Detects car crashes and call or message a person along with your location

2.

Accident alert

3.6

Gives caution about the black spot and over speed

3.

Crash scan

4.0

Detects abnormal things such as quick shaking

4.

Safe-personal

4.1

Provides speed data and alert notification

5.

Life 3600

4.4

Caution to appropriate location

6.

SOS alert

4.4

Emergency app and send the location

Fig. 3 Smart car

5 Autonomous Navigation Using a predictive algorithm [27], the vehicle can navigate the destination by RFID tag, which measures the distance up to 5 km. It can be set up in experimental output based on the hidden Markov method. Figure 3 displays the vehicle as a smart vehicle, which means that sensors are attached in wearing a helmet, steering, tires, and camera for monitoring abnormal activity.

6 VANET To overcome the routing issues, the work establishes a context-aware system that sends HELLO messages to nearby vehicles in the network. The HELLO message represents a change of vehicle direction and speed for safe traveling, hereby vehicle ad hoc network (VANET) plays an important role; in MANET, it monitors the vehicle

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Fig. 4 V2V communication in cloud server

movement. It is based on the dynamic Bayesian network (DBN) for real-time monitoring of the driver’s behavior and communications from nearby vehicles [29, 30]. The work provides in scenario of traffic congestion, and the emergency vehicle goes through to destination. Figure 4 presents vehicle-to-vehicle communication in cloud technology. The method addresses the traffic congestion and traveling time of each street from the beginning to the end of the street in the mode of fastest drive-in emergency condition with help of V2X and V2V communication in VANET. Here, the overview of work can be expressed in simulation using a cluster of OMNET++ simulator (network simulator) and SUMO [31]. Using a combination of Wi-Fi and ZigBee, here improves the throughput and consumed power in moving vehicles. Furthermore, routing techniques and monitoring of the vehicle’s movement and communication by combined mobile 3G/4G networks and Wi-Fi [32, 33].

7 V2X Communication Using ML/DL Techniques Machine/deep learning is designed based on the human brain, and it analyzes and senses the activity and does the work as per protocol and gives the corresponding output.

7.1 V2X Communication Based on ML/DL Techniques The challenges [34] arise between vehicle-to-vehicle communication and vehicle to infrastructures communication for safety applications based on machine learning techniques shown in Table 4.

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Table 4 V2X communication using machine and deep learning techniques Study

Proposed work

Parameters

ML/DL techniques

Limitation

[37, 38] The simulation result shows that come up with standard power transmission in communication

Allocation of power Deep neural scheme network (DNN)

[39–41] The development of 3GPP connectivity on 80 vehicles in communication at the time in places can be set up in simulation

QoS parameters Deep reinforcement DRL algorithm learning (DRL) makes the design 1. Latency complex constraint 2. Reduce distortion

[42, 43] The simulation-based on numerical value arises for V2X communication based on IEEE 802.11p. It tackles the complication in channel estimation

1. Transmission power 2. Delay 3. Doppler

Artificial neural network (ANN) in machine learning

Observe the limited parameters only

1. Latency 2. Reliability 3. Path loss 4. Shadowing 5. LoS status

Deep reinforcement learning (DRL)-based decentralized algorithm

The meta upon DRL algorithm can reach the target easily, but not efficiently

[44]

The graph output displays the cellular V2X communication to increase the volume of V2I users and simultaneously also meet the requirements of V2V services

Autoencoder is not suited for a real vehicular environment

8 Measuring the Parameters in Accident Scenario The section presents the specifications involved in accidents such as the speed of a vehicle, orientation, change of lane information, detection rate, false rate, and the cluster of vehicles in the region are all computed by a combined algorithm [45] for sensing the type of injury and weather conditions for drivers developed an algorithm called random forest (RF) and hybrid K-means. Random forest can be as follows, f (x) =

J 1E f j (x) j j=1

where f j (x) is an average value of bias in RF.

(5)

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For [46], accident detection by deep learning in ODTS is utilized for observing specific parameters such as stop, wrong driving information, and fire. It can detect within 10 s on a freeway [47].

9 Discussion The work is based on monitoring the hazards that happen in roadways through different methods using IoT technology. The review reflects vehicle tracking using GPS technology and collects the techniques involved in accident detection. The issue in the work spot only monitors the accident, and the location then provides an ambulance in the accident spot and also passes the information to the respective person but navigation to a nearby hospital after the injury is not effective here. Furthermore, multiple sensors working effectively in the area that is enabled in a single module are not enough. To overcome the issues in the previous work enable monitoring the parameters like network coverage versus distance, data quality versus distance and few are involved in V2X communication using LSTM in neural network. Also, the vehicular parameters will be observed, and the application layer of the work which involves autonomous navigation to nearby hospitals can be a workout by trend tech of LPWAN such as LoRa and NB-IoT in the separate phase. The upcoming work makes the system efficient and accurate and also superior to other methods.

10 Conclusion The study focused on the Internet of Intelligent Transportation systems including the Vehicular communication, driver’s behavior, rescue method, and accident detection based on deep learning techniques. The work could be divided the chapters as the chain method. First, briefly explain the several ways of monitoring the driver’s behavior for accident detection in modes such as high-fi camera usage, ZigBee, speed control, and solar power. Also, some hazards occurred, tracking the spot with the help of utilizing GPS and GSM for providing ambulances or giving first aid which is a rescue method with help of IoT technology also discussed clearly. The second chapter shows V2X communication for safe speed and avoids abnormalities throughout the study. We addressed various strategies for not only detecting but also preventing accidents. For accident detection, these strategies used various sensors such as accelerometer sensors, shock sensors, pressure sensors, and so on, as well as various machine learning techniques such as neural networks, support vector machines, and representation learning. Various accident prevention strategies were also discussed, such as detecting drunk and drowsy drivers, regulating vehicle speed, preserving a secure distance from obstacles, and so on. More than that, it displays the pros and cons of V2X communication through the parameters involved in communication. The reviewer trusts that the above comprehensive study of V2X

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communication for accident detection shows a fair understanding concept for new authors and a clear crystal of issues in the field. Also, it assures trend work for the future.

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LoRaWAN-Based Intelligent Home and Health Monitoring of Elderly People S. Elango, K. P. Sampoornam, S. Ilakkiya, N. Harshitha, and S. Janani

Abstract Wireless sensor network-based intelligent home monitoring system helps older people to ease their daily activities and provide them with safe and secure living. Wireless sensor networks allow the devices to measure and aid in monitoring, tracking and sensing their household appliances often. An innovative home monitoring system and intelligent healthcare monitoring system are integrated for real-time data evaluation of the older adults’ daily activities. This paper involves the intimation of the mechanism in various sensing devices based on their usage of household appliances. The efficiency of wellness functions predicts the abnormal behavior of the elderly during their need for electrical appliances. Smart home automation and smart health care are blended with the help of LoRaWAN communication network protocol. The implementation of the two methods in this technology includes tracking and determining the status of the elderly based on the performance of the functional assessment of day-to-day activities. The proposed system, which monitors and estimates their essential activities, is tested at homes when they live alone. The usage of LoRaWAN technology achieves the fruitful response of data validation. Keywords LoRaWAN gateway · Health monitoring · Elder people monitoring

S. Elango (B) · K. P. Sampoornam · S. Ilakkiya · N. Harshitha · S. Janani Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu 638401, India e-mail: [email protected] K. P. Sampoornam e-mail: [email protected] S. Ilakkiya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_22

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1 Introduction Elderly people desire to lead an independent lifestyle at their old age, but they are prone to different accidents. So, living alone has high risk and danger in secure living. Older adults face many injuries when they are alone and isolated during their aged times [1]. Elders tend to have reduced mobility, which continues due to ageing. This situation improves medical care and takes care of their regular activities by monitoring them for a better lifestyle. Elders are needed to stay always socially connected through networks for maintaining their physical and mental well-being. The recent advances in technology pave the way to the developed system, which monitors the activities of older adults living alone and subsequently provides statistical data [2]. An intelligent home monitoring system based on the LoRaWAN wireless sensor network designed and innovated to monitor and evaluate the well-being of elderly living alone in the home. The wellness of the elderly can be estimated by forecasting and tracking the unsafe conditions by monitoring their regular activities. An ingenious software conspires with an electronic system that monitors the usage of different appliances and recognizes for determining the status of the well-being of older people. Data processing in real time is the most critical aspect of identifying their activities and behaviors at home. To overcome the difficulties faced by older adults, a system has been designed and developed to monitor and track the expected behavior and well-being of the elderly. The complete data can be detected by triggering the appropriate messages through LoRaWAN protocol that determines the care in functional abilities when older people are isolated [3].

2 Related Works WSN-Based Home Monitoring System for Wellness Determination of Elderly surveys intelligent home automation to determine older people’s activities and functional assessment using Zigbee [1]. The fingerprint-integrated E-Health Monitoring System using IoT investigates and proposes the Tele-Health condition for peoples using biometrical sensors for data manipulation. It is accessed through the wireless network protocol of GSM, Wi-Fi and Cloud for communication. The system consists of a Security Alert System alert message for mismatching and loss of data, a health monitoring system for scanning the various parameters like temperature, pressure and an Emergency Alert System to predict the disorder in the preliminary stage diagnose for better lifestyle quickly [4]. The authors of a paper on LoRa and server-based home automation using IoT track the result in the field of IoT, which performs the efficiency in transmission delay communication. This paper proposes the capable architecture in home automation for both long-range and short-range communications in utilizing multiple communication technologies [3]. An article entitled Wristwatch-based Wireless Sensor Platform for IoT Monitoring Applications uses the wristwatch frequency at the bandwidth of 868 MHz and 2.45 GHz

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ISM bands to avoid the coexistence issues of data packet loss and transmission delay. Wireless bracelet is integrated with the antenna technology for low data rate, low power and low complexity and uses the invasive optical PPG-BPSK modulation [2]. Monitoring the human body signal through IoT-Based LoRa Wireless Network System investigates the CBSN used with Zigbee Wireless Network for data gathering, which provides the information of functional assessment of daily activities. This paper involves the overall monitoring system of ECG, My Signals and LoRa wireless network system [5]. A health monitoring in smart homes utilizes IoT surveys. The blending technology comprises CPS, Machine Learning for monitoring the data for evaluation. It is of tailored medical care, which uses WBAs with a broader area for network protocol. Data analytics, communication data and visualization are the essential terms used for older ones to analyze their activity [6]. The IoT-Based Home Monitoring System with LoRa Communication Technology gives overviews about the communication with hyper-terminal programs using LoRa, which had been implemented. An IoT-based health monitoring system is developed in the My Signals platform. The author says that the supporting sensors integrated with IoT health care can effectively analyze and gather physical health data, making IoT health care ubiquitously acceptable [7]. Home Automation Architecture based on LoRa Technology and Message Queue Telemetry Transfer protocol describes an IoT-oriented architecture for intelligent homes based on long-range and low-power technology. LoRa Message Queue Telemetry Transfer protocol is used as domotic middleware for data transmission [8]. Data Acquisition and Control System in Smart Homes based on IoT is presented about the innovative home environment and control strategy of three layers based on IoT is designed to improve the intelligent home uses the behavior patterns on mining system. The paper reveals his idea on automation of the devices at the houses with the IoT technology [9].

3 Proposed Methodology Wireless sensor networks (WSNs) are integrated with LoRaWAN protocol that creates wireless mesh networks. Data are communicated with a single hop which transmits the data directly to the gateways at the end-devices for data abstraction. In this project, a sensor module is used to analyze the sensor data that tracks the usage of household appliances. Health conditions were also evaluated from the collected data through the gateway. The status of the sensor can be detected by the processed data stored for data evaluation. It can be estimated based on the condition of the devices, whether it is active or inactive while processing the information. The designed system can scan the activity behaviors of elderly well-being through sensing units’ values and access the electrical appliances as shown in Fig. 1. It monitors the sensing units and household functionalities frequently. The functions of microwave, water kettle, toaster, bed, toilet, chair, electronic gadgets, TV, a dishwasher can be determined and evaluated from the given input during the operation of the devices.

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Fig. 1 Smart automation of the home appliances

The output of these devices is connected through electrical appliances. This system manages the sensors’ values through data flow that predicts the monitored person’s abnormal behavior or critical condition. To minimize the cost and power, three electrical appliances on a single power inlet help to detect particular devices with more intelligence. To achieve this, one sensing unit is required. Analog values which are obtained are converted to digital signals by ADC. Analog values from force sensors are determined. It is operated by the coordinating system that recognizes the status, whether it is active or inactive. To access the health of elderly, a wearable wireless bracelet is used. The developed system has monitored and recorded the event functionality of the devices in determining the agilities of elderly well-being.

3.1 Transmitter Section Mobile devices are connected through Bluetooth connectivity. Wireless Application Protocol (WAP) is used for exchanging data through nearby devices over a particular distance. Application software is installed in the mobile for transmitting the signals for data transmission. Bluetooth connection is set up between Arduino and application which is shown in Fig. 2. The successful notification appears on the screen when it receives the data. If a failed notification occurs, the data sending process continues until the successful notification is displayed on the screen for evaluation. When the successful data are received from the application, it checks the data validity of the

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Fig. 2 Block diagram of a transmitter section

Fig. 3 Block diagram of a receiver section

command received. If it gets the valid data, it sends data to the LoRa kit from the Arduino. The unique character data will be sent to the receiver end.

3.2 Receiver Section The antenna receives the data from the transmitting antenna. The received digital signal is sent as an alert to LoRa RYLR400 , as shown in Fig. 3. Microcontroller stores the data and processes the data for indicating the devices which are in progress. It checks incoming data from the sender. The data are converted in the form of code for signal processing. When there is any loss of data, it discards the request, and no action is performed. If there is no loss in corruption data, it sends data toward the corresponding electronic components for activation. This provides the status of the indicator of whether the switch is ON/OFF.

3.3 Home Monitoring System Using LoRaWAN LoRaWAN technology implemented in home monitoring system works on the basis of low-power wide area (LPWA) networking protocol. It ensures privacy and security for end-to-end communication. The devices are connected in star topology that relay information to the end-server. LoRa gateways deployed in the network have standard IP address allotted for unique devices with reference ID. RFID, a new intelligent gateway, helps in the conversion of RF packets into IP packets from the sensed individual devices. The physical world and computing devices are connected through this network model. The network protocol is equipped with a low-cost, high performance, safe and reliable network of multi-type sensors. Sensor types vary according to pressure, door contact, infrared, cliff, Hall effect, reed, ultrasonic, water, temperature, humidity, electrical, gas, smoke and ultrasonic sensors as shown in Fig. 4. The LoRa gateway provides information about the multi-sensors used for the signal process in receiving and transmitting when they are accessed to the Internet. The end terminals

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Fig. 4 Types of sensors used in the devices

are set up with the help of smartphones, laptops, tablets, computing devices with an installation of developed intelligent ware. It helps in monitoring and collecting data for determining the activity behavior of the elderly. Smart home monitoring and intelligent healthcare system is integrated for predicting the wellness of the elderly during their old-aged times. It prevents and guides them to live an independent and secured life happily when they are living alone.

3.4 Health Monitoring System Using LoRaWAN LoRa technology provides smart healthcare applications for critical situations by ensuring low-power, low-cost and reliable performance. Wireless wearable waterproof bracelet is used for monitoring the physical and mental activities of people. The bracelet is integrated with temperature, pressure and humidity sensors, as shown Fig. 5. An intelligent gateway between the sensors and microcontroller provides the data collected from the sensing units. Suppose there is any mismatch in the accuracy of temperature limit, pressure limit, blood oxygen level, heart rate and humidity level, in that case, the bracelet warns the elder ones by either a vibrating or red signal in LCD. Alert messages are triggered to the caretaker or the person who monitors them.

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Fig. 5 Block diagram of the bracelet with modules

4 Experimental Observations and Discussion 4.1 Data Acquisition The sensors can be sensed and evaluated through the communication protocol. Sensors read the data through the network based on the information accessed during data processing. The data collection process involves the activity of collecting the sensed data for evaluation. The data collected are displayed and written in a database to estimate the usage of household appliances and the health of the elderly. The sensor reads the data through the communication network. The data are collected from the sensor which are written to the database. The database storage system consists of a large number of data that synchronizes the data to write operations. The data are captured dynamically, changing and demanding to meet the requirements in forecasting the abnormal activities of the elderly. To obtain data accuracy, the storage mechanism of sensor data onto a computer system is efficient. Therefore, the proposed system can deal with storage requirements in generating the data to monitor irregular activities. It is an essential technique in reducing the size of storage and data manipulation. Whenever there is a change in sensor events, sensors stream the data in a continuous flow. The sensor can be monitored based on the event-based storage system. The activities of the elderly can be detected by the status of the active/inactive position of the sensor. Data mining and data collecting have enormous benefits in analyzing the data storage and data process for real-time applications.

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4.2 Activity Annotation The activity of individuals is monitored by labeling the activities of the elderly, as shown in Fig. 6. Sensor events provide data information through the LoRa gateway. Sensor ID and time duration of each activity give the report in predicting their activities. Data abstraction is achieved by identifying the sensors through the sensing units. Appropriate and accurate data are collected based on the observation of actions. Each activity is allocated with a particular duration of time and indicates the system whenever the duration period exceeds a specific value. Alarms/buzzers/reset buttons indicate emergency help and deactivation of the devices. Maximum and minimum duration levels are fixed for helping them when they need emergency. Every activity is rooted in the particular operation of devices implemented in the homes. Bracelets are provided to monitor their physical and mental illnesses, as shown in Fig. 7. It gives the complete database of temperature, pressure, blood saturation, cardiac activity during their entire lifestyle. The data are monitored for every five minutes continuously. Whenever the duration periods lag behind a specific value, intensive care is required. Activities are recognized at every interval of time.

Fig. 6 Daily activities of elderly

Fig. 7 Block diagram of data collection

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5 Wellness Determination of Elderly The proposed health monitoring system assists the elderly people to look after them when they are living alone. Furthermore, the wellness of the elderly care unit provides us with a more comprehensive, longitudinal evaluation of monitored elderly activities and displays the snapshot of an annual physical examination assessment. In addition, the developed system assists the smart kit in managing their household activities. Activity of Daily Livings (ADLs) is used in health care to refer the people’s daily self-care activities. ADLs help in scanning personal hygiene, dressing, eating, ambulating, bathing, washing and toileting, as shown in Tables 1 and 2. It gives us an index or scale to measure the degree of their independence. Instrumental Activities of Daily Living (ADLs) reflect on a person’s ability in living independently and thrive. Table 2 gives the complete data obtained at the regular interval of time. This includes securing assistance in mental support, preparing meals, managing a person’s household and managing medications. The wellness function of the elderly is determined by analyzing two functions based on the written database to the computer system. Figure 8 shows the pie chart of the predicted output of the elderly when using their electrical and household appliances. The functions β1 and β2 are two terms used for determining the wellness of an elderly. The wellness function of an elderly is calculated using the formula, which depends on the activities of elderly behavior. 1. The wellness function of the elderly based on the non-usage or inactive duration of the appliances is termed β1. 2. The wellness function of the elderly based on the over-usage or long active duration of appliances is termed as β2. β1 and β2 can be obtained from the regular activities of older adults. The data are evolved from the mentioned article [1].

6 Conclusion Wellness is an active process of being aware and making choices accordingly toward a healthy and fulfilling life. Therefore, the wellness of the elderly is more critical to be scanned at a particular time on a daily basis. In this proposed system, 16 types of sensors were used to monitor 20 different activities of the elderly. The developed home monitoring system using LoRaWAN effectively monitors the appliances in real time. This system provides monitoring of both physical and mental health conditions of the elderly. The health perceptions and daily activity behavior recognition are estimated and evaluated in determining the wellness of the elderly.

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Table 1 Daily activities at the regular interval of time Sensor ID status

Connected to devices

Type of sensor

Activities monitored

Time duration

1

Bed/couch/sofa

Pressure sensor

Sleeping (SL)

9.00 p.m.–6.00 a.m.

2

Refrigerator/doors/windows

Door contact sensors

Opening and closing (OC)

Anytime

3

TV

Infrared sensor

Watching (W)

10.00 a.m.–4.00 p.m.

4

Vacuum cleaner

Cliff sensor

Cleaning (CL)

9.00 a.m.–10.00 a.m.

5

Washing machine

Hall effect sensor

Washing (WS) 8.00 a.m.–9.00 a.m.

6

Water heater

Reed sensor

Heating (H)

7.00 a.m.–8.00 a.m.

7

Fan

Ultrasonic sensor

Rotating (RO)

Anytime

8

Water pipes

Water sensor

Flowing (F)

Anytime

9

Bracelet

Temperature sensor

Temperature (TP)

Anytime

10

Bracelet

Humidity sensor

Water vapor (WV)

Anytime

11

Toilet

Pressure sensor

Toileting (TO)

Anytime

12

Grooming cabinet

Contact sensor

Self-grooming (SG)

7.00 a.m.–9.00 a.m.

13

Bracelet

Pressure sensor

Pressure (P)

Anytime

14

Microwave oven/water kettle/dishwasher/toaster

Electrical sensor

Breakfast (BF) 8.00 a.m.–10.30 a.m.

15

LPG gas

Gas sensor

Gas leakage (GL)

Anytime

16

Instant sticks

Smoke sensor

Smoke blowing (SB)

Anytime

17

Door way

Ultrasonic sensor

Person entry (X)

Anytime

18

Iron box

Electrical sensor

Usage of clothes (CL)

10.00 a.m.–11.45 a.m.

19

Bracelet

Optical heart rate sensor

Pulse rate (HR) Anytime

20

Bracelet

Oximeter

Blood oxygen (BO)

Anytime

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Table 2 Predicted output of the observed data S. No.

Annotated activity

Day 1

Day 2

1

SL

2021/08/04 21:09:30 ON SL BEGIN 2021/08/05 05:30:00 OFF SL END

2021/08/05 21:29:30 ON SL BEGIN 2021/08/06 05:35:00 OFF SL END

2

OC

2021/08/04 08:09:30 ON OC BEGIN 2021/08/04 08:30:00 OFF OC END

2021/08/05 10:09:30 ON OC BEGIN 2021/08/05 11:30:00 OFF OC END

3

W

2021/08/04 10:30:30 ON W BEGIN 2021/08/04 15:30:00 OFF W END

2021/08/05 11:09:30 ON W BEGIN 2021/08/05 14:30:00 OFF W END

4

CL

2021/08/04 09:19:30 ON CL BEGIN 2021/08/04 09:50:00 OFF CL END

2021/08/05 09:09:30 ON CL BEGIN 2021/08/05 09:30:00 OFF CL END

5

WS

2021/08/04 08:09:30 ON WS BEGIN 2021/08/04 08:50:00 OFF WS END

2021/08/05 08:19:30 ON WS BEGIN 2021/08/05 08:30:00 OFF WS END

6

H

2021/08/04 07:09:30 ON H BEGIN 2021/08/04 07:30:00 OFF H END

2021/08/05 07:19:30 ON H BEGIN 2021/08/05 07:50:00 OFF H END

7

RO

2021/08/04 09:09:30 ON RO BEGIN 2021/08/04 15:30:00 OFF RO END

2021/08/05 11:09:30 ON RO BEGIN 2021/08/05 15:30:00 OFF RO END

8

F

2021/08/04 10:09:30 ON F BEGIN 2021/08/04 18:30:00 OFF F END

2021/08/05 11:09:30 ON F BEGIN 2021/08/05 19:30:00 OFF F END

9

TP

2021/08/04 10:09:30 ON TP BEGIN 2021/08/04 17:30:00 OFF TP END

2021/08/05 11:09:30 ON TP BEGIN 2021/08/05 16:30:00 OFF TP END

10

WV

2021/08/04 12:09:30 ON WV BEGIN 2021/08/04 14:30:00 OFF WV END

2021/08/05 13:09:30 ON SL BEGIN 2021/08/05 17:30:00 OFF SL END

11

TO

2021/08/04 06:20:30 ON TO BEGIN 2021/08/04 06:30:00 OFF TO END

2021/08/05 07:09:30 ON SL BEGIN 2021/08/05 07:15:00 OFF SL END (continued)

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Table 2 (continued) S. No.

Annotated activity

Day 1

Day 2

12

SG

2021/08/04 07:09:30 ON SG BEGIN 2021/08/04 08:30:00 OFF SG END

2021/08/05 07:15:30 ON SL BEGIN 2021/08/05 08:30:00 OFF SL END

13

P

2021/08/04 08:09:30 ON P BEGIN 2021/08/04 18:30:00 OFF P END

2021/08/05 11:09:30 ON SL BEGIN 2021/08/05 19:30:00 OFF SL END

14

BF

2021/08/04 08:09:30 ON BF BEGIN 2021/08/04 09:30:00 OFF BF END

2021/08/05 08:50:30 ON SL BEGIN 2021/08/05 09:45:00 OFF SL END

15

GL

2021/08/04 11:09:30 ON GL BEGIN 2021/08/04 15:30:00 OFF GL END

2021/08/05 14:09:30 ON SL BEGIN 2021/08/05 21:30:00 OFF SL END

16

SB

2021/08/4 10:09:30 ON SB BEGIN 2021/08/4 20:30:00 OFF SB END

2021/08/5 11:09:30 ON SL BEGIN 2021/08/5 185:30:00 OFF SL END

17

X

2021/08/04 12:09:30 ON X BEGIN 2021/08/04 15:30:00 OFF X END

2021/08/05 14:09:30 ON SL BEGIN 2021/08/05 17:30:00 OFF SL END

18

CL

2021/08/04 10:09:30 ON CL BEGIN 2021/08/04 10:45:00 OFF CL END

2021/08/05 10:09:30 ON SL BEGIN 2021/08/05 10:30:00 OFF SL END

19

HR

2021/08/04 11:09:30 ON HR BEGIN 2021/08/04 20:45:00 OFF HR END

2021/08/05 12:09:30 ON HR BEGIN 2021/08/05 15:45:00 OFF HREND

20

BO

2021/08/04 11:09:30 ON BO BEGIN 2021/08/04 22:45:00 OFF BO END

2021/08/04 13:09:30 ON BO BEGIN 2021/08/04 20:45:00 OFF BO END

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Fig. 8 Pie chart of elderly activities

References 1. Suryadevara NK, Mukhopadhyay SC (2012) Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sens J 12:1965–1972 2. Kumar S, Buckley JL, Barton J, Pigeon M, Newberry R, Rodencal M, Hajzeraj A, Hannon T, Rogers K, Casey D, Sullivan DO, Flynn BO (2020) A wristwatch-based wireless sensor platform for IoT health monitoring applications. Sensors 20:1675 3. Islam R, Rahman MW, Rubaiat R, Hasan MM, Reza MM, Rahman MM (2021) LoRa and server-based home automation using the internet of things (IoT). J King Saud Univ Comput Inf Sci 4. Keerthana N, Lokeshwaran K, Kavinkumar PS, Kaviyan T, Manimaran M, Elango S (2021) Fingerprint integrated E-health monitoring system using IoT. Springer, Singapore 5. Islam MS, Islam MT, Almutairi AF, Beng GK, Misran N, Amin N (2019) Monitoring of the human body signal through the internet of things (IoT) based LoRa wireless network system. Appl Sci 9 6. Linkous L, Zohrabi N, Abdelwahed S (2019) Health monitoring in smart homes utilizing internet of things. In: Proceedings of 4th IEEE/ACM conference on connected health: applications, systems and engineering technologies, CHASE 2019, pp 29–34 7. Misran N, Islam MS, Beng GK, Amin N, Islam MT (2019) IoT based health monitoring system with LoRa communication technology. In: Proceedings of international conference on electrical engineering and informatics, July 2019, pp 514–517 8. Gambi E, Montanini L, Pigini D, Ciattaglia G, Spinsante S (2018) A home automation architecture based on LoRa technology and message queue telemetry transfer protocol. Int J Distrib Sens Netw 14 9. Hu Z (2016) A data acquisition and control system in smart home based on the internet of things. Int J Simul Syst Sci Technol 17:17.1–17.5

A Hybrid Guided Filtering and Transform-Based Sparse Representation Framework for Fusion of Multimodal Medical Images S. Sandhya, M. Senthil Kumar, and B. Chidhambararajan

Abstract Fusion of medical images aims at integrating the harmonizing features of medical images obtained from multimodalities to generate a single image which possess superior visual quality, thus aiding the process of clinical diagnosis in a better way. The challenging task exists when the significant features are extracted simultaneously using the multi-scale transform (MST) methods. To overcome the above-mentioned limitation, fusion framework for multimodal medical images is proposed at two-scale level. Using the two-scale framework, both the structural and texture information from the input medical images are extracted by performing the decomposition of images using a guided filter technique. The base layers are fused in order to preserve the structural details from the images using the integrated DWT and SR pair rule in which the meaningless details are excluded from the source images by constructing the image patch selection-based dictionary, thus enhancing the sparse depiction proficiency of the DWT-decomposed low-frequency layer. A guided filter scheme is applied to combine the detailed layers by enhancing the level of contrast by filtering the noise to the possible level. Finally, the base and detailed parts are combined together to attain the fused image. Experimental results of the proposed framework are assessed objectively which demonstrates the better visual quality by preserving the significant and meaningful features by eliminating the meaningless information. Keywords Multi-scale transform · Sparse representation (SR) · Guided filtering · Medical images · Multimodalities

S. Sandhya (B) Department of IT, SRM Valliammai Engineering College, Kattankulathur, India e-mail: [email protected] M. Senthil Kumar Department of CSE, SRM Valliammai Engineering College, Kattankulathur, India B. Chidhambararajan SRM Valliammai Engineering College, Kattankulathur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_25

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1 Introduction Image fusion in the field of medical imaging has gained research focus in recent years as it is about to extract the complementary features of the medical images from multimodalities, thus making the clinical diagnosis an easier task for the medical field experts [1, 2]. Fusion techniques have been classified as (i) multi-scale decomposition (MSD), (ii) sparse representation (SR), (iii) spatial domain and (iv) hybrid transformation techniques. Multi-scale transform (MST) schemes and spatial filtering schemes are the mostly used MSD-based methods [3]. MST-based fusion techniques include Laplacian Pyramid (LP), Gradient Pyramid (GP), Ratio of Low-pass Filter (RP) wavelet-based techniques like discrete wavelet transform (DWT), DualTree Complex Wavelet Transform (DTCWT), Stationary Wavelet Transform (SWT), Curvelet Transform (CVT) and Non-Subsampled Contourlet Transform (NSCT) [4]. All these MST-based techniques are excellent in extracting the significant information of the images, but still it faces its limitations when single transform technique is applied for extraction of information and quality of images is degraded while applying the inverse transform. Guided filtering is the popularly used spatial domainbased fusion scheme, in which an average weighted strategy is applied to obtain the edge information of the images from the base and the detailed layers. This method also suffers from the drawback of low contrast. Sparse representation-based fusion scheme relies on training and the dictionary optimization, thus making it a timeconsuming process. The aforementioned limitations cannot be overcome by a single transform technique with simple fusion rule as it fails in identifying the significant information from the decomposed coefficients of the source images. Thus a hybrid transform-based fusion strategy is needed to extract the significant information from the multimodalities thus by involving the advantages of the multi-transforms. MST methods tend to extract meaningless information in the fused images which leads to degradation in the visual quality in the regions like bone structures. The idea of using two-scale decomposition arises to eliminate the drawback of MST [1].

2 Materials and Methods Spatial filtering-based decomposition technique is a recent research area in the applications of medical image fusion. This technique utilizes the filters to preserve the edge information at distinct scales thus maintain the shift variance in retaining the edge features. In order to preserve more effective details of structure, rolling guidance filter-based decomposition method is used in [5]. Gaussian filter is used for preserving the significant information of the source images by using two-scale decomposition by integrating the layers with various fusion rules [6]. A guide filtering scheme is implemented as a hybrid multi-scale decomposition technique by using three different fusion rules at various scales [7]. Two-scale decomposition is applied to the medical images with three rules of fusion inherently in [8]. Studies reveal that

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principal component analysis (PCA) performs better in identifying the enhanced structural information of the medical images by eliminating the color misrepresentation. A two-scale exemplification of the input medical images, algorithm for extracting features and sparse representation is used to obtain the fused image. Sparse representation techniques are widely used in various applications of image fusion [9]. Learning method is used to construct dictionary which is trained adaptively by utilizing fixed dictionary models. Image patches constructed from the medical images exhibit redundant information which holds no certain and valued information during the process of sparse coding which is difficult to be used in learning the dictionary by considering it as a training set. To solve the problem of such sparse coding, an innovative dictionary learning technique was proposed in [10]. Kim et al. [11] proposed a clustering-based dictionary learning technique as a joint patch for the multimodal medical images. Separable dictionary learning along with Gabor filtering was utilized in solving the inconsistencies arise in the flat regions of the medical images [12]. For the fusion of medical images, a new dictionary learning method was proposed using the sparse and low rank decomposition [13]. Li et al. [14] uses a discriminative dictionary learning technique to preserve the structural details of the components that are present coarsely along with noises. Finally, sparse representation methods perform better with good dictionary learning.

3 Proposed System The proposed system primarily focuses the aspects of spatial filtering-based decomposition, two-scale fusion strategies for extracting the meaningful information from the source images and the dictionary learning. The two-scale decomposition is achieved by using the guided filtering scheme in which the base and detailed layers are constructed respectively to preserve the edge information at the base layer and the texture information and noises are captured in the detailed layer. This two-scale decomposition achieves the retention of edge details of the images that are fused by reducing the complexity of computation. DWT-SR is used as a hybrid transformbased fusion rule to preserve the image information at the base layer. In order to eliminate the noises and the meaningless information at the detailed layer, guided filter technique is incorporated thus maintain the spatial consistency. Firstly, twoscale decomposition of the input images is performed by processing the guided filter and its difference. Next, the base and the detailed layers are fused using the DWT-SR and mean weight based on guided filtering scheme to its corresponding characteristics of the image pixels. The proposed fusion framework considers CT and MRI as the input images, respectively, on which the two-scale decomposition is accomplished. The architecture diagram for the proposed fusion framework is depicted in Fig. 1. The detailed layers D1 and D2 are generated by taking the difference in the exact values of the input medical images and the foundation layers into consideration, and it is shown in (1) and (2)

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Fig. 1 Illustration of the proposed framework architecture for fusion

B1 = GFr 1,ε1 (I 1, R1) B2 = GFr 1,ε1 (I 2, R2),

(1)

D1 = |I 1 − B1| D2 = |I 2 − B2|,

(2)

where GFr,ε (x, y) denotes the guided filtering process with r as indigenous window range and ε as the regularization element for the guided filter; x and y are measured, respectively, as the source and guidance images. In (2), |.| is considered as the absolute value operator. DWT is used for the multi-scale depiction of the base layers. Low-frequency and high-frequency sub-bands are constructed by decomposing the multimodal images. Then, the process of dictionary construction is carried out by using the sliding window technique over the overlapping image patches, respectively. Once the dictionary has been constructed, fusion of lower sub-band is accomplished by computing the sparse coefficients. Then, the fusion of detail layer is performed by using the guided filteringbased weighted average for enhancing the level of contrast by eliminating the noise. Laplacian filtering is applied, then followed by Gaussian convolution filtering which is applied to reduce noise. Laplacian filtering is applied to the detailed layers D1 and D2 to acquire the feature map of the edge details as follows: H 1 = D1 ∗ L 3∗3 H 2 = D2 ∗ L 3∗3 .

(3)

Here, L 3∗3 denotes Laplacian filter with size of 3 × 3. Guided filtering scheme is used to optimize the primary weight map for the equivalent detailed layer, and it is represented in (4). W f 1 = GFr 2,ε2 (W 1, D1) W f 2 = GFr 2,ε2 (W 2, D2),

(4)

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where W 1 and W 2 represent the initial weight maps, and W f 1 and W f 2 represents the final weight maps of the detailed layer images D1 and D2, respectively. Finally, the pixel-level weighted mean technique is used to attain the merged detailed layer. D f = W f 1 D1 + W f 2 D2 .

(5)

The concluding target image is attained by combining the merged base layer and the merged detailed layer B f and D f , respectively, and it is given in (6). F = Bf + Df .

(6)

4 Experimental Results and Discussion The proposed framework uses CT and MRI as input images of size 256 × 256 obtained from BraTS 2017 online dataset from which the harmonizing features are preserved for bter clinical diagnosis using MATLAB2016a. To analyze the proposed fusion framework against the different fusion techniques, parameters like feature mutual information metric Q FMI , Q G —gradient-based quality metric and standard deviation function are used. Q FMI is used to measure the feature data from the input and the target image by the edges as features in global scope. Q FMI is measured as Q FMI =

MIa, ˜ f˜ H f˜ + Ha˜

+

MIb,˜ f˜ H f˜ + Hb˜

,

(7)

where a, ˜ b˜ and f˜ are the feature mappings of input images a, b and fused image f correspondingly. The evaluation metric Q G is used to calculate the quantity of boundary details that is transformed from the input images to the final fused images, a it is given in (8). ∑J (

∑I QG =

i−1

) Q a f (i, j )W a (i, j ) + Q b f (i, j )W b (i, j) . ) ∑I ∑ J ( a b i−1 j−1 W (i, j ) + W (i, j )

j−1

(8)

Q a f and Q b f denote the strength of the edge and values of preservation at the pixels (i, j). Contrast level of the combined image is evaluated as given in (9). [ | | SD = √

I J 1 ∑∑ ( f (i, j ) − m)2 , I × J i=1 j=1

‘m’ represents the average value of the target image.

(9)

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MRI image

LP-SR

RP-SR

DWT-SR (Proposed)

Fig. 2 Example for DWT-SR-based fusion

Table 1 Comparison of proposed fusion framework

Fusion schemes

Q FMI

QG

SD

LP-SR

0.770

0.551

55.390

RP-SR

0.661

0.465

76.508

DWT-SR proposed

0.807

0.633

82.287

The fusion outcomes of the proposed framework are related with the existing fusion scheme LP-SR and RP-SR by considering the CT and MRI images as source and depicted in Fig. 2. The experimental outcomes of the proposed fusion framework are analyzed with the existing fusion schemes like LP-SR, RP-SR, respectively, and it is given in Table 1. The evaluation results of the proposed system against the existing fusion techniques are represented in Fig. 3a–c, respectively, for the measures Q FMI , Q G and SD, and it shows that the proposed DWT-SR-based fusion scheme performs better, thereby preserving the significant information of the medical images from the multimodalities like CT and MRI.

5 Conclusion The proposed two-scale decomposition model for the multimodal medical-based images was implemented using the guided filtering technique and DWT-SR fusion technique, respectively, for preserving the edge details of the multimodal input images, and then, the base and detailed layers are fused together thus eliminating the noise and the meaningless information. Dictionary learning is incorporated for the better sparse representation of the image patches. Finally, the target image is acquired by associating the base and the detailed layers by applying weighted average strategy. The evaluation metrics were compared among the existing fusion techniques, and it is evident that the proposed DWT-SR-based fusion yields better results thereby reducing the contrast level; noise and edge information of the medical multimodalities were preserved for the clinical diagnosis. The fusion framework shall be extended by considering the other hybrid transform-based methods for the various multimodalities in future.

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Q FMI

1 0.8 0.6 0.4 0.2 0 LP-SR

RP-SR

DWT-SR

(a)

QG

DWT-SR

RP-SR

LP-SR 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

(b)

SD 100 50 0

LP-SR

RP-SR

DWT-SR

(c)

Fig. 3 a Comparison for the measure Q FMI . b Comparison for the measure Q G . c Comparison for the measure SD

References 1. Pei C, Fan K, Wang W (2020) Two-scale multimodal medical image fusion based on guided filtering and sparse representation. IEEE Access 8:140216–140233 2. Du J, Li W, Tan H (2019) Intrinsic image decomposition-based grey and pseudo-color medical image fusion. IEEE Access 7:56443–56456 3. Liu Y, Chen X, Wang Z, Wang J, Ward RK, Wang X (2018) Deep learning for pixel-level image

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fusion: recent advances and future prospects. Inf Fusion 42 4. Zhou F, Li X, Zhou M, Chen Y, Tan H (2019) A new dictionary construction based multimodal medical image fusion framework. Entropy 21:1–20 5. Jian L, Yang X, Zhou Z, Zhou K, Liu K (2018) Multi-scale image fusion through rolling guidance filter. Future Gener Comput Syst 83:310–325 6. Ma T, Ma J, Fang B, Hu F, Quan S, Du H (2018) Multi-scale decomposition based fusion of infrared and visible image via total variation and saliency analysis. Infrared Phys Technol 92:154–162 7. Zhu J, Jin W, Li L, Han Z, Wang X (2016) Multi-scale infrared and visible guided filter based on three layer decomposition. Sensors 16:1068–1082 8. Du J, Li W (2020) Two-scale image decomposition based image fusion using structure tensor. Int J Image Syst Technol 30:271–284 9. Maqsood S, Javed U (2020) Multimodal medical image fusion based on two-scale image decomposition and sparse representation. Biomed Signal Process Control 57 10. Zhu Z, Chai Y, Yin H, Li Y, Liu Z (2016) A novel dictionary learning approach for multimodality medical image fusion. Neurocomputing 214 11. Kim M, Han DK, Ko H (2016) Joint patch clustering based dictionary learning for multimodal image fusion. Inf Fusion 27:198–214 12. Hu Q, Hu S, Zhang F (2020) Multimodality medical image fusion based on separable dictionary learning and Gabor filtering. Signal Process Image Commun 83 13. Li H, He X, Tao D, Tang Y, Wang R (2018) Joint medical image fusion, denoising and enhancement via discriminative low rank sparse dictionaries learning. Pattern Recogn 79:130–146 14. Li H, Wang Y, Yang Z, Wang R, Li X, Tao D (2020) Discriminative dictionary learning based multiple component decomposition for detail preserving noisy image fusion. IEEE Trans Instrum Meas 69:1082–1102

Design of Wearable Antenna for Biomedical Telemetry Application A. L. Sharon Giftsy, K. Usha Kiran, and Ravi Prakash Dwivedi

Abstract In this paper, a dual-band low profile flexible microstrip patch antenna with low specific absorption rate (SAR) value is designed for wearable application and ISM band biomedical applications. Here, dual-band microstrip antenna is printed on denim jeans substrate and has total area of 52.42 × 48.35 × 1.6 mm3 with a full ground plane. The proposed dual-band patch antenna with slot loaded covers frequencies of 2.4 and 5.8 GHz (lower sub-band of 5G communication) with the return loss of − 19 and − 16 dB. The designed antenna deliberates excellent performance higher values which gain up to 4.8 dB and low SAR value 0.298946 W/kg at 5.8 GHz and 0.152577 W/kg at 2.4 GHz. This dual-band antenna provides great solution for ISM band biomedical applications for wireless health monitoring. Keywords ISM band · 5G communication · Wireless health monitoring · Low specific absorption rate · Dual-band antenna

1 Introduction The uses of biotelemetry is in cardiac care, body temperature, glucose monitoring, heart beat rate, etc., in the hospitals. Virtually, any physiological signal could be transmitted; however, the application is typically constrained to health monitoring. The necessity for the health monitoring is driven by a quick increase in the field of medical application devices, enhancement in wireless communication technologies, and it is required to upgrade the quality and reliability of the healthcare system. A. L. Sharon Giftsy (B) · K. Usha Kiran · R. P. Dwivedi Vellore Institute of Technology, Chennai, Tamil Nadu, India e-mail: [email protected] K. Usha Kiran e-mail: [email protected] R. P. Dwivedi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_26

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Microstrip patch antenna because of its versatile advantages of low profile, light weight, easily modified to dual-band operation by inserting some slots and cuts in the patch and easy fabrication (photolithography) is used in wide application from commercial to military application. The fore-mentioned advantages of microstrip antenna are suitable for wearable applications. For biomedical and wearable applications, the microstrip antenna is usually built on a fabric substrate materials [1]. The Wi-Fi frequency ranges between 2.4 and 5.8 GHz. As per ISM band, 2.4 GHz is proposed to use for wireless communication using Wi-Fi for wireless health monitoring biomedical applications. Further, in the present scenario, larger bandwidth and higher data rate are main significant aspects of modern biomedical health monitoring applications which could be achieved using 5G communication lower spectra (5.7–5.8 GHz). The use of different materials and techniques for the fabrication of wearable antennas using a textile substrate has been studied [1–10]. In these literatures, biomedical antennas were developed and studied for various substrates such as Wash cotton [2], liquid crystal polymer [3], polymer [4], denim jeans [5, 7, 9], semi-flexible Rogers [6, 10], polydimethylsiloxane (PDMS) [11], Leather [9] substrate. Here, denim jean is quite suitable for flexible substrate material and it is cost-effective. Performance of an antenna will be better when an antenna is fixed near to the human skin by sustaining good return loss, good radiation; gain and low SAR [5] value will determine for patient safety [10]. The conducting materials for wearable antennas which are often used are adhesive copper tape, adhesive conductive fabric and conductive thread for designing the flexible antenna [8]. In this paper, we have used copper tape to realize the conducting material of the microstrip antenna because of the low cost, ease of fabrication and availability. This paper explains about the design of a wearable microstrip patch antenna results in dual-band operation for biomedical applications. The proposed design will work at two resonant frequencies i.e., 2.4 and 5.8 GHz, and it performs on dualband operation with lower SAR value. The dimension size of the patch antenna is 52.42 × 48.35 × 1.6 mm3 . The paper is arranged as follows: Sect. 2 illustrates the configuration of an antenna and Sect. 3 explains the discussion of the results about the antenna behavior and the parametric analysis of an antenna.

2 Design Configuration of an Antenna The proposed wearable dual-band microstrip patch antenna is designed on denim jean as dielectric substrate size of 29.2 × 29.2 × 1.6 mm3 , and dielectric constant is 1.7. The rectangular microstrip line feeding is provided as antenna feed at the center of the patch along the length. The designed dual-band antenna uses center feed which provides better match with the patch and also in performance [12]. The design formulas for both patch and the microstrip line feed are from Ref. [13]. The

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Fig. 1 Patch antenna geometry

Table 1 Dimensions of the patch and feed

Parameters

Units (mm2 )

Patch length (L p × W p )

29.2 × 29.2

Ground dimensions (L × W )

52.42 × 48.35

Microstrip line feed (L f × W f )

4×3

configuration of the dual-band rectangular patch antenna is shown in Fig. 1 and its parametric values are given in Table 1. Later on, two rectangular slots were loaded on the patch to achieve dual-band frequency as shown in Fig. 2. The loading of slots in the patch perturbs the current paths of the conventional antenna with no slots providing new frequency resonances. Therefore, the designed patch antenna works at two points such as 2.4 and 5.8 GHz. In the proposed antenna design for dual-frequency operation, the first slot S1 (SW1 × SL1 = 20 × 3.3 mm) is loaded parallel to the radiating edge at a spacing of 1 mm. he second slot S2 (SW2 × SL2 = 11 × 3 mm) was located in the middle of the patch at a spacing of 19 mm from the radiating edge. The parametric analysis carried out for the slot S2 such as length (SL2), width (SW2) and position of the slots is varied from the center of the patch toward the radiating edge and presents in Sect. 3.2 of this paper. Fig. 2 Patch antenna with slot loaded

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In the next section, the results are discussed for the proposed slots antenna and its parametric antennas.

3 Results and Discussion 3.1 Proposed Dual-Band Wearable Antenna In this section, the dual-band antenna is characterized for return loss, SAR and radiation pattern. The antenna design and parametric analysis are carried out through CST Microwave Studio Suite software version 2018. Return Loss Figure 3 shows the return loss characteristics of the proposed dual-band slot-loaded wearable antenna. From Fig. 3, it is seen that the slot antennas resonate at 2.4 and 5.8 GHz. The obtained return loss shows good return loss characteristics at both the frequency resonances. Radiation Pattern and Gain Characteristics The result for the antenna radiation pattern at different phi values (0° and 90°) for 2.4 and 5.8 GHz is shown in Figs. 4 and 5. Therefore, the designed antenna delivers better performance in radiation pattern for the unidirectional (front). The gain of an antenna is defined as the ability of an antenna power to emit high or low between input and output in any direction. The rectangular dual slotted antenna provides the gain of 6.3 dB and 2.17 dB for both 2.4 GHz and 5.8 GHz frequencies, respectively.

Fig. 3 Return loss for proposed antenna after slot is introduced

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Fig. 4 Radiation pattern at 2.4 GHz

Fig. 5 Radiation pattern at 5.8 GHz

SAR The rate of measuring the absorption of RF energy by the tissue or human body when exposed to a electromagnetic radiation or radio frequency (RF). RF energy absorption can be measured as mass per unit. The material changes due to environmental condition will avoid the impact of body coupling absorption. Antenna based on biomedical application should have minimum SAR biological effect (MSBE) to obtain the low SAR. The full ground in back plane will protect effectively against the RF absorption by the human tissue. The SAR simulation values are obtained as 0.298946 W/kg at 5.8 GHz and 0.152577 W/kg at 2.4 GHz which is less than 1.6 W/kg as per FCC standards. Simulation of SAR of 5.8 and 2.4 GHz is shown in Figs. 6 and 7. SAR = σ E 2 /m d , where • σ = Material conductivity (s/m). • E = Electric field (V/M). • md = Mass density (kg/m3 ).

(1)

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Fig. 6 SAR value at 5.8 GHz

Fig. 7 SAR value at 2.4 GHz

3.2 Parametric Analysis of Dual-Band Wearable Antenna The antenna parameters’ parametric analysis is also done for the designed antenna and presented in this section. Effect of Variation of Slot S2 Spacing The results of the parametric analysis are given as graphs by varying the center slot (S 2 ) with spacing from 17 to 21 mm from the radiating edge. The s-parameter graph is shown in Fig. 8. As the slots’ spacing is increased, the frequency is also increasing.

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Fig. 8 S 11 -parameter with variation of center slot with spacing from the radiating edge

Return loss of − 28 dB at 18 mm spacing is achieved at the resonance frequency of 2.4 GHz. Effect of Variation of Slot S2 Width The parametric analysis is also done by varying the center S2 with width (SW2 ) from 2 to 4 mm. The s-parameter graph is shown in Fig. 9. Significant change in the resonant frequency is observed at lower resonance. However, for 3 mm slot width, good resonance can see at 2.4 GHz when compared to other values. Effect of Variation of Slot S2 Length The parametric analysis is also done by varying the center with length (SL2 ) from 9 to 13 mm. The s-parameter graph is shown in Fig. 10. Again here, there is no significant change in the upper frequency to be found, but, however, in the lower frequency band, there is a drastic variation in the returns loss; also length decreases,

Fig. 9 S 11 -parameter with variation of center slot width (SW2 ) from 2 to 4 mm

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Fig. 10 S 11 -parameter with variation of center slot length (SL2 ) from 9 to 13 mm

the returns loss increases. However, slot length of 11 mm is chosen because of good return loss near to our desired frequency.

3.3 Comparison of the Proposed Work The review is surveyed based on the flexible materials with respective to SAR and gain as shown in Table 2. The size of an antenna is formulated and designed which was drew from the operational frequency. The substrate material is selected as denim jean due to its cost effective as it is compared with other material which is little expensive [14, 15]. Most of the papers are worked on single-band frequency at 2.4 GHz ISM band operation, but the proposed antenna works on a dual-band operation at 2.4 and 5.8 GHz [2, 16, 17]. Both frequencies are operated under ISM band frequency using slot loading technique that provides lowest SAR value and moderate gain when differentiate with other reviewed papers [18].

4 Conclusion In this work, a wearable dual-band antenna for biomedical applications has been designed at 2.4 and 5.8 GHz by using denim jeans. The slot loading techniques help in achieving the dual-frequency operation. Desired results have been achieved, and the value of gain and SAR values shows that the antenna is perfectly suitable for biomedical telemetry applications. Parametric analysis of the slots in the antenna helps in obtaining the desired frequency resonances. Proposed antenna will be more economical and flexible using denim jeans which is designed using cost-effective material.

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Table 2 Literature review for the designed antenna Ref. No.

Antenna size (mm3 )

Substrate used

Operational frequency

SAR (W/kg)

Gain (dB)

[2]

56.5 × 48.2 × 3

Wash cotton tan δ = 0.02 εr = 1.51

2.4 GHz

8.16

6.091

[16]

64 × 71 × 3

Cotton jean tan δ = 0.025 εr = 1.67

2.45 GHz

–

7.54

[14]

17 × 25 × 0.787

Semi-flexile RT duriod 5880 tan δ = 0.0004 εr = 2.2

2.4 GHz

0.602

1.35

[17]

81 × 81 × 4

Wool felt tan δ = 0.02 εr = 1.2

2.4 GHz

0.554

5.15

[18]

25 × 25 × 1

Denim jean tan δ = 0.025 εr = 1.7

(2.9–11.6) GHz 7.3 GHz

1.6018

3.32

[15]

43.2 × 43.2 × 4.6 RO 4003 tan δ = 0.0027 εr = 3.38

f1 = 5.2 GHz f2 = 5.8 GHz

0.84 0.56

6.05

This work

52.42 × 48.35 × 1.6

f1 = 2.4 GHz f2 = 5.8 GHz

0.1525 0.2989

6.3 2.17

Denim jean tan δ = 0.02 εr = 1.7

References 1. Purohit S, Raval F (2014) Wearable-textile patch antenna using jeans as substrate at 2.45 GHz. Int J Eng Res Technol (IJERT) 3(5):2456–2460 2. Ali U, Ullah S, Khan J, Shafi M, Kamal B, Basir A, Flint JA, Seager RD (2017) Design and SAR analysis of wearable antenna on various parts of human body, using conventional and artificial ground planes. J Electr Eng Technol 12(1):317–328 3. Abbasi QH, Rehman MU, Yang X, Alomainy A, Qaraqe K, Serpedin E (2013) Ultrawideband band-notched flexible antenna for wearable applications. IEEE Antennas Wirel Propag Lett 12:1606–1609 4. Singh N, Singh AK, Singh VK (2015) Design & performance of wearable ultra wide band textile antenna for medical applications. IEEE Access 5:117–123 5. Ashya AYI, Abidin ZZ, Dahlan SH, Majid HA, Kamarudin MR, Abd-Alhameed RA (2017) Robust low-profile electromagnetic band-gap based on textile wearable antennas for medical application. IEEE Access 9:5090–5177 6. Shah SM, Kadir NFA, Abidin ZZ, Seman FC, Hamzah SA, Katiran N (2019) A 2.45 GHz semi-flexible wearable antenna for industrial, scientific and medical band applications. IEEE Access 15(2):814–822 7. Grilo M, Correra FS (2013) Parametric study of rectangular patch antenna using denim textile material. In: 2013 SBMO/IEEE MTT-S international microwave & optoelectronics conference (IMOC), pp 1–5

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8. Monti G, Corchia L, Tarricone L (2013) Fabrication techniques for wearable antennas. In: 2013 European microwave conference, pp 1747–1750 9. Chilukuri S, Gogikar S (2019) A CPW-fed denim based wearable antenna with dual bandnotched characteristics for UWB applications. IEEE Access 94:233–245 10. Kirtonia P, Hosain MK, Rahman T (2019) A circularly polarized implantable wideband antenna for bio-telemetry applications. In: 2019 international conference on electrical, computer and communication engineering (ECCE), pp 1–4 11. Joshi R et al (2020) Analysis and design of dual-band folded-shorted patch antennas for robust wearable applications. IEEE Open J Antennas Propag 1:239–252 12. Ashok Kumar S, Arun Raj M, Shanmuganantham T (2018) Analysis and design of CPW fed antenna at ISM band for biomedical applications. Alex Eng J 57(2):723–727 13. Pozar DM (2012) Microwave engineering, 4th edn. Wiley, New York 14. Smida A, Iqbal A, Alazemi AJ, Waly MI, Ghayoula R, Kim S (2020) Wideband wearable antenna for biomedical telemetry applications. IEEE Access 8:15687–15694 15. Mu G, Ren P (2020) A compact dual-band metasurface-based antenna for wearable medical body-area network devices. J Electr Comput Eng 20:1–10 16. Santhakumar G, Vadivelu R, Perumal A, Selvaraj D (2020) A flexible microstrip antenna for health monitoring application in wireless body area network. Int J Sci Technol Res 9(3):7088– 7092 17. Gao G, Hu B, Wang S, Yang C (2018) Wearable circular ring slot antenna with EBG structure for wireless body area network. IEEE Antennas Wirel Propag Lett 17(3):434–437 18. Yadav A, Kumar Singh V, Kumar Bhoi A, Marques G, Garcia-Zapirain B, de la Torre Díez I (2020) Wireless body area networks: UWB wearable textile antenna for telemedicine and mobile health systems. Micromachines 11(6)

Hand-Off Selection Technique for Dynamic Wireless Network Scenario in D2D Multihop Communication D. Shobana, B. Priya, and V. Samuthira Pandi

Abstract Multihop MH-D2D communication boosts throughput in 5G cell networks by extending the distance between devices. The study proposes a transfer choice strategy for multihop communication in the unique organisation situation associated with 5G cell organisations. This plan is semi-base station based, with participating devices determining various limits and departing from the base station. Based on some specified parameters, the base station chooses two gadgets to operate as a transfer between the accessible gadgets. The chosen gadgets chip away at elective methodology, i.e. on the off chance that one chose transfer will leave the organisation because of any explanation; at that point, the elective gadget goes about as a transfer. The analytical result demonstrates that the proposed conspire provides a critical advantage over the alternative transfer choice strategies. Keywords D2D interchanges · Cell communication · 5G networks · Multihop · Limit

1 Introduction In the future, communication will be conducted on a local level to the extent that it exceeds 90% [1]. As a result, gadget-to-gadget (D2D) communication has attracted significant interest, notably in the context of 5G cell communication [2]. When compared to cell information rate, the D2D correspondence minimises base station

D. Shobana (B) · B. Priya Rajalakshmi Engineering College, Chennai, India e-mail: [email protected] B. Priya e-mail: [email protected] V. Samuthira Pandi SRM Institute of Science and Technology Ramapuram, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_27

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(BS) side, moderates device transmission intensity, and gives a high nearby information rate. As long as D2D coverage is completely enabled, it is possible for all devices to communicate regardless of the base station (BS) [2]. In a unique organisational circumstance, each device in a D2D pair can change his place, obviating the possibility of instant communication. In this instance, the D2D pair may either transmit in cell mode in the future or seek out a device capable of transmitting in D2D mode. Uni-bounce D2U communication occurs when only the objective is interested in multi-targeted data transmission (D2D pair). The device that tracks interest is referred to as a transfer hub [3]. By placing gadgets closer together, the MH-D2D communication boosts throughput. The base station selects the hand-off of the existing proximity devices between the D2D pairs in MHD2D on the basis of Signal-to-impedance and Noise Ratio (SINR). In specifically, in the transfer phase and asset component of MH-D2D communication, there are two major challenges. To tackle the transfer choice issue, there are a few transfer determination plans for cell communication [4]. Proposed a maximum–minimum and maximum–maximum hand-off selection strategy based on the optimal method of communication from start to completion without the cushion space. Riihonen et al. [5] proposes a maximum–max hand-off choice strategy with cradle in which BS determines the optimal path for the two jumps with N variety. To expand the variety request, creators [6] proposed max-connect hand-off determination plot where the BS selects most grounded connection for the two jumps alongside 2N variety request. The developers of [7] presented a maximum proportion conspire in which the BS finds the best link that maximises gain. Michalopoulos and Karagiannidis [8] proposes a secure transfer choice strategy for 5G networks. The previously mentioned transfer plans are base-driven, meaning that the BS selects the transfer that drives the BS’s heap. A few gadget-driven transfers are suggested in [9] to reduce the BS load. The creators proposed a gadget-driven transfer determination plot for multihop D2D communication. This plan thought about different boundaries for choosing the hand-off. The signal-to-commotion (SNR) or SINR was the only factor evaluated in the present hand-off selection procedures. They overlooked the unshakable quality, battery life, and bundle misery that can enhance the organisation’s administration nature. Nonetheless, the authors of [10] investigated several boundaries for selecting the hand-off, but they do not depict what would happen if a selected transfer was to quit the company. As a result, it is necessary to propose a transfer determination plot that will operate in the event that a chosen hand-off does not operate. The proposed plot’s objectives are as follows: (a) To suggest a plan for hand-off choice. This plan will likewise give an elective hand-off. Also, this plan incorporates a few boundaries in front of SINR. (b) The hand-off or elective transfer that is chosen will increase throughput. The remainder of the article is organised in the following manner. Section 3 concludes with the completion of the framework model and issue plan. We represent the proposed plot in Sect. 4. In Sect. 5, we discuss the conclusion and conversation.

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2 Research Challenges for D2D Systems We shall look at D2D communication mechanisms and the use of multihop communications in order to secure public security in this section’s partial covering. From a technological point of view, using the neighbourhood property can give several benefits. D2D UEs may appreciate the fast data rate and quick start-to-finish times at first. Secondly, direct communication utilises less energy while improving asset utilisation. Thirdly, switching from a framework to an instantaneous mode offloads cell traffic, alleviating congestion. Another advantage is the ability to expand the cell range via UE-to-UE hand-off. At any given time, some UEs can act as a backbone for remote transfers, consolidating with the MBS and forming what is referred to as an impermanent HetNet [11]. As previously stated, clients located in unprotected regions can benefit from these HetNets during times of crisis, as they act as extensions of dynamic cells. Additionally, the benefit of increasing force and phantom asset utilisation, as well as organisation inclusion, is critical for public health in areas where resources are typically extremely few. Several of the basic capabilities that enable LTE D2D to address the aforementioned possible administrations are as follows: • D2D disclosure It utilises LTE radio technology to enable close-proximity gadgets to communicate with one another. Generally, the disclosure cycle occurs inside the LTE network’s inclusion and is highly impacted by the organisation’s administrator. Then again, revelation in districts with fractional and non-inclusion is additionally wanted. In this case, UEs should uphold bigger inclusion reach, and it will be needed to occasionally send/get discovery signals with fundamental data about recognisable proof, area, channel status, and so forth, which has an effect on the battery life of UEs directly. D2D revelation is critical since it enables the start of any D2D correspondence and enables proximity-based administrations [12]. • D2D information correspondence It enables direct communication between general D2D UEs without passing through base stations. Regardless, some level of administrator control may be required for information/traffic assurance, as well as secure transfers. Furthermore, the administrator’s control enables a QoS structure that provides differentiated correspondence treatment based on D2D administrations, channel circumstances, endorsers, and gadget innovation, among other factors. • D2D hand-off It allows multi-jump links to be established between both D2D gadgets or cell base and UE endpoint. D2D with UE hand-off enhances cell edge client data output, which can also be utilised to connect EUs without cell coverage to base stations.

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1. D2D Communication Process D2D correspondence information regularly includes information streaming, neighbourhood gaming, and so forth, and in this manner can be a lot heavier than straightforward D2D disclosure. What is more, the absolute traffic differs a tonne in time. Consequently, utilisation of devoted assets can be wasteful for D2D information correspondences. Covering assets with cell transmission are profoundly wanted to improve utilisation of the range, which is basic in open well-being situations. The critical issue in covering asset use is the interference the board. To ensure cell transmission when D2D UEs are included or not integrated, the organisation should establish limits on obstructing cell transmissions from D2D. In crisis points, it is not uncommon for a large number of people to attempt to impart simultaneously, leading the effectively restricted or harmed assets to decline. By and large, to facilitate D2D correspondence with cell network in request to decrease impedance and to accomplish proficient asset reuse, the accompanying methods can be utilised [13]: • Resource allotment To reduce intracell impedance, resources can be symmetrical between D2D and cell transmissions, as well as between D2D transmissions. Be that as it may, the range may be underutilised, which prompts otherworldly proficiency issues. The reuse of intracell assets, based both on the data from the spot and SINR, is promise for optimal utilisation of the assets. • Control of power Another component that can be employed for impedance management is power control using the proximity of D2D UEs. Reduce the transmission power of similar D2D UEs to reduce cellular and other D2D communications’ impedance while maintaining optimum QoS. • Hybrid Automatic Repeat Request (HARQ) HARQ could improve D2D performance in spatial reuse conspiracies by simply redunding D2D transmissions in several subframes. This technology is particularly advantageous for D2D transmissions outside of an organisation’s firewall, when interference can be rather strong and the transmission range required for public health applications can be quite long. • Mode selection Mode selections provide a seamless transition between conventional uplink/downlink (UL/DL) mode and D2D correspondences for optimal asset utilisation. 2. D2D Communications Routing Algorithms When the disclosure cycle characterises conceivable D2D organisations, the utilisation of multi-bounce procedures will ensure the communication among source and objective by utilising middle UEs as remote transfers. Multi-bounce steering is critical in D2D correspondences because, as previously stated, a few measurements

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associated with the multi-bounce computation can affect the D2D correspondence’s execution [14]. Multi-jump steering has previously been widely studied for remote interchanges, and a few computations for supporting D2D interchanges have been proposed [15]. Nonetheless, contingent upon their particular qualities, these calculations could possibly be applied to explicit correspondence situations. All in all, the principle challenge for directing in D2D organisations includes three distinct angles. To begin, the communication information limits imposed by the limited battery life of UEs influence the course selection essentially as much as the tolerable throughput. Second, the mathematical data of the D2D organisation, interference imperatives, and the D2D rate prerequisites (in view of the nearness administrations upheld) should be mutually thought of 914 to enhance the directing systems. Thirdly, it is necessary to consider the computational complexity of D2D computations, since this has a significant impact on energy efficiency and the requirement for rapid data transmission [16]. The complete number of jumps, bounce distance, complete upheld correspondence limit, energy productivity, and likelihood of directing achievement are some examples of measurements that we can use to look at changed steering calculations. A portion of these directing calculations additionally considers the point of bounce for choosing the following hand-off.

3 Framework Model and Problem Formulation In order to organise 5G cells, we analyse an uplink communication scenario. As indicated in Fig. 1, one cell network consisting of a single BS is considered (eNodeB). In a cell, there are C numbers of CU clients, B numbers of D2D clients, and MH numbers of MH-D2D clients. D2D and MH-D2D clients are regarded auxiliary clients, while CU clients are regarded important. For D2D and MH-D2D clients are represented by a red, while CU clients are listed as blue. Rayleigh blurring is used to describe the channel model, and the reaction follows the free complex Gaussian circulation (Fig. 2). The MH-D2D pair (UE1 and UE2) can speak with the assistance of any transfer among accessible transfers (A1, A2, A3). The CU clients (CU1…CU8) communicate with the base station in uplink mode. K asset blocks are available for CU, D2D, and MH-D2D clients. There are a couple MH-D2D sets (UE6, UE7) that fall under a similar eNodeB (UE8, UE9). What is more, there are various D2D sets (UE3, UE4, and UE5, UE10) accessible for correspondence. A gathering is considered fruitful in this paradigm whenever the SINR at the recipient exceeds a SINR edge threshold. Table 1 contains the material that is referred to throughout the work. Multihop D2D communication: In this section, we explore two-jump D2D communication, which is also expandable to multihop. When finding the optimal course of action for MH-D2D correspondence, a few limitations are considered. Underneath, the pre-owned limits are inspected.

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Fig. 1 D2D multihop communication

Fig. 2 End-to-end communication via relay selection

Table 1 Notation and description

Notation

Description

A

Where 1 < a < A is number of CU users

B

Where 1 < b < B is number of D2D users

N

Where 1 < n < N is number of resource blocks

BW

Each user’s bandwidth is allotted

SINR Constraint: Optional clients have a lot in common with CU clients and other auxiliary clients. Consequently, they make obstruction to one another. So that, the SINR at the hand-off side: Battery Power Constraint: To ensure the organisation’s long-term viability, an appropriate battery intensity of transmission is required [13]. As a result, devices

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with lower energy consumption (as measured by limit esteem) are not optimal for the transfer determination approach. Dependability Constraint: Reliability (α) can be figured as: if the chose transfer goes about as a hand-off, the unwavering quality worth will increment by + 1 and in the event that it is would not, at that point the unwavering quality worth is diminished by − 0.5. In this way, the dependability of any transfer is Cushion Constraint: In MH-D2D correspondence, buffer increases organisation throughput. Gadgets have a modest cradle size used for data storage. In comparison to the second jump, the initial bounce has a superior channel character; the bundle will be lost at the transfer end. Following that, it is necessary to select a hand-off that has appropriate support space.

4 Proposed Scheme This section offers a methodology to determine the transmission of MH-D2D communication. This plot of hand-off determination is dependent on a variety of boundaries. This plan improves the achievement likelihood and organisation throughput contrasted and other existing hand-off choice plans. The suggested transfer determination graphic is based on a semi-base model. The BS is used to determine transfer, whereas the D2D pair is used to compute the momentary SINR and reveal neighbours. During the neighbour disclosure step, the participating D2D pair broadcasts a signal bundle across the organisation in order to search for nearby devices. On the off chance that any gadget listens the guide parcel, it answers back to the communicated gadget. Following that, the conveyance gadgets transmit the required info to the BS for hand-off determination. After obtaining the required data from the conveying D2D pair, the BS initiates the hand-off selection technique. In the event that there is a solitary neighbour basic between D2D pairs, at that point it will be legitimately chosen by the BS. Due to the abundance of standard neighbour gadgets, the BS selects the best neighbour gadget among the available neighbour gadgets utilising our hand-off determination plot. To choose the most ideal transfer from among accessible transfers, the BS figures the dynamic edge an incentive for every boundary. The dynamic edge conspire for SINR is distinctive as it thinks about other edge boundaries. We propose three ways to transfer determination that are each tailored to a unique presentation metric determined by the application at hand. The offered strategies complement the three procedures described previously: • Maximum connect determination, in which just one connection is picked from the available SR and RD connections, results in more variability. Reference presented a max interface with power fluctuation to reduce force use. • Successive astute transferring (SOR), in which two chosen transfers replicate FD activity, with one acquiring the source’s sign and the other advancing a previously

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acquired sign to the target, resulting in horrific proficiency. Reference has concentrated fixed-power SOR for throughput augmentation. Additionally, discussed SOR with energy conversion for the purpose of minimising power consumption. • FD pioneered transferring, in which one selects hand-off sends and receives on the same channel using its two reception apparatuses, resulting in increased throughput. Reference focussed FD with power variation in order to reduce the organisation’s force consumption. The base station examines only those devices that are within D2D pair proximity for computing the edge estimation of SINR (source D2D to beneficiary D2D). Due to obstruction, we compare the SINRs of the two bounces (source D2D-to-proximity gadget and closeness gadget to beneficiary D2D). Essentially, a similar cycle is performed for each D2D pair’s nearby device. Furthermore, we gather all of the contrasts between the source D2D and the proximity gadget, as well as the proximity gadget and the receiver D2D, and save them in an exhibit. From that moment on, we assume the typical. This normal value is referred to as the dynamic limit and serves as an incentive for SINR. We eliminate features whose two bounce distinctions are more significant than the SINR advantage. This is because the SINR of a single leap is greater/less than the SINR of a second bounce, implying that either more parcels will be disposed of or transmission capacity will be underutilised. The eNodeB considered just those proximity gadgets that had fewer contrasts when compared to their edge value. To determine the superior two proximity gadgets, the BS reduces the cluster values in diving request and brings the superior two characteristics. The BS additionally stores their comparing cradle esteem, unwavering quality worth and battery life. Then again, the BS additionally figures unwavering quality, outstanding battery force and cradle space boundary’s edge esteem. To ascertain the dependability limit, take the normal of partaking transfers unwavering quality worth. Essentially, the BS likewise ascertains the limit an incentive for different boundaries’ battery force and support space. The BS previously had many boundaries for the top two proximity devices and began the examinations with a single boundary to relate the dynamic limit. On the off chance that condition will fulfil, the main gadget will choose as a hand-off in any case second gadget will choose a transfer. The unselected gadget fills in as an elective hand-off. This vicinity gadget is known as transfer gadget. The programme is run numerous times to perform the mathematical assessment, and then, the normal is used to produce the diagram. The two-bounce D2D communications and uplink correspondence situations for cell clients are considered in this work. For a multiuser multi-sharing situation, the proposed solution is compared to three distinct plans: irregular transfer choice, max–min hand-off plan, and max–max hand-off choice plan. The devices are transported arbitrarily inside the cell region. Each CU client is allocated to a single asset block. Table 2 contains a list of boundaries and their default esteem. Figure 3 shows a graph showing the probability of achieving a chosen handoff versus a boundary limit. We started with a dynamic restriction of 0.1 ara as an incentive. It is observed that the probability of achieving maximum–minimum SINR and maximum–maximum SINR is approximately 70% and 54%, respectively, and

Hand-Off Selection Technique for Dynamic Wireless Network Scenario … Table 2 Parameters and values

293

Parameter

Value

Type of system

Single cell

BW

10 MHz

Radius (cell)

250 m

Number of CU users

50

Maximum device power

50 dBm

Number of resource block

50

Proximity distance

80 m

that the proposed plot offers up to 83%. By increasing the edge value and setting it to 0.2, the concept of the chart is altered. The achievement likelihood of various hand-off choice plans is debased more contrasted with proposed plot. At the point when we set the edge an incentive at 0.9, it is seen that our plan performed very much contrasted with other hand-off choice plan. The throughput is plotted against the number of two-bounce D2D sets in Fig. 4. In this scenario, we use the insatiable technique to distribute assets. For their correspondence, the two-bounce D2D pair needed two asset blocks. The primary asset is required between the source D2D pair and the hand-off to the objective D2D pair, whereas the secondary asset is required between the hand-off and the objective D2D pair. We have identified 50 single-bounce and double-dipond clients together. The suggested conspiracy is considered as providing the highest possible transfer throughput. Due to the fact that the best transfer was chosen, it is evident that the proposed plot’s organisation throughput is superior to that of alternative hand-off selection plans. Fig. 3 Threshold value and value of success probability

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Fig. 4 Throughput of number of D2D pairs

The proposed solution considered the throughput of the optimal transfer and elective hand-off in Fig. 5. In this case, 50 CU clients and a variable number of two-bounce D2D sets (between 10 and 50) are considered. With two-bounce communications, it is very possible that some of the selected hand-off will exit the organisation. The elective hand-off will function in this case. It is evident that the suggested system has a higher throughput than alternative options. Fig. 5 Throughput of number of D2D pairs

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5 Conclusion This paper presents a transfer determination scheme for 5G cell structure using MHD2D correspondence. The transfer determination plot thought about different boundaries for choosing most ideal two hand-off among the accessible transfers. If the best hand-off is not available, the information will be moved to the objective side using the other hand-off. The presentation assessment validates the planned conspiracy’s prevalence when compared to numerous transfer choice plans. This work will be expanded to allocate the asset block for MHD2D correspondence.

References 1. Demestichas P, Georgakopoulos A, Karvounas D, Tsagkaris K, Stavroulaki V, Lu J, Xiong C, Yao J (2013) 5G on the horizon: key challenges for the radio-access network. IEEE Veh Technol Mag 8(3):47–53 2. Niyato D, Wang P (2012) Cooperative transmission for meter data collection in smart grid. IEEE Commun Mag 50(4):90–97 3. 3GPP TR 36.814 v.9.0.0 (2010) Technical specification group radio access network; evolved universal terrestrial radio access (E-UTRA); further advancements for E-UTRA physical layer aspects, Mar 2010 4. IEEE 802.16j (2009) IEEE standard for local and metropolitan area networks. Part 16: air interface for broadband wireless access systems amendment 1: multihop relay specification, June 2009 5. Riihonen T, Werner S, Wichman R (2011) Mitigation of loopback self-interference in fullduplex MIMO relays. IEEE Trans Signal Process 59(12):5983–5993 6. Nomikos N, Charalambous T, Krikidis I, Skoutas DN, Vouyioukas D, Johansson M (2013) Buffer-aided successive opportunistic relaying with inter-relay interference cancellation. In: IEEE international symposium on personal, indoor and mobile radio communications (PIMRC), London, Sept 2013, pp 1321–1325 7. Bletsas A, Khisti A, Reed D, Lippman A (2006) A simple cooperative diversity method based on network path selection. IEEE J Sel Areas Commun 24:659–672 8. Michalopoulos DS, Karagiannidis GK (2008) Performance analysis of single relay selection in Rayleigh fading. IEEE Trans Wireless Commun 7:3718–3724 9. Krikidis I, Charalambous T, Thompson JS (2012) Buffer-aided relay selection for cooperative diversity systems without delay constraints. IEEE Trans Wireless Commun 11:1957–1967 10. Nomikos N, Charalambous T, Krikidis I, Vouyioukas D, Johansson M (2014) Hybrid cooperation through full-duplex opportunistic relaying and max-link relay selection with transmit power adaptation. In: IEEE international conference on communications (ICC), Sydney, June 2014 11. Ikhlef A, Michalopoulos DS, Schober R (2012) Max-max relay selection for relays with buffers. IEEE Trans Wireless Commun 11:1124–1135 12. Nomikos N, Vouyioukas D, Charalambous T, Krikidis I, Makris P, Skoutas DN, Johansson M, Skianis C (2013) Joint relay-pair selection for buffer-aided successive opportunistic relaying. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.2718 13. El Gamal A, Mammen J, Prabhakar B, Shah D (2006) Optimal throughput-delay scaling in wireless networks—part II: constant-size packets. IEEE Trans Inf Theory 52(11):5111–5116 14. Horizon 2020—work programme 2014–2015. http://ec.europa.eu/research/participants/portal/ doc/call/h2020/common/1587758-05i_ict_wp_2014-2015_en.pdf

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15. Rankov B, Wittneben A (2007) Spectral efficient protocols for half-duplex fading relay channels. IEEE J Sel Areas Commun 8(2):379–389 16. Nomikos N, Makris P, Vouyioukas D, Skoutas DN, Skianis C (2013) Distributed joint relay-pair selection for buffer-aided successive opportunistic relaying. In: IEEE international workshop on computer aided modeling and design of communication links and networks (CAMAD), Berlin, Sept 2013

Fabrication of Hexagonal Fractal Antenna for High-Frequency Applications Perarasi, Leeban Moses, Rajkumar, and Gokula Chandar

Abstract In the revolutionary mobile world, antennas play a most important role on the technical perspective. There are many configurations of antenna, among which the patch is a family. Patch can be designed with various shapes, structures, and substrates. Gain and directivity play a vital effect. Hexagonal-shaped fractal antenna is proposed to analyze the impact of radiation, gain, and directivity. Arrow-shaped fractal antenna structures for mobile terminals are analyzed. Proposed antennas are designed and fabricated to resonate at different frequencies 6.325, 7.464, 10.60 GHz, while Sierpinski triangle-shaped configuration is selected. The fabricated radiating element resonates at the expected frequency, − 11 dB return loss, and 6 dBi gain that offers a range of cellular modems’ applications such as image sensing and RF applications, detector applications, IEEE 802.11a, UWB applications, large satellite requirements. Keywords Efficiency · Fractal antenna · Gain · Return loss · Wide bandwidth

1 Introduction Antennas are elements that are essential for all navigation systems: television monitoring, antennas for mobile phones and fiber optics, as well as other products such as for doors, portable microphones, Bluetooth sensors, wireless transmission networks, security cameras, and intelligent sensors. Antennas are part of all radio equipment

Perarasi (B) · L. Moses Bannari Amman Institute of Technology, Erode, India e-mail: [email protected] Rajkumar SNS College of Engineering, Coimbatore, India G. Chandar Sri Venkatesa Perumal College of Engineering and Technology, Puttur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_28

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that is necessary. An ultra-wideband (UWB)-printed antenna described in [1] monitors the current distribution at different frequencies with a combination of types of traveling waves. Left circular polarized (CP) technique is described in [2] enabling meta-surfaces and meta-resonators to fill the gap. For better performance and reduction in size of antennas, square patches filled with a slot are preferred. These antennas resonate at 3.3 GHz (around 0.262). An analysis of 34 × 43 mm2 described in [3] mixes circular and rectangular forms with four adjacent variations to obtain the geometric form of the radiator. In the ground plane, two substrates have been introduced that optimize input impedance matching with less than 2 VSWR and 11.9% bandwidth in the 2.79–11 GHz frequency range. Multiband and designed structure for wireless applications is focused in [4] by altering the fractal configuration of the Sierpinski layout. Multiband efficiency is manifested, resulting in broadband performance, size reduction, and improved directivity. Proposed design is suitable for Wi-Fi, Wi-Max, WLAN, Bluetooth, ISM, and more wireless sector. Circular-type polarizer described in [5] expands the bandwidth of applications. The suggested antenna activates a basic 50 microstrip feed line with a resistance adjustment transformer. Measurements like 450 MHz (2.35–2.8 GHz) and 4.1 GHz (3.3–7.4 GHz), − 10 dB impedance bandwidth, 12% (2.35–2.65 GHz) 3 dB axial ratio bandwidth, 10% (3.3–3.65 GHz), and 4.4%, respectively (5.6–5.85 GHz), were observed. Sierpinski antennas with frequency from 3.8 to 14 GHz are described in [6]. In the X-band, fractal crystal structure has been identified and clinically tested [7]. To build a four-factor structure with 1.4 GHz bandwidth and 2 GHz resonating component, an optimized section was used. Promising short-range wireless networking solutions with license-free broadband frequencies for ultra-wideband (UWB)-focused commercial, military, medical, private and public applications are being discussed [8], in which an antenna provided in 1.6 mm3 measurements is a compact size. A fractal slot antenna with a coplanar fractal waveguide (CPW) loaded with a dielectric resonator that complies different wireless requirements is provided in [9]. Multilayer surface (SFS) with supporting fractal element is preferred. The upper fractured patches are inductive and non-resonant, and the ground fractal slits provide capacitances [10]. Octagonal-shaped fractal ultraviolet band multiinput/multi-output compact tuple antenna is presented, and its characteristics are investigated [11]. The antennas that offer higher bandwidths of 670 and 1070 MHz at 10.89 and 15.92 GHz resonant frequencies are described in [10]. Therefore, triangular patches are used in this fractal antenna, adding some widespread use for the wireless industry. A small single-feed circular polarization (CP) patch has been proposed in [12] and tested based on the antenna strategy in which the meta-surface is combined with a meta-resonator. For better performance and smaller size of antennas, they all consist of well-designed square patches filled with well-designed false interconnection surfaces and are better at 4.15 dB in portable and handheld communication systems [13].

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The research focuses on the design of dual-band structure operated at different frequency ranges. For wide band, three different structures were proposed and compared. The designed filter achieved two passbands centered at 2.51 and 3.59 GHz with 3 dB bandwidth of 15.94 and 15.86% [14]. Antenna designed at 915 MHz is optimized on low profile FR4 substrate with reflection of less than − 10 dB and gain of 6.928 dB at desired frequency [15]. A simulated gain of 7.9 dB is achieved with a linear antenna efficiency of 91% [16]. An original coplanar waveguide (CPW) antenna for the ultra-wideband applications is tested in 34 mm × 43 mm at 3.1–0.6 GHz using Epoxy FR4 [17].

2 Design of Fractal Antenna The ultimate focus of fractal antenna array is to achieve special characteristic impedance. The variance in the fractal component of the array distribution has been shown to influence the filter performance of such arrays of antennas [18]. Using arbitrary fractals reduces the edge detection, contributing to better regulation of the differential lobe. A very popular method used for feeds to printed monopole antennas is the coplanar feed or probe stream [19]. The coplanar connector’s external cord spreads through the electrodes and is welded to the radiator, while the opposite side is attached to the antenna as shown in Fig. 1. That represents, the increased probe duration allows better suitable to the transmission line of heavier substrates, resulting in matching issues. An emitting layer with slot antenna connecting wires is shielded by the dipole antenna [20]. The relationship between the layer and the reflection coefficient makes a slot or an angle in the conductor as shown in Fig. 2. The connecting area is normally operated by the aperture’s design; the density determines the amount of contact from the power supply to the patch [21]. Since the input impedance removes the layer and the connecting wires, high emission is minimized. It is seen that the slot antenna line feed and the transceiver feed suffering from multiple drawbacks for a thick dielectric layer provide large bandwidth [22]. These problems are solved by the quasi-feed methods. The utility of multiple fractal geometries has been experimented by Cohen. The Koch curves, kinds of energy curves, and the Sierpi´nski triangle are included [23]. This fractal also begins in the plane as a solid electrical circuit, as shown in Fig. 2. In the development of new architecture strategies for antennas and fading channels’ surfaces, different parts based on solely linear or random geometric trees have also known to be highly useful. Figure 3 provides an example of a linear ternary (three branches) fractal tree. The energy features of the Hilbert curve and associated functions make them ideal options for use in fractal antenna architecture in Fig. 4. It demonstrates the first four steps in the orthogonal curve layout. The Koch’s have mostly used modern ones for miniaturized loop as well as multipatch antennas. New architectures have also been designed for miniaturized dipole antennas. There are a significant number of edges and sides in these geometries, a fact that will help

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Fig. 3 Initiator (m = 0) and first three (m = 1, 2, and 3) iterations of the Sierpi´nski triangle fractals

Fig. 4 One to four orders of Hilbert curves

increase the effectiveness of the antenna. For the same reason, fractal trees were explored and were found to have multiband features.

2.1 Features of Fractal Antennas Fractal antennas use fractal geometry for their architecture, as already explained, in contrast to standard antennas using hyperbolic geometry. The two fundamental properties of fractals establish distinct features for these fractal constructed antennas, which are shown in Fig. 5 with appropriate implementation areas.

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Fig. 5 Ternary complex tree associated to the Sierpinski triangle

2.2 Multiband/Wideband Performance Every successful balance guarantees that variable components remain consistent with measurements. Because of the geometric self-similarity of the fractal composition, regardless of size development, it can be assumed that fractal architectures can be employed to build antenna designs throughout a wide frequency spectrum. The antenna can be controlled in the same way at different wavelengths, ensuring that the antenna radiates the same amount of energy across multiple bands.

2.3 Compact Size Small size is another prerequisite of wireless communication systems for antenna construction. Dimensionality and loading property of fractal shape allow fractalshaped antennas to use the limited space around them effectively. This reduces the loss and helps to preserve the compact bandwidth for application on mobile phones. In the receiver part, since fractal antennas are thin, they are more compact. Many mobile phones employ patch antenna monopolies, which are basically radioactive wires chopped to a specific length. Despite their simplicity, they exhibit exceptional radiation characteristics. This time of exploitation is often longer than the smartphone for gadgets with varied speeds. Building an antenna based on a fractal design with the frequency range as patch antenna is extremely beneficial.

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3 Proposed Antenna Design The characteristics and application of antenna and different types of antenna with fractal geometry-based analysis for multiband operation were discussed. An arrowshaped fractal antenna using ADS tool was designed with the specified frequency and dimensions. The proposed arrow-shaped fractal antenna as in Fig. 6 has the following dimensions: L 1 = 27 mm, L 2 = 9 mm, L 3 = 3 mm, and the width of the proposed antenna is W 1 = 9 mm, W 2 = 3 mm, W 3 = 1 mm. The length and width of the slot are L s = 3 mm and W s = 6 mm.

3.1 Design Considerations for Fractal Antennas A region was developed based on the definition of time, fractal width for a specified absorption coefficient, and expressive speed. A monopole antenna system can alone be employed or an array of elements may be taken into account. In either of the cases, the planners should use a phased approach while designing. The main goal is usually to achieve unique performance criteria at a specified data rate. The technique described in the next category can be used to create a fractal antenna. Fig. 6 Geometry of the proposed square shape

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3.2 Substrate Selection A similar dielectric base with a default tangent depth of h and loss is selected as the design stage. A thicker substrate can improve power output, reduce conductor loss, and decrease magnitude. The weight, return loss, waveguide loss, and outward radioactive materials from the sensor feed would therefore improve. The absorption coefficient substrate εr plays a function similar to that of the thickness of the layer. A low value of εr would be enhanced for the ground at the edges of the field, and therefore, reflection coefficient. Often preferred are substrates with εr = 4.4. Substrate thickness h = 1.6 mm and dielectric constant εr = 4.4 are assumed for optimal antenna (using FR4).

3.3 Feed Point Location The next task is to determine the feed point after choosing the patch parameters L and W for a particular frequency to achieve a reasonable patch impedance bandwidth. It is remembered that the feed location alteration results in a lack of transition to modification and thus provides a simple method of measuring bandwidth. The antenna geometry is represented in Fig. 7. Four key criteria like the antenna bandwidth, the antenna gains, the antenna return loss, and the antenna radiation pattern are to be estimated. These criteria will help us understand whether the optimal design in the antenna is developed for the expected application.

Fig. 7 Proposed antenna geometry

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4 Results and Discussions The antenna is modeled using the ADS simulation tool to determine the recommended antenna characteristics. This specifically shows that the multiband antenna resistance frequency is 6.3 at 6.62 GHz, 7.42 at 7.58 GHz, 10.45 at 10.83 GHz and the resonances are 6.325, 7.464, and 10.60 GHz, respectively. For this reason, four key parameters: antenna bandwidth, antenna gain, antenna loss return, and antenna radiation pattern must be met. In case of the antenna design, an omni-directional radiation pattern as in Fig. 8 was observed. The quality of power supply from the microstrip antenna is estimated by input impedance. The frequency components of the virtual antenna are 2.625, 5.156, and 5.857 GHz. The planned arrow form variation has a return loss of − 5.841 dB and an efficiency of 100% as in Fig. 9.

Fig. 8 Magnitude and phase response of square patch antenna

Fig. 9 Gain and efficiency

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Fig. 10 VSWR measurements

An equally distributed view for radiation power across the antenna in both directions was observed. The simulated radiated pattern of the E plane and H plane is obtained at 2.625, 5.156, and 5.857 GHz as in Fig. 10. The area of coverage of the proposed one with 3D view is shown in Fig. 11. The planned transmitter input power is obtained by calculating the appropriate structure parameter at frequency of 2.625, 5.156, and 5.857 GHz, such as in figure. Because of its present spread at the patch’s surface, the minimum radiation generated at several quantities can be seen. The designed antenna is fabricated using MITS eleven fabrication machine given in Fig. 12, and the parameters are tested using Vector Network Analyzer.

5 Conclusion A fractal-shaped antenna was developed that resonates at frequencies of 6.325, 7.464, 10.60 GHz and offers a range of wireless communication applications such as remote sensing and RF applications, radar applications, IEEE 802.11a, UWB applications, high-frequency satellite applications. Various antenna parameters like 6 dBi directivity, 9 dB gain, return loss of below − 11 dB are determined for the proposed structure. The shape of the proposed scheme validates for various bands and with required specifications. A perfect impedance matching is attained which depicts a VSWR below 2, and the radiation patterns indicate good gain for these antennas.

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Fig. 11 3D radiation pattern

Fig. 12 Fabricated proposed antenna

References 1. Fereidoony F, Chamaani S, Mirtaheri SA (2012) Systematic design of UWB monopole antennas with stable omnidirectional radiation pattern. IEEE Antennas Wirel Propag Lett 11:752–755 2. Zarifi D, Soleimani M, Nayyeri V, Rashed-Mohassel J (2012) On the miniaturization of semiplanar chiral metamaterial structures. IEEE Trans Antennas Propag 60(12):5768–5776

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3. Li D, Mao JF (2012) A Koch-like sided fractal bow-tie dipole antenna. IEEE Trans Antennas Propag 60(5):2242–2251 4. Li JF, Chu QX, Huang TG (2011) A compact wideband MIMO antenna with two novel bent slits. IEEE Trans Antennas Propag 60(2):482–489 5. Xu HX, Wang GM, Qi MQ (2012) Hilbert-shaped magnetic waveguided metamaterials for electromagnetic coupling reduction of microstrip antenna array. IEEE Trans Magn 49(4):1526– 1529 6. Xu HX, Wang GM, Liang JG, Qi MQ, Gao X (2013) Compact circularly polarized antennas combining meta-surfaces and strong space-filling meta-resonators. IEEE Trans Antennas Propag 61(7):3442–3450 7. Azari A, Ismail A, Sali A, Hashim F (2013) A new super wideband fractal monopole-dielectric resonator antenna. IEEE Antennas Wirel Propag Lett 12:1014–1016 8. Marnat L, Carreno AA, Conchouso D, Martı MG, Foulds IG, Shamim A (2013) New movable plate for efficient millimeter wave vertical on-chip antenna. IEEE Trans Antennas Propag 61(4):1608–1615 9. Fallahi H, Atlasbaf Z (2013) Study of a class of UWB CPW-fed monopole antenna with fractal elements. IEEE Antennas Wirel Propag Lett 12:1484–1487 10. Li L, Wu Z, Li K, Yu S, Wang X, Li T, Li G, Chen X, Zhai H (2014) Frequency-reconfigurable quasi-Sierpinski antenna integrating with dual-band high-impedance surface. IEEE Trans Antennas Propag 62(9):4459–4467 11. Ouedraogo RO, Tang J, Fuchi K, Rothwell EJ, Diaz AR, Chahal P (2014) A tunable dual-band miniaturized monopole antenna for compact wireless devices. IEEE Antennas Wirel Propag Lett 13:1247–1250 12. Chatterjee S, Majumder A, Ghatak R, Poddar DR (2014) Wide impedance and pattern bandwidth realization using fractal slotted array antenna. IEEE Trans Antennas Propag 62(8):4049–4056 13. Su HL, Ho WP (2012) Compact metamaterial-inspired broadband monopole antenna for WLAN/WiMAX applications. In: 2012 international symposium on antennas and propagation (ISAP). IEEE, pp 588–591 14. Mabrok M, Zakaria Z, Masrukin YE, Sutikno T, Alsariera H (2021) Effect of the defected microstrip structure shapes on the performance of dual-band band pass filter for wireless communications. Bull Electr Eng Inform 10(1):232–240 15. Keriee HH, Rahim MKA, Nayyef NA, Zakaria Z, Al-Gburi AJA, Al-Dhief FT, Jawad MM (2020) High gain antenna at 915 MHz for off grid wireless networks. Bull Electr Eng Inform 9(6):2449–2454 16. Jawad MM, Abd Malik NNN, Murad NA, Ahmad MR, Esa MRM, Hussein YM (2020) Design of substrate integrated waveguide with Minkowski-Sierpinski fractal antenna for WBAN applications. Bull Electr Eng Inform 9(6):2455–2461 17. El Hamdouni A, Tajmouati A, Zbito J, Tribak A, Latrach M (2019) Novel fractal antenna for UWB applications using the coplanar waveguide feed line. Int J Electr Comput Eng 9(4):3115 18. Dhar S, Patra K, Ghatak R, Gupta B, Poddar DR (2015) Dielectric resonator-loaded Minkowski fractal-shaped slot loop heptaband antenna. IEEE Trans Antennas Propag 63(4):1521–1529 19. Tripathi S, Mohan A, Yadav S (2015) A compact Koch fractal UWB MIMO antenna with WLAN band-rejection. IEEE Antennas Wirel Propag Lett 14:1565–1568 20. Wu XT, Lu WJ, Xu J, Tong KF, Zhu HB (2014) Loop-monopole composite antenna for dualband wireless communications. IEEE Antennas Wirel Propag Lett 14:293–296 21. Reddy VS, Shreyas KS, Ghosh T (2020) Design and analysis of an arrow-headed patch antenna with microstrip feed for WLAN and exclusive cellular applications. In: 2020 fourth international conference on inventive systems and control (ICISC). IEEE, pp 586–589 22. Majumdar A, Das SK (2021) A multiband arrow shaped patch antenna based on apollonian gasket and Soddy’s circle for application in LTE and UWB range. IEEE Antennas Wirel Propag Lett 9:293–296 23. Baudh RK, Kumar R, Singh VK (2013) Arrow shape microstrip patch antenna for WiMax application. J Environ Sci Comput Sci Eng Technol 3(1):269–274

Study and Comparison of Various Metamaterial-Inspired Antennas Sathyamoorthy Sellapillai, Rajkumar Rengasamy, and V. Praveen Naidu

Abstract In this paper, various metamaterial-inspired antenna performances are studied and compared. Here, shapes such as square, ring, and triangle-shaped antenna are considered. All three designs used a single metamaterial-based splitring resonator (SRR) as the main radiating element. Coplanar waveguide (CPW) transmission is used to give input to the antenna design, and it has a size of 23.7 × 32 × 1.6 mm3 . Three proposed antennas are designed for 4.9 GHz (safety applications). Various antenna parameters such as reflection coefficient, bandwidth, VSWR, radiation pattern are studied for three different metamaterial structures and compared their performance. Keywords Metamaterial · Split-ring resonator · Coplanar waveguide

1 Introduction The quick improvement in wireless communications has to urge wideband antennas to help specialized gadgets like PDAs, PCs, tablets, satellite applications, aircraft, and automated airborne vehicle radar. It has additionally requested to reduce the size of wireless gadgets that permit more space to accommodate other electronic parts. Various methodologies are presented to attain a compact size such as increasing length by adding additional stubs [1], introducing resonators [2], and using a better feeding technique which also aid in a reduction in the size of the antenna device [3]. S. Sellapillai (B) · R. Rengasamy Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, India e-mail: [email protected] V. Praveen Naidu Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520007, India R. Rengasamy CREAT (Center for Research, Engineering and Advance Technologies), Uno Minda Limited, Pune, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_29

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The design flexibility is good at coplanar waveguide, and the size reduction of the antennas is achieved better in CPW antennas than microstrip antennas [4]. Though the above-discussed methods work effectively, size reduction is not sufficient enough to cater to future demand. These limitations can be overcome by using metamaterial concepts. A metamaterial is an artificial material and it can be used to attain various characteristics such as size reduction, improving gain, bandwidth, isolation enhancement which are discussed in [5–8]. Minkowski fractal antenna with SRR structure [5] is used to attain multiband response with compact antenna design. In [6], metamaterial super-substrate is discussed to improve the gain of the antenna by employing two layers of meta super-substrates in the desired frequency range. Bandwidth enhancement is achieved using metamaterial resonators, as discussed in [7]. Four metamaterial structures are used along with monopole to improve the isolation of the multiband MIMO antenna [8]. Various metamaterial shapes such as double-ring square, hexagonal, and circular performance are discussed and tabulated in [9]. An absorber for wideband absorption can be achieved by inserting a graphite disc cavity resonator for generating and combining the multiple modes [10]. For absorption enhancement trapping, the electric charge in metamaterial-based graphene surface and arranging them in desired way to improve the performance of the absorber [11]. For perfect absorption and tenability, the magnetic and dipole field is altered by a non-resonant graphene ring [12]. Metamaterial absorber which is insensitive to angle was achieved with the conventional FR4 substrate without resistive lumped elements [13]. Three square split-ring resonators (SSRRs) are interconnected with a strip line just as two parts in three square split-ring resonators are applied to build the electrical length and coupling impact [14]. By interconnecting the three hexagonal split-ring resonators with a strip line, the electrical length and coupling impact have increased [15]. In this paper, various single-ring metamaterial-inspired resonators are implemented as an antenna. Their performance, such as better size reduction performance, bandwidth, and better reflection coefficient among different shapes of metamaterial inspired antennas are discussed.

2 Antenna Design The metamaterial inspired antennas are designed on FR4 substrate with a thickness of 1.6 mm. The antenna ring has width of 23.7 × 32 mm2 , and the length is varied based on antenna design to obtain the desired frequency. The schematic diagram of various metamaterial antennas is shown in Figs. 1 and 2, all the antennas are designed on a 1.6 mm thickness, the length and width of the substrate and ground are common for all three designs, the substrate length Al = 32 mm and width Aw = 23.7 mm, and the ground length Gl = 10 mm and width Gw = 10.9 mm. The feed width is F w = 1.3, and the antenna ring dimensions are Pl = 10.41 mm, Pw = 9.41 mm, Rw = 0.5 mm, and Rg = 1.15 mm. A single split-ring resonator (SSRR) equivalent circuit is shown in Fig. 2b. All the antennas are designed to produce one resonance frequency (4.9 GHz).

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Fig. 1 SQSRR antenna

To attain the desired performance, other parameters are varied. The designed antennas’ reflection coefficients are plotted in Fig. 3a–c. All three antennas are designed to achieve 4.9 GHz resonance frequency. The comparison of all the above three reflection coefficients is plotted in Fig. 4. It shows that all antennas are resonating at 4.9 GHz, and the SQSRR reflection coefficient is better than the other two antennas. The performance of all three antennas is analyzed and plotted in Table 1. Bandwidth of CISRR is better than other two, area length of TISRR is lesser, and antenna length and reflection coefficient of SQSRR are better than other two antennas. All the three rings which are shown in Figs. 1 and 2a are opened and analyzed; if the rings are closed, it will not meet the metamaterial properties. SQSRR antenna needs less length (10.41 mm) to produce 4.9 GHz, while TISRR and CISRR need 11.36 and 15.62 mm as shown in Table 1. It is confirmed that the SQSRR antenna is better than the other two antennas, which require less electrical length to produce desired resonance frequency. Voltage standing wave ratio is one of the important factors in antenna design. Its value must be less than 2. The VSWR of all the three antennas is plotted in Fig. 5a–c, respectively, where all the three antennas exhibit the value range from 1 to 2 for the desired frequency. The radiation patterns for both E and H planes for all the three antennas are plotted in Fig. 6a–c. The antennas exhibit omnidirectional pattern at H plane and a bidirectional pattern at the E plane for the desired frequency 4.9 GHz.

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(a)

(b) Fig. 2 a CISRR and TISRR antenna. b Equivalent circuit of single SRR

3 Conclusion The metamaterial-inspired antennas have been designed for different shapes. All the antennas are designed for 4.9 GHz by keeping the same length and width. Among all three designs, the square-shaped split-ring resonator has a better size reduction than the other two types. λ/12 length only requires to attain the 4.9 GHz with a reflection coefficient of − 45.02 dB. From this study, it is confirmed that shapes are playing a vital role in achieving a compact design with better performance. Further, SSRR is easy to do fabrication when compared to other shapes due to the shape of edges. It could be a better candidate for modern wireless communications.

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Fig. 3 Reflection coefficient of the metamaterial antennas

Fig. 4 Comparison of reflection coefficients of the metamaterial antennas

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Table 1 Performance of various metamaterial antennas SRR shapes

Frequency (GHz)

Antenna length

Lower frequency (GHz)

Upper frequency (GHz)

Bandwidth (GHz)

S 11 (dB)

CISRR

4.9

λ/8

15.625 mm

4.1

5.75

1.65

− 24.61

TISRR

4.9

λ/11

11.36 mm

4.2

5.3

1.1

− 17.18

SQSRR

4.9

λ/12

10.41 mm

4.2

5.7

1.5

− 45.02

Fig. 5 VSWR of the metamaterial antennas

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Fig. 6 Radiation pattern of the metamaterial antennas

References 1. Sung Y (2015) Simple inverted-F antenna based on independent control of resonant frequency for LTE/wireless wide area network applications. IET Microw Antennas Propag 9(6):553–560 2. Rosaline SI, Raghavan S (2015) CSRR based compact penta band printed antenna for GPS/GSM/WLAN/WiMAX applications. Microw Opt Technol Lett 57(7):1538–1542 3. Ameen M, Mishra A, Chaudhary RK (2020) Asymmetric CPW-fed electrically small metamaterial-inspired wideband antenna for 3.3/3.5/5.5 GHz WiMAX and 5.2/5.8 GHz WLAN applications. AEU-Int J Electron Commun 119:153177 4. Wen CP (1969) Coplanar waveguide: a surface strip transmission line suitable for nonreciprocal gyromagnetic device applications. IEEE Trans Microw Theory Techn 17(12):1087–1090 5. Rengasamy R, Dhanasekaran D, Chakraborty C, Ponnan S (2021) Modified Minkowski fractal multiband antenna with circular-shaped split-ring resonator for wireless applications. Measurement 109766

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6. Rajkumar R, Kiran KU (2018) Gain enhancement of compact multiband antenna with metamaterial superstrate. In: Optical and microwave technologies. Springer, Singapore, pp 91–98 7. Wu W, Yuan B, Guan B, Xiang T (2017) A bandwidth enhancement for metamaterial microstrip antenna. Microw Opt Technol Lett 59(12):3076–3082 8. Rajeshkumar V, Rajkumar R (2021) SRR loaded compact tri-band MIMO antenna for WLAN/WiMAX applications. Progr Electromagn Res 95:43–53 9. Saha C, Siddiqui JY (2011) A comparative analysis for split ring resonators of different geometrical shapes. In: IEEE applied electromagnetics conference (AEMC), pp 1–4. https://doi.org/ 10.1109/AEMC.2011.6256871 10. Varshney G, Giri P (2021) Bipolar charge trapping for absorption enhancement in a graphenebased ultrathin dual-band terahertz biosensor. Nanoscale 3. https://doi.org/10.1039/D1NA00 388G 11. Khan MS, Varshney G, Giri P (2021) Altering the multimodal resonance in ultrathin silicon ring for tunable THz biosensing. IEEE Trans Nanobiosci 20(4):488–496. https://doi.org/10. 1109/TNB.2021.3105561 12. Varshney G (2021) Wideband THz absorber: by merging the resonance of dielectric cavity and graphite disk resonator. IEEE Sens J 21(2):1635–1643. https://doi.org/10.1109/JSEN.2020. 3017454 13. Hannan S, Islam MT, Sahar NM, Mat K, Chowdhury MEH, Rmili H (2020) Modifiedsegmented split-ring based polarization and angle-insensitive multi-band metamaterial absorber for X, Ku and K band applications. IEEE Access 8:144051–144063. https://doi.org/ 10.1109/ACCESS.2020.3013011 14. Almutairi AF, Islam MS, Samsuzzaman M, Islam MT, Misran N, Islam MT (2019) A complementary split ring resonator based metamaterial with effective medium ratio for C-band microwave applications. Results Phys 15:102675. ISSN 2211-3797. https://doi.org/10.1016/j. rinp.2019.102675 15. Islam MS, Samsuzzaman M, Beng GK, Misran N, Amin N, Islam MT (2020) A gap coupled hexagonal split ring resonator based metamaterial for S-band and X-band microwave applications. IEEE Access 8:68239–68253. https://doi.org/10.1109/ACCESS.2020.2985845

Wireless Sensor Network-Based Agriculture Field Monitoring Using Fuzzy Logic Ashwini Bade and M. Suresh Kumar

Abstract The economic, industrial, cultural, commercial, and human development of any developing country hugely depends upon the agriculture sector. Recently, technological growth and innovation are widely being used for the improvement of quality and production of the agricultural products. Tremendous growth in Wireless Sensor Network (WSN) and Internet of Things (IoT) has attracted researcher’s attention toward use of automation in agriculture sector. This paper presents Wireless Sensor Network-based agriculture field monitoring based on fuzzy logic. It considers temperature, humidity, and moisture as the input parameters for the watering time management. The proposed system provides better irrigation management in extreme environmental conditions. Keywords Fuzzy logic · Internet of Things · Precision agriculture · Wireless sensor network

1 Introduction Agriculture is the backbone of the economic growth of any country, and it is an important source of livelihood [1]. In spite of larger scope, agriculture sector is not yet fully digitized because of several factors such as complexity, natural, chemical, biological, climate, and economy. Agriculture data collection is critical due to several issues such as geographical variations, climate change, market proximity, infrastructure for storage, and transportation and individual farmer’s activities [2]. Traditional crop production includes sequence of tasks, such as plantation, fertilizing, and harvesting with preset schedule. However, precision agriculture has emerged as promising field in agriculture automation that collects the environmental and agricultural data for the monitoring, controlling, and decision-making of agriculture activities to minimize the labor cost and haptic manual monitoring [3]. A. Bade (B) · M. Suresh Kumar Department of Electrical and Electronics Engineering, Sandip University, Nashik, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_30

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Wireless sensor network is collection of various sensor modules aimed for the data collection, processing, analysis, and communication. Each sensor module consists of transducer, signal conditioning unit, processor, memory, transmitter–receiver module, and battery. The sensor nodes usually placed over the agriculture field in structured and un-structured ways. In WSN data is communicated over the network in between sensor cluster-head, and base station using proper routing technique. WSNs are widely used for the weather forecasting, habitat monitoring, military applications, agriculture, earthquake detection, food industry, process monitoring, etc. [4]. The Internet of Things has shown greater usefulness for controlling the remote systems via internet, and it enables the large data processing using cloud environment. It has shown wide potential in controlling various agricultural, social, industrial, and commercial applications [5–7]. The generalized framework for the use of WSN for various agriculture applications is shown in Fig. 1. It consists of sensor nodes deployed at the agriculture field to detect the various inputs, such as temperature, humidity, moisture, water level, light intensity, soil nutrition, and crop parameters. Data acquired using various sensor nodes is collected at the base station and then communicated to the central processing center using GPRS or GPS. This data is processed, analyzed, stored, and normalized at the processing center. The control information or parameter details are then communicated with the user by the mean of message or mobile applications [8].

Interne t

GPRS

Communication Server

Database

Gateway Agriculture Parameter Monitoring (Humidity, Moisture, temperature, etc)

Sensor

Base Station

Fig. 1 Generalized framework for WSNs in agriculture

User

Wireless Sensor Network-Based Agriculture Field Monitoring Using …

319

This paper presents the WSN and IoT-based irrigation management system using fuzzy logic. It controls the irrigation time based on various real-time parameters of the agriculture field obtained using the sensor nodes placed over the agriculture field. The remaining paper is arranged as follows: Sect. 2 gives the previous work carried out for various applications of WSNs in agriculture field monitoring. Section 3 provides description of proposed irrigation management method. Section 4 provides simulation results and discussions. At last, Sect. 5 describes the conclusion and future directions for the improvement of the work.

2 Related Work Irrigation management is important due to limited availability of groundwater, water resources, and drought conditions. It helps to manage the water resources efficiently and to avoid the wastage of water. Bhanu et al. [9] designed WSNs to improve the crop quality and yield by monitoring the temperature, water level, and humidity. It uses agriculture filed parameters for the irrigation management based on real-time data obtained from the field. Khan and Kumar [10] proposed frontward communication area (FCA) routing algorithm to minimize the delay and energy consumption using mobile sink-based WSN to improve the packet delivery and network lifetime. Vijayakumar and Balakrishnan [11] presented Artificial Neural Network (ANN) for the agriculture automation based on humidity, temperature, atmosphere pressure, PH level in soil or water, etc. It focused on the water management and soil quality management. Sadowski and Spachos [12] suggested that Long Range Wireless Area Network (LoRaWAN) can give optimal solution for the efficient field monitoring compared with IEEE 802.15.4 (Zigbee) and IEEE 802.11g (WiFi 2.4 GHz). It has shown better energy harvesting capabilities for the agriculture filed management. Khelifi [13] proposed region-based sensor deployment and periodic hybrid routing algorithm for the agriculture field monitoring based on environmental and soil parameters. It has given low power solution with higher network lifetime. Barkunan et al. [14] explored water irrigation based on real-time rainfall information and agriculture field data. They have used microcontroller along with GSM module to communicate the rainfall information to user on mobile. Cardoso et al. [15] used previously collected agriculture data acquired through WSN to decide the best time of the day for the water irrigation. It minimizes the wastage of water caused due to fixed scheduled irrigation. Anguraj et al. [16] presented combination of Convolution Neural network (CNN) and Support Vector Machine (SVM) for the irrigation management based on soil, crop, weather, and available water data collected using WSN. Various systems have been presented in the past for the agriculture field monitoring using machine and deep learning algorithms but, the performance is limited due to bulky system, high cost, sensitivity to different environmental changes, less robustness, complex interactive environment, and lack of explainable control system. Most of the systems produces the correct control signal for agriculture field monitoring but fails to convince the customer about logic behind the control action generation. It

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is essential to provide explainable and promising reasons about particular control signal generation for irrigation management to improve the trust and empathy of farmers toward the devices of the precision agriculture.

3 Irrigation Management Using Fuzzy Logic The flow diagram of proposed agriculture irrigation management using fuzzy logic can be shown in Fig. 2 that consists of input variables such as Humidity (H), Temperature (T ), and Moisture (M); and output variables such as Watering Time (WT). The proposed IoT-based agriculture field monitoring system is simulated using Mamdani Fuzzy Controller to monitor the PH sensor, temperature sensor (DHT11), moisture sensor (hygrometer sensor), wireless communication model (ESP8266 WiFi Module), and motor. The proposed system accepts the different agriculture and environmental parameters using moisture sensor, PH sensor, and temperature sensor to monitor the irrigation timing of the field. The Mamdani Fuzzy model decides the watering time for the crop based on the rule base. Total 45 rules designed based on different levels of the input variables. The fuzzy model has high level of explainability because of its simple decision generation based on available rule base that increases the trust and empathy of the customer toward the proposed irrigation management system. The webpage was developed using hypertext preprocessor that can be accessed through android mobile phones that provides and interactive environment for monitoring and controlling of the systems parameters. The cotton plant requires the humidity in between 50–80% and 25–35 °C temperature in the Indian subcontinent. The moisture sensor predicts the dry and wet status of the agriculture area [17, 18]. The fuzzy rules for the irrigation management are shown in Table 1. We have constructed total 45 rules for the control of watering time for the crop based on

Humidity

Temperature

Mamdani Fuzzy System

Output Control System

Moisture

Fuzzy Rule Base Fig. 2 Fuzzy logic-based agriculture field monitoring system

Irrigation Management

Wireless Sensor Network-Based Agriculture Field Monitoring Using …

321

humidity, temperature, and moisture value. The temperature has five levels, such as very cool, cool, medium, hot, and very hot. Moisture signal splits into dry, wet, and medium. Humidity has three levels, such as high, medium, and low. The watering time (0–15 s) has five levels, such as very short, short, average, long, and very long. The analytical functions for various fuzzy input variables such as humidity, temperature and moisture considering trapezoidal and triangular membership function are given in Eqs. 1–11. 

70−x 10

HumidityMedium (x) = HumidityHigh (x) =

 x−75

 x−80 10

1

TemperatureVery Cool (x) = TemperatureCool (x) =

TemperatureHot (x) =

80 < x ≤ 90 90 ≤ x ≤ 100

(3)

1 20−x 10

7 30−x 8

7 35−x 5

 x−30 7 45−x 15

1

1 20−x 5

25 < x ≤ 30 30 ≤ x ≤ 35

30 < x ≤ 38 30 ≤ x ≤ 45

 x−40 7

0 ≤ x ≤ 10 10 ≤ x ≤ 20

15 < x ≤ 22 22 ≤ x ≤ 30

 x−25

TemperatureVery Hot (x) = 

(2)

 x−15

TemperatureMedium (x) =

(1)

65 < x ≤ 75 75 ≤ x ≤ 85

10 85−x 10



MoistureDry (x) =

50 ≤ x ≤ 60 60 ≤ x ≤ 70

1

HumidityLow (x) =

40 < x ≤ 45 45 ≤ x ≤ 55

0 ≤ x ≤ 15 15 ≤ x ≤ 20

⎧ x−15 ⎨ 10 15 ≤ x ≤ 25 MoistureModerate (x) = 1 25 ≤ x ≤ 35 ⎩ 45−x 35 ≤ x ≤ 45 10  x−40 40 < x ≤ 45 5 Moisturewet (x) = 1 45 ≤ x ≤ 55

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

The analytical equation for various output functions is given by Eqs. 12–15.  WTVery Short (x) =

1 3−x 2.5

0 ≤ x ≤ 0.5 0.5 ≤ x ≤ 3

(12)

322 Table 1 Fuzzy rules for irrigation management

A. Bade and M. Suresh Kumar Humidity

Temperature

Moisture

Watering time

Low

Very cool

Wet

Very short

Low

Cool

Wet

Short

Low

Medium

Wet

Average

Low

Hot

Wet

Long

Low

Very hot

Wet

Long

Low

Very cool

Moderate

Short

Low

Cool

Moderate

Short

Low

Medium

Moderate

Long

Low

Hot

Moderate

Long

Low

Very hot

Moderate

Very long

Low

Very cool

Dry

Short

Low

Cool

Dry

Average

Low

Medium

Dry

Long

Low

Hot

Dry

Very long

Low

Very hot

Dry

Very long

Medium

Very cool

Wet

Very short

Medium

Cool

Wet

Very short

Medium

Medium

Wet

Short

Medium

Hot

Wet

Average

Medium

Very hot

Wet

Long

Medium

Very cool

Moderate

Very short

Medium

Cool

Moderate

Short

Medium

Medium

Moderate

Average

Medium

Hot

Moderate

Average

Medium

Very hot

Moderate

Long

Medium

Very cool

Dry

Average

Medium

Cool

Dry

Average

Medium

Medium

Dry

Long

Medium

Hot

Dry

Very long

Medium

Very hot

Dry

Very long

High

Very cool

Wet

Very short

High

Cool

Wet

Very short

High

Medium

Wet

Very short

High

Hot

Wet

Short

High

Very hot

Wet

Average

High

Very cool

Moderate

Very short

High

Cool

Moderate

Short (continued)

Wireless Sensor Network-Based Agriculture Field Monitoring Using … Table 1 (continued)

323

Humidity

Temperature

Moisture

Watering time

High

Medium

Moderate

Average

High

Hot

Moderate

Average

High

Very hot

Moderate

Long

High

Very cool

Dry

Long

High

Cool

Dry

Average

High

Medium

Dry

Long

High

Hot

Dry

Very long

High

Very hot

Dry

Very long

⎧ x−0.5 ⎨ 1.5 0.5 ≤ x ≤ 2 WTShort (x) = 5−x 3.5 ≤ x ≤ 5 ⎩ 1.5 1 2 ≤ x ≤ 3.5 ⎧ x−3.5 ⎨ 2.5 3.5 ≤ x ≤ 6 WTAverage (x) = 1 6 ≤ x ≤ 8.5 ⎩ 11−x 8.5 ≤ x ≤ 11 2.5 ⎧ x−9 ⎨ 1.5 9 ≤ x ≤ 10.5 WTLong (x) = 1 10.5 ≤ x ≤ 11.5 ⎩ 13−x 11.5 ≤ x ≤ 13 2.5  x−11.5 11.5 ≤ x ≤ 13 1.5 WTVery Long (x) = 1 13 ≤ x ≤ 15

(13)

(14)

(15)

(16)

4 Simulation Results and Discussion The proposed agricultural field monitoring (cotton field) using fuzzy logic is simulated using MATLAB R2018 on the Windows environment. The membership functions for input variables and output variables are shown in Fig. 3. Table 2 provides the level, range, and membership functions details of the input and output variables used for agriculture field monitoring using fuzzy logic (Fig. 4). The performance of the system is estimated for the different random temperature, humidity, and moisture condition that results in 96.00% accuracy. Some of the sample conditions for different input conditions are shown in Figs. 5, 6 and 7.

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Fig. 3 Fuzzy membership function for a input variable humidity (H), b input variable temperature (T ), c input variable moisture (M), and d output variable watering time (WT) Table 2 Input and output variables for irrigation management Variables

Input

Level

Range

Membership function

Input variable

Humidity (H) (%)

Low

50–75

Trapezoidal

Medium

70–80

Triangular

High

75–100

Trapezoidal

Very cool

0–20

Trapezoidal

Cool

15–30

Trapezoidal

Medium

25–35

Trapezoidal

Hot

30–45

Trapezoidal

Very hot

40–55

Trapezoidal

Dry

0–25

Trapezoidal

Moderate

15–45

Trapezoidal

Wet

35–60

Trapezoidal

Very short

0–3

Trapezoidal

Short

0.5–5.1

Trapezoidal

Average

4.5–11

Trapezoidal

Long

9–13

Trapezoidal

Very long

12–15

Trapezoidal

Temperature (°)

Moisture (M)

Output variable

Watering time (WT) (s)

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325

Fig. 4 Surface plot for a humidity and temperature versus watering time, b humidity and moisture versus watering time

5 Conclusions Thus, this paper presents agriculture field monitoring using fuzzy logic algorithm based on temperature, humidity, and moisture. The fuzzy-based system provides 96.00% accuracy for the agriculture field monitoring using real-time agricultural and environmental data. The proposed method provides the explainable solution for the irrigation management to improve the trust of customer. The proposed system helps to monitor the agriculture irrigation based on multiple field parameters. The performance of the proposed system can be improved in future by considering soil nutritional parameters and crop type.

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Fig. 5 Fuzzy rule viewer for very long watering time (14 s) for input variables humidity = 57.5%, temperature = 35.8 °C, moisture = 11.9%

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Fig. 6 Fuzzy rule viewer for very short watering time (0.98 s) for input variables humidity = 95.5%, temperature = 23.5 °C, moisture = 37.7%

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Fig. 7 Fuzzy rule viewer for average watering time (7.5 s) for input variables humidity = 95.5%, temperature = 23.5 °C, moisture = 9.75%

Acknowledgements I would like to express our sincere thanks to Siddhant College of Engineering, Sudumbare, Pune for continuous support of research work. Also, I would like to express our gratitude toward Dr. Rahul Agrawal, Professor, Department of Electrical and Electronics Engineering, Sandip University, Nashik for his valuable guidance.

References 1. Madiga Bala D, Venkata NSP, Suresh R, Mude RN (2020) Agriculture, economics, ecology and trade: a way forward for better world. World Food Policy 6(2):96–104 2. Yang X, Shu L, Chen J, Ferrag MA, Wu J, Nurellari E, Huang K (2020) A survey on smart agriculture: development modes, technologies, and security and privacy challenges. IEEE/CAA J Autom Sin 8(2):273–302 3. Grace KSV, Kharim S, Sivasakthi P (2015) Wireless sensor based control system in agriculture field. In: 2015 global conference on communication technologies (GCCT), pp 823–828

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4. Kumar SA, Ilango P (2018) The impact of wireless sensor network in the field of precision agriculture: a review. Wireless Pers Commun 98(1):685–698 5. Mapari R, Bhangale K, Deshmukh L, Gode P, Gaikwad A (2022) Agriculture protection from animals using smart scarecrow system. In: Soft computing for security applications. Springer, Singapore, pp 539–551 6. Sarraf R, Ojha S, Biraris D, Bhangale KB (2020) IoT based smart quality water management system. Int J Adv Sci Res Eng Trends 5(3):12–16 7. Biradar P, Kolsure P, Khodaskar S, Bhangale KB (2020) IoT based smart bracelet for women security. Int J Res Appl Sci Eng Technol (IJRASET) 8(11):688–691 8. Ramson SRJ, Moni DJ (2017) Applications of wireless sensor networks—a survey. In: 2017 international conference on innovations in electrical, electronics, instrumentation and media technology (ICEEIMT). IEEE, pp 325–329 9. Bhanu B, Rao R, Ramesh JVN, Ali M (2014) Field monitoring and analysis using wireless sensor networks for improving crop production. In: Eleventh international conference on wireless and optical communications networks (WOCN) 10. Khan THF, Kumar DS (2020) Ambient crop field monitoring for improving context based agricultural by mobile sink in WSN. J Ambient Intell Humaniz Comput 11(4):1431–1439 11. Vijayakumar V, Balakrishnan N (2021) Artificial intelligence-based agriculture automated monitoring systems using WSN. J Ambient Intell Humaniz Comput 1–8 12. Sadowski S, Spachos P (2020) Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities. Comput Electron Agric 172:105338 13. Khelifi F (2020) Monitoring system based in wireless sensor network for precision agriculture. In: Internet of things (IoT): concepts and applications. Springer, Cham, pp 461–472 14. Barkunan SR, Bhanumathi V, Balakrishnan V (2020) Automatic irrigation system with rain fall detection in agricultural field. Measurement 156:107552 15. Cardoso J, Glória A, Sebastião P (2020) Improve irrigation timing decision for agriculture using real time data and machine learning. In: 2020 international conference on data analytics for business and industry: way towards a sustainable economy (ICDABI). IEEE, pp 1–5 16. Anguraj DK, Mandhala VN, Bhattacharyya D, Kim T (2021) Hybrid neural network classification for irrigation control in WSN based precision agriculture. J Ambient Intell Humaniz Comput 1–12 17. Alomar B, Alazzam A (2018) A smart irrigation system using IoT and fuzzy logic controller. In: 2018 fifth HCT information technology trends (ITT). IEEE 18. Abdullah N et al (2020) Towards smart agriculture monitoring using fuzzy systems. IEEE Access

Cylindrical Dielectric Resonator Antenna with a Key-Shaped Microstrip Line for 2.4 GHz Wireless Applications B. Manikandan, D. Thiripurasundari, R. Athilingam, G. Karthikeyan, and T. Venish Kumar

Abstract This work presents a small cylindrical DRA antenna with a partial ground plane fed by a key-shaped microstrip line. It is made up of a 9.8 relative permittivity resonator and a dielectric constant of 4.4 FR4 substrate. To improve the antenna’s performance, partial ground plane and feeding mechanism techniques were used. HEM11δ basic mode is transmitted through DRA via the key-shaped microstrip line connected to the antenna. DRA excited the fundamental frequency by aligning the DRA’s position, which results in more coupling. The proposed antenna has a band that goes from 2.03 to 3.06 GHz and a fractional bandwidth of 40%. The concept proposed here is ideal for Wi-Fi applications. Keywords Compact · DRA · Key-shape · Microstrip fed

1 Introduction In today’s world, wireless communication is essential. Dielectric resonator antennas could be utilized as a feasible alternative to conventional antennas. Dielectric resonator antenna (DRA) exhibits very attractive features for wireless communication like wide bandwidth (BW), high efficiency etc. DRA scores over the microstrip antenna in the term of impedance bandwidth as no conduction loss occurs in DRA due to the absence of any conducting material. Besides microstrip antenna radiates only within a specific area of patch [1] while in DRA fields are radiated from the B. Manikandan (B) · R. Athilingam · T. V. Kumar Department of Electronics and Communication Engineering, Nadar Saraswathi College of Engineering and Technology, Theni, India e-mail: [email protected] D. Thiripurasundari Department of Electronics and Communication Engineering, VIT University, Chennai, India G. Karthikeyan Department of Electronics and Instrumentation Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_31

331

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whole structure. It is possible to activate DRAs with a coaxial probe, microstrip line, microstrip slot and a coplanar wave [2, 3]. A different shape of DRA has been described, such as hemispherical, cylindrical and rectangular. Among these structures, rectangular shape DRA offers a higher degree of freedom, i.e. two, as compared to cylindrical and hemispherical shape for optimizing the physical dimension of the DRA [4]. However, due to low dielectric constant material usage, DRA has a huge physical volume. In addition to that, high permittivity leads to compact DRA [5]. Adding a metal plate to the symmetry plane of the DRA is another way to construct a miniature DRA, electric monopole in DRA, sectored cylindrical DRA, etc. are some of the methods used for designing compact DRAs while hybrid DRA, stacked DRA [6], composite shapes, notched DRAs, ring DRAs [7–9], fractal DRAs, loaded DRA, partial ground plane DRAs, etc., DRA Array [10–13] these are the techniques used for enhancing the bandwidth and performance of the DRA. On the basis of extensive simulations utilizing CST, a partial ground plane with compact DRA is proposed, which is activated by a key-shaped microstrip line. The key-shaped microstrip line excitation has been used to give better impedance bandwidth response and efficient coupling. With the partial ground plane for DRAs, bandwidth is significantly improved. There is a reasonable gain and bandwidth in the 2.4 GHz wireless frequency. Antenna design is discussed in Sect. 2 whereas antenna parameters are discussed in Sect. 3. Sections 4 detail the findings and implications of the research.

2 Geometry Figure 1 shows the proposed antenna, which has a microstrip line feed that looks like a key. Table 1 shows the specifications of the proposed antenna in terms of geometrical shape. The typical impedance of a microstrip line with a width of W = 2.7 mm and a strip thickness of t = 0.035 mm is 50 ohms. There are three things that make up FR4: its loss tangent is 0.001 and it has a relative permittivity of 4.4. It’s also 1.6 mm thick. The alumina dielectric material is placed on top of the key-shaped patch, with its dielectric constant (εr = 9.8) and loss tangent (tanδ = 0.002). There are two dimensions of dielectric material that can be used to modify an antenna’s bandwidth performance: height d = 8 mm and radius g = 12 mm.

3 Parametric Analysis Figure 2 hierarchically depicts the antenna’s evolution, and the associated return loss characteristic is depicted in Fig. 3. It all starts with antenna 1 having a microstrip line with no resonating frequency when it comes to antenna design. This means that the antenna doesn’t match up properly. In ant 2, the partial ground plane slightly changed the return loss. A single resonance frequency at 3.4 GHz was made possible

Cylindrical Dielectric Resonator Antenna with a Key-Shaped …

(a)

333

(b)

(c) Fig. 1 Schematic representation of the antenna design Table 1 The proposed antenna’s geometrical dimensions

Parameter Symbol

Parameter name

Dimension (mm)

Lg

Length of substrate

48

Wg

Width of substrate

48

hs

Height of substrate

1.6

Xj

Length partial ground

15

l1

Length of the feed

15.5

W

Width of the feed

2.7

R

Outer ring

9.7

r

Inner ring

7

t

Key formation

1.8

g

DRA radius

12

hd

DRA height

8

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B. Manikandan et al.

by this. To introduce the key-shaped formation in the ring microstrip line resulting frequency has been moved to the lower band at 3.2 GHz frequency. The microstrip line design’s key-shaped ring serves as a bandpass filter at this stage. In order to generate the 2.4 GHz Wi-Fi frequency, an antenna resonated with a cylinder DRA installed on the ground plane, as shown in Fig. 2d. DRA has been recommended to be kept on a strip line’s upper-most corner, which shifts the resonance peak’s frequency downward, leading to slow-wave effects and a rise in effective permittivity. As a result, the antenna is small and low-profile because of the partial ground plane and key-shaped microstrip line. In the y-direction, Fig. 4 shows how DRA’s position changes with regard to the centre of the microstrip line. The DRA position indicates a name as “d”. The impedance bandwidth is varied and frequency response is shifting towards the left while moving the cylindrical DRA upward. Therefore impedance bandwidth can be easily tuned for different values of “d”. A wide impedance bandwidth and good return loss is observed at the optimum value of d = 8. Figure 5 illustrates the impact of altering the proposed design’s DRA radius “g”. A drastic change occurs whiles varying the radius of the DRA. If DRA size is varying, a drastic change occurs in the return loss and slightly affects the impedance bandwidth. A good return loss is obtained at case g = 12. Figure 6 shows the significant impact of adjusting the DRA height “hd ” as return loss versus frequency curve. A drastic change occurs while varying the DRA height from the proposed antenna. Variations in DRA height result in a frequency shift to a lower frequency, minorly impacting impedance bandwidth. In case hd = 8 attains the better result in the parametric study. Figure 7a shows the key-shaped formation Fig. 2 Steps of antenna evolution a. ring antenna (Ant 1), b. partial ground with ring feed (Ant 2), c. key-shaped strip feed (Ant 3), d. key-shaped fed with DRA (Ant4)

(a)

(b)

(c)

(d)

Cylindrical Dielectric Resonator Antenna with a Key-Shaped …

335

Fig. 3 The return loss of antenna designs

Fig. 4 Effect of varying the DRA position

of feed variation on the reflection coefficient. The feed element has been modified by key-shaped fashion at t = 1, which improves return loss characteristics. If we further disturb the feedline structure, impedance matching of |S11| becomes poor and reduces the gain shown in Fig. 7a. Figure 7b shows the changing of the ring-shaped microstrip line into a key-shaped microstrip line. A significant variation occurs in |S11| while varying the microstrip line into key-shape formation. Also, impedance bandwidth and return loss improve while changing the strip line’s shape to key-shape. The proposed antenna’s final optimized key-shaped microstrip line is depicted in Fig. 7b. Figure 8 indicates the radiation pattern of 2.4 GHz frequency different at respective plane. The antenna gain

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Fig. 5 Effect of changing DRA radius

Fig. 6 Effect changing the DRA height

has shown on Fig. 9 shows that maximum gain of 6.2 dbi. The comprehensive comparison of the proposed design with the earlier design implemented is summarized in Table 2.

4 Conclusion This letter studied using a key-shaped microstrip line in conjunction with a cylindrical dielectric resonator antenna. A two-ring shape structure is modified into keyshape formation by adding half of the ring at the bottom, resulting in the 2.4 GHz

Cylindrical Dielectric Resonator Antenna with a Key-Shaped …

337

Fig. 7 a. Effect of varying the ring microstrip line into key-shape, b. Effect of changing the keyshaped

Fig. 8 Radiation pattern at 2.4 GHz, a XZ-plane b YZ- plane

frequency. The DRA position is varied to achieve a good impedance bandwidth at the desired band. A partial ground plane has been introduced to obtain the high return loss characteristics at 2.4 GHz frequency, and gain is improved. The proposed antenna operates in the 2.4 GHz frequency band, which is compatible with Wi-Fi and Bluetooth applications.

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Fig. 9 Gain of the proposed antenna

Table 2 Comparison performance of the antenna References

Dimension (mm × mm × mm)

Height of the DRA (mm)

Frequency (GHz)

Bandwidth (%)

Gain (dbi)

Feeding techniques

[5]

50 × 50 × 1.56

18

2.45

5.28

5.4

Microstrip feed

[14]

40 × 40 × 1.6

18

2.4

20.67

1.5

Meander line microstrip feed

[15]

50 × 50 × 1.6

18

2.49, 3.40

11.85,9.11

2.8

Cross-shaped slot

[16]

60 × 50 × 1.6

6.72

2.4

6.6

3.74

Coplanar waveguide

[17]

80 × 80 × 1.6

40

2.4

71

7.34

Coaxial probe

Proposed antenna

48 × 48 × 1.6

8

2.4

40

6.2

Key-shaped feed

References 1. Petosa A, Ittipiboon A (1998) Recent advances in dielectric-resonator antenna technology. IEEE Antennas Propag Mag 40(3):35–48 2. Luk KM (2003) Dielectric resonator antenna. Research Studies Pr. 3. Petosa A (2007) Dielectric resonator antenna handbook. Artech House, USA 4. Dash SKK, Khan T, Antar YM (2018) A state-of-art review on performance improvement of dielectric resonator antennas. Int J RF Microwave Comput Aided Eng 28(6):e21270 5. Makwana GD, Vinoy K (2009) Design of a compact rectangular dielectric resonator antenna at 2.4 GHz. Prog Electromagnet Res C 11:69–79 6. Zakariya MA, Shahidi MI, Sanmugan L, Witjaksono G, Nor NM, Khir MH, Khairuddin AS, Yahya N, Fadzil FA, Nor MF (2018) The development of dual band DRA hybrid antenna. In:

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International conference on intelligent and advanced system (ICIAS), pp 1–6. IEEE, Malaysia 7. Haddad A, Aoutoul M, Rais K, Faqir M, Essaaidi M, Lakssir B, Jouali R (2018) Efficient stacked cylindrical dielectric resonator antenna for anticollision short range radar at 79GHz. In: 2018 international symposium on advanced electrical and communication technologies (ISAECT), pp 1–4. IEEE, Morocco 8. Tandy A, Chauhan M, Mukherjee SG, Gupta S (2018) A compact notched chamfered RDRA with edge groundi ng for wide-band application. In: 11th international congress on engineered materials platforms for novel wave phenomena (Metamaterials), pp 343–345. IEEE, France 9. Gupta S, Kshirsagar P, Mukherjee B (2019) A low-profile multilayer cylindrical segment fractal dielectric resonator antenna: usage for wideband applications. IEEE Antennas Propag Mag 61(4):55–63 10. Mishra M, Chaudhuri S, Kshetrimayum RS, Bhunia S (2020) Highly compact DRA array using metallic grid-shaped partial ground plane. In: 2020 IEEE MTT-S international microwave workshop series on advanced materials and processes for RF and THz applications (IMWSAMP). IEEE, pp 1–3, China 11. Chen Z et al (2020) Millimeter-wave rectangular dielectric resonator antenna array with enlarged DRA dimensions, wideband capability, and high-gain performance. IEEE Trans Antennas Propag 68(4):3271–3276 12. Jovi´c S, Clénet M, Antar YM (2021) Versatile wideband annular DRA with loaded core. In: 2021 15th European conference on antennas and propagation (EuCAP), pp 1–5. IEEE, Germany 13. Laribi M, Elkarkraoui T, Hakem N (2020) Compact high-gain dra antenna array at 28 GHz. In: 2020 IEEE international symposium on antennas and propagation and North American radio science meeting. IEEE, pp 1489–1490, Canada 14. Kumar R, Kumar Chaudhary R (2017) Wideband circularly polarized dielectric resonator antenna coupled with meandered-line inductor for ISM/WLAN applications. Int J RF Microwave Comput Aided Eng 27(7):e21108 15. Kumar R, Chaudhary RK (2019) A dual-band dual-polarized cubical DRA coupled with new modified cross-shaped slot for ISM (2.4 GHz) and Wi-MAX (3.3-3.6 GHz) band applications. Int J RF Microwave Comput-Aided Eng 29(1):e21449 16. Das S, Islam H, Bose T, Gupta N (2019) Coplanar waveguide fed stacked dielectric resonator antenna on safety helmet for rescue workers. Microw Opt Technol Lett 61(2):498–502 17. Rani D, Kumar D, Mondal M, Nandi D (2020) Design of mushroom shaped dielectric resonator antenna for communication applications. In: 2020 national conference on emerging trends on sustainable technology and engineering applications (NCETSTEA). IEEE, pp. 1–4, India

A Dual Mode Quad-Band Microstrip Filter for Wireless Applications P. Ponnammal and J. Manjula

Abstract A novel quad-band bandpass filter (BPF) is proposed by employing dual mode resonators. The proposed BPF is designed to support GPS (1.575 GHz), WLAN (5 GHZ), IoT applications (3.1 GHz) and FCC unlicensed frequency band (6.6 GHz). The filter generates five transmission zeros to facilitate the enhanced isolation between the pass bands. A fixed odd mode frequency and controllable even-mode frequency response is achieved using the symmetric structure of the proposed filter. The simulation results show that there is a significant reduction in insertion loss when compared to existing results. Keywords Quad band · Transmission zero · Even and odd mode

1 Introduction Multiband bandpass filters (BPFs) with compact size are vital for incorporating different communication modes such as GPS and Wi-Fi into a single device without interference. Microstrip filter has become so popular for the mentioned application due to its features like small size, low cost, ease of fabrication and planar structure [1, 2]. Towards this direction, many results have been reported by the researchers such as dual and triple band BPFs on various methods such as ring resonator, embedded stub resonator, stub loaded resonator (SLR), step impedance resonator (SIR) and multimode resonator [3–11]. In [3], a dual band filter consisting of two open circuited stubs attached on either side of square ring resonator has been proposed. However, it is complicated to design higher-order filters since the structure is not fully symmetric. A dumbbell-shaped P. Ponnammal (B) · J. Manjula Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India e-mail: [email protected] J. Manjula e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_32

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dual band filter has been reported in [4] with SLR. The main drawback of this design is poor common mode suppression and requirement of huge circuit area. This drawback has been addressed and overcome in [5] using embedded coupled resonator. Nevertheless, the complex structure of the proposed filter will incur high cost for manufacturing. Utilizing high-temperature super conducting material, a triple band filter has been proposed in [7] for WIMAX, WLAN and GSM standards. The limitation is its large size due to usage of three single band filters to form triple band filter. The authors of [8] exploited uniform SIRs and open stub loaded SIRs to enhance the insertion loss and fractional bandwidth. Utilizing left-handed and right-handed resonators, a novel triband compact BPF has been reported in [9]. Even though the filter has good selectivity, there was poor insertion loss in it. In [10], by employing ring multimode resonators, a triband response of 1.2/2/1/3.1 GHz has been presented. The proposed filter has only four transmission zeros which resulted in poor selectivity. By employing slotted feed lines, a quad-band BPF has been reported in [11]. Manageable bandwidth and high selectivity are the key features of this design. However, losses greater than 3 dB of second and third bands are major limitation of the proposed filter. In the paper, a very simple and compact filter is designed to achieve four pass bands. The proposed filter consists of two input ports, two output ports and two symmetric SIRs. This structure achieves quad-band response at 1.57/3.1/5/6.7 GHz. This filter is designed on Duroid 5880 substrate with thickness of 0.787 mm, loss tangent of 0.0009 and a dielectric constant of εr = 2.2.

2 Geometry and Resonator Analysis of the Proposed Filter The proposed quad-band BPF is designed using RT Duroid 5880 substrate and simulated using CST microwave studio 2016. The filter contains 50Ω input and output feed lines as shown in Fig. 1. The dimensions of various stubs are listed in Table 1. Based on T-shaped dual mode resonators having length of λg /4, the proposed filter has been designed. There is one shorted stub and two open-ended stubs on each T-shaped resonator as shown in Fig. 3. The admittance of the transmission L 1 and L 2 is Y 1 and Y 2 , respectively. On A-A’ plane, the proposed design has its symmetry. Hence, even–odd mode analysis can be applied. The resonator’s odd mode can be found using the relation given below L 1 = λg /2,

(1)

√ where λg = c/ f εeff is the guided wavelength, εeff is the effective dielectric constant, and c is the velocity of light. Hence, the odd mode resonant frequency is given by f odd =

(2n + 1)c √ 2L 1 εeff

(2)

A Dual Mode Quad-Band Microstrip Filter for Wireless Applications

Fig. 1 Geometry of the proposed filter

Fig. 2 a Double mode resonator structure and b equivalent circuit

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Table 1 Dimensions of the proposed filter (in mm) L

24

L2

13.27

L5

9.36

W1

0.8

W

35

L3

11.83

L6

3

W2

0.6

L1

19.30

L4

4.65

L7

4

W3

0.3

W4

3.7

Fig. 3 Frequency response of the proposed structure

Similarly, the even-mode resonant frequency can be given as f even =

nc √ (L 1 + 2L 2 ) εeff

(3)

It is apparent from the investigation shown in Fig. 4 that even-mode frequency not only depends on L 1 , the odd mode frequency is fixed, and it is dependent only on L 1 .

3 Simulation Results The simulated S-parameters of the proposed filter are shown in Fig. 3. The designed filter exhibits four operating bands at 1.57, 3.1, 5 and 6.6 GHz. These bands find application in GPS, WLAN and FCC’s unlicensed bands. It can be noticed from Fig. 3 there is five transmission zeros (TZ). The initial pass band is centred at 1.57 GHZ for GPS having return loss of 19 dB and incurs an insertion loss (IL) of only 0.42 dB. The two TZ can be noticed neighbouring the pass band at 1.03 and 1.86 GHz. The next pass band is observed at 3.1 GHZ with the return loss of 30 dB and an IL of 0.25 dB. Two TZ can be observed at 2.06 and 3.8 GHz. The centre frequency of the third pass band is observed at 5 GHz with an IL of 0.23 dB and a return loss of

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Fig. 4 Variation of S 11 against the parameter L 1

− 35 dB. The fourth pass band is noticed at 6.6 GHz with the return loss and IL that are 43 dB and 0.24 dB, respectively. A TZ is realized at 5.6 and 7.3 GHz. The simulated surface current density is depicted in Fig. 5. It can be noticed that Fig. 5b, c and (d) exhibits the even symmetry, whereas the odd mode frequency at 1.57 GHz (Fig. 5a) is not exhibiting the symmetry of current distribution. Table 2 shows the comparison of the proposed filter performance over the existing filters reported in the literature. It can be noticed that there is a significant reduction in the insertion losses. Figure 6 depicts the surface current density at 1.57, 3.1, 5 and 6.6 GHz.

Fig. 5 Variation of S 21 against the parameter L 1

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Table 2 Performance comparison of the proposed filter with the available filters Reference

No. of operating bands

Operating band frequencies (GHz)

IL in dB

Number of TZs

Proposed

4

1.57/3.1/5/6.6

0.43/0.24/0.23/0.24

5

[7]

3

1.8/2.4/3.5

0.07/0.08/0.11

3

[10]

3

1.21/2.16/3.1

1.6/1.6/1

4

[12]

3

1.575/2.4/3.4

0.74/1.14/0.3

5

[13]

3

2.35/3.44/5.2

1.1/2.2/3

2

[12]

3

1.575/2.4/3,45

0.74/1.14/0.3

5

[14]

3

2.4/3.4/5.5

1.2/1.9/2.02

8

Fig. 6 Surface current density at a 1.57 GHz, b 3.1 GHz, c 5 GHz and d 6.6 GHz

4 Conclusion A novel quad-band BPF is presented in this paper by exploiting the double mode resonator. The proposed filter exhibits resonance at GPS and WLAN frequency bands. Through comparison, it is depicted that there is a significance reduction in insertion losses. Odd and even-mode analysis is performed and observed that the even-mode frequency can be shifted by varying the stub length. The proposed filter produces five transmission zeros which helps in isolating the pass bands. This feature implies that this filter can be suitable for incorporating in front-end circuits in wireless transceivers.

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References 1. Wei F, Yue HJ, Zhang XH, Shi XW (2019) A balanced quad-band BPF with independently controllable frequencies and high selectivity. IEEE Access 7:110316–110322 2. Yang Q, Jiao YC, Zhang Z (2018) Compact multiband bandpass Filter using low-pass filter combined with open stub-loaded shorted stub. IEEE Trans Microw Theory Techn 66(4):1926– 1938 3. Ren B, Liu H, Ma Z, Ohira M, Wen P, Wang X, Guan X (2018) Compact dual-band differential bandpass filter using quadruple-mode stepped-impedance square ring loaded resonators. IEEE Access 6:21850–21858 4. Zhou LH, Ma YL, Shi J, Chen JX, Che W (2016) Differential dual-band bandpass _lter with tunable lower band using embedded DGS unit for common-mode suppression. IEEE Trans Microw Theory Techn 64(12):4183–4191 5. Guo X, Zhu L, Wu W (2016) A dual-wideband differential filter on strip-loaded slot line resonators with enhanced coupling scheme. IEEE Microw Wireless Compon Lett 26(11):882– 884 6. Bagci F, Fernández-Prieto A, Lujambio A, Martel J, Bernal J, Medina F (2017) Compact balanced dual-band bandpass filter based on modified coupled-embedded resonators. IEEE Microw Wireless Compon Lett 27(1):31–33 7. Song F, Wei B, Zhu L, Feng Y, Wang R, Cao B (2016) A novel tri-band superconducting filter using embedded stub-loaded resonators. IEEE Trans Appl Supercond 26(8):1–9 8. Zhu C, Xu J, Kang W, Wu W (2018) Synthesis design of microstrip triple-passband dual-stop band _lter based on/4 uniform-impedance resonators. IEEE Microw Wireless Compon Lett 28(3):209–211 9. Mohan MP, Alphones A, Karim MF (2018) Triple band filter based on double periodic CRLH resonator. IEEE Microw Wireless Compon Lett 28(3):212–214 10. Li D, Wang D, Liu Y, Chen X, Wu H (2019) Compact tri-band bandpass filter based on ring multi-mode resonator. In: IEEE MTT-S Int Microw Symp Dig, pp 1–3 11. Zhang SX, Qiu LL, Chu QX (2017) Multiband balanced filters with controllable bandwidths based on slotline coupling feed. IEEE Microw Wireless Compon Lett 27(11):974–976 12. Rahman M, Park JD (2018) A compact tri-band bandpass Filter using two stub-loaded dual mode resonators. Prog Electromagnet Res M 64:201–209 13. Zhang et al S-F (2019) Design of dual-/tri-band BPF with controllable band- width based on a quintuple-mode resonator. Prog Electromagnet Res Lett 82:129–137. https://doi.org/10.2528/ PIERL18111305 14. Ma MM, Tang ZX, Cao X, Qian T (2018) Tri-band cross-coupling bandpass _lter with rectangular defected ground structure array. J Electromagnet Waves Appl 32(11):1409–1415

Integer and Fractional Order Chaotic Systems—A Review G. Gugapriya and A. Akilandeswari

Abstract This paper reviews various chaotic systems, its synchronization methods, and its real-time implementation. Recently, the chaotic phenomenon has gained its interest among various researchers. Most of the physical systems are nonlinear in nature, and for solving nonlinear equations, chaos theory outperforms other analytical methods. This review presents a survey of both integer and fractional order chaotic systems. Chaotic systems with new features are based on their equilibrium points. This review also deals with the various chaotic systems with many equilibrium points. Keywords Chaotic systems · Synchronization · Integer chaotic systems · Fractional chaotic systems · Cryptography

1 Introduction Chaos is a nonlinear phenomenon which is been under research for the last three decades. The sensitive nature of chaotic systems is generally referred as the butterfly effect. Lorenz in 1963 initially investigated the chaos phenomenon in weather models [1]. Lorenz used a computer model to run a weather prediction. He modeled the atmosphere with a set of three differential equations. The previous day he got the output as 0.50612. He ended the simulation with that output. The next day he wanted to restart his computations. He entered 0.506 instead of 0.50612 and expected the same results. But he got different results. He predicted that a small change in the initial conditions may cause a different result in the output. This particular behavior, he named it as chaos. G. Gugapriya (B) Vellore Institute of Technology, Chennai, India e-mail: [email protected] A. Akilandeswari Saveetha School of Engineering, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_33

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In this paper, review on various chaotic systems, types of synchronization, chaotic systems with new features, reviews on fractional order chaotic systems, applications of chaotic signals in cryptography, and its FPGA implementation are discussed. Section 2 gives an overview of chaos. Section 3 deals with the types of synchronization of chaotic systems. Section 4 discusses the popularly used chaotic systems. Section 5 discusses the chaotic systems having many equilibrium points, and Sect. 6 discusses the chaotic attractors with symmetry property, respectively. Section 7 deals with the chaotic systems with multi-scroll property. Section 8 gives the advantages of chaotic signals in communication field. Sections 9 and 10 discuss the integer and fractional order chaotic systems in cryptography, respectively. Finally, Sect. 11 reviews various literatures in FPGA implementation of chaotic systems.

2 Chaos—An Overview Following Lorenz, up to 2005, many chaotic systems were introduced by Rossler [2], Hindmarsh–Rose [3], Ruck-lidge [4], Chua [5], Chen [6], Sprott [7], Rikitake [8], Shimizu–Morioka [9], Lü [10], and Liu [11]. Hyperchaotic system will be having than one positive Lyapunov exponent. As hyperchaotic systems are having the characteristics like high security and efficiency, it has wide applications in neural networks, lasers, secure communications, biological systems, and so on. The hyperchaos phenomenon was first observed by Rössler [12]. Chaotic system is a nonlinear system which is having one positive Lyapunov exponent, whereas hyperchaotic system will be having more than one.

3 Synchronization of Chaotic Systems As chaotic signals depend very sensitively on the initial conditions, it will be having unpredictable features and wide band spectrum. Even though two identical autonomous chaotic systems start with the same initial points, due to butterfly effect they will be having different trajectories after some period of time. Therefore, synchronization of two chaotic systems is a very challenging research problem. Pecora and Carroll in 1990 identified the synchronization property of the chaotic systems [13]. This property may occur when more than one chaotic oscillators are coupled together or when one chaotic oscillator drives the other. Authors have used common signals to link two chaotic systems and proved that synchronization is possible when the Lyapunov exponents of the subsystems are negative. During synchronization, the trajectories of one of the system converge with that of the other. Master–slave response formalism is used in most of the synchronization approaches. One chaotic system acts as a master system, and the other acts as a slave system. The output of the master system controls the slave system till its

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output is tracked by the slave system asymptotically. As chaotic system possesses self synchronization property, these circuits are used to transmit secure messages. Pecorra and Carroll (1997) have reviewed the geometry of synchronization [14]. The authors have examined widely used coupling configurations and also have done experimental analysis to show their feasibility. The authors have also described the synchronization method using scalar chaotic coupling signals. A general mathematical framework was outlined to analyze the stability of arrays. Generalized synchronization along with data analysis techniques was also discussed by the authors. The main types of synchronization explained in the literature are as follows: Complete synchronization: This is the earliest and simple form of synchronization and is characterized by the requirement that the difference in the output of the master system and slave systems converges to zero. Anti-synchronization: When the sum of the outputs of the master system and slave system converges to zero, it is called anti-synchronization method. Hybrid synchronization: Both complete and anti-synchronization coexist together in hybrid synchronization. The difference of the odd outputs of the master and slave systems converges to zero, and the sum of the even outputs of the master system and slave system converges to zero. Let the master and slave system be described as x˙m = Axm + f (xm )

(1)

x˙s = Bxs + g(xs ) + u

(2)

where xm ∈ R n and xs ∈ R n are the states of the system, A and B are the n ×n matrix of the system parameters, and f : R n → R n and g : R n → R n be the nonlinear part of the system. u ∈ R n is the adaptive sliding mode nonlinear controller to be designed. The chaotic systems are said to be identical if A = B and f = g. If A /= B or f /= g, then the chaotic systems are said to be different. Figure 1.6. gives the schematic representation of synchronization of chaotic systems. For the complete synchronization of the master and slave systems using adaptive sliding mode control, the synchronization error is defined as e = xs − xm .

(3)

In adaptive control for complete synchronization of master and slave systems, u is designed such that lim ||e(t)|| = 0 For all e(0) ∈ R n

t→∞

(4)

For anti-synchronization of the master and slave systems using adaptive sliding mode control, the anti-synchronization error is defined as

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Fig. 1 Schematic representation of chaotic synchronization [15]

e = xs + xm .

(5)

In the anti-synchronization of symmetrical oscillators, state vectors will be having the same absolute values with opposite sign xm (t) + xs (t) → 0 as t → ∞.

(6)

In adaptive sliding mode control for anti-synchronization of master and slave systems, u is designed such that lim ||e(t)|| = 0 For all e(0) ∈ R n .

t→∞

(7)

A chaotic system having both synchronization and anti-synchronization is called hybrid synchronization. If /\ is replaced by minus sign as in Fig. 1, it is called the complete synchronization, and if it is replaced by plus sign, it is called antisynchronization. Other types of synchronization methods reported are generalized synchronization, projective synchronization, lag synchronization, phase synchronization, impulsive synchronization, adaptive synchronization, etc. Active control approach can be used, when the parameters of the master and slave systems considered for synchronization are available. When the parameters are either uncertain or unknown, adaptive control approach can be used. Adaptive sliding mode control (ASMC) technique was developed by Cheng et al. 2012 [16] to synchronize two identical chaotic systems with matched and mismatched perturbations. The authors used Lyapunov stability theorem and linear matrix inequality method to develop the technique. Tamba et al [17] studied a simple system which has only one nonlinear term [17]. It is noted that the system shows variable chaotic attractors which is rarely investigated. Adaptive synchronization scheme was implemented, and its circuit implementation showed its simplicity and effectiveness.

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3.1 Sliding Mode Control Sliding mode control is a nonlinear control technique which has high accuracy, easily tunable, and can be easily implemented. Sliding mode surface systems drive the system states into a surface called sliding surface [18]. Sliding mode control keeps the states of the system in the close neighborhood of the sliding surface. Steps involved in the design of a sliding surface are as follows: A sliding surface has to be designed first, and the next step is to select a control law which makes the switching surface gets attracted to the system. The adaptive sliding mode control approach is considered as an efficient tool to design robust controllers which operates under uncertainty conditions.

3.2 Design of the Parameter Update Law Let the parameters of both the master and slave systems are unknown. Let the vectors α and β represent the parameters of the master and slave system, respectively. Let synchronizing adaptive sliding mode controller be ˆ u(t) = u(x, y, α, ˆ β),

(8)

where αˆ and βˆ are estimates of the unknown parameter vectors α and β. Identification of unknown/uncertain parameters is mandatory for both chaotic synchronization and control applications. The identification is usually done from building parameters update laws in adaptive control theory. Lyapunov functions are used to determine the stability of the system. A quadratic Lyapunov function can be defined as follows: V =

1 2 (e + e22 + · · · + en2 + eβ21 + eβ22 + · · · + eβ2k + eα21 + · · · + eα2l ), 2 1

(9)

where α = (α1 , α2 , ..., αl ) and β = (β1 , β2 , ..., βk ). and where V is the Lyapunov function, it is differentiated along the trajectories and the parameter update law is designed such that V˙ should be a negative definite function. Therefore, the error between the master and slave systems outputs and the parameter estimation error, ˆ decays to zero exponentially. viz. eβ = β − βˆ and eα = α − α,

4 Popular Chaotic Systems The chaotic systems which are quite popular and are extensively used nowadays are as follows:

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(i) Chua’s Circuit: Leon O. Chua in 1983 introduced a simple electronic third-order circuit called as Chua’s circuit. It exhibits only one nonlinear element and exhibits a double-scroll attractor. (ii) Duffing Oscillator: This system is an example for continuous time dynamical system and is a periodically forced oscillator with a nonlinear elasticity [19]. (iii) Henon Map: Michel Henon introduced a discrete time dynamical hyper chaotic system called Henon map. It is one of the most studied nonlinear dynamical systems which is a simplified model of the Poincare section of the Lorenz model [20].

5 Chaotic Systems with Equilibrium Points Most of the discoveries of chaotic systems with new features are based on their equilibrium points. Jafari et.al proposed a three-dimensional autonomous chaotic system with one stable equilibrium with hidden attractors [21]. With the variation of the chosen parameter values, the system is found to have the coexistence of stable equilibrium point. Based on the specific choice of linear controllers and RouthHurwitz conditions, the system can be controlled to its equilibrium point. Wei [22] has developed a simple autonomous chaotic system which is having no equilibrium points. The system consists of a constant controller which will adjust the type of chaotic attractors. The authors proved that for the existence of chaos, a positive largest Lyapunov exponent and a fractal dimension are sufficient. Molaie [23] proposed a system which is having infinite number of hidden attractors including tori, limit cycles, and strange attractors. Wang [24] developed a chaotic system which is having one stable equilibrium point. The authors proved that the existence of largest positive Lyapunov exponent and a fractal dimension will lead to chaos. Barati [25] designed a class of chaotic systems with a curve equilibrium point which also contains hidden attractors. Jafari [26] developed a chaotic system which is having a plane of equilibria. Jafari [27] proposed a three-dimensional chaotic system with surfaces of equilibria where basins of attraction intersect the equilibrium surface in some sections. Therefore, it is impossible to identify the chaotic attractor nearer to the unstable equilibrium points. In other words, these attractors are hidden. Wei [28] proposed a jerk system with unstable periodic orbits. The system consists of one non-hyperbolic equilibrium point with a pair of complex conjugate eigen values and one zero eigen value. The system has no classical Hopf bifurcations. Authors have proved the existence of periodic orbits by using averaging theory. A new type of chaotic system which contains hidden attractors and having some mysterious features of chaos is discussed in literature [29].

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6 Chaotic Attractors with Symmetry Property Various chaotic systems with new features related to symmetry are discussed in many literatures. The chaotic systems are said to exhibit symmetric property if they are having strange attractors in both forward and reverse time without altering the parameter values [30, 31]. Sprott [31] described a simple three-dimensional chaotic system which is having polynomial nonlinearity and a strange attractor. These systems will be having the property of coexistence of strange attractor with tori. Field and Goubitsky [32] constructed chaotic attractors with symmetry property by reducing the equivariant complex function. They formulated chaotic systems with cyclic symmetric attractors. Recently, enormous works have been reported in cyclic symmetry systems which uses general polynomial or trigonometric functions. Brisson et al. [33] presented chaotic attractors with cube shape symmetry structure. Reiter [34] reported chaotic attractors formulating hypercube symmetry (1996) and tetrahedral shape symmetry [35]. Carter et al. [36] and Dumont [37] discussed frieze type and wall paper symmetries. All these methods are limited to polynomial-based functions. Wang et al. [38] used trigonometric function to formulate various symmetries. The authors have also reported dihedral symmetries. Various similar methods are identified to give a number of aesthetic patterns. Different types of chaotic systems with conservative and dissipative nature are discussed in [39]. In dissipative systems, attractors define the longtime bounded motion of the trajectories. When the trajectory of the dissipative system starts from an arbitrary initial condition, after some time it approaches an attractor asymptotically. The attractor may be a periodic orbit, an equilibrium point, or even a chaotic orbit. As the phase space volume is conserved in the conservative systems, the trajectory cannot reach an equilibrium point asymptotically. Therefore, nodes and spirals are not possible in conservative systems. However, saddle equilibrium points and center can occur. A family of conservative chaotic systems is discussed in [40].

7 Multi-scroll Chaotic Systems One of the interesting areas in nonlinear dynamical systems is the generation of multi-wing and multi-scroll chaotic systems [41, 42]. Dynamical systems having multi-scroll attractors present more complex dynamics than chaotic systems with mono-scroll attractors. As the chaotic system with multi-scroll attractors is more complex, it found its significance in secure communication [43]. Various methods have been proposed to generate multi-scroll attractors. A family of n-double-scroll chaotic attractors was introduced by Suykens and Vandewalle [43– 45]. The authors used quasi-linear function approach to generate chaotic attractors. Suykens and Vandewalle [46] also designed a neural network model to generate a chaotic attractor which is similar to Chua’s circuit. Li [47] introduced generalized ring

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transformation in Chua system and proposed a ring-scroll Chua system. The authors have investigated the periodic orbits in Chua attractor which are mapped with the ring-scroll Chua chaotic attractor and the topological horseshoe of the system. The authors have also implemented the ring-scroll Chua attractor by using digital signal processors. Qi [48] modified the Lorenz system and reported a three-dimensional chaotic system. The system contains a single quadratic cross-product term. The dynamic analysis of this system showed some complex dynamics. The equilibrium point of a dissipative system can be controlled to generate multiscroll attractors [49]. The equilibrium point is governed by switching control signal. According to the number of scrolls of the attractor, the control signal is changed. Therefore if two system exhibits different number of scrolls, they will be having different control signals. Yue Wang [50] reported a time-delayed hyperchaotic system for producing multi-scroll attractors. Hyperchaotic system consists of multiple positive Lyapunov exponents (LEs). Time-delayed systems are having high complex dynamical characteristics than chaotic system without time delay. When time delay is increased, number of positive LEs and number of scrolls also increase. The authors have also performed PSpice simulations to verify the numerical results. Salama [51] reported a chaotic oscillator whose nonlinearity function can be represented by a hyperbolic tangent function. The chaotic oscillator uses nonlinear transconductor to generate n-scroll chaos. The authors proved that this method was simpler than other n-scroll chaos generators. To maintain the n-scrolls, a square waveshaped transconductor was used. Kehui Sun [52] designed a continuous nonlinear function from a hyperchaotic system of order six and presented single direction and grid multi-scroll models. A generalized two-dimensional multi-scroll chaotic system is introduced in [53]. These single and two-dimensional chaotic systems are used in encryption. A state controlled cellular neural network (SC-CNN)-based model was used to generate multi-scroll attractors [54]. After having double-scroll attractors, the system generates multi-scroll attractors by adding hyperbolic tangent function. Alaoui et al. (1999) [55] demonstrated the development of dynamics and a mechanism for the fundamental routes to “multispiral chaos” and bifurcation phenomena. Multispiral chaotic attractors using Chua’s piecewise linear function approach were used for autonomous and non-autonomous differential equations. To generate a family of multi-scroll hyperchaotic systems, a circuit model was proposed by Yalcin et al. [56]. The authors discussed how simple models can be generalized to get a family of multi-scroll chaotic attractors. By using current feedback, Op-Amp (CFOA) circuit implementations were carried out. Lü et al. [57] proposed a method to develop multi-scroll chaotic attractors using saturated function series, hysteresis series method, and thresholding approach. These methods use techniques like adding break points or adding switching functions of nonlinear portions in the system to generate multi-scrolls. Presence of these functions increased the system complexity tremendously, and it is proved by various analyses. Apart from these, many other methods are also discussed in literature to generate n-scroll chaotic attractors. Stepping circuit by Yalcin et al. [58, 59], sine function method by Tang et al., [60] is suggested for the creation of n-scroll chaotic attractors.

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To generate 1D n-scroll to 3D n-scroll hyperchaotic attractors coupling Chua’s circuit method was used by Cafagna and Grassi [61]. Yu et al. [62] used adjustable triangular, saw tooth, and transconductor wave functions to create one-dimensional multi-scroll chaotic attractors. Deng (2007) [63] used stair function to create three-dimensional scroll grid attractors for fractional order systems. Deng and Lu [64] used switching control method to design a fractional form of differential equations which shows multidirectional multiscroll chaotic attractors. Chen et al. [65] implemented the fractional order chaotic system in hardware which produces multi-scroll attractors. Cang et al. [66] discussed a four-dimensional smooth quadratic autonomous chaotic system which generates a four-wing hyperchaotic attractor. When the parameters are changed, the system shows periodic orbit, chaos, and hyperchaos. For two symmetrical initial conditions, the system generates a pair of coexistent double-wing hyperchaotic attractors. Xu and Yu [67] used hyperbolic tangent function in a chaotic system to generate multi-scroll attractors and achieved chaos control and chaos synchronization. Recently, Chen et al. [68] produced grid multi-scroll chaos by using hyperbolic tangent function series and showed the successful implementation of the system using circuits. Wang et al. [69] analyzed the variable-boostable chaotic system and presented the generalized algorithm to generate multi-double-scroll chaotic attractors. Bifurcation analysis was also carried out to show the significance of parameter variation and its influence in the system. Muñoz-Pacheco et al. [70] verified experimentally the optimized multi-scroll chaotic attractors which are designed using irregular saturated function.

8 Advantages of Chaotic Signals The properties of the chaotic signals can be used in different communication fields especially in spread spectrum analysis. The advantages of the chaotic signals when used in communication field are as follows: (i)

For the transmission of broad band signal, synchronization is easy to obtain with chaotic signals. (ii) As the chaotic signals are aperiodic in nature, the long-term prediction of trajectories is impossible. Therefore, it is more difficult to develop a forecasting model for non-periodic system than for a periodic one. (iii) As the chaotic system is extremely sensitive to initial conditions and parameters, even a small change in these values will make the trajectory to diverge exponentially. This property is used in the concept of generating hardware key for secure communications. Chaotic cryptography is considered as an alternative solution to classical cryptography. Security can be achieved directly at the physical level by implementing chaotic signals [71]. Initially, the pseudorandom properties of the trajectories of the

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system are used in secure communication. Before the evolution of chaos synchronization, basic models of chaotic maps were used for cryptographic techniques [72]. The evolution of chaos synchronization has changed the approach of security in communication area. The driving system acts as transmitter, and the response system is the receiver. At the transmitter side, the message is masked with the chaotic signal and the same synchronized chaotic signal is decrypted at the receiver side. With the help of chaotic synchronization where the master system drives the slave system by its output signal, chaotic systems increase security in the communication systems. To increase security and confidentiality of data to be transmitted, cryptographic protocol is required for every communication. Chaos-based encryption method was proposed by Baptista which seems to be a better encryption method [73].

9 Integer-Order Chaotic Systems in Cryptography Cuomo et al [74] implemented the chaotic Lorenz system. The chaotic behavior of the implemented Lorenz system matches with the results from the numerical simulations. The authors have demonstrated two approaches to secure communication by using synchronized chaotic systems (SCS’s) with the Lorenz circuit in both transmitter and receiver. According to May [75], simple nonlinear systems following iterative dynamics are potential generators of complicated dynamics. This complicated dynamics is more important in encryption/decryption algorithms of cryptography. Many chaotic systems are designed recently to generate chaos for applications such as encryption, signal masking, and secure communication [76, 77]. Yang [78] explained about the advantages and challenges of first three generation schemes. The author has also analyzed the need for the fourth generation and proposed a new scheme by combining impulsive synchronization of chaotic systems and conventional cryptographic method. He used Chua’s oscillator to prove the effectiveness. For analog signal transmission, Hua et al. [79] proposed a chaotic secure communication method. The signal to be transmitted is encrypted using the chaotic system, and the information is masked into the system parameter. Applications for the masking of an information signal with chaos can be found in Lawande et al. [80]. This scheme exploited the broadband or noise-like characteristics of chaos to hide an information signal. The main drawback is that as the masking is accomplished by adding a chaotic signal to the information signal, masked as well as the chaotic masking signal has to be sent to the receiver to retrieve the information back. In order to achieve good security, the strength of the information signal should be smaller than the masking signal. An innovative chaotic encryption approach through the use of parameter modulation of a chaos system and the use of a nonlinear filter for decryption is presented [81]. The scheme can be implemented with any chaos system, and through the correct choice of the encryption parameter, the encryption/decryption system can

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withstand non-idealities in the communication link. A chaotic encryption scheme using difference equations can also be found in Papadimitriou [82]. Modulation techniques using chaos systems are presented [83]. The noise-like spectral characteristics of chaos systems have been proposed for spread spectrum and multi-user communications [84–87] which are typically suited for digital communications systems. These techniques were proposed using controlled chaotic systems in the direct creation of PN sequences for CDMA communications system. Mauricio Zapateiro et al. [88] proposed a new chaotic secure communication scheme using duffing oscillator and frequency estimation. A scaling parameter is introduced in the oscillator to improve the frequency estimator response performance. W. Liu proposed a new two-dimensional sine Iterative Chaotic Map with Infinite Collapse (ICMIC) modulation map (2D SIMM) whose chaotic performance was analyzed by its phase diagram, complexity, and Lyapunov exponent spectrum [89]. A fast image encryption algorithm was also proposed with this map. A class of chaotic systems shows different types of infinite equilibrium points were designed and the existence of special properties increases the complexity of the dynamical systems. The designed chaotic systems were used for chaos-based communication application using the differential chaos shift keying method (DCSK). The special properties support the application of such systems in chaos-based secure communication [90]. A novel hyperchaos-based image encryption algorithm was proposed by Norouzi [91]. The initial conditions of the hyperchaotic system are generated with the help of a 256 bit-long secret key by making some algebraic transformations. By combining DNA sequence operations, SHA 256 hash, and chaotic system, Chai presented a novel image encryption scheme [92]. Liu proposed an image encryption algorithm for hyperchaotic system. The proposed hyperchaotic system has bigger Lyapunov exponent than many classical hyperchaotic systems. The hyperchaotic system generates key streams to permute and substitute the image pixels [93]. Y. Zhang proved that encryption scheme with only one round diffusion process can be easily broken with chosen plaintext attacks [94]. C. Li proved that permutation operation alone is not sufficient for high level of security. It should be combined with other substitution functions [95]. Rhouma proposed two different attacks on an image based on hyperchaos. It required only three couples of plaintext to break the cryptosystem [96]. AbundizPérez presented a robust and fast fingerprint image encryption algorithm scheme by using a hyperchaotic map [97]. Mollaeefar proposed a scheme for image encryption by using two chaotic maps having high Lyapunov exponents [98]. Ahmad et al. [99] made necessary amendments in the image cryptosystem designed by Xu et al. [100] and increased the encryption strength. The experimental analysis is carried out to justify the improved performance in secure communication. An attacker can easily recover the original image from the encrypted image with the knowledge of the generated codes. Therefore instead of knowing the initial values of secret key, the attacker can have a plan to deduce the generated codes. Improved encryption process consists of two phases: pixel shuffling and pixel encoding. The

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information of the plain image depends on pixel shuffling and pixel encoding. Therefore, different sets of permutation sequence gets generated which increases the performance of the encryption process.

10 Fractional Order Chaotic Systems in Cryptography Even though the fractional calculus was introduced 300 years ago, the research on fractional order dynamical systems has been receiving increasing attention only in this millennium [101]. Many new mathematical models were introduced for fractional order chaotic oscillators which are used in the fields of science and engineering. Still majority of researchers are not aware of simulating fractional order (FO) chaotic oscillators. The fractional calculus is the generalization of the integer-order analysis. Fourier, Euler, and Laplace have contributed much more in the field of fractional calculus. Good works are recently published in the fields of biology, botany, digital circuits, ciphers, cryptography, image processing, etc. These works demonstrated that fractional order analysis provides an excellent approach to design a real phenomenon with good accuracy. With the fractional derivatives, many systems in interdisciplinary fields can be clearly described. As R-L fractional operator has initial value problem [102], Caputo fractional operator is widely used. Various fields of science and technology use fractional order models of real dynamical objects [103]. Ahmed et al [104] proposed the predator–prey and rabies model in fractional order. The authors discussed the relation between fractional mathematics and memory function. They have studied the existence and uniqueness of solutions of systems and proved the stability of fractional order equations. With the help of numerical solutions, the authors showed that the solution of integer-order analysis is a center, whereas for fractional order analysis it is stable. Zambrano-Serrano et al [105] designed a fractional order double-scroll chaotic attractor. Authors proposed a fractional order unstable dissipative system by using switching law. When the order is less than 2.568, the authors have obtained the chaotic attractors. The fractional order system is then implemented in an ARM board by using Python programming. By using fractional order chaotic attractor, authors have also proposed a random number generator. Muthukumar et al. [106] have designed a reverse butterfly-shaped fractional order chaotic attractor. For synchronizing the fractional order system, authors have designed a feedback controller and used the system in digital cryptography to obtain a secured key. By using numerical simulations, the correctness of the proposed key was validated.

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11 Real-Time Implementation of Chaotic Systems In the implementation of chaotic systems, non-optimal VHDL code is generated using automatic code generation tools [107]. Since the code is automatically generated, users need not have the knowledge of physical implementation. A novel approach for real-time implementation of Chen’s system using fourth-order Runge–Kutta method (RK-4) to solve the differential equations is discussed in literature [108]. Fixed point arithmetic representation is used as it takes minimum chip area and is simple. To design a digital system, Xilinx ISE suite design environment which includes “Xilinx system generator” (XSG) is considered as an efficient technology. VHDL code is generated from simple blocks design which can be later used to configure the target FPGA board. Previous knowledge on register transfer knowledge (RTL) design methodologies is not needed to use a system generator. Designs are captured in the Simulink modeling environment with the help of a Xilinx block set. FPGA implementation steps are performed automatically, and FPGA programming file is generated [109]. Many research works on chaos theory have been implemented with fieldprogrammable gate array (FPGA). Cuautle et al. [110] discussed the challenges of electronic device capabilities to generate multi-scroll attractors and used fieldprogrammable gate arrays (FPGAs) to generate 50 scrolls at 66 MHz. Realization as well as resource utilization and power utilizations are also shown. Yan-Xia Tang et al. [111] investigated 2D nonlinear oscillator which produces an infinite number of coexisting strange attractors. Discrete state equation is derived using forward Euler method, and to show the hardware realization, the system is implemented in field-programmable gate arrays (FPGAs). Karthikeyan et al. [112] designed a fifth-order hyperchaotic circuit with two memristors and analyzed its spice model. The design is numerically valuated using LabVIEW results. The authors have also shown the resource and power utilization. Ana Dalia Pano-Azucena et al. [113] have implemented different families of fractional order chaotic oscillators in FPGA. The authors have used Grünwald– Letnikov method to solve the mathematical models and also highlighted the short memory principle. Due to their memory dependency, fractional order systems are little bit complicated to translate into hardware. For implementing complex systems, FPGA systems are more suitable. Before implementing fractional order integrators and differentiators in hardware system quality, hardware cost and speed have to be carefully considered.

12 Conclusion Review on various integer-order chaotic and fractional order chaotic systems and their applications in cryptography are done in this paper. After analyzing the literatures, it is found that the complexity of the cryptographic algorithm can be increased by

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increasing the complexity of the chaotic system. The complexity can be increased by designing chaotic systems which can generate multi-scroll attractors and is more complex in nature. Also by using fractional order chaotic system, complexity of the system can be increased. Also the keys produced from the encryption algorithms discussed in the literature are static in nature. The algorithm complexity can be increased by producing dynamic keys.

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Machine Learning-Based Binary Regression Task of 3D Objects in Digital Holography R. N. Uma Mahesh and Anith Nelleri

Abstract A binary regression task is applied to the retrieved digital holographic phase images of 3D objects using machine learning algorithms. The machine learning algorithms considered are k-nearest-neighbor (KNN) regressor, support-vectormachine (SVM) regressor, and multi-layer-perceptron (MLP) regressor. The results are presented using the evaluation metrics like r2_score, explained_variance_score, and mean_absolute_error to show the proof of the concept. Keywords Machine learning · Regression · Digital holography · 3D objects

1 Introduction Machine learning is a subdomain in deep learning that consists of different kinds of algorithms like k-nearest-neighbor (KNN), support-vector-machine (SVM), and multi-layer-perceptron (MLP) [1]. Even though deep learning has varieties of deep neural networks, machine learning is found in a lot of applications in digital holography [2–5]. Ren et al., Xu et al. proposed the convolutional neural network (CNN) to perform regression task in digital holography for holograms of amplitude objects and phase objects [1]. Here, in this paper, the binary regression task of 3D objects is performed using machine learning algorithms like KNN regressor, SVM regressor, and MLP regressors for the retrieved 2D phase images. The retrieved phase information from a digital hologram contains the 3D object information. The KNN regressor classifies the set of points in n-dimensional space by observing the knearest-neighbors across query point where k is the value specified by the user. The SVM regressor is also a machine learning algorithm that classifies the set of points in n-dimensional space through the hyper plane. A MLP is a supervised neural network that consists of an input layer, hidden layer, and output layer. The input layer consists R. N. U. Mahesh · A. Nelleri (B) School of Electronics Engineering, Vellore Institute of Technology (VIT), Chennai, Tamilnadu 600127, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_34

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of n-number of neurons, and there can be several hidden layers with each hidden layer having a different number of neurons. Finally, the output layer also contains several neurons or a single neuron according to the problem at hand. The major difference between the proposed work and previous work [1] is that here the machine learning algorithms are used to perform binary regression task of 3D objects for retrieved 2D phase images from an off-axis digital Fresnel hologram. By applying binary regression task to 2D phase images, it is equivalent to applying binary regression task to 3D objects.

2 Methodology The machine learning algorithms considered to perform binary regression task of 3D objects for digital holographic phase images are KNN regressor, SVM regressor, and MLP regressors. KNN regressor is a machine learning algorithm that produces the output as continuous labels instead of discrete labels. KNN regressor takes the input as digital holographic 2D phase images reconstructed from the digital hologram. KNN regressor takes the input of size 1000 × 1000 from 1029 × 1029. The number of nearest-neighbors considered to get the output is (k = 1). In the same manner, the SVM regressor, MLP regressors take the input as digital holographic 2D phase images of size 1000 × 1000 reconstructed from the digital hologram. In the SVM regressor, the regularization constant considered to get the output is (c = 1). In a MLP regressor, the number of hidden layers considered to get the output is one. The activation function used in the MLP regressor is rectified linear unit (ReLU) activation function, and the MLP network is solved using Adam optimizer with the learning rate of 0.001 to get the output. The learning rate is kept constant throughout the process to generate the result.

3 Experimental Results and Discussion The five different 3D objects considered to perform binary regression task were circle-triangle, pentagon-square, rectangle-square, square-pentagon, and squarerectangle as shown in Fig. 1. The 3D object with features square in the first plane and pentagon in the second plane named ‘square-pentagon’ was separated by a distance of d = 8mm. Similarly, the same separation was given to form the remaining 3D objects with different features. The hologram of the 3D object was recorded using an off-axis digital holographic geometry scheme. In the off-axis digital holographic geometry scheme, the He–Ne laser of wave length 632.8 nm was used and the 3D object was placed at different distances like d 1 = 180 mm, d 2 = 200 mm, and d 3 = 300 mm, respectively, from the CMOS sensor. The CMOS camera with square pixel pitch of 6µm × 6µm was used to record the digital hologram of the 3D object. The size of the recorded hologram considered was 1029 × 1029. After recording the

Machine Learning-Based Binary Regression Task of 3D Objects …

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hologram of the 3D object, the numerical reconstruction algorithm was applied on the recorded hologram of each 3D object. The numerical reconstruction algorithm yields a 2D digital complex image that contains both intensity and phase information. The phase image contains the 3D information of the object. The numerical reconstruction algorithm used was a complex wave retrieval method [6]. The phase images were extracted from the 2D digital complex images and further rotated in steps of five degrees to form a dataset of 525 images, respectively. The dataset was prepared in two sets with the first set consisting of ‘circle-triangle’ and the second set consisting of ‘square-pentagon’, ‘pentagon-square’, ‘rectangle-square’, and ‘square-rectangle’, respectively. The training set consists of 336 images belonging to two sets. The validation set and test set consist of 84 images and 105 images, respectively, corresponding to two sets. The binary regression task of 3D objects for retrieved 2D phase images was performed using machine learning algorithms. For the implementation of binary regression task, the size of the phase image considered was 1029 × 1029. Figure 2 shows the 2D phase images of 3D objects used to perform binary regression task using machine learning algorithms. The KNN regressor was trained on the training set first with a batch size of 21 images. In the same manner, the SVM regressor and MLP regressor were trained on the training set first with a batch size of 21 images, respectively. The evaluation metrics like r2_score, explained_variance_score, and mean_absolute_error are calculated on the test set with a batch size of 21 images. Table 1 shows the evaluation metrics on the test set obtained from the KNN regressor. From Table 1, it can be observed that the KNN regressor has poor performance on the binary regression task of 3D objects for retrieved 2D phase images because of the negative value of r2_score. Table 2 shows the evaluation metrics on the test set obtained from the SVM regressor.

Fig. 1 3D objects used in the off-axis digital holographic geometry scheme a circle-triangle, b pentagon-square, c rectangle-square, d square-pentagon, and e square-rectangle

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Fig. 2 Retrieved 2D phase images of 3D objects a ‘circle-triangle’, b ‘pentagon-square’, c ‘rectangle-square’, d ‘square-pentagon’, and e ‘square-rectangle’

Table 1 Performance metrics of binary regression task from KNN regressor

Metric Mean absolute error r2_score Explained_variance_score

Table 2 Performance metrics of binary regression task from SVM regressor

Metric

Value 0.24 − 0.31 0.00

Value

Mean absolute error

0.34

r2_score

0.00

Explained_variance_score

0.01

From Table 2, it can be observed that the SVM regressor has constant performance on the binary regression task of 3D objects for retrieved 2D phase images because of constant values of r2_score and explained_variance_score, respectively. Table 3 shows the evaluation metrics on the test set using MLP regressor. From Table 3, it can be observed that the MLP regressor has poor performance on the binary regression task of 3D objects for retrieved 2D phase images because of the negative value of r2_score.

Machine Learning-Based Binary Regression Task of 3D Objects … Table 3 Performance metrics of binary regression task from MLP regressor

Metric Mean absolute error r2_score Explained_variance_score

371 Value 0.38 − 1.64 0.00

4 Conclusion In this paper, a binary regression task of 3D objects is carried out by applying to the retrieved 2D phase images retrieved from the digital hologram using machine learning algorithms. The evaluation metrics like r2_score, mean_absolute_error, and explained_variance_score are shown on the test set for the confirmation of the work. The SVM regressor has constant r2_score and explained_variance_score, whereas the KNN regressor and MLP regressor have a negative value of r2_score. Therefore, it can be concluded that the SVM regressor has got better regression performance on 2D phase images retrieved from the 3D objects compared to KNN regressor, MLP regressor, respectively. Acknowledgements This work was supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under the grant no. CRG/2018/003906.

References 1. Ren Z, Xu Z, Lam EY (2018) Learning-based nonparametric autofocusing for digital holography. Optica 5:337–344 2. Bianco V, Memmolo P (2020) Holographic imaging boosts machine learning for accurate microplastics recognition in seawater sample. OSA imaging and applied optics congress 2020 3. Shao S, Mallery K (2020) Machine learning holography for 3D particle field imaging. Optics Express, 2987–2998 4. Pan F, Dong B (2020) Stitching sub-aperture in digital holography based on machine learning. Optics Express, 6537–6557 5. Dubey V, Ahmad A (2019) Digital holographic microscopy and machine learning approach for the classification of inflammation in macrophages. Digital Holography and 3-D Imaging 2019, OSA 6. Liebling M, Blu T, Unser M (2004) Complex-wave retrieval from a single off-axis hologram. J Opt Soc Am A 21:367–377

Performance of the RoF Network with Multi-carrier Modulation Scheme Praveen Yadav and R. G. Sangeetha

Abstract Nowadays to fulfill the demand for a high data rate, radio-over-fiber (RoF) technology is a very good candidate. RoF technology has become the backbone of the front-end transport network for 5th generation (5G) wireless communication. Many modulation schemes are available for the radio frequency (RF) section, including orthogonal frequency division multiplexing (OFDM), a scheme that plays a better role in digital multi-carrier modulation. In this paper, we analyze the front-haul network of 5G wireless network which is based on the RoF. RF part of RoF is operated by OFDM with quadrature amplitude modulation (QAM). Using a simulation environment, we analyze the performance of OFDM and determine the Q-value to the fiber length relationship for RoF. The result shows how the Q-value varies over the fiber length for the RoF system. Also, the bit error rate (BER) performance of RoF link analyze for various fiber length. Keywords RoF · Q-value · OFDM · BER

1 Introduction Over the past two decades, the wireless communications industry has faced growing global subscriber growth and demand for broadband data transmission. The foremost objectives of the 5G network include many times more capacity to handle the traffic than fourth generation (4G) networks, gigabit service capacity to each user, latency is low, and spectral efficiency is high [1]. As the implementation of 5G networks is everywhere, the optical fiber provides a new age of growth. High frequency signal not able to cover long distances and network of next generation uses a high frequency signal for communication. To P. Yadav · R. G. Sangeetha (B) Vellore Institute of Technology (VIT), Chennai, India e-mail: [email protected] P. Yadav e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_35

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solve this problem fiber optic is only the solution. With this condition requirement of fiber optic base stations will much large for the network. Optical fiber is only efficient cable material that gives the essential high data transfer rates [2]. The leading converging wireless fiber networks rising as favorable links are radio-and-fiber (R&F) network and radio-over-fiber (RoF) network. Central gateway and radio antennas units (RAUs) are form these networks. In radio-and-fiber networks, the optical signal transmitted by central gateway via fiber to each RAUs. This optical signal is converted first into an electrical signal by RAUs. Now, as shown in Fig. 1, this electrical signal is modulated with an RF carrier and then transmits to the users. The central gateway in an RoF network first modulates the signal which contains the information by using a radio carrier signal. As shown in Fig. 2. After RF modulation an optical modulator, modulate the RF signal with an optical carrier which can be a light amplification by stimulated emission of radiation (LASER) and transmitted through the optical fiber to every RAUs. The optical signal is converted into an electrical RF signal by RAUs and sends RF signal for wireless transmission. One of the main drivers and requirements of the system for RoF link is to implement the RAUs which are uncomplicated and cost-effective [3]. For radio frequency modulation OFDM is one of the options. The key factor behind the OFDM is that since multipath not affects the low-rate modulations, the right mode is to transmit more than a few low-rate streams in parallel than transmitting one high-rate waveform. OFDM can do this exactly. The OFDM divides

Fig. 1 Structural diagram for radio-and-fiber (R&F) [3]

Fig. 2 Structural diagram for radio-over-fiber (RoF) [3]

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the frequency spectrum into small sub-bands these sub-band are enough so that the channel effects are flat (constant) over a given sub-band. After this, a conventional in-phase and Quadrature (IQ) modulation (M-QAM, Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying modulation (QPSK), etc.) is sent over the sub-band [4, 5]. BER of any electrical signal is easy to compute and demonstrate to the user with the help of a visualizer. The estimation of BER can be done by different algorithms such as Chi-Squared and Gaussian and obtain a variety of parameters from the eye diagram, like extinction ratio, eye-opening, eye-closure, eye height, Q factor, jitter, etc., [5]. The quality of the transmitted signal is also one of the factors at which receiving and transferring data rate depends. If the Q-value of the signal is high, the quality of the signal also increases. In this paper, we find the interconnection between Q-value and length of fiber for the RoF network based on the subcarrier modulation scheme [6, 7].

2 Architecture An RoF link build-up with OFDM multi-carrier modulation scheme. Quadrature amplitude modulation is used to modulate the digital information signal generated by a pseudo-random bit generator followed by a non-return to zero (NRZ) generator, which generates a sequence of return to zero pulses coded through input digital information. In phase and quadrature-phase signals are modified with the OFDM system. Now the output of the OFDM system is modulated by an optical modulator using LASER at 1550 nm (Fig. 3). The performance valuation of the RoF link where the operating frequency is 5 GHz, and the 1 Gb/s, 2 Gb/s and 5 Gb/s are the data rate. We design an RoF link using the OFDM modulation technique with the optical phase modulation schemes and examine the performance of combined RF modulation and optical modulation schemes through OptSim software. Here we use a simplified continuous wave (CW)

Fig. 3 Digital optical communication link

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laser. Value of Full Width Half Maximum (FWHM) for lorentzian emission line shape which, represents the importance of Laser phase noise, is designed by parameters. Optical phase modulator, which modifies the phase of the optical input signal depending on the electric drive voltage. The transfer function in terms of the applied voltage vs phase shift is expected to be linear. At the receiver side, a tunable Mach– Zehnder interferometer is used to demodulate the output of optical phase modulator. PIN photodiode optical signal convert into electrical signal. With appropriate Bessel low pass filter we get the information and estimate the Q-value through estimator. Samples of the signal are taken and calculate the mean and standard deviation of the samples. With the help of the mean and standard deviation of the signal, samples measured the Q-value. These samples taken at the optimum sampling instant and considering the optimum decision threshold. In the literature, the RoF network is based on arrayed waveguide (AWG) cascaded with the tunable optical transmitter. Subcarrier modulation schemes ASK, FSK and PSK are used with RF 1 GHz and 100 Mb/s. For 60 GHz RF link with 1 Gb/s data rate used optical signal sideband (OSSB) and determined the Fiber length versus Q-value. In this work OFDM with QAM is used as a subcarrier modulation scheme with 5 GHz RF, different data rates (1 Gb/s, 2 Gb/s and 5 Gb/s) without AWG and determine the Q-value versus fiber length.

3 Analysis of Simulation Results The performance of a communication channel determines by a key parameter known as Q-value. Q-value gives the SNR for RoF link and makes easier system performance analysis. The parameters of the simulation are a radio carrier frequency of 5 GHz, wavelength of an optical carrier is 1550 nm (C-band) for different data rates. Responsivity of PIN diode is set to be at 0.8 A/W. Figure 4 shows the comparison of Q performance at a data rate of 1 Gbps, 2 Gbps and 5 Gbps. As Fig. 4 shows increase in the length of optical fiber, decrease in Q-value of the optical communication link. The signal quality of the received signal for transmitted digital data is measure by BER.BER is calculated numerically by:   Q 1 BER = erfc √ 2 2

(1)

Q represents the Q-value and erfc is the complementary error function. Figure 5 shows an increase in the length of optical fiber, increase in BER of the RoF communication link. BER is lower for a data rate of 1 Gbps as compared to the BER at a data rate of 5 Gbps for the same length of the fiber. So as the rate of data increases, BER increases. An increase in the both length of the fiber and rate of data increases the BER for RoF communication link.

Performance of the RoF Network with Multi-carrier Modulation Scheme

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20 Q value [dB] at 1 Gbps Q value [dB] at 2 Gbps Q value [dB] at 5 Gbps

Q value (dB)

18 16 14 12 10 8 6

0

10

20

30

40

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70

80

Length of Fiber (km)

Fig. 4 RoF link’s Q-value performance with OFDM

BER-1 at 1Gbps BER-2 at 2Gbps BER-3 at 5Gbps

1.00E-69 1.00E-61

BER

1.00E-53 1.00E-45 1.00E-37 1.00E-29 1.00E-21 1.00E-13

0

10

20

30

40

50

60

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80

1.00E-05

Fig. 5 BER performance of RoF link with OFDM

4 Conclusion We presented an RoF system which transmits combination of QAM-OFDM RF signal and optical signal. The high data rate is today’s requirement which is achievable by combining the processing of an optical signal and electrical signal for enhancement of RoF link. The obtained results show that the Q-value performance with OFDM on the optical fiber link is satisfactory. We studied RoF links with radio frequency of 5 GHz and found the OFDM modulation scheme fitting to get a RoF link of 20–60 km. BER performance also support the RF and optical signal transmission over the RoF link. The studying of multi-carrier modulation and RoF technology became very essential because both systems have been developed to improve the new wireless link.

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References 1. Pandey G, Choudhary A, Dixit A (2021) Wavelength division multiplexed radio over fiber links for 5G Fronthaul networks. IEEE J Sel Areas Commun 8716(c) 2. Beas J, Castanon G, Aldaya I, Aragon-Zavala A, Campuzano G (2013) Millimeter-wave frequency radio over fiber systems: a survey. IEEE Commun Surv Tutorials 15(4):1593–1619 3. Dixit A (2018) Architectures and algorithms for radio-over-fiber networks. J Opt Commun Netw 10(5):535–544 4. Raddo TR, Rommel S, Monroy IT, Vagionas C, Kalfas G, Pleros N (2019) Analog radio-overfiber 5G fronthaul systems: BlueSPACE and 5G-PHOS projects convergence. In: European conference on networks and communications, EuCNC 2019, 479–484 5. Konstantinou D, Morales A, Rommel S, Raddo TR, Johannsen U, Monroy IT (2019) Analog radio over fiber Fronthaul for high bandwidth 5g millimeter-wave carrier aggregated OFDM. In: International conference on transparent optical networks, 2019–2022 6. Fu M, Zhuge Q, Liu Q, Fan Y, Zhang K, Hu W (2019) Advanced optical transmission technologies for 5G Fronthaul. In: OECC/PSC 2019—24th opto electronics and communications conference/international conference photonics in switching and computing 2019, 1(c), 1–3 7. Burdah S, Alamtaha R, Samijayani ON, Rahmatia S, Syahriar A (2019) Performance analysis of Q-factor-optical communication in free space optics and single mode fiber. Univ J Electr Electron Eng 6(3):167–175 8. Lannoo B, Dixit A, Colle D, Bauwelinck J, Dhoedt B, Jooris B, Moerman I, Pickavet M, Rogier H, Simoens P, Torfs G (2015) Radio-over-fibre for ultra-small 5G cells.:17th international conference on IEEE transparent optical networks (ICTON) 9. B. G. Kim, S. H. Bae, H. Kim, and Y. C. Chung.: Feasibility of RoF-based optical fronthaul network for next-generation mobile communications.: Opto-Electronics and Communications Conference (OECC) and Photonics Global Conference (PGC), Singapore, pp. 1–3(2017). 10. Perez-Galacho D, Sartiano D, Sales S (2019) Analog radio over fiber links for future 5G radio access networks. In: 21st international conference on transparent optical networks (ICTON), Angers, France, pp 1–4 11. Kim H (2017) RoF-based mobile Fronthaul network implemented by using directly modulated laser. In: Asia communications and photonics conference (ACP), paper Su4E.2, Guangzhou, China, pp 1–3

Performance Analysis of User Pairs in 5G Non-orthogonal Multiple Access Downlink Transmissions J. Arumiga and C. Hemanth

Abstract Non-orthogonal Multiple Access (NOMA) being the promising candidates of 5th generation mobile system can increase spectral efficiency by adding multiple users in the single resource block than the conventional Orthogonal Multiple Access system (OMA). NOMA is one of the key enabler of 5th generation mobile systems. In this paper, the performance of different user pairing schemes are analyzed and the simulated result exhibit that NOMA can offer higher sum rate over the conventional Orthogonal Multiple Access, and it can further be amplified by selection of user pairs with different channel conditions. Keywords Non-orthogonal multiple access (NOMA) · Orthogonal multiple access (OMA) · User pairs and outage probability

1 Introduction NOMA is known as one of the key enablers of 5G systems [1–4]. Power domain NOMA allocates users with different power coefficients. In NOMA, the near user does successive interference cancelation (SIC) before it decodes its own [5]. In multiple access (MA) scheme, if there is weak user with worst channel state indicated as X, a specific time slot will be allocated to this far user, so that no other user can make use of this slot. But in NOMA, a near user with a best channel state, indicated as Y, is added into this time slot. Since X channel state is worst, it will not suffer from the interference of Y, and hence the total system throughput is considerably enhanced since the base station (BS) as well as Y can receive more information.

J. Arumiga · C. Hemanth (B) VIT, Chennai, India e-mail: [email protected] J. Arumiga e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_36

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Given that many users are added in the same resource blocks, the co-channel interference will be more in NOMA system. So one solution would be is to form a hybrid MA system, where NOMA can be accompanied by any conventional MA schemes. The users can be divided into multiple groups and NOMA can be applied in every group and the orthogonal bandwidth resources are divided among the groups. How hybrid NOMA scheme performs depends purely on the users grouping. The purpose of this paper is to analyze the performance of user pairing schemes. Specifically, the focal point is on downlink communication scenario with one Base Station and multiple users ordered according to their channel conditions to the Base Station. For the Fixed Power-NOMA (F-NOMA) scheme, the sum rates achieved by F-NOMA with different pairing schemes are studied along with the conventional MA. The performance of the different user pairs depend purely on the channel conditions of the selected users.

2 NOMA With Fixed Power Allocation Our system model Fig. 1 consists of a BS and 4 mobile users where these users share the same resource blocks. The received signal is corrupted by  Gaussian  additive white noise (AWGN). The users’ channels have been ordered as h 1 |2 ≤ . . . ≤h m |2 where h m denotes Rayleigh fading channel coefficient between the mth user and the BS. We have considered a scenario in which the users are paired with different pairing strategy to perform NOMA. We have calculated the sum rate of all the three different pairing strategy and the outage probability of these pairing schemes. In this paper, the focal point is on F-NOMA, where the amount of transmit power is fixed to every users. The power allocation coefficients for the user pair are represented as am and an , where these power coefficients values are also fixed and am2 + an2 should be equal to 1. Where am ≥ an since the channel with the worst condition will be given more power. F-NOMA can be implemented when the order of users’ channel condition are known.

Fig. 1 System model of fixed power NOMA scheme

Performance Analysis of User Pairs in 5G Non-orthogonal Multiple …

The achievable rate of the users is, ⎛ ⎜ ⎜ ⎜ Rm = log⎜1 + ⎜ ⎝

381

⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠

|h m |2 am2 2 |h m | an2 +

(1)

1 ρ

Rn = log l + ρan2 |h n |2

(2)

Here ρ is the transmit Signal-to-Noise Ratio. Similarly, for any OMA scheme, take Time Division Multiple Access [TDMA], the rate is as follows: where i ε {m, n}. R1 =

1 log l + ρan2 |h n |2 2

(3)

3 Design of the Hybrid NOMA Pairing Scheme As known, NOMA can serve many users concurrently. However, when more users are added above a certain limit, there will be degradation in the sum throughput of the system. So, we cannot keep on increasing the users. One way of solving this problem is to take on Hybrid NOMA. This is a way of combining NOMA technique with either one of the Orthogonal Multiple Access technique. Let’s consider a downlink communication system with a BS and 4 users. Let d i 1, d i 2, d i 3 and d i 4 denote the distances of the users U#1, U#2, U#3 and U#4, respectively, to BS. U#1 is near user with best channel condition to the base station (considered as stronger) and U#4 is the far user from as weaker). Therefore, their channel conditions are  theBS (considered   ordered as h 1 |2 h 2 |2 h 3 |2 |h 4 |2 Here, the user pairing is done based on the channel condition in terms of their distance from the base station.

3.1 Near User-Far User Pairing (NU-FA) In this pairing scheme, the strongest user with best channel condition and weakest user with worst channel condition are paired. Similarly, subsequent strong user and penultimate user are paired. In our system model, U#1 is the strongest user (near user) and U#4 is the weakest user (far user). So, in the NU-FA User pair, are there are two pairs, U#1 and U#4 in one time slot and U#2 and U#3 in the next time slot. In the first pair, the power allocation coefficients are provided in such a way that α1 would be less than α4. So, U#1 should execute SIC, while U#4 should execute Direct Decoding [DD]. Similarly in the next pair, the power allocation coefficients

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are provided in such a way that α2 would be less than that of α3. Here, U#2 should execute SIC while U#3 should execute DD. The rates achievable by the users in the first pair are,

R1,n f = log l + ρan2 |h n |2

(4) ⎞

⎛ R4,n f

⎜ ⎜ ⎜ = log⎜1 + ⎜ ⎝

⎟ ⎟ ⎟ ⎟ ⎟ ⎠

|h m |2 am2 2 |h m | an2 +

(5)

1 ρ

Similarly, for the next pair,

R2,n f = log l + ρan2 |h n |2

(6) ⎞

⎛ R3,n f

⎜ ⎜ ⎜ = log⎜1 + ⎜ ⎝

⎟ ⎟ ⎟ ⎟ ⎟ ⎠

|h m |2 am2 2 |h m | an2 +

(7)

1 ρ

The sum rate of the NU-FA scheme will be the aggregate of all the rates together.

3.2 Near User-Near User, Far User-Far User pairing (NU-NU, FA-FA) In this pairing scheme, U#1(strongest) and U#2 (2nd strongest) are the first pair. U#3(weakest) and U#4(2nd weakest) are the second pair. In the first pair, α1 would be less than α2. U#1 should execute SIC while, U#2 will execute DD. In the second pair, α3 would be less than α4. U#3 should execute SIC, while U#4 should execute DD. The rates achievable by the users in the first Near User-Near User and Far User-Far User pair are,

R1,n f = log l + ρan2 |h n |2

(8)

Performance Analysis of User Pairs in 5G Non-orthogonal Multiple …

⎞

⎛ R2,n f

⎜ ⎜ ⎜ = log⎜1 + ⎜ ⎝

383

⎟ ⎟ ⎟ ⎟ ⎟ ⎠

|h m |2 am2 2 |h m | an2 +

(9)

1 ρ

In similar fashion, the achievable rates for the next pair are,

R3,n f = log l + ρan2 |h n |2

(10) ⎞

⎛ R4,n f

⎜ ⎜ ⎜ = log⎜1 + ⎜ ⎝

⎟ ⎟ ⎟ ⎟ ⎟ ⎠

|h m |2 am2 2 |h m | an2 +

(11)

1 ρ

The sum rate of the Near User-Near User-Far User-Far User scheme will be the aggregate of all the rates together.

3.3 Alternate Near User–Far User (A NU-FA) Pairing One of the other ways to pair users is to pair the strongest user with the second weakest user and the second strongest user with the weakest. In our case, the first pairs has U#1 and U#3 and second pair has U#2 and U#4. In the first set, α1 is less than α3. U#1 should perform SIC while U#3 would perform DD. For the second set, α2 would be less than that of α4. User#2 should execute SIC while U#4 should execute DD. The rates achievable by the users in the first Alternate Near User–Far User (A NU-FA) pair are,

R1,n f = log l + ρan2 |h n |2

(12) ⎞

⎛ R3,n f

⎜ ⎜ ⎜ = log⎜1 + ⎜ ⎝

⎟ ⎟ ⎟ ⎟ ⎟ ⎠

|h m |2 am2 2 |h m | an2 +

(13)

1 ρ

In similar fashion the achievable rates for the next pair are,

R2,n f = log l + ρan2 |h n |2

(14)

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⎞

⎛ R4,n f

⎜ ⎜ ⎜ = log⎜1 + ⎜ ⎝

⎟ ⎟ ⎟ ⎟ ⎟ ⎠

|h m |2 am2 2 |h m | an2 +

(15)

1 ρ

4 Simulation Results In this section, matlab simulations are used to compare and calculate how well the hybrid user pairing schemes perform. We have considered a downlink system with 4 users and the sum rate is calculated for the three different pairing schemes along with Single Carrier NOMA (SC-NOMA) and TDMA. And finally Outage Performance of the three different pairing schemes has been evaluated. In Fig. 2, we can infer that when NU-FA user pairing method is used; a higher sum rate is achieved. We also know NOMA perform well, when the user pairs are of different channel conditions. When NU-NU-FA-FA pairing, NOMA exhibits higher sum rate than Time Division Multiple Access, but the performance is not that distinct. The alternate pairing, where the second farthest user is paired with the second nearest performs similar with the NU-FA pairing, so a higher sum rate is achieved. The performance of SC-NOMA is not good when compared to TDMA, because, squeezing all the users into the same carrier creates interference among the users. While coming to outage probabilities, the inherent differences in the channel gains are exploited to aid the power domain multiplexing. In Fig. 3 both the users experience lesser outage for the pair (1, 4) and for the (2, 3) pair, the outage probability is high for both the users. This confirms the reasoning that we made before. That is, NOMA gives superior performance when the user pairs are of different channel conditions. In Fig. 4 both the users experience higher outage for the pair (1, 2) since both the users are far from the BS and for the (3, 4) pair, the outage probability is still high for both the users. In Fig. 5, both the users experience higher outage for the pair (1, 3) and for the (2, 4) pair, the outage probability is still high for both the users.

5 Conclusion The Performance analysis of user pairing in NOMA with F-NOMA has been analyzed in this paper. For F-NOMA, numerical studies shows that it can provide higher sum rate over TDMA and user pairs with most different channel states perform well. Similarly, in outage probabilities, the inherent differences in the channel gains are

Performance Analysis of User Pairs in 5G Non-orthogonal Multiple …

Fig. 2 Sum rate with signal-noise ratio for different user pairing

Fig. 3 Outage probability of near-far (NU-FA) user pair

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Fig. 4 Outage probability of near-near far-far (NU NU-FA FA) user pair

Fig. 5 Outage probability of alternate near-far (A NU-FA) pairing

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exploited to aid the power domain multiplexing and NOMA gives superior performance than OMA when pairing users from set of users with most different channel conditions.

References 1. Saito Y, Benjebbour A, Kishiyama Y, Nakamura T (2013) System level performance evaluation of downlink non-orthogonal multiple access (NOMA). In: Proceedings IEEE annual symposium on personal, indoor and mobile radio communications (PIMRC), London, UK 2. Al-Imari M, Xiao P, Imran MA, Tafazolli R (2014) Uplink non orthogonal multiple access for 5G wireless networks. In: Proceedings 11th international symposium on wireless communications systems (ISWCS), Barcelona, Spain, pp 781–785 3. Ding Z, Yang Z, Fan P, Poor HV (2014) On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users. IEEE Signal Process Lett 21(12):1501–1505 4. Choi J (2014) Non-orthogonal multiple access in downlink coordinated two point systems. IEEE Commun Letters 18(2):313–316 5. Cover T, Thomas J (1991) Elements of information theory, 6th edn. Wiley and Sons, New York

Image Encryption Based on Watermarking and Chaotic Masks Using SVD R. Girija , S. L. Jayalakshmi , and R. Vedhapriyavadhana

Abstract In an era of large information and systems administration, it is an important to create a safe and hearty computerized watermarking plan with high computational productivity to secure copyrights of advanced works. Nonetheless, a large portion of the current strategies centre on strength and implanting limits, dismissing security or requiring huge computational assets in the encryption cycle. It’s very crucial for any user to protect not only the data, but also images, audio and video files. Protection of the data is equally important to protect all the other files. Even though, there are numerous encryption and decryption algorithms, attacking the cryptosystem are rapidly increasing in the same frequency. This paper proposes a cryptosystem for images which also includes computerized picture watermarking model dependent on scrambling calculation, bit plane slicing and compression. In the place of random phase masks in double random phase masks (DRPE), chaotic masks have been generated. DRPE in chaotic phase masks embedded with watermarking and SVD could be a great support for image encryption and decryption systems. It has been proposed in fractional Fourier transforms. It has been performed to guarantee the safety of the top-secret data at the establishment of enormous installing limit, great strength and high computational efficiency. For encryption and decryption of images, cryptosystem has been proposed which uses the watermarking and singular value decomposition. Many analyses have been conducted to show the robustness of the system. Keywords Watermarking · Singular value decomposition · Chaotic masks R. Girija (B) Manav Rachna University, Faridabad, India e-mail: [email protected] S. L. Jayalakshmi Pondicherry University (Main Campus), Puducherry, India e-mail: [email protected] R. Vedhapriyavadhana Vellore Institute of Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_37

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1 Introduction In today’s world, protecting data is a very challenging task. Not only the data, protecting the audio, video and images also. Various encryption and decryption algorithms are available worldwide attackers are coming with daily new attacking techniques. The Internet had become everyone’s primary need a few years ago. It is an incredibly simple and fast method of moving and accessing data and knowledge all over the world. As far as automated data goes, this data is crucial (content, pictures, sound, video). Everyone makes use of the Internet, whether for personal or professional reasons. As a result, it is important to protect consumer data from unauthorized access. At the time, copyright protection implied an unapproved singular case in which he recreated data. Watermarking is portrayed as a means of making data accessible on the Internet or apart from the modifying process, and it often examines unapproved access to the data. The high-level checking technique is used when modernized data is combined with watermarks. The hidden data is a watermark, and the comparable is embedded with other types of data such as sound, video, or even substance, which can be used to introduce [1]. There are two types of progressed watermarking: visible and imperceptible watermarking. The possibility of steganography is particularly associated with cutting-edge watermarking [2]. Steganography is defined as a form of getting creating in which basic printed data is secretly stored using other media and a high level. For putting a stop to this problem to secure data from unauthorized copies or movement, a digital watermarking scheme is used. It’s a way of hiding data in cutting-edge data so that unapproved individuals can’t view or copy it for their own purposes. A watermark is a piece of data that is embedded in cutting-edge media. It is a piece of information associated with the data (any name, references, creator name, or id). In [3] in the year 1995, projected the DRPE method, numerous good-looking methods have been proposed in past periods in more than a few extents such as exclusive rights protection, watermarking and Quick Response code etc., [2–9]. Various domains such as Fresnel transform (FrT) [10–12], fractional Fourier transform (FrFT) [16–18], Gyrator transform (GT) [13–15] and Fractional Hartley transforms (FrHT) [19]. To intensify the safety and arbitrariness, chaotic phase masks [17] remain fashioned founded on the logistic map, Fresnel zone plates (FZP) [18, 19] and radial Hilbert mask (RH) [18, 19].

2 Mathematical Contextual 2.1 CSPM DRPE uses traditional phase masks. These random phase masks (RPM) be situated correspondingly erected as of divergent frenzied maps. Chaos utility describes from organized form to disorganized form. Chaotic map engenders huge amount of

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haphazard continuous standards and nature of these standards are non-correlation, statistically random numbers and point of moving systems. The simplistic (1D) non-linear chaos purpose is named as logistic chart and it is distinct as, f (x) = p · x · (1 − x)

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where p is divergence parameter 0 < p < 4. Equation (1) is repeated and it is designated as, xn+1 = p · xn · (1 − xn )

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where xi ∈ (0, 1). Reshuffle the arrangement X as Y, by erecting as smooth matrix and it is epitomized in Eq. (4), Y = {yi, j , |i = 1, 2, . . . , M; j = 1, 2, . . . , N }

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(6)

where r, f and λ is the radius, focal length and wavelength, respectively. The structured phase mask (SPM) produced for restrictions r = 3 mm, f = 40 mm and λ = 632.8 nm and it is portrayed in Fig. 1b. In order to make the limits of an copy better, radial Hilbert function is expressed as, RH(r, θ ) = exp(imθ )

(7)

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Fig. 1 a CRPM b SPM based on FZP c SPM based on RHM d SPM

  where r, θ are log polar coordinates; r 2 = x 2 + y 2 and θ = tan−1 xy . m is the order of transformation called as topological charge. Figure 1c shows the topological charge m = 5. HSPM(x, y) = FZP(r ) × RH(r, θ )

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HSPM is shown in Fig. 1d. f, λ and m are measured as decryption secrets. Lastly, the CSPM is twisted by with Eqs. (5)–(7) as keep an eye on, CSPM(x, y) = exp{i{arg{CRPM(x, y)} × arg{FZP(r )} × arg{RH(r, θ )}}}

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where arg{.} represents stage operation. CSPM covers frequent security limits and it is shown in Fig. 1e.

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2.2 Fractional Fourier Transforms The FrFT is a generalization of usual FT and is separate in footings of the fractional imperative p. It is an powerful and commanding instrument for indication, significant mechanism, time-variant sifting, multiplexing and visual info dispensation. It can be exactly applied in optics with a solo lens by altering the detachments accomplishment the Fourier transform. WatermarkingI in fractional-order area delivers additional safety in contradiction of incidences since slight instructions of transform deliver encryption restrictions. +∞ F { f (x)}(u) = K p (x, u) f (x).dx p

(10)

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where the kernel function K p (x, u) is denoted as, ⎧ ⎨ A exp[iπ(x 2 cot ϕ − 2xu csc ϕ + u 2 cot ϕ)], α /= nπ ; K p (x, u) = δ(x − u), α = 2nπ ; ⎩ δ(x + u), α = (2n + 1)π ;

 exp −i π sgn(ϕ) − ϕ2 4 (11) A= √ | sin ϕ|

2.3 Singular Value Decomposition The SVD method is used to mathematically separate the estimations. The organizations are subdivided into three sub-cross sections using SVD, and the recovered lattices are the same size as the primary matrices. An image is described as a collection of non-negative scalar segments that can be considered the organization, according to the possibility of straight factor-based math (Fig. 2).

3 Proposed Module Proposed cryptosystem consists of two steps: (1) Encryption process and (2) Decryption process. The pictorial representation for the proposed module is given in Fig. 3.

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Fig. 2 SVD diagram

Fig. 3 Encryption process of the proposed module

Steps of the encryption process are listed below: 1. Let the watermark image of the cryptosystem is I (x, y). This input image is multiplied to chaotic structured phase masks CSPM1. The equation of the step 1 is indicated below: w(ζ, η) = I (x, y). ∗ CSPM1

(12)

where I (x, y) is an input image and CSPM1 is the first chaotic phase masks. 2. The product is applied to the fractional Fourier transform using two security parameters (α, β).

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w(ζ, η) = frft((α, β)[w(ζ, η)])

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frft(α, β) Represents the fractional Fourier transform with two security parameters. 3. The transformed output is getting multiplied with another CSPM2. Both the phase masks are statistically independent of each other. w(ζ, η) = w(ζ, η). ∗ CSPM2

(14)

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(15)

5. The resultant must be added with the host image h(ζ, η). The obtaining image is watermarked image w(ζ, η). w(ζ, η) = w(ζ, η) + h(ζ, η)

(16)

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(17)

Steps of the decryption process are listed below: I (x, y) = ISVD'{frft((α, β){[w(ζ, η)]. ∗ CSPM2}. ∗ CSPM1}

(18)

4 Simulation Results In order to assess the safety of our scheme, numerous simulations are completed for Grey scale image of Lena (256 × 256) pixels in MATLAB 2019. It is shown in the Fig. 4.

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Fig. 4 Encryption result: a input image b host image c CSPM d encrypted image e U component f S Component g V component

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1. Performance analysis [20–23] The MSE and RE checked for the proposed cryptosystem. MSE is the mean square error and it is mentioned in the Eq. (19). MSE =

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The RE for our proposed system is 1.5333 × 10−26 where I (x, y) and D(x, y) are original image and decrypted image, respectively.

5 Occlusion Analysis Some portions of the masks are occluded and made the analysis. The corresponding result is shown in Fig. 5, [20–23]. Figure a, c and e represents 15, 25 and 75% occlusion. The corresponding results are denoted in Figure b, d and f.

6 Conclusion The proposed symmetric cryptosystem uses the watermarked image and multiplied with chaotic random phase masks with respect to the fractional Fourier transforms (using two security parameters). It also getting multiplied with another chaotic phase masks and also getting multiplied with fractional Fourier transform (two security parameters). The resultant added with host image and applied to SVD. SVD is the one which delivers three components. Many analyses made for this proposed work to show the system is secure.

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Fig. 5 Occlusion analysis

References 1. Javidi B (2005) (Ed.) Optical and digital techniques for information security. Springer Sci Bus Media 2. Cai L-Z, He M-Z, Liu Q et al (2004) Digital image encryption and watermarking by phaseshifting interferometry. Appl Opt 43:3078–3308 3. Kishk S, Javidi B (2002) Information hiding technique with double phase encoding. Appl Opt

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41:5462–5470 4. Matoba O, Nomura T, Perez-Cabre E, Millan MS, Javidi B (2009) Optical techniques for information security. Proc IEEE 97:1128–1148 5. Liu S, Guo C, Sheridan JT (2014) A review of optical image encryption techniques. Opt Laser Technol 57:327–342 6. Chen W, Javidi B, Chen X (2014) Advances in optical security systems. Adv Opt Photonics 6:120–155 7. Kumar P, Joseph J, Singh K (2016) Double random phase encoding based optical encryption systems using some linear canonical transforms: weaknesses and countermeasures. In: Linear canonical transforms, Springer New York, pp 367–396 8. Chen J, Zhang Y, Li J, Zhang LB (2018) Security enhancement of double random phase encoding using rear-mounted phase masking. Opt Lasers Eng 101:51–59 9. Refregier P, Javidi B (1995) Optical image encryption based on input plane and Fourier plane random encoding. Opt Lett 20:767–769 10. Situ G, Zhang J (2004) Double random-phase encoding in the Fresnel domain. Opt Lett 29(14):1584–1586 11. Rajput SK, Nishchal NK (2013) Image encryption using polarized light encoding and amplitude and phase truncation in the Fresnel domain. Appl Opt 52:4343–4352 12. Singh H, Yadav AK, Vashisth S, Singh K (2015) Optical image encryption using devil’s vortex toroidal lens in the Fresnel transform domain. Int J Opt 13. Rodrigo JA, Alieva T, Calvo ML (2007) Applications of gyrator transform for image processing. Opt Commun 278:279–284 14. Singh H, Yadav AK, Vashisth S, Singh K (2015) Double phase-image encryption using gyrator transforms, and structured phase mask in the frequency plane. Opt Lasers Eng 67:145–156 15. Girija R, Singh H (2018) A cryptosystem based on deterministic phase masks and fractional fourier transform deploying singular value decomposition. Opt Quant Electron 50(5):1–24 16. Unnikrishnan G, Singh K (2000) Double random fractional fourier-domain encoding for optical security. Opt Eng 39:2853–2859 17. Unnikrishnan G, Joseph J, Singh K (2000) Optical encryption by double-random phase encoding in the fractional fourier domain. Opt lett 25:887–889 18. Girija R, Singh H (2020) An asymmetric cryptosystem based on the random weighted singular value decomposition and fractional Hartley domain. Multimedia Tools Appl 79(47):34717– 34735 19. Girija R, Singh H (2018) Symmetric cryptosystem based on chaos structured phase masks and equal modulus decomposition using fractional fourier transform. 3D Research 9(3):1–20 20. Girija R, Singh H (2018). Enhancing security of double random phase encoding based on random S-Box. 3D Research 9(2):1–20 21. Girija R, Singh H (2017, October) A new substitution-permutation network cipher using Walsh Hadamard transform. In: 2017 International conference on computing and communication technologies for smart nation (IC3TSN), IEEE, pp 168–172 22. Girija R, Singh H (2019) Triple-level cryptosystem using deterministic masks and modified Gerchberg-Saxton iterative algorithm in fractional Hartley domain by positioning singular value decomposition. Optik 187:238–257 23. Girija R, Singh H (2021) Security-enhanced optical nonlinear cryptosystem based on modified Gerchberg-Saxton iterative algorithm. Optik 244:167568

Complementary Planar Inverted-L Antennas (PILAs) for Metal-Mountable Omnidirectional RFID Tag Design Jiun-Ian Tan, Eng-Hock Lim, Yong-Hong Lee, and Kim-Yee Lee

Abstract A new miniature omnidirectional tag antenna, which is designed by using two complementary planar inverted-L antennas (PILAs), is proposed for RFID metallic objects tagging applications. The two PILAs, which are placed in rotational symmetrical style, are complementing each other to exhibit a steady omnidirectional radiation pattern with a gain variation of not more than 0.6 dBi. Here, a simple feeding mechanism is employed to excite both the PILAs simultaneously. As such, additional circuitry such as Wilkinson power divider is not required. Despite having a miniature size of 35 × 35 × 3.2 mm3 , the proposed omnidirectional antenna can obtain a high realized gain of − 4.4 dBi in the entire directions in the azimuth plane while it is attached on a conductive surface. In addition, the tag antenna does not require any additional matching circuit as the antenna input impedance could be simply modified by varying the dimensions of the circular loop, shorting stubs, and notches of the PILAs. Keywords Omnidirectional · Planar Inverted-L Antenna (PILA) · Metal-Mountable

1 Introduction The Radio Frequency Identification (RFID) is one of the contactless communication technologies that incorporates the use of electromagnetic waves to track objects automatically. The ultrahigh frequency (UHF) band, typically ranges from 860 to J.-I. Tan · E.-H. Lim (B) · Y.-H. Lee · K.-Y. Lee Department of Electrical and Electronic Engineering, Universiti Tunku Abdul Rahman, Selangor, Malaysia e-mail: [email protected] Y.-H. Lee e-mail: [email protected] K.-Y. Lee e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_38

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960 MHz, is a widely used RFID spectrum. This is because it can provide a faster data transfer rate and a higher antenna gain than other designated bands that operate at a much lower frequency. Currently, the UHF RFID technology has a high degree of commercial usage, and it can be easily found in many industrial applications such as warehouse management, transportation, animal tracking, patient monitoring, and access control [1]. Tag antenna plays a crucial role in a typical RFID system. It is used by the tag to communicate with the RFID reader using radio frequency waves. As such, tag antenna needs to have high gain, miniature size, wide coverage area, and insensitive to backing platform [2]. In [3], multiple metallic vias are used to short patches to their ground plane for achieving a miniature size of 26 × 14 × 1.6 mm. Besides, vias can also be used for increasing the antenna bandwidth or varying antenna impedance, as demonstrated in [4]. However, employing vias in tag design can increase the complexity of the assembling process and, therefore, they are not economical for mass production. Alternatively, meandered feedline can also be employed to design a miniature tag antenna for metal-mountable applications [5]. However, this can enhance the current crowding effect in the tag antenna and, thus, result in a poor read distance. Although the tag antennas in [3–5] can perform stably on metal, they are not omnidirectional antennas. Therefore, they are not suitable for applications that require a full 360° spatial coverage. Modified dipole and monopole tag antennas presented in [6, 7] can exhibit a stable omnidirectional radiation pattern when they are being tested in the free space environment. However, when they are placed on a conductive platform, they lose their omnidirectional characteristics immediately and become directional antennas. Currently, there are very few omnidirectional tags that can be used for on-metal applications and their read ranges are usually not more than 7 m [8, 9]. In this paper, a new miniature omnidirectional antenna is designed for the metal-mountable UHF RFID tagging applications. This can be realized by tactfully combining two identical PILAs, where each of them can be functioning as a directive antenna on its own, into rotational symmetrical style for producing a stable radiation in the entire directions in the azimuth plane. The two PILAs are simultaneously excited by a circular feeding loop at the center of the tag so that the radiation of both the PILAs can complement each other to produce an omnidirectional radiation pattern with a gain variation of not more than 0.6 dBi. Apart from functioning as a feeding mechanism, the circular loop can also be used to provide additional inductive reactance for enhancing the impedance matching level. This paper is arranged as follows. The antenna configuration is illustrated and explained in Sect. 2. Next, the characterization of the proposed tag antenna is performed and the results are discussed in Sect. 3. In the last section, a conclusion is drawn.

2 Antenna Configuration Figure 1 shows the proposed omnidirectional tag antenna’s configuration. Referring to Fig. 1, the antenna comprises of two conductive layers: two identical PILAs which

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are fed by a circular loop on the top surface and a full ground plane on the bottom surface. In between, a piece of flexible polyethylene foam substrate [10] is inserted so that it can provide structural support to the tag antenna. The relative permittivity and loss tangent of the foam are εr = 1.06 and tan δ = 0.0001, respectively. In the design, there are thin polyethylene terephthalate (PET) layers between the conductive layers and the foam substrate. These thin PET layers are the substrates of the flexible printed circuit board. Referring to Fig. 1 again, an inductive shorting stub (s), which is formed when a long notch (wn ) is etched on the top conductive surface, is connecting each of the patches to the ground plane. It should be mentioned that the notches are deliberately etched in the opposite direction so that the PILAs can be realized in a rotational symmetrical style for achieving good omnidirectionality. It will be shown later that the incorporated notches and shorting stubs are also working as an effective impedance tuning mechanism for this design. A simple circular loop at the center, which is used to excite both the PILAs simultaneously, has an RFID microchip [11] bonded on it. As the loop is made of long concentric microstrip line, it can provide sufficient inductive reactance to the input impedance for enhancing the impedance matching between the proposed omnidirectional tag and the RFID chip.

3 Results and Discussion The proposed omnidirectional tag antenna is optimized by using the CST simulation software. The proposed omnidirectional tag is situated at the center of a piece of 200 × 200 × 10 mm3 aluminum plate during the simulation process because the proposed omnidirectional antenna is devised for metal-mountable applications. The

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optimization process is conducted by using the genetic algorithm and the optimized values are found to be la = 35.0 mm, wa = 35.0 mm, h = 3.2 mm, wp = 17.25 mm, r f = 5.4 mm, wf = 0.8 mm, ln = 5.5 mm, wn = 0.5 mm, g = 0.2 mm, and s = 4.0 mm.

3.1 Simulated Results The 3-D radiation pattern of the proposed omnidirectional tag is simulated and shown in Fig. 2. It can be found that the proposed omnidirectional tag can exhibit a steady omnidirectional radiation pattern with a maximum realized gain of − 4.4 dBi in all directions at the resonant frequency of 0.921 GHz. This is also equivalent to a read range of 10.9 m if estimated using the Friis transmission equation [12] and a reader power of 4 W Effective Isotropic Radiated Power (EIRP). In Fig. 3a, a constant realized gain, with gain fluctuation of not more than 0.6 dBi, is detected in the azimuth plane. While in Fig. 3b, the xz and yz planes show doughnut-shaped radiation patterns with nulls points situated at θ = 0° and θ = 180°. This is similar to the radiation pattern of a vertical dipole. However, due to the presence of the metal platform underneath, a maximum realized gain of ~ − 4.4 dBi is shifted upward to the elevated angles of θ = ± 55°. The surface current distribution at 0.921 GHz is also simulated and shown in Fig. 4. The notches, shorting stubs, and circular loop of the omnidirectional tag are

Fig. 2 3-D radiation pattern of the omnidirectional antenna at the resonance

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having high current densities. It shows that the notches’ width (wn ), stubs’ width (s), and dimension of the circular loop (r f and wf ) can be utilized for adjusting the tag’s input impedance and resonant frequency. The electric and magnetic field distributions of the tag are also simulated and shown in Fig. 5. Referring to Fig. 5a, the electric field shows that the tag is radiating vertically polarized waves in all directions in the azimuth plane. Whereas in Fig. 5b, magnetic field distributions are found to be stronger at the circular loop, notches, and stubs of the PILAs, which can be attributed to the high surface current densities in those regions. The field distributions in Fig. 5 also indicates that both PILAs are excited in-phase by the circular loop. The input impedance, Z in and reflection coefficient, S 11 of the omnidirectional tag antenna are displayed in Fig. 6. As plotted in Fig. 6a, the tag input impedance (6.47 + j192.07 Ω) is almost conjugately matched with the microchip impedance (13−j191 Ω) at the resonance. The reflection coefficient vs frequency plot is given in Fig. 6b. Result shows that the tag is resonating at 0.921 GHz with achievable power transmission coefficient of 88.5%.

3.2 Parametric Analysis Subsequently, a parametric analysis is conducted to identify the effects of the important design parameters. The effects of varying the radius (r f ) and width (wf ) of the circular loop are first discussed and the changes in the antenna impedance are plotted in Fig. 7a and b, respectively. Increasing r f or reducing wf can cause the antenna to become more inductive. These parameters are able to introduce reactance tuning rates of 282 Ω/mm and 116 Ω/mm, respectively. Besides, increasing r f can also tune

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Fig. 4 Surface current distribution of the omnidirectional antenna at the resonance

Fig. 5 a Electric field and b magnetic field distributions of the omnidirectional antenna at the resonance

down the tag antenna’s resonant frequency at a rate of ∂f/∂r f = 47.25 MHz/mm. It shows that r f is suitable for coarse-tuning purposes. From the simulation, it is found that r f can also be used to improve the matching level and, thus, the power transmission coefficient.

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4 Conclusion A new type of miniature omnidirectional RFID tag has been designed for metalmountable tagging applications. When the omnidirectional tag is situated on the conductive surface, it can generate a steady omnidirectional radiation pattern, with a maximum realized gain of − 4.4 dBi found at the elevated plane at θ = 55°. Positioning the PILAs into rotational symmetry configuration allows them to complement each other for achieving good omnidirectional characteristics. As a result, the gain variation in the azimuth plane is capped below 0.6 dBi. This is practically useful when it comes to the applications that require 360° coverage. The dimension of the circular loop, shorting stubs, and notches of the PILAs can be varied to fine-tune the antenna’s input impedance.

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References 1. Melanie R, Crispo B, Andrew T (2006) The evolution of RFID security. IEEE Pervasive Comput 5(1):62–69 2. Fuad E et al (2020) U-shaped inductively coupled feed UHF RFID tag antenna with DMS for metal objects. IEEE Antennas Wirel Propag Lett 19(6):907–911 3. Jun Z, Yunliang L (2014) A novel metal-mountable electrically small antenna for RFID tag applications with practical guidelines for the antenna design. IEEE Trans Antennas Propag 62(11):5820–5829 4. Wang L, Yu J (2018) A novel UHF-RFID tag using a planar inverted-F antenna mountable on the metallic objects. In: 2018 IEEE International conference on computer and communication engineering technology (CCET), IEEE, pp 146−149 5. Faudzi N, Ali M, Ismail I, Jumaat H, Sukaimi N (2014) Metal mountable UHF-RFID tag antenna with meander feed line and double T-match. In: 2014 International symposium on technology management and emerging technologies, IEEE, pp 33−38 6. Yun Jing Z, Dan W, Mei Song T (2017) An adjustable quarter-wavelength meandered dipole antenna with slotted ground for metallically and airily mounted RFID tag. IEEE Trans Antennas Propag 65(6):2890–2898 7. Abdulhadi EA, Ramesh A (2012) Design and experimental evaluation of miniaturized monopole UHF RFID tag antennas. IEEE Antennas Wirel Propag Lett 11:248−251 8. Shin-Rou L, Wai-Hau N, Eng-Hock L, Fwee-Leong B, Boon-Kuan C (2020) Compact magnetic loop antenna for omnidirectional on-metal UHF tag design. IEEE Trans Antennas Propag 68(2):765–772 9. Yong-Hong L, Eng-Hock L, Fwee-Leong B, Boon-Kuan C (2020) Loop-fed planar inverted-L antennas (PILAs) for omnidirectional UHF on-metal tag design. IEEE Trans Antennas Propag 68(8):5864–5871 10. ECCOSTOCK PP. http://www.eccosorb.com/Collateral/Documents/English-US/PP.pdf. Last accessed 1 Jan 2017 11. UCODE 8/8m Chip DataSheet Rev. 3.4, SL3S1205_15. https://www.nxp.com/docs/en/datasheet/SL3S1205-15-DS.pdf. Last accessed 2 Jan 2021 12. Juha V, Toni B, Leena U, Lauri S (2010) Passive UHF inkjet-printed narrow-line RFID tags. IEEE Antennas Wirel Propag Lett 9:440−443

Interactive Chatbot for Space Science Using Augmented Reality—An Educational Resource N. Shivaanivarsha and S. Vigita

Abstract Traditional method of learning by reading books is becoming a thing of the past. Everything is becoming increasingly digitized and is being driven by technology innovations. Bringing new learning formats based on technology, in the classrooms can lead to increased student engagement which in turn will make the knowledge and skills stay longer. Thus, adapting technological solutions to education is very important. Augmented Reality (AR) is an enhanced version of the real world which is achieved using digital visual elements, audio and other sensory stimuli. This is a growing technology. Since it provides many powerful visualizations, AR can be used as a powerful learning tool. In this paper, we are proposing to use Augmented Reality technology to develop a book and an AR application for space science that will help students easily acquire, process and remember the information. Space science is one of the most exciting and rapidly evolving branches of science. It encompasses all of the scientific disciplines that involve space exploration and study natural phenomena and physical bodies occurring in outer space. Learning space science is very important because it has led to development of various technologies such as GPS, weather prediction, solar cells. Students are fascinated to understand these. So, in the work proposed we are developing a whole book from scratch on basics of space science, which is our physical data (Image Targets). We are developing an AR application which will detect the Image Targets and layer virtual content (AR Visualizations) over them. These visualizations will help to understand the physical data better thus providing a fun way of learning. To make the learning interactive, a chatbot will be added. Keywords Chatbot · Mobile App · Digital Education · Space science · Augmented Reality · Virtual model · 3D objects

N. Shivaanivarsha · S. Vigita (B) Department of ECE, Sri Sairam Engineering College, Chennai, India N. Shivaanivarsha e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_39

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1 Introduction Augmented Reality (AR) technology has seen a great growth since 2020. It has come a long way from fiction to reality. It provides an interactive experience between the user and his/her environment, where the physical world elements in the environment is enhanced by a computer-generated input. The inputs range from images to sound to videos to graphics and so on. To put in simple words, AR is a technology that puts virtual objects in the real world. It can be experienced through AR glasses or through mobile devices. It is a growing trend among mobile computing companies.

2 Proposed Method The proposed work has a book and a mobile device. An AR-based application is created and installed in the mobile device. The book is specifically written to explain the basics of the space science. The mobile application displays AR content on top of the book when scanned using the AR camera. A chatbot is present which helps in making the learning interactive.

2.1 Overall Setup and Block Diagram The overall setup is shown in Fig. 1. The block diagram of the proposed work is shown in Fig. 2.

2.2 Block Diagram Description The AR application is installed in the mobile phone. The mobile phone should have the following sensors: Gyroscope, Camera, Accelerometer and Light sensor. The application has three buttons: AR, chat and exit. The AR button opens the mobile camera. It scans the image target. The image targets are from the book. Then the image target is processed by the application and appropriate virtual models (Game Objects) are placed on top of the book by AR camera. AR camera displays the content from the main camera along with the virtual models on the mobile screen. The chat button opens a dialog flow messenger, where we can ask questions related to space bot and receive answers. The exit button closes the application.

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Fig. 1 Overall setup

Fig. 2 Block diagram

3 Hardware and Software Component Hardware components for Augmented Reality are: a processor, display, sensors and input device. Modern mobile devices like smart phones and tablet-computers contain these elements. The main hardware required is,

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• Smartphone that supports Vuforia The software tools used are as follows: • Unity Game Engine • Vuforia • Dialog flow. The programming language use is • C#.

3.1 Smart Phone AR glasses are very expensive. The device which we can get our hands on easily for AR applications is mobile phone. Smart phones may be ordinary looking on the outside, but on the inside, they have various sensors like accelerometer, gyroscope, camera, light sensor and magnetometer which play a very important role in AR. Long [5] did research to compare a needle placement AR performance between smartphone and AR glass. The placement time of virtual objects for both these devices were almost same and very less compared to the placement time of other AR platforms. Siriwardhana et al. [8] explains by enhancing the quality of user experience, MobileAR applications can bring value to various application domains. De Miguel et al. [2] did non-experimental research with 1716 subjects to see the usability of phones among students between primary educations to university. His analyses revealed the perception of higher use in an intermediate age. Though high usage can be seen in middle age students, children by two years of age gets access to smartphone, says Yadav et al. [9]. Hence for affordability purpose, the smartphone for AR is the most preferred choice.

3.2 Unity Game Engine and Vuforia Unity is a cross-platform game engine initially released by Unity Technologies. Unity features a full toolkit for designing and building various applications. It includes interfaces for graphics, audio and level-building tools. It requires only a minimal use of external programs to work on projects. Varun [4] and his team created an interesting and interactive 3D model of the solar system using Unity for middle school students. Midak et al. [6] developed a mobile application to visualize the chemical structure of water and to represent video materials of lab experiments. Flores et al. [3] proposed an AR application aiming to help students understand the geometric shapes better. Flores’s study aimed in exploring the potential of AR among the 6th grade (age 11–12 years old) students. Arslan et al. [1], in his study examined the techniques to

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increase the learning performance in biology. So, he developed an application using Unity3D. Steps to create a Unity Project • • • •

Open the Unity App Go to New Project → Create project Add the project name and select ‘Create’ The Unity IDE opens. Then import the Vuforia package.

The proposed work is mainly focused on Image target. Using Image Targets Image Targets are images that Vuforia can detect and track. It detects and tracks the image by comparing extracted natural features from the camera image against a known target database. The images should have sharp features. Figure 3 shows Vuforia image target with good feature points. Figure 4 shows images with less and zero feature point. Vuforia can’t process images with zero feature points.

Fig. 3 Good Vuforia Image target

Fig. 4 Poor Image targets

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Once the Image Target is detected, Vuforia Engine will track the image and augment the virtual model on top of it. In the work proposed the image targets are created within the Unity editor. Now to place the 3D models (Game Objects) on top of the Image target, the 3D models are made as a ‘child’ to the Image target. Now when the application scans the image, Vuforia engine places the 3D model on top of it.

3.3 Dialog Flow Dialog flow is a platform to design and integrate a conversational user interface into the mobile app, web application, device, bot, interactive voice response system and so on. Using dialog flow, one can provide new and engaging ways for users to interact with the product. After completing the chatbot, select the suitable integration and integrate them to the application.

3.4 C# Scripting tells the Game Object how to behave (rotate, revolve, translate, change scene, etc.). In Unity, scripting is done using C# language. It is an object-oriented scripting language.

4 Results and Discussion In a study performed by Sahin and Rabia [7], it was understood that the AR was indeed game changing in education. Initially we did a small survey to see ‘space science’ related topics in school syllabus. The topics identified in the school syllabus are: • • • •

What is universe? Definition of astronomy Solar system and some facts about the planets And a few basic space physics

They are not sufficient. So, we wrote a book consisting of 6 parts, covering the basics of space science. They are • • • •

Part 1—Going around the sun Part 2—Life of stars Part 3—Galaxies a trip beyond our solar system Part 4—Black holes

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• Part 5—Mysteries of the universe • Part 6—Are we alone? Traditional method of learning is boring. In order to create interest among the students, an Augmented Reality Application for the book-written, is created using the Unity Application Software. The application can be marker-based or marker-lessbased. The proposed application focusses only on marker-based approach because many mobile phones don’t support marker-less-based AR. When a student reads the book, he/she needs to know which pages have Augmented Reality models (3D objects/virtual objects/game objects) on top of it. So, some kind of indication must be given. We use an icon/symbol for this purpose. Figure 5 shows the icon used in the proposed work. In Unity three scenes were created. Scenes are similar to routing links in websites. Each page is called scenes in Unity. To open appropriate scenes, C# scripts are written. Figure 6 shows the scenes and the scripts written to navigate through the scenes. In the AR scene, Image targets and Game objects were brought together. This is possible only after importing the Vuforia package and license key. Using the images marked with indication, we superimposed the game objects on top of the images, in the Unity Game Engine Software using Vuforia. Figure 7 shows the game object as a child of the image target.

Fig. 5 Icon used to identify the pages with AR models

Fig. 6 Scenes created

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Fig. 7 Game object on top of Image target

The chatbot was created using dialog flow. In dialog flow, the required entities were created, to define and extract information. Figure 8 shows one of the entities created. Intents were created, and it is where we train the user phrases with the help of entities and come up with appropriate response. Figure 9 shows one of the intents created. To integrate the dialog flow plugin into Unity, we used web-view plugin. It works on Android, IOS, Unity web player and MAC. It doesn’t support windows. After all the virtual objects were created and chatbot was integrated, the next step was to build the application for appropriate platform. To build the application for android services Windows OS is enough. But, to build the application for IOS devices, we need Apple laptop. Before building, SDKs of the required platform was installed. In this project we are using android application. The result of the build was a ‘.apk’ file.

Fig. 8 Entity created in dialog flow

Fig. 9 Intent created in dialog flow

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The ‘.apk’ file was downloaded to the target mobile device and installed. Figure 10 shows the final application built. On opening the application, the following user interface appeared. Figure 11 shows the User Interface in the application. There were three buttons in the opening UI. Figure 12 shows the functions of the buttons. On clicking the chat button, the chatbot opened. In the dialog flow, small talk was enabled. Small talk helps with basic user-bot conversation such as greeting, introduction. We named our chatbot as ‘Space BOT’. Figure 13 shows a sample small talk conversation. With the help of entities and intents, Space BOT was trained to answer basic questions. Figure 14 shows the interaction between the user and space BOT. On clicking the AR button, the AR camera opened. Through AR camera the image targets were scanned, and appropriate 3D/virtual objects were placed on top of the image. Displaying the 3D objects can be ‘Tracked model’ or ‘Extended tracked Fig. 10 Final application

Fig. 11 User Interface

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Fig.14 Interaction between the user and the space BOT

model’. In Tracked model, the 3D object will be displayed only when the image target is present in the focus of the camera. In Extended tracked model, the 3D object will be displayed once the image target is scanned, and the object will remain in position without disappearing even when the image target disappears (out of focus of the camera). Figure 15 shows an example of Tracked model. Here when the image target was scanned and identified, the 3D object was seen on top of it. When I placed my hand on top of the image target, Vuforia couldn’t find the target and hence the 3D object disappeared. This is ‘Tracked AR model’–(The image target should be in focus for displaying the 3D object). Figure 16 shows an example for Extended tracking model. When the Image target was scanned and identified, the 3D object was seen on top of the target through our mobile screen. Later when the image was blocked by hand, the 3D model was still present. This is Extended tracked model– (The image target is required only to trigger the 3D model. Once the 3D model is triggered, it is displayed even after the image target is lost). In the work proposed, Tracked models are extensively used compared to Extended tracked models. Each type, has its own benefit. Extended tracked models glitch/lag in mobile phones with low FPS. In the work proposed, we use Extended tracking when we need to manipulate the 3D model. In the Fig. 17, it is seen that, on scanning an image target, a 3D object (Earth and Moon) is seen on top of it. Additional scripts were written, to manipulate the game object. In the Fig. 18, the 3D object is bigger compared to what was seen in Fig. 17. This is because the user has zoomed the image. When viewed from a far distance, the image target was lost, but the 3D object was still visible (Extended Tracking). In the Fig. 19, the 3D model is not seen in the

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Fig. 15 Tracked AR model

same location as seen is Fig. 18. This is because the user dragged and moved the 3D object, away from the image target. The image target is not visible, but the 3D model is. So, when we need to manipulate the 3D models, we need Extended Image tracking. Extended tracking is also used when our 3D objects are very big. Figure 20 shows another application of Extended tracking in our work, where we show an AR of galaxy model, which is very big. AR is not just about 3D objects. Figure 21 shows a 2D video playing on a 2D plane object above the image target. Figure 22 shows some of the other 3D models in this work. The final products of the project are an AR application and a book, ‘GIVE ME MORE SPACE’. Figure 23 shows the final book cover and the app icon. After the final application was created a small study was performed. A few models were sent to THE PACKIANATH PUBLIC SCHOOL, Kattathurai, Tamil Nadu 629158, India. The feedback from the teachers and students were highly pleasing. Figure 24 shows the study performed in the school by the school’s principal.

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Fig. 16 Extended tracked AR model

Fig. 17. 3D model of earth and moon

5 Conclusion This work presents the design and development of an AR-based booklet and application, focusing on space science. With AR, learning becomes engaging and fun. Hence, it helps the students to easily grasp, process and remember the information. It is not limited to a particular age group. It can be used in all schools of learning or even at work. AR has a potential to replace paper textbooks, hence education

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Fig. 20 Virtual model of milky way galaxy

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Fig. 21 2D game object

Fig. 22 Some of the other 3D models in the work

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Fig. 23 Cover of the final book and icon of the final application

Fig. 24 Study performed in THE PACKIANATH PUBLIC SCHOOL

becomes more accessible. It doesn’t require any expensive hardware because 73% percent of teens currently own smartphone which is sufficient to use AR applications. To sum it all, AR pushes the traditional limits and provides a fun way to learn thus creating a powerful impact among the learners.

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References 1. Arslan R, Kofoglu M, Dargut C (2020) Development of augmented reality application for biology education. J Turkish Sci Educ 17(1):62–72 2. de Miguel CR, Pérez DD, Sánchez GR (2021) Perception of the use of the mobile phone in students from primary education to university degree. Digital Educ Rev 39(2021):23–41 3. Flores-Bascuñana M, Diago PD, Villena-Taranilla R, Yáñez DF (2020) On augmented reality for the learning of 3D-geometric contents: a preliminary exploratory study with 6-grade primary students. Educ Sci 10(1):4 4. Kapoor V, Naik P (2020) Augmented reality-enabled education for middle schools. SN Comput Sci 1:1–7 5. Long DJ, Li M, De Ruiter QMB, Hecht R, Li X, Varble N, Blain M et al. (2021) Comparison of smartphone augmented reality, smartglasses augmented reality, and 3D CBCT-guided fluoroscopy navigation for percutaneous needle insertion: a phantom study. CardioVascular Interv Radiol 44(5):774–781 6. Midak L, Yaroslavivna, Kravets IV, Kuzyshyn OV, Pahomov JD, Lutsyshyn MV (2020) Augmented reality technology within studying natural subjects in primary school. published on CEUR Workshop Proceedings (CEUR-WS. org) 7. Sahin D, Rabia MY (2020) The effect of augmented reality technology on middle school students’ achievements and attitudes towards science education. Comput Educ 144(2020):103710 8. Siriwardhana Y, Porambage P, Liyanage M, Ylianttila M (2021) A survey on mobile augmented reality with 5G mobile edge computing: architectures, applications, and technical aspects. IEEE Commun Surv Tutorials 23(2):1160–1192 9. Yadav S, Chakraborty P, Kochar G, Ansari D (2020) Interaction of children with an augmented reality smartphone app. Int J Inf Technol 12(3):711–716

Interference Power Reduction Algorithm for Massive MIMO Linear Processing ZF Receiver Abdul Aleem Mohammad and A. Vijayalakshmi

Abstract Through large number of antennas, frequency reuse concept enables to suppress interference and to increase the spectral efficiency. To achieve high speed data transmission and to increase capacity, it is very important to focus on spectrum efficiency and to overcome the channel fading in multipath channel environment. Existing traditional modulation techniques such as Multiple-input multiple-outputOrthogonal frequency division multiplexing (MIMO-OFDM) system, combining the OFDM and MIMO technologies can meet the requirements. A group of independently operating terminals transmitting data streams instantaneously to a closely gathered antennas arranged as an array. This antenna array transmits pilot signals to gather the required information. As the Channel State Information (CSI) is imperfect, the antennas transmit pilot signals to acquire the CSI as well as the transmitted power rates from the terminals. To compensate the loss and without reducing the performance levels at the base station end, the power dissipated is maintained reciprocally proportional to the square of the root of the total used antennas. But when CSI is known, the transmitted power is made oppositely symmetrical to the total number of antennas. For Zero forcing (ZF) and Maximum Ratio Combining (MRC) detection, lower capacity bounds are been derived. It is been observed that ZF outperformed MRC. A power scaling method is considered for the analysis of uplink sum rate with imperfect and perfect CSI, the increase in the antenna numbers shows that the sum rate on the uplink side between ZF and MRC reduces and with a constant increase in the number of antennas there won’t be any difference between ZF and MRC. In this paper Algorithmic-based Interference Power reduction Linear Processing ZF Receiver is proposed for Massive MIMO also the need of beamforming techniques in Massive MIMO systems in overcoming the technically developed obstacles in the deployment of Massive MIMO system is studied. Simulation is carried out by using Python, the SNR values for Maximal Ratio Combiner (MRC), Zero forcing (ZF) in A. A. Mohammad (B) · A. Vijayalakshmi Department of ECE School of Engineering VISTAS, Chennai, India e-mail: [email protected] A. Vijayalakshmi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_40

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a 2 * 2 MIMO are also compared. With the proposed receiver performance enhancement of Massive MIMO systems and interference cancelation with and without Power Scaling is been observed. Keywords Beamforming · ISI · Massive MIMO · ZF

1 Introduction With recent advancements in Massive MIMO had become the most favorable and commonly used technology for the fifth generation (5G) cellular networks when combined with beamforming antenna array techniques has a promising potential to achieve greater improvements in Spectral efficiency (SE). While upgrading 5G, antenna selection for beamforming and power consumption had become a new frontier. Network capacity can be increased by making the cell size smaller that reduces the distance between Base Station’s (BS’s) and introduces the inter-cell cooperation but which on the other hand increases, Inter Symbol Interference (ISI) and other interference related issues. To achieve better spatial diversity in Massive MIMO, intelligent antenna beamforming with power scaling Algorithmic-based Interference Power reduction Linear Processing ZF Receiver is proposed. Simulation is carried √ out in Python, the power scaling for perfect CSI by reducing the power to 1/ M and for MRC, ZF in a 2 * 2 MIMO are compared. Enhancement of performance in Massive MIMO systems and interference cancelation is also been observed. Whereas advanced techniques like Massive MIMO can productively make use of the spatial realm for mobile fading channels to significantly improve the performance levels of wireless communication systems. In Massive MIMO Zero forcing (ZF) is useful in eliminating interference [1]. In this paper we have highlighted the outcomes of using different technologies that can be implemented in 5G [2]. The rest of the paper is organized in the following manner. In Sect. 2 major challenges and requirements in the deployment of 5G are discussed. In Sect. 3, importance of Massive MIMO is mentioned. Section 4 is all about Interference. In Sect. 5 MIMO receiver design and detection schemes have been pointed out. Section 6 contains simulation and results.

2 Requirements in Implementing 5G 2.1 Interference The major restriction in wireless communications is with frequency spectrum. For a limited band of below 6 GHz, it is difficult to accommodate a greater number of users and to answer the never-ending need of higher data rates as in [3]. To

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resolve the above-mentioned challenges Massive MIMO stood as effective method. Anyhow during implementation, frequency reuse introduced new difficulties like ISI and other interferences related to frequency sharing of multiple nearby users. Interference reduction techniques can be implemented for both uplink and downlink [2].

2.2 Capacity and Coverage 5G is supposed to have data rates up to 10 Gbps as in [1, 2]; it is difficult to find a particular modulation scheme to achieve the same and existing 4G networks are not capable to support the unexpected demand for the digital data rates. Providing massive connectivity and achieving higher spectral efficiency, linear receivers with a basic knowledge of CSI is possible [2, 4].

2.3 Throughput and Latency One of the major technological challenges in implementation of 5G is controlling the access points for effective and efficient handling of multiple users and providing high throughput with the existing non competent infrastructure for deploying smaller and denser cells. Lack of coordination among the cells will increase the latency as in [2].

3 MIMO Because of its promising advantages, 5G is going rule the market from 2020 and a lot of changes and enhancements in communication services can be experienced very soon. The 5G deployment is mainly focused on flexible baseband and RF technologies, hybrid beamforming and Massive MIMO system supports in the implementation of macro, micro, pico, femto cells. In the wireless communication technology, MIMO are defined as communication systems with multiple antennas transmitter and multiple antenna receiver. These systems serve for a purpose of achieving higher data rates and the spectral efficiency. Higher data rates are anyhow promised by effectively utilizing the resource. Without increasing the power allocation at transmitter or without changing the bandwidth, significant increase in data rates can be achieved even the complexity of the system is increased. To enable high frequency implementations and to serve multiple UEs with single antenna, multiuser-MIMO (MU-MIMO) with supportive antennas as intelligent arrays at every BSs are required. Using these smart antenna arrays MUMIMO systems have evolved into Massive MIMO systems [3]. As many antennas

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are equipped, multiple signal paths are attained, and good link reliability can be observed. Due to the scarcity of microwave band spectrum, millimeter wave (mmW) bands can provide considerable allocations for enabling 5G cellular networks. The smaller wavelengths of mmW can be used to employ, not less than hundred antenna elements in an array within a smaller physical platform, near the BS, as a Massive MIMO. These antennas help in attaining a great directivity over a smaller region to bring improvements in radiated energy. Few Massive MIMO benefits, include use of low-power components, end to end latency of < 10 ms. In addition to that Beam Forming (BF) in MIMO provides directional signal transmission or reception [5, 6]. Beam forming is employed either at transmitter or receiving ends to achieve a good spatial selectivity by combining the elements in a phased array. Multiuser interference: Interference because of other devices would show a considerable impact on performance levels [7]. Few interferences minimization or cancelation techniques, like multiuser maximum likelihood detection at uplink, and techniques at downlink like dirty paper coding (DPC) which are complex to implement [8]. Several wireless technologies which are already in use, such as, IEEE 802.11 for Wi-Fi WLAN’s, 3GPP for LTE can be utilized together for effective usage of mmW and massive antenna arrays to achieve densification of the network by forming a Heterogeneous wireless network (HWnet) [9]. Using IEEE 802.11 standards, multiple smaller cell sites controlled by BSs forming multiple hotspots (micro cell) supporting short range communications and can be considered as low-cost radio access nodes having a covering area around 15–300 m which are further controlled by comparatively multiple high-capacity BSs covering larger cell sites (macro cell) may provide a solution for increasing demand of high data rates and for a good area throughput. In any HWnet, wireless network (Fig. 1). Area throughput is an important parameter and can be measured through a standard procedure as follows: Fig. 1 A heterogeneous network

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where B denotes the bandwidth in Hz, DC is for cell density in cells per square kilometer and N is the number of information bits transferred per second over one Hertz of frequency, (which defines spectrum efficiency). Anyhow signal path and propagation challenges at smaller wavelengths in a denser urban environment are high. Densification process involves bringing BSs near to the User Equipments (UEs) and may introduce shadowing of signals. As Massive MIMO is flexible, multiple Cooperative methods can be implemented to avoid interference in denser networks, one such method is Cooperative MIMO (CoMP) as in [2]. CoMP has emerged as a useful method to combat the ICI. In CoMP as shown in Fig. 2, all the BSs communicate with a central processor, share the Channel State Information (CSI) and also jointly send signals to the UEs. Apparently, a UE is monitored by multiple BSs. This cooperation provides a constructive generation of signals, so that a great spectral efficiency is achieved it also reduces inter-cell interference and outage as well especially at cell edge as in [10]. Further moving forward, Interference Coordination (IC), IC aims to reduce interference within cells sharing same spectrum as in [11]. Coordination among resources between adjacent cells during uplink and downlink for the data channels Physical Downlink Shared Channel (PDSCH) is considered, and Physical Uplink Shared Channel (PUSCH), for uplink control. Fig. 2 A cooperative MIMO (CoMP)

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3.1 Beamforming: Construction and Directing the Beams Beamforming algorithm plays the key role in focusing the beam on the desired direction. Antenna configurations with arrays and sub arrays control the beams. Beamforming can be implemented either in analog, digital or RF front end. Digital beamforming method allows coefficients to get multiplied per Radio Frequency chain, over modulated signals, near Fast Fourier Transformation (FFT). Using FFT latency can also be improved [12]. Analog beamforming is the process of assigning coefficients to a modified Radio Frequency signal in time domain itself. Sharper and much focused beams are possible with Hybrid beamforming with both analog and digital domain [7]. Antennas with high directivity are important for future 5G development. Design of such antennas anyhow involves much complexity. Attenuated beams from antennas may increase the delay or even shutdown the communication. Misalignment of antennas will increase power loss by transmitting signals in unwanted directions. Directing and controlling antennas and beam directions at UEs and BS for communication is possible with beamforming protocols as in [13]. Determining antenna beam directions is possible by measuring and computing angular distances between multipaths. Complexity is more with increase in the number of antennas and extracting the information of channel at every single antenna in a Massive MIMO integrated mm-wave system is difficult [3]. The problem of link budget can be alleviated by continuously changing the position and direction of beams for both transmitter and receiver as discussed in [1]. Directional switching antenna beam systems use fixed impressions for transmitting or receiving from specific directions. A Sectored antenna model is suitable for such systems. Different multiple access techniques along with beamforming algorithms could increase spectrum capacity and frequency reuse as in [5, 14]. Capacity of Massive MIMO cellular networks depends on algorithmic procedures employed, and on extraction of CSI.

3.2 Massive MIMO Architecture The basic concepts of Massive MIMO with M T antennas, transmitting X signals and M R receiving antennas receiving Y signals simultaneously is shown in the Massive MIMO architecture Fig. 3. Massive MIMO technology employs the base station with antenna array, generally a hundred or several hundred antennas, which is a higher order than the antennas in the existing communication system.

3.3 A Characteristic Expression for the Massive MIMO LetX 1 (k), X 2 (k), . . . , X MT (k) = X (k)

(2)

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Fig. 3 Massive MIMO architecture

be M T transmitted symbols at time K on M T antennas. Equation (2) is the transmit vector of the order of M T × 1. ⎡ ⎢ ⎢ where, X (k) = ⎢ ⎣

X1 X2 .. .

⎤ ⎥ ⎥ ⎥ ⎦

X MT LetY1 (k), Y2 (k), . . . , Y M R (k) = Y (k)

(3)

be M R symbols received at time K on M R antennas. Equation (3) is the receive vector of the order of M R × 1. ⎡ ⎢ ⎢ where, Y (k) = ⎢ ⎣

Y1 Y2 .. .

⎤ ⎥ ⎥ ⎥ ⎦

YMR The relation between symbols of transmitted antenna 1 and received antenna 1 is given as: Y1 (k) = X 1 (k)h 11 + X 2 (k)h 21 + . . . .. + X MT (k)h MT 1 + W1 (k)

(4)

The relation between symbols of transmitted antenna 2 and received antenna 2 is given as:

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Y2 (k) = X 1 (k)h 12 + X 2 (k)h 22 + . . . .. + X MT (k)h MT 2 + W2 (k)

(5)

Similarly, between M T M R antennas: Y M R (k) = X 1 (k)h 1M R + X 2 (k)h 2M R + . . . .. + X MT (k)h MT M R + W M R (k)

(6)

where W i (k) is the noise at receiver antenna ‘i’, (i = 1,2,3,.) and hij is the relational coefficient between transmitting antennas ‘i’ and receive antennas ‘j’. The Massive MIMO channel matrix arrangement will be like: ⎤ ⎡ h 11 h 12 y1 ⎢ y2 ⎥ ⎢ h 21 h 22 ⎢ ⎥ ⎢ ⎢ .. ⎥ ⎢ .. .. ⎢ . ⎥=⎢ . . ⎢ ⎥ ⎢ ⎢ . ⎥ ⎢ . . .. ⎣ .. ⎦ ⎣ .. yMR h MT 1 h MT 2 ⎡

⎤⎡ x1 . . . . . . h 1M R ⎢ x2 . . . . . . h 2M R ⎥ ⎥⎢ .. ⎥⎢ .. ⎢ ... ... . ⎥ ⎥⎢ . ⎥ .. ⎦⎢ ⎣ ... ... ... . . . . . . . h MT M R x MT

⎡

⎤

⎤ w1 ⎥ ⎢ w2 ⎥ ⎥ ⎢ ⎥ ⎥ ⎢ .. ⎥ ⎥+⎢ . ⎥ ⎥ ⎢ ⎥ ⎥ ⎢ . ⎥ ⎦ ⎣ .. ⎦ wMR

The total received signals of the whole system can be written as: T

T

T

Y (k) = X (k)H + W (k)

(7)

where k = 1, 2, …., N are the transmission instants. Let us consider ‘N’ as transmission pilot vectors: X (1), X (2), . . . X (N )

(8)

Let N corresponding received vectors as: Y (1), Y (2), . . . Y (N )

(9)

Then the corresponding matrix is: ⎡ ⎢ ⎢ y=⎢ ⎣

y1 y2 .. . yMR

⎤

⎡

h 11 h 21 .. .

⎥ ⎢ ⎥ ⎢ ⎥H = ⎢ ⎦ ⎣

h MT 1

⎤ ⎡ x1 h 12 · · · h 1M ⎢ x2 h 22 · · · h 2M ⎥ ⎥ ⎢ .. . . .. ⎥x = ⎢ .. ⎦ ⎣ . . . . h MT 2 · · · h MT M R x MT

⎤

⎡

⎥ ⎢ ⎥ ⎢ ⎥w = ⎢ ⎦ ⎣

w1 w2 .. .

⎤ ⎥ ⎥ ⎥ ⎦

wMR

tacking as a matrix, we get Y = H ·X +W

(10)

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4 Interference The radio waves originated from most wireless systems propagate through much complex environments which consists of various obstructing objects making the radio waves to get reflected, scatter in different directions and sometimes to diffract depending on the physical properties and boundary conditions of the objects. For less complex conditions Maxwell’s equations help to estimate the requirements but for the scenarios with more complex environments few estimates are being developed to calculate the path loss [15]. Path loss is defined as the ratio between received power (Pr) to the Power transmitted (Pt) of any propagation path and it varies with propagation distance. Considering a path loss system, Signal to Noise Ratio SNR = Pr/W. In a wireless communication channel BER is always varies with its SNR. As the path loss is always proportional inversely to, the square of the frequency of the signal and moreover the path loss decides signal power levels in relation to the distance [16]. Wireless communication systems with less path loss cover larger areas and vice versa. Depending on path loss the power level of the received signal is kept constant [11]. On the other hand, multipath gives rise to two serious channel deteriorations, one is flat fading and the other is Inter Symbol Interference (ISI). Rapid fluctuation in the signal power over a short period of time because of multipath can cause constructive or destructive interference. The BER will be increased if the received signal power is less than its average value, this is one of the noticeable adverse effects of flat fading [9, 17]. If the received signal power falls below 10–20 dB of its average, the channel becomes deep fade, anyhow this condition is experienced rarely and only over a short period of time. In this paper our major point of discussion will be interference. ISI will become a crucial difficulty especially when the delay caused by the multipath transmission is much higher than the bit time. This becomes general cause for self-interference, this is the reason why most of the times ISI is termed as frequencyselective fading. In ISI, bit error rate is higher, and it cannot be decreased just by boosting the signal power levels, which in turn increases the power levels of the self-interference [11, 18]. This again leads in applying limitations to the data rates, both in indoor and outdoor scenarios, thus ISI compensation is much required [19].

5 ISI Counter Measures The objectives for designing the receiver will consider the presence of interference and channel estimation error for a Massive MIMO system. The decision device at the receiver side detects errors which occur because of the ISI. Initial requirement in designing a receiver is to have the property to minimize the probability of error. As the objective is to lessen the ISI effects, and there upon to deliver the data and to achieve a SNR. In MIMO wireless communication system, multipath fading becomes the general reason that introduces ISI in a transmitted signal. To

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completely eliminate ISI from transmitted signal, a very strong equalizer at receiver side is mandatory. Few detection schemes available include MRC and ZF. Maximum Ratio Combining restores the signal and has a limitation of performing poorly in interference limited scenarios, it has very less complex structure. Zero forcing performs well in suppressing interference, it is very complex in nature and robust for scenarios like Massive MIMO [20]. In a linear zero forcing detector, interference is canceled but noise will amplify along with the signal for which an algorithm for interference power reduction is proposed to reduce or control the transmitted power for any individual user with the help of combining beam former and by maintaining a steady Signal to Information plus Noise Ratio (SINR) [21, 22]. A simple matched filter has been employed to subdue the multiuser interference as a greater number of antennas used in Massive MIMO. Power is decreased as (E u /M) [3]. Zero forcing, the name relates to reducing the ISI to almost zero for a noise free environment [23]. This equalization method is helpful in a case where ISI is significant when compared to noise. ISI is completely nulled out by multiplying received vector with Pseudo-inverse of the channel matrix H. Considering a single or first user as the desired one in the array of users, the received signal is represented as y=

√

Pu g1 x1 +

√

Pu

k ∑

gi xi + w

(11)

i=2

where received signal is ‘y’, ‘Pu ’ is the power of individual user, ‘gi ’ is the channel vector, ‘xi’ is the received vector and ‘w’ is the noise (i = 1,2,3,…). The flow chart explains the steps involved in the design.

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6 Simulation and Results 6.1 Simulation Analysis The simulation results for MRC and a Zero forcing equalizer receiver comparing their BER performance characteristics are presented here. As mentioned earlier, the simulation is carried out on a 2 × 2 MIMO network with BPSK scheme of modulation for a Rayleigh channel which clearly shows that results obtained for a 1 × 1 system with BPSK modulation in the Rayleigh channel shown in Figs. 4 and 5. When compared with the MRC equalizer, ZF equalizer has achieved the high data rate gain. It is not possible to achieve a double data rate improvement in different channel conditions as the channels are correlated with the same coefficients. Figure 6 Spectral Efficiency of a Massive MIMO channel with MRC, ZF equalizers clearly indicates that due to an increase in number of antennas the matched filter suppresses multiuser interference, and the Zero forcing equalizer suppresses ISI [24]. BER in case of MRC increases due to the increase in number of receiving antennas. The below observations are made. The Zero forcing linear equalizer removes or cancels all ISI when the channel is noiseless

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Fig.4 BER plot for a 2 × 2 MIMO channel with MRC equalizer (BPSK modulation in Rayleigh channel)

Fig. 5 BER plot for a MIMO 2 × 2 channel with ZF equalizer (BPSK modulation scheme for a Rayleigh channel)

Figure 7 shows the power scaling performance analysis in Massive MIMO receivers proposed by the author in this paper. Serving 10 users with perfect CSI in the uplink and pilot being transmitted with power 10 dB for each, a constant rate has been achieved and continued despite the transmitted power is weighted to as 1/M. It follows that, for each user the power very likely be decreased as oppositely symmetrical to the number of BS antennas. Furthermore, desired signal power level

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Fig. 6 Spectral efficiency in a massive MIMO channel having MRC, ZF equalizers

accelerates M times the multiuser interference added along with the noise, and when M increases substantially large, the pairwise orthogonality of user channel vectors is achieved which conquers the multiuser interference completely, with the help of a very simple and less complex matched filter. In Fig. 8 it can be noted that in case of incomplete CSI the power can’t be 1/M as the transmitted power reduces and CSI estimation error increases at a level M.ρ*number of users, and hence, it is additionally √ analyzed that sum-rate consistency is obtained if the power is scaled done to 1/ M.

6.2 Conclusion In this paper the analysis of data transmitted from number of terminals of antenna array has been done. It has been identified that there is a difference in the spectral efficiency between perfect and imperfect CSI. As the CSI is imperfect the antennas in the array transmit the pilots signal to find the CSI and power transmission information. As the number of antennas is increased, then the performance of the system may be affected because of the Inter Symbol Interference. To remove ISI from the transmitted signal, ZF equalizers along with Linear Precoding and Conjugate Beamforming at receiver side are used and their performances for Massive MIMO channel are been compared. Results clearly show that Zero forcing with linear precoding outperforms MRC in case of MIMO and Massive MIMO.

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Fig. 7 Power scaling achieved for a massive MIMO network with perfect and complete CSI

Fig. 8 Power scaling achieved in massive MIMO with imperfect CSI

References 1. Choi S Implementation of a zero-forcing precoding algorithm combined with adaptive beamforming based on WiMAX system. Application Article|Open Access Volume 2013|Article ID 976301|https://doi.org/10.1155/2013/976301 2. Noha Hassan ID (2017) Massive MIMO wireless networks: an overview. Electronics 6(63). https://doi.org/10.3390/electronics6030063. http://www.mdpi.com/journal/electronics 3. Aditya KJ, Vasudevan K, Hanzo L Fellow IEEEUplink Sum-rate and power scaling laws for

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multi-user massive MIMO-FBMC systems. http://arxiv.org/abs/1901.10239v2 4. Shukla P, Tharani L Comparison of various equalization techniques for MIMO system under different fading channels. In: Proceedings of the 2nd international conference on communication and electronics systems (ICCES 2017) IEEE Xplore Compliant—Part Number:CFP17AWO-ART. ISBN: 978-1-5090-5013-0 5. Abdulah NF Beamforming techniques for massive MIMO systems in 5G: overview, classification, and trends for future research. Front Inf Technol Electr Eng. www.zju.edu.cn/jzus; engineering.cae.cn; www.springerlink.com ISSN 2095-9184 (print); ISSN 2095-9230 (online) 6. Abd El-Rahma AB, Kawasaki Z (2013) Modified Zero Forcing Decoder for Ill-conditioned Channels. 978-1-4799-0543-0/13/$31.00©2013 IEEE 7. Ahmed MR (2020) Power scaling and antenna selection techniques for hybrid beamforming in mmWave massive MIMO systems. INTL J Electr Telecommun 66(3):529–535 Manuscript received May 1, 2020: Revised July 2020. https://doi.org/10.24425/ijet.2020.134009 8. Vardhan P (2017) Design, simulation & concept verification of 4 × 4, 8 × 8 MIMO with ZF, MMSE and BF detection schemes. Electr Control Commun Eng 13(1):69–74 9. Kountouris M (2016) Deploying dense networks for maximal energy efficiency: Small cells meet massive MIMO. IEEE J Sel Areas Commun 34(4):832–847 10. Björnson EL, Sanguinetti, Debbah M (2016c) Massive MIMO with imperfect channel covariance information. In: Proceedings Asilomar 11. Saha A, Ghosh S (2010) OFDM System analysis for reduction of inter symbol interference using the AWGN channel platform. (IJACSA) Int J Adv Comput Sci Appl 1(5) 12. Sai Krishna KV (2020) Implementation of massive MIMO systems for 512-point FFT processor using VLSI technology. MuktShabd J 9(6) ISSN NO: 2347-3150 13. Semenova A, Mikhailov V (2019) 5G Base station prototyping: massive MIMO approaches. 978-1-7281-0339-6/19/$31.00 ©2019 IEEE 14. Marzetta TL (2013) Energy and spectral efficiency of very large multiuser MIMO systems. IEEE Trans Commun 61(4):1436–1449 15. Liang N, Zhang W, Shen C (2015) An uplink interference analysis for massive MIMO systems with MRC and ZF receivers. In: 2015 IEEE Wireless communications and networking conference (WCNC 2015)–Track 1: PHY and Fundamentals 16. Nguyen HH (2018) Power scaling laws of massive MIMO full-duplex relaying with hardware impairments. IEEE Access 6:40860–40882 17. Poornima A (2018) BER reduction using zf-sic and MMSE-SIC algorithm in LTE-A network. Int J Eng Manufac Sci 8(3) © Research India Publications ISSN 2249–3115. http://www.rip ublication.com 18. Qi XF The effect of diversity combining on ISI in massive MIMO. arxiv.org.1811.00534v1 19. Larsson EG (2013) Uplink performance analysis of multicell MU-SIMO systems with ZF receivers. IEEE Trans Veh Tech 62(9):4471–4483 20. Guerreiro J A low complexity channel estimation and detection for massive MIMO using SC-FDE. http://dx.doi.org/https://doi.org/10.3390/telecom1010002. http://www.mdpi. com/journal/telecom. 21. Yeoh PL, Evans J (2015) An SNR approximation for distributed massive MIMO with zero forcing, IEEE, pp 1089–7798 22. Ngo HQ, Ratnarajah T Performance of massive MIMO uplink with zero-forcing receivers under delayed channels. IEEE Trans Vehic Technol. https://doi.org/10.1109/TVT.2016.2594031 23. Hossain MA (2020) Performance analysis of zero forcing and MMSE equalizer on MIMO system in wireless channel. J Network Inf Secur 8(1&2):19–25 24. Jha RK A survey of 5G network: architecture and emerging technologies special section on recent advances in software defined networking for 5G networks

Currency Identifier for Visually Impaired People S. Rohit and N. Bhaskar

Abstract Visually impaired people are struggling to identify the denominations of the currency. The transactions that are done are basically out of trust but then the current scenario survey says that many are getting cheated because of the cruel people in this society. Visually impaired people are confident enough to rely on the existing system. The braille system is not flooded everywhere, even though people teach braille many who lacks education or cannot afford it cannot learn it. Nowadays, everything has become automated with the implementation of IoT and machine learning so new systems for visually impaired people are integrated with mobile phones so in order to overcome the existing crisis we bring the hardware prototype method The datasets of various currencies are taken and trained to the model; if the shown currency matches with the currency in the database, then the denomination is recognized, and information is passed to the user. Keywords Machine learning · Internet of Things · Crisis · Braille

1 Introduction This project is purely based upon the core concepts of IoT and machine learning. It is a currency reader for the visually impaired people out there who are getting tricked by many people. It is not possible for everybody to learn the braille system and implement them in real-world cases. Therefore, this currency reader helps the visually impaired people to know what currency they are receiving currently or while paying it to the vendors basically the transactions. The upcoming growing technology Internet of Things and machine learning is being used which will benefit hundreds of people out there. The IoT module is primarily aimed at mapping out a complex information system with the combination of sensor data, efficient data exchange through networking. Machine learning artificial intelligence plays a vital role in this S. Rohit (B) · N. Bhaskar Department of Information Technology, KCG College of Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_41

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field. On the other hand, collecting information and maintaining, running together with privacy and security provision in IoT are the main issues. But then everything has a solution as in this case blockchain can be implemented and information can be secured [1]. The classification predictive model is the task of approximating the mapping function from input to discrete output variables. According to the experiments by researchers, the proportion of visually impaired people is higher. Braille system does not work with everybody The smartphone application development makes it possible to identify currency with an attractive UI and voice recognition. Over the past few years, machine learning is playing a vital role in society, its contribution to the field of information technology and to society is rapidly increasing and turning everybody to the new era of smart living. As the data collected is increased rapidly smart technological system has become a need for the upgrowing technology. Machine learning is a key to solving the problems that are faced in day-to-day lives. With machine learning, we introduce new forms of prototypes to reduce the scaling range of different kinds of problems. Some of the well-known applications that we see around include speech recognition, self-driving cars, web search recommendations, etc. [2].

2 Methodology Firstly, the device will be in the form of a pen where the bottom opening will be fitted with a micro camera and the top opening will consist of an output loudspeaker, camera will be connected to an RPi where the implementation part takes place. As soon as the camera senses a currency note image it will trigger the tiny ML model where the model is stored in the cloud and the model will be trained using the dataset. After finding out the type of currency, the data sends the currency number as text [7]. Using the text-to-speech module, we convert the above text to a speech machine and implement it in the Arduino Nano 33 BLE Sense microcontroller. The Raspberry Pi will help us to content to the google cloud database, and it is retrieved using query optimization algorithm for more operations [5, 6]. We can classify the above into two different parts in order to understand the working: Part 1: Emulation of the machine learning model and speech Google Assistant on an RPi. Part 2: Implementing a KWS on the Arduino Nano for the output and trigger. The training test data for this can be taken from Kaggle and the model can be trained. We will use the below-listed modules: • gTTS—Google Text-To-Speech, for converting the given text to speech, • PyAudio module.

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The dataset contains these four input characteristics: The variance of the image transformed into wavelets and then processed further. The asymmetry of the image transformed into wavelets and the execution takes place. Kurtosis of the image transformed into wavelets. Image entropy. We need to balance our data, the easiest way to do this is to drop a number of instances of the target function. This is called random undersampling. We will use Logistic Regression for Fake Currency Detection and classification. Open CV module will be used in order to detect the input image and the process happens then the google assistant speech module is triggered, and it says the type of currency [3].

3 Proposed System 3.1 Serviceability 3.1.1

Automated

As the advancements in technology have happened a lot in the past years, everybody expects automation in the current scenario so this prototype will be automated using the Internet of Things and integrated with a cloud-based server. Such that there is no confusion created for the person who is using, and it will be handy.

3.1.2

Human Intelligence

The product is based on IoT automation the programmed module will be able to tackle all the difficult possible circumstances that are in the day-to-day life. It is a quite unique and advanced technology that is there in the field, future enhancements can be made such as introducing personal assistance and integrating with the old module.

3.2 Technical Background In the preprocessing methodology, the model is trained with different images of currency notes, and these will include the different currency denominations. Then the process of extraction comes into play by resizing the image that is present. Later the image is enhanced to make them brighter and darker points up to the contrasting level. This process will lead to feature extraction which produces the required feature

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Fig. 1 Source INDIAN CURRENCY RECOGNITION FOR BLIND PEOPLE, Rohith Pokala1

points. The descriptors from the extraction will compare the sample imputed image and query image to find the probability of the highest match; the final result will be retrieved in the form of text format and will be converted to speech module (Fig. 1). • The above-mentioned diagram explains the basic flow of image processing and enhancements. These steps are used in both Brute Force Matcher and the Convolution Neural Network. The CNN algorithm is mainly used in the classification of images which will be effective for very large datasets. • The SIFT and SURF detector and descriptor that was used for a very long time and even though it is comparatively old it has proved its success rate in a number of applications, including object recognition, image classifying and stitching, visual mapping development. It brings up large computational problems. Efficient replacement to the above-mentioned methods which has quite a large similarity in matching performance and is less affected by the general constraints and is highly capable of being deployed for real-time performance known as ORB. 3.2.1

Image Classification Algorithm

The image classification algorithm used in this project is Convolution Neural Network (CNN) the scanned image undergoes various steps for the classification to occur. Firstly, the input image dimensions are flattened to one dimension (width pixels × height pixels) normalize the image pixel values by dividing them with standard values (255). Next, a model architecture has to be built with dense layers,

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and the model has to be trained and should make predicted accordingly. One major advantage of using CNNs over NNs is that you do not need to flatten the input images to 1D as they are capable of working with image data in 2D. This helps in retaining the “spatial” properties of images.

3.3 Challenges in the Domain • • • •

Poor quality of data Time consumption is more Needs battery backup Nonrepresentative training data.

3.3.1

Poor Quality of Data

To properly train the data to the model, one must have the required data set but then some problems arise here where the government or the organization keeps changing the physical features of the denominations every five years once where large computational process comes into play. Secondly, if the quality of the data set is poor, then the accuracy rate decreases rapidly.

3.3.2

Nonrepresentative Training Data/Inconsistent

This would be a major drawback as the government is imparting new notes every 2 years once so training the model will get complicated.

3.3.3

Time Consumption Is More

As it requires parts to be assembled and algorithms to be implemented and trained, labor requirement is quite more and the rate at which the implementation is done takes quite a lot of time.

3.3.4

Needs Battery Backup

As the hardware is powered by low consumption elements battery will not be much of a problem but then charging it or might drain out quickly if it is used for a long time.

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4 Need of the Proposed Prototype As we can see from the above stats many people in the current scenario do not utilize the current system effectively and the blame is not on them since many might not be educated to use braille or to check for a symbol identifier on a currency, basically each currency has a unique symbol to identify the denominations for visually impaired people but then in today’s scenario nobody is able to identify it as they are not well educated. Therefore, bringing this technology into the market will help many visually people out there who are struggling/getting cheated by the cruel people out there (Figs. 2 and 3).

Fig. 2 Source https://www.thinkerbelllabs.com/need

Fig. 3 Source https://www.npr.org/sections/alltechconsidered/2012/02/13/146812288/brailleunder-siege-as-blind-turn-to-smartphones

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5 Future Enhancements The proposed system will be able to solve most of the crises that are currently occurring, this prototype is integrated with machine learning and the Internet of Things which are mostly used in the current era. Visually impaired people will be able to identify the denominations perfectly with the proposed system and can avoid getting cheated. Many more enhancements can be made to the proposed system when it is brought into use, the better quality of data can be trained to the model such that the accuracy of prediction will be high. Secondly, with the overuse of the proposed system battery may get degraded soon so in order to enhance new lithium batteries can be updated. Thirdly, small assistance can be integrated into the system such that it will accompany the person and help their peers to track them if they are lost. Many more enhancements can be made depending upon the user’s interest. The product can also be brought out with assistants for visually impaired people and schedule their medical details in the cloud such that assistants can help the people with their medical intake by notifying them through the speaker [8].

6 Conclusion The model detects the denomination of currency with the help of an image processing and classification algorithm and a handy hardware prototype, and it can be implemented in real-time applications. Much future enhancement can be brought to the existing module such as handy personal assistance. This will require the inclusion of a few more preprocessing techniques and some modifications to the existing prototype to incorporate assistant features. Besides, the current system has poor accuracy for identifying coins other than notes due to the reflective nature of the material. Suitable lighting sources can prevent this problem in the future. Therefore, the proposed prototype can rectify the main issue of visually impaired people and will help them to avoid getting cheated on.

References 1. Currency recognition system for visually impaired: Egyptian banknote as a study case. In: IEEE 2015 International conference on information and communication technology and accessibility, ICTA 2015 2. NRC (National Research Council) (1993) Counterfeit deterrent features for the next-generation currency design. National Materials Advisory Board, NRC. National Academy Press, Washington, D.C 3. Pokala R, Teja V Indian currency recognition for blind people. Int Res J Eng Technol 4. Shweta Y (2020) Currency detection for visually impaired. JETIR May 2020 7(5)

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5. Bhaskar N, Kumar PM (2020) Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm. Soft Comput. https://doi.org/10.1007/s00 500-020-04722-0. ISSN 1432-7643 6. Bhaskar N, Mohan Kumar P, ArokiaRenjit J (2020) Evolutionary fuzzybased gravitational search algorithm for query optimization in crowdsourcing system to minimize cost and latency. Comput Intell. https://doi.org/10.1111/coin.12382. Online ISSN:1467-8640 7. Cloudin S, Mohan KP (2018) Adaptive mobility-based intelligent decision-making system for driver behavior prediction with motion nanosensor in VANET. Int J Heavy Veh Syst. https:// doi.org/10.1007/s40430-018-1286-2 8. Frank Vijay J (2019) Cloud data analysis using a genetic algorithm-based job scheduling process. Expert Syst. https://doi.org/10.1111/exsy.12436 9. Currency Recognition on Mobile Phones, Suriya Singh, CVIT, IIIT Hyderabad, India 10. Currency Recognition System For Visually Impaired, IJARIIE-ISSN(O)-2395-4396

Machine Learning Enabled Performance Prediction of Biomass-Derived Electrodes for Asymmetric Supercapacitor Richa Dubey and Velmathi Guruviah

Abstract Biomass-derived carbon materials have gained momentum due to their natural abundance, unique porous structures and environmental friendly behavior. In this work, different machine learning methods have been applied for studying the effect of surface chemistry on the in-operando behavior of various biomassderived carbon electrode materials with different structural properties, such as pore size, specific surface area of mesopores and micropores. Further, quantitative descriptions have been established for predicting the electrochemical properties of electrode materials. In addition, the effect of key features on energy density was also investigated. ANN model for both the parameters showed the highest correlation coefficient value as 0.99 suggesting good fit of the predicted and experimental data. Through an exhaustive analysis of literature-driven experimental data with various machine learning methods, we report the quantitative correlations between the different physiochemical features of carbon-based electrodes which would be useful for their efficiency optimization in energy storage devices. Keywords Artificial Neural Network · Biomass-derived electrodes · Capacitance prediction · Flexible Asymmetric supercapacitor · Machine learning · Porous electrode

1 Introduction The consumption of available non-renewable fuel resources at an alarming rate has imposed a serious crisis on the energy demands [1, 2]. Thus, the development of green renewable and cost effective electrode materials having quick ion/electron transport speed, high adsorption rate and tunable surface morphology is the need of the hour. Biomass-derived from marine organism, terrestrial plants and daily waste have been R. Dubey (B) · V. Guruviah School of Electronics Engineering, Vellore Institute of Technology, Chennai, TN 600127, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_42

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Fig. 1 Energy source transformation scenario

applied for preparing active carbon in electrode materials for asymmetric supercapacitor [3–5]. Recently, biomass-based products or materials have been utilized as potential sustainable precursors for preparing carbon nanomaterials for energy storage like orange peel algae, rice stem, coconut shell, almonds, etc. The synthesized functional materials mainly consisted of carbon elements other than H, N, O and S [6–8]. Apart from these elements, various other mineral substances like Ca, Mg, Na, and Si are also found [9]. Figure 1 shows the energy source transformation from petrol, diesel to the green and clean energy source. Biomass-derived from renewable crops or animals is an eco-friendly precursor as it creates less pollution. Besides plants, different microorganisms such as bacterial cellulose, fungus, Tremella, Bradyrhizobium Japonicum have been used for preparing biomass-derived active carbon material. The hyphae of microorganism interconnect to form 3D network and transform into honeycomb-like structure thereby providing an effective way for producing unique porous structures for electrodes [10]. Animals and their metabolism such as crustaceans, insects, and mollusks act as good resources for the production of biomass-derived carbon materials. Numerous detailed experimental results and reviews focused on biomass-derived carbon materials have been published in literature though computational studies for establishing the relationship between the structural features, chemical composition and the surface morphology characteristics, like pore size distribution, surface area, etc., have been rarely reported [11–14]. Through a comprehensive analysis of the literature-driven data, physics-informed different machine learning methods (ML) are proposed in this work for unraveling the quantitative correlations between the different performance parameters and the in-operando performance parameters of biomass-derived carbon electrode materials. Though all the proposed machine learning methods showed the right trend of data prediction, the artificial neural network (ANN) provided the best predicted result which is evident by the highest value of correlation coefficient (0.99) and the lowest values of root mean square error (RMSE—10–5 ) obtained for the ANN model.

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2 Data Collection and Feature Selection All data used in this work is obtained from the literature based on biomass-derived electrode materials for asymmetric supercapacitors [15–20]. As per its ability, the ANN can define the complex relationship between the inputs and outputs without understanding the sophisticated function mechanism. For our case, we have applied it for analyzing the relationship between different performance parameters and the in-operando performance parameters of biomass-derived carbon electrode materials. For building our database, we have collected more than 150 articles published on biomass-derived carbon electrode materials for asymmetric supercapacitor. After that a set of data was extracted considering the different physical and chemical features of carbon-based electrode materials, like surface area, micropore size, total pore volume, micropore volume, ID /IG (intensity ratio), cellulose ratio, aspect ratio, internal resistance, contact resistance and different elements doping levels (N%, O%, C%). After the data collection process, the feature selection was conducted based on the gaps identified from the literature. Since our research mainly focuses on studying the capacitance prediction based on effect of electrolyte selection and pore size, we have selected various variables namely specific surface area (SSA), applied voltage (V), pore size (PS), total pore volume (TPV), cellulose ratio, aspect ratio, and ID /IG for ANN and ML modeling. Figure 2 shows the sequential approach followed for capacitance prediction of biomass-derived electrodes.

3 Model Evaluation On the basis of database and the selected physical and chemical features, the ANN model was designed using the conventional model comprising of three layers, namely input layer (at input node), hidden layer (at intermediate node) and output layer (at output node). The input layer has eight neurons connected individually for each parameter, the hidden layer consisting of 20 neurons, and the output layer is having single neuron. To eliminate dimension differences and accelerate convergence efficiency, original inputs were normalized in advance according to given equation: X norm =

X − X min X max − X min

(1)

The selected physical and chemical features of porous carbon electrode materials and the applied voltage are summarized in Table 1. Along with the parameters, the minimum, maximum, mean and standard deviation values corresponding to each parameter are also mentioned. For better modeling and analysis of our data, the capacitance value calculations at low current density (i.e., < 1Ag−1 ) have been selected.

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Fig. 2 Workflow representing the sequential approach for predicting EDL capacitance

Table 1 Different input variables selected for each model Parameter

Minimum

Maximum

Voltage

0.5

4

Specific surface area

3.89

3143

Total pore volume

0

3.14

Mean 1.44 1033.7

Standard deviation 0.845 892.7

0.832

0.68 17.52

Pore size

0.189

163.9

5.852

I D /I G

0.46

3.1

1.16

Capacitance

4.2

2111

346.14

0.505 405.98

The algorithm of the different ML methods was conducted in the ML open-source package WEKA. WEKA is a collection of different machine learning algorithms used for data mining process (https://www.cs.waikato.ac.nz/ml/weka/). It has different tools for data classification, pre-processing, clustering, regression, visualization and association rules. Four different ML methods considered for capacitance prediction model are: Random Tree (RT), Multilayer Perceptron (MLP), Linear Regression (LR), Random Forest (RF) and Support Vector Machine Regression (SVR) [21–24]. The relative contribution of different variables to predictive EDL capacitance is shown in Fig. 3.

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17

SBET

30.1

RS TPV Vmes

18.7

19.4

rL/D ID/IG PS Vmic N% CL%

Fig. 3 Relative contributions of different variables to the predictive EDL capacitance

4 Results and Discussion 4.1 Quantification Assessment of ANN The supercapacitor data set consisting of different features (like SSA, TPV, PS, V, I D /I G ) was trained using different ML methods, namely MLP, LR, RT, SVR, RF and ANN for the prediction of capacitance. The training set and validation set regression function results obtained using ANN model are shown in Fig. 4a. It shows that the value of correlation coefficient for the training data samples was 0.99 and most of the predicted values lie on the vertical line which suggests that the maximum predicted capacitance values are nearly the same as the real (experimentally observed) capacitance values appearing in the literature. As our entire data samples were segregated into train sets, test sets and validation sets hence the test set regression function results obtained using ANN model is presented in Fig. 4b. The result in Fig. 4 indicates that the real and the predicted values are in line with each other which ensure the better prediction capability of our designed model. The extracted data samples have also been trained using different ML methods, and Table 2 presents the statistical results for the prediction of capacitance obtained for each ML model. Among all the ML methods proposed for the designing of the capacitance prediction model, the ANN captures the best correlation established between the different physiochemical features of carbon-based materials and their influence on the EDLC capacitance.

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(a)

(b) Fig. 4 ANN results showing the different correlation coefficient values for a training set and validation set b test set and overall samples Table 2 Statistical results for the prediction of capacitance obtained for each ML model ML method

Performance parameter CC

MAE

RMSE

RAE

RRSE

MLP

0.85

36.78

39.87

0.19

0.20

RT

0.98

15.21

9.86

0.14

0.02

SVR

0.74

49.36

54.72

0.32

0.17

RF

0.96

28.60

15.94

0.26

0.19

ANN

0.99

9.32

3.47

0.11

0.01

LR

0.51

54.71

64.39

0.51

0.28

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5 Conclusion Active carbon derived from biomass-based precursors has emerged as the most effective tool toward coping with severe energy crises and environmental issues. The electrochemical performance of any supercapacitor is having strong dependence on experimental conditions, pore structure and chemical composition. Therefore, for attaining well-quality biomass-based product proper care should be given on the selection of electrode materials. A quantitative correlation between the different features of carbon-based electrode material and EDLC performance using physics-informed ML methods (namely MLP, SVR, RT, RF and LR) was established in this research. Among all the ML methods considered, ANN model gives good fit of the predicted values with the literature collected data samples. The highest correlation coefficient value of 0.99 obtained for ANN makes it the most suitable candidate for prediction models. It provides accurate capacitance prediction dependent on specific surface area over a broad range of electrolyte selection. However, the research can be further carried out using the doping elements or considering the electrode mass ratio. Further, the research could be focused on using different training functions like Bayesian regularization back propagation which usually avoids the overfitting with sparse and scarce data.

References 1. Wang J, Zhang X, Li Z, Ma Y, Ma L (2020) Recent progress of biomass-derived carbon materials for supercapacitors. J Power Sour 451:22779 2. Dubey R et al (2019) Review of carbon-based electrode materials for supercapacitor energy storage. Ionics 25:1419–1445. https://doi.org/10.1007/s11581-019-02874-0 3. Rajesh M et al. (2020) Pinecone biomass-derived activated carbon: the potential electrode material for the development of symmetric and asymmetric supercapacitors. IJER 4. Zhang M, Song Z, Liu H, Ma T (2019) Biomass-Derived highly porous nitrogen-doped graphene orderly supported NiMn2 O4 nanocrystal as efficient electrode materials for asymmetric supercapacitors. Appl Surface Sci S0169–4332:33882–33886 5. Shan D, Yang J, Liu W, Yan J, Fan Z (2016) Biomass-derived three-dimensional honeycomblike hierarchical structured carbon for ultrahigh-energy-density asymmetric supercapacitors. J Mater Chem A 6. Ranaweera CK, Kahol PK, Ghimire M, Mishra SR, Ram KG (2017) J Carbon Res 3(3) 7. Pourhosseini SEM, Norouzi O, Salimi P, Naderi HR, Sustain ACS (2018) Chem Eng 6(4):4746– 4758 8. Tian Q, Wang X, Xu X, Zhang M, Wang L, Zhao X, An Z, Yao H, Gao J (2018) Mater Chem Phys 213:267–276 9. Xia J, Zhang N, Chong S, Li D, Chen Y, Sun C (2018) Green Chem 20(3):694–700 10. Zeng F, Li Z, Li X, Wang J, Kong Z, Sun Y, Liu Z, Feng H (2019) Appl Surf Sci 467(2019):229– 235 11. Chaoui H, Ibe-Ekeocha CC, Gualous H (2017) Elec Power Syst Res 146:189–197 12. Mozaryn J, Chmielewski A (2018) IFAC PapersOnLine 51:23–30 13. Tabor DP, Roch LM, Saikin SK, Kreisbeck C, Sheberla D, Montoya JH, Dwaraknath S, Aykol M, Ortiz C, Tribukait H, Amador-Bedolla C, Brabec CJ, Maruyama B, Persson KA, AspuruGuzik A (2018) Nat Reviews Mater 3(5):5–20

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14. Perea JD, Langner S, Salvador M, Sanchez-Lengeling B, Li N, Zhang C, Jarvas G, Kontos J, Dallos A, Aspuru-Guzik A, Brabec CJ (2017) J Phys Chem C 121(33):18153–18161 15. Wang Z, Guo H, Shen F, Yang G, Zhang Y, Zeng Y, Wang L, Xiao H, Deng S (2015) Chemosphere 119:646–653 16. Kang D, Liu Q, Gu J, Su Y, Zhang W, Zhang D (2015) ACS Nano 9(11):11225–11233 17. Fu H, Xu Z, Li R, Guan W, Yao K, Huang J, Yang J, Shen X, Sustain ACS (2018) Chem Eng 6:14751–14758 18. Qiu X, Wang L, Zhu H, Guan Y, Zhang Q (2017) Nanoscale 9(22):7408–7418 19. Xiao Z, Chen W, Liu K, Cui P, Zhan D (2018) Int J Electrochem Sci 13:5370–5381 20. Li Y, Yu N, Yan P, Li Y et al. (2015) Fabrication of manganese dioxide nanoplates anchoring on biomass derived cross-linked carbon nanosheets for high-performance asymmetric supercapacitors. J Power Sour 300(2015):309e317. http://dx.doi.org/https://doi.org/10.1016/j.jpowsour. 2015.09.077 21. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning 22. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199– 222 23. Tin Kam H (1998) The random subspace method for constructing decision forests. IEEE Trans Patt Anal Mach Intell 20(8):832–844. https://doi.org/10.1109/34.709601 24. Rokach L, Maimon O (2014) Data mining with decision trees

Impact of High-K Material on the Short Channel Characteristics of GAA-Field Effect Transistor Lucky Agarwal, Saurav Gupta, and Ranjeet Kumar

Abstract The current paper presents the modeling of low-power Gate-All-AroundField Effect Transistor structure (GAA-FET) for application in digital circuit. With the advancement in technology, the size of the devices is scaling down, which leads to many short channel effects. To overcome this, an analogy GAA-FET is proposed in this paper. In the proposed work an innovative approach, negative capacitance has been included to satisfy the sub-threshold swings. In order to attain this approach, high-k dielectric material is used as gate insulator material. Furthermore, the thickness of oxide layer is optimized to obtain the best performance GAA-FET. The significance of the high-k dielectric material reduces the total oxide capacitance in GAAFET device such that the proposed structure acquires the better performance such as low sub-threshold swing (SS), and high rectification ratio. The simulation outcomes show a high on current of 0.06 mA in the forward direction with a reduced DIBL and sub-threshold swing. The simulation study conducted by TCAD tool ATLAS, reveals that GAA-FET with high-k dielectric draws an attention for nano-scale application. Keywords GAA-FET · Negative capacitance · High-k · Short channel effects

1 Introduction Transistors have been a very integral part of the VLSI industry and as predicted by Moore’s law; the miniaturization of devices is still a primary focus area to be worked upon. The prime focus lies in getting large number of structures accommodated with L. Agarwal (B) · S. Gupta · R. Kumar School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamilnadu 600127, India e-mail: [email protected] S. Gupta e-mail: [email protected] R. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_43

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lesser power being consumed. But such a need for miniaturization has brought up several short channel effects (SCEs). Sub-threshold swing and high off-state current are the SCEs occurring when the channel length of the devices goes below 100 nm as the devices start losing the gate control. This brings various challenges to satisfy the demands of a structure for low-power applications. Many devices like multiple gate, FinFET (Fin shaped Field Effect Transistor)/TriGate were designed and investigated in recent days to overcome these SCEs [1–4]. FinFET attained its compatibility with semiconductor processing. But there were a lot of disadvantages coming up due to lower values of on current [1]. The applications of reported transistor structures are limited by the significantly low on current (Ion), low rectification ratio, and a high value of sub-threshold swing (SS) [5]. To further improve the performance of transistors for low-power applications, applications of new structures with a new defined material have been proposed in recent years [5]. For example, GAA-FET is proposed which has better control over the channel [3]. With the GAA structure, the short channel parameters of the transistors are improved and results to a device that is well suited for low-power application. To further improve the device performance, high Ion is required. Therefore, the concept of negative capacitance (NC) is proposed which founds to be helpful to increase Ion and satisfy the sub-threshold swings [2]. The negative capacitance is a concept in which scaling down of the voltage leads to a large amount of charge in the channel [6]. This allows the engineer to design a device for low-power application. The important aspect of negative capacitance is the material that is being used as the gate oxide. Recent studies have suggested that the use of high-k dielectric materials has helped to improve the Ion thus reducing the short channel effects. An approach of using an electronic property of high-k dielectric material as an gate insulator material leads to step slope effect, which improves the performance of GAA-FET [2, 3]. In the paper, authors have proposed a GAA-FET with a concept of NC to improve the performance of the device. To validate the importance of high-k material in NCFET, the proposed structure is simulated with different gate oxide materials. The Silvaco Atlas tool has been used to simulate the proposed device.

2 Device Structure Simulation and Discussion The rectification ratio in FET-based devices can be increased by providing excellent electrostatic control over the channel. Therefore, a novel GAA-FET architecture with a high-k layer and polysilicon layer as a gate stack structure is reported in this paper as shown in Fig. 1. 3-D simulation has been done using software Silvaco ATLAS to obtain the cylindrical structure of the proposed device. The total length of the device is 30 nm and the radius is 1.5 nm. The source and gate form the two end terminals of the device whereas the gate which surrounds the channel forms the third terminal. This cylindrical structure of the gate allows better channel control. The gate surrounded by the channel has a work function of 4.7 eV. The parameter used

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Fig. 1 a 3-dimension view of the proposed structure, b 2-dimension view of the proposed structure, c energy band diagram of proposed structure

464 Table 1 GAATFET-device parameters

L. Agarwal et al. Device parameters

Values

Gate length

30 nm

Radius of silicon body

5 nm

Oxide thickness

3 nm

Polysilicon thickness

1 nm

Length of source/drain

10 nm

Source, channel, and drain doping

P, Type 5 * 1020 /cm3

Drain doping

N, Type 2 * 1019 /cm3

Channel doping

N, Type, 1016 /cm3

Source work function

Øs = 5.93 eV

Gate work function

Øg = 4.73 eV

in simulation of 3-D structure is indexed in Table 1. Employing HfO2 as a high-k dielectric with polysilicon stack reduces the overall capacitance and hence found a good affinity for the standard CMOS design. In comparison with its perovskite counterparts, HfO2 has a lower dielectric constant. This property of HfO2 allows it to be used in thin films, which reduces fringing even more. The simulation results were obtained by substituting the HfO2 as a oxide gate material with a dielectric constant of 25 [7]; silicon dioxide (SiO2 ) with a dielectric constant of 3.9 [7]; and aluminum oxide (Al2 O3 ) with a dielectric constant of 9 [7]. The materials with dielectric constants above 3.9 are defined as high-k dielectrics [8]. It was observed that the dielectric constants have a great effect on various SCEs of the device. While simulating the structure, the band-to-band tunneling model has been incorporated in the Silvaco code.

3 Results and Discussion The present section deals with the electrical characterization of the proposed device that is obtained using the Silvaco ATLAS TCAD tool with varying the dielectric material. Various performance parameters are also taken into consideration.

3.1 Output Characteristics and Transfer Characteristics The threshold voltage (V th ) is the value of the gate-source voltage (V gs ) of any transistor when its conducting channel just starts to connect the source and drain contacts of the transistor to initiate conduction in the device. This parameter has been studied for the three different dielectric materials, and the effect is depicted in a graphical form in Fig. 2. It can be observed from Fig. 2 that the V th reduces with

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Fig. 2 Transfer characteristics of the proposed device [for different values of dielectric constant]

the increase in dielectric constant of the gate oxide material. This shows that high-k material is a good choice for a low-power device. It is also observed from Fig. 2 that the I on increases as the dielectric constant of the material is increased. The I on of the proposed NC-GAA-FET is 3.5 mA which is higher value the reported earlier. Figure 3 shows the output characteristics of the proposed device. As observed from Fig. 3, the value of drain current (I d ) increases with the increase of drain to source voltage (V ds ) up to certain value the drain current becomes constant. The Id seems to improve with higher values of V gs . The output a characteristic of the simulated device follows the similar pattern of the transistors characteristics as reported in the literature [9]. Further, Fig. 4 shows the transfer characteristics of the proposed device. It is observed from Fig. 4 that, I d with respect to V gs increases with an increase of V ds . The values of V ds were varied in a range of 0.2 V to 1 V. It can be seen that the drain current increases and reaches a maximum value of 3.5 mA. This value is much higher than reported in literature [10]. The high value of on current makes the device suitable for various electronic and optoelectronic applications.

3.2 Effect of DIBL (Drain-Induced Barrier Lowering) and on Current with Varying Dielectrics The source potential barrier is lowered by interactions between depletion regions of the source and drain regions near the channel surface, resulting in DIBL. When a high drain voltage is applied to a short channel device, the barrier height is reduced,

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Fig. 3 Plot for drain current corresponding to drain-source voltage

Fig. 4 Output characteristics of the proposed structure

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Fig. 5 On current and drain-induced barrier lowering values obtained for different dielectrics

lowering the threshold voltage further, as shown in Fig. 2. The carriers are injected into the channel which is independent of the gate voltage. The effect of DIBL increases at shorter channel length and higher drain voltages [11, 12]. High value of dielectric constant reduces the effect of DIBL as seen from Fig. 5. The on current is the current that flows between the source and drain terminals. As shown in Fig. 2, the on current increases as the value of the dielectric material increases. Since the high-k dielectric materials introduce the NC in the channel that increases the charge and reduces the drain control over the channel [13].

3.3 Effect on Rectification Ratio The current rectification ratio determines the ratio of reverse and forward currents at the same source-drain bias. The lowering of reverse leakage current improves the rectification ratio thus making the device suitable for low-power applications. The rectification ratio can be improved either by decreasing the reverse leakage current or by increasing the on current or both. In the proposed structure, the on current increases but the reverse current does not show much decrement. Overall, rectification ratio is well seemed to be increasing with the use of high-k materials as depicted in Fig. 6.

3.4 Effect of Transconductance (gm ) and SS The variation of sub-threshold swing and transconductance with different dielectrics is shown in Fig. 7. The sub-threshold swing is a feature of the device’s I-V characteristics. In the threshold region, drain current (I d ) is controlled by the gate terminal,

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Fig. 6 Rectification ratio values obtained for different dielectrics

so in this structure, the gate is surrounded by all around the channel which in turn reduces the sub-threshold swing. The SS also reduces with the use of high-k material that moves the proposed device closer to the ideal transistors. For a constant drain to source voltage, transconductance is defined as the ratio of change in drain current to change in gate to source voltage. The transconductance defines the amplification property of the transistor. The higher the value of transconductance, the better it shows amplification. SCEs parameters have been compared in Table 2. GAA-FET with HfO2 as the oxide material is found to be the optimum device for low-power applications.

Fig. 7 Transconductance and sub-threshold swing values obtained for different dielectrics

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Table 2 Comparison of sces parameters for different dielectric material Dielectric material

Transconductance (Amp/V)

SS (mV/Decade)

Rectification ratio

I on (mA)

DIBL(mV/V)

SiO2

0.00209389

76

1.66E + 5

2.03

128

Al2 O3

0.00343238

69

2.55E + 6

2.9

93

HfO2

0.00518354

59

5.41E + 6

3.5

87

4 Conclusion GAA-FET with the concept of high-k material has been proposed in the paper. In proposed structure, the simulation outcome shows that GAA-FET with gate terminal surrounding the channel produces efficient gate electrostatic control over the channel. This makes the proposed structure a good device for low-power applications. Further through a comparative analysis with different dielectrics, it has been observed that for a high-k dielectric material, HfO2 is used as an oxide material that reduces the short channel effects. The structure proposes a high on current of 3.5 mA with a rectification ratio of 5.41E + 6. The presence of an additional gate terminal in the device improves the performance of the GAA-FET. As explored from the DIBL, rectification ratio, and sub-threshold parameters, the GAA-FET structure is resistant to SCE and may be able to replace the FinFET structure in future technology nodes. Acknowledgements The authors are highly obliged and grateful to MNNIT Allahabad for providing access to Silvaco tool and guidance by its esteemed faculty members.

References 1. Dutta U, Soni MK, Pattanaik M (2018) Design and optimization of gate-all-around tunnel FET for low power applications. Res Int J Eng Technol 2. Gaidhane AD, Pahwa G, Verma A, Chauhan YS (2018) Compact modeling of drain current, charges, and capacitances in long-channel gate-all-around negative capacitance MFIS transistor. IEEE Trans Electron Devices, pp 0018–9383 3. Mohan C, Choudhary S, Prasad B (2017) Gate all around FET: an alternative of FinFET for future technology nodes. Int J Adv Res Sci Eng 6(7) 4. Agarwal L, Singh BK, Mishra RA, Tripathi S (2016) Short channel effects (SCEs) characterization of underlaped dual-K spacer in dual-metal gate FinFET device. In: 2016 international conference on control, computing, communication and materials (ICCCCM), IEEE 5. Piccinini G, Graziano M, Vacca M, Vergallo D (2018) Analysis and simulation of emerging FET devices: FinFET, TFET” POLITECNICO DI TORINO,Corso di Laurea Magistrale in Ingegneria Elettronica, Tesi di Laurea Magistrale April 2018 6. Khandelwal S, Duarte JP, Khan AI, Salahuddin S, Hu C (2017) Impact of parasitic capacitance and high-k parameters on negative capacitance FinFET characteristics. IEEE Electron Device Lett 38(1):142–144

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7. Usha C, Vimala P (2021) Influence of high-k material in gate engineering and in multi-gate field effect transistor devices. In: High-k materials in multi-gate FET devices, pp 33–54. CRC Press 8. Philip A, Kumar RK (2011) Preparation and characterization of high k aluminum oxide thin films by atomic layer deposition for gate dielectric applications. Shodhganga 9. Rabaey JM (1996) Digital integrated circuits. Englewood Cliffs, NJ: Prentice-Hall, ch 2, pp 55–56 10. Saeidi A, Rosca T, Memisevic M (2020) Nanowire tunnel FET with simultaneously reduced subthermionic subthreshold swing and off-current due to negative capacitance and voltage pinning effects. Nano Lett 20(5):3255–62 11. Vijh M, Gupta RS, Pandey S (2019) Graphene based tunnel field effect transistor for RF applications. PIERS-Spring, pp 256–259 12. Kumar U, Bhattacharyya TK (2020) Corrections to opportunities in device scaling for 3nm node and beyond: FinFET Versus GAA-FET Versus UFET. IEEE Trans Electron Dev 67(8):3496–3496 13. Atlas user’s manual (2016) Silvaco International Software, Santa Clara, CA, USA

Disaster Management Solution Based on Collaboration Between SAR Team and Multi-UAV Amina Khan, Sumeet Gupta, and Sachin Kumar Gupta

Abstract From the past few years, unmanned aerial vehicles (UAVs) have become a top focus in both academic research and industry. Because of the rapid advancement in wireless networking technologies, they are now extensively employed in a variety of applications like search and rescue (SAR), remote monitoring, surveillance, disaster management, and so on. One of the most crucial necessities during and after natural and man-made catastrophes is emergency communication for first-responders and victims. In this situation, the existing cellular infrastructure gets destroyed, and it puts a huge burden on the remaining base station to handle high traffic coming from the users. It is critical to keep communication infrastructures operational during catastrophe recovery and mitigation. In this situation, a UAV will serve as an excellent replacement for a disaster-damaged terrestrial base station. In this article, we propose a collaborative approach between the SAR team and multi-UAV. To boost its connection, we suggest an appropriate SAR angle of elevation for the SAR team and multi-UAV to efficiently and effectively govern wider disaster zones. We evaluate the proposed model’s performance for coverage probability, throughput, and pathloss. The findings show that the proposed strategy provides an important improvement in helping SARs. Keywords SAR · Collaboration · SAR team · Multi-UAV · Disaster management · QoS · Angle of elevation

A. Khan · S. Gupta · S. K. Gupta (B) School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Kakryal, Jammu and Kashmir 182023, India e-mail: [email protected] S. Gupta e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_44

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1 Introduction Large-scale natural as well as man-made catastrophes like Irma and Hurricanes Sandy, and other cases such as flooding and forest fires, can cause damage to ground communication networks [1]. Because of the disaster’s scale and magnitude, it is extremely difficult for individuals to confront and respond to the issue immediately. In such cases, people will not respond to catastrophe right away. At the moment, attempts are being made to predict and anticipate the possibility of a disaster in the future, as well as to better retort to the aftermath of a catastrophe, to assess the consequences, resolve conflicts, and restore normalcy in a timely and accurate manner. With the advancement in technology, unmanned aerial vehicles (UAVs) can come to the rescue [2]. In beyond fifth-generation (B5G) networks, UAVs can be used as flying base stations (BSs) to provide coverage to the vast geographical regions with optimum signal strength and efficient connection [3, 4]. Moreover, temporarily restoring destroyed communication facilities within catastrophe zones [5] and thereby assisting public safety efforts [6]. The UAV-based network is made up of an airborne access point that provides an airto-ground channel model [7] as well as a device-to-device and ground network system that gives wireless communication between dysfunctional and functional locations [8]. For instance, in the B5G communication network UAVs can be exploited to address particular mission-critical communications requirements like catastrophe recovery and preparedness [9, 10]. In the incident of a cellular base station failure, flying UAV base stations can be immediately deployed to meet the demand for wireless services. In comparison with traditional techniques, these ad hoc cellular systems based on UAVs provide significant advantages. Some of the key benefits of the UAVs are fast deployment across a large geographic area, disaster resistance, end-to-end communication between the search and rescue (SAR) team and the main control center, better signal receiving line of sight (LoS), and enhanced signal transmission than ground base stations due to higher altitudes [11, 12]. The flying UAV base stations must be implemented in such a way that the maximum users can be assisted. One of the most important success aspects is connectivity analysis, which allows for very reliable communications over a wide range using LoS communication, while also ensuring maximum network coverage area and increasing energy efficacy [4]. Moreover, UAV energy efficacy may be accomplished by using a blind-beam tracking strategy and by improving the trajectory to reduce the total amount of power essential to broadcast conferring to the SAR’s rate necessities [13, 14]. The position and angle of elevation between SAR and UAV are also a major factor that affects communication services and pathloss. However, the deployment of a single-UAV can lead to partial coverage for the SAR team due to the inadequate distance for communication. To deliver communication services to a greater area, coordination between the multi-UAV and SAR teams is required. As a result, this article offers a collaboration between SARs and multi-UAVs, with performance measured using the SAR elevation angle.

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The remainder of the paper is carried out as follows: Sect. 2 discusses the related work. Section 3 presents the proposed model based on multi-UAV in disasters to deliver wireless services to the SAR. Section 4 demonstrates the simulation setup and gives result discussion on pathloss, throughput, and probability of coverage; and finally, Sect. 5 presents a conclusion and future directions are drawn.

2 Related Work In this paper, [15] a reliable public safety network was developed based on the Internet of Public Safety (IOPS) and UAV technology. It provides efficient communication facilities to evacuate people who are caught in disaster with the help of police, rescue, and relief team. Here, it mainly focuses on throughput and delay as the quality of service parameters for enhancing communication connection by collaborating UAV and IOPS. To increase the energy transfer performance for long-term network connectivity, the authors [9] suggested a different emergency communication system (ECS) based on the optimum cluster head method to provide extended coverage and reliable transmission, whereas the authors in [16] discuss various UAV communication toward 5G or B5G wireless networks and also focus on numerous 5G techniques based on UAV platforms categorizing them into distinct categories such as joint communication, caching, physical layer, and network layer. Moreover, the authors in [7] discuss various uplink as well as downlink channel models for UAV communications. It also compares various performance metrics based on different altitudes of UAVs and their different operating frequencies. In addition, it also gives layered architecture for disaster management systems based on Low Altitude Platform (LAP) UAVs as well as High Altitude Platform (HAP) UAVs. Most of the research work was done based on UAV’s altitude and its operating frequency, and only a few researchers focus on the angle of elevation between SAR and UAV. Hence, the main contribution of this article is to provide collaboration between multi-UAV and SAR based on the optimum angle of elevation between SAR and UAV to enhance the area of coverage and throughput. Table 1 summarizes the latest work in the field of UAV and SAR collaboration. From the above literature survey, it is found that none of them take into consideration the collaboration between the SAR team and UAVs. In this article, the authors mainly focus on how the SAR team will communicate with different UAVs based on the elevation angle between the SAR team and various UAVs to achieve maximum coverage and connectivity. The authors then focus on the proposed SAR and multiUAV collaboration paradigm for accomplishing tasks efficiently and effectively in real time. Finally, we measure the throughput, pathloss, and probability of coverage to assess the proposed model.

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Table 1 Summary of the latest work in the field of UAV and SAR collaboration References

Highlights

Data gathering

UAV

IoT

SAR

Optimum θ

[15]

UAVs act as relay nodes to provide full-duplex communication services [15]

✓

✓

✕

✕

✕

[9]

Different emergency communication systems based on cluster head method for extended coverage and reliable transmission [9]

✓

✓

✓

✕

✕

[16]

HAP UAVs to provide mobile communications [16]

✕

✓

✓

✕

✓

[7]

Different uplink and ✓ downlink channel models for LAP and HAP UAVs to provide maximum coverage and connectivity [7]

✓

✕

✓

✕

This article

SAR team and ✓ multi-UAV collaboration to enhance QoS and connectivity [This article]

✓

✓

✓

✓

3 Proposed Model As illustrated in Fig. 1, we discuss the use of multi-UAV in disasters to deliver wireless services to the SAR in this article. The homogeneous Poisson point process (PPP) is used to deploy the SARs spatially Φ S AR with intensity ρ. . Moreover, to offer wireless communication facilities to the SAR teams, UAVs are positioned at a fixed location (x n, y n , z n ) with height H n . Let the positions of SARs be SARi (x i , y i ), SARj (x j , y j ), SARk (x k , y k ). The UAVs, namely UAV1 , UAV2 , and UAV3 , are in direct connection with the SARs. In catastrophe cases, the angle of elevation for every SAR is coupled with the UAVs to provide reliable communication and maximize coverage probability.

3.1 Pathloss Between UAV and SAR The coverage of the UAV is considered to be circular with 250 m as a diameter. The distance between SAR and UAVs for different UAVs in their coverage zone is written as R i , R j , R k , and the SAR angle of elevation within the coverage range of UAVs

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UAV2

UAV1

UAV3

h2

h1

h3 H= UAV altitude R= Distance between SAR and UAV = SAR elevation angle Φ= Distribution range of SAR

H2

Rj2

Ri1 Rk3

User

H1

H3

User

j rj SARj

User

User

User User

k

i ri SARi

SARk

rk

User User

User

Damaged base station

User

Fig. 1 Use of multi-UAV in disasters to deliver wireless services to the SAR

is denoted as θ i , θ j , and θ k , respectively. The mth active SAR distance from the nth UAV is given as R(m,n) =

/ (U AVn − xm )2 + (U AVn − ym )2 + Hn2

(1)

where SAR members are denoted by m ∈ i, j, k and n = 1, 2, 3 are the serial number of UAVs. The LoS probability associated with SAR members aided by the UAVn is calculated as a function of the θm the angle of elevation of SAR and a, b as environmental parameters [17] is as follows: PLoSm = (

1 ) 1 + ae−b(θm −a)

(2)

The pathloss transmission between the SAR team and UAV via the air-to-ground (A2G) channel is a crucial component that affects the wireless connection. Total pathloss is the sum of Free Space Pathloss (FSPL) and Additional Pathloss (APL) of the free space. As a result, the pathloss can be calculated by using the following formula: PL(dB) =

4π f c Rm,n + ηLoS PLoS + ηNLoS PNLoS c

(3)

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PL(dB) = FSPLm + APLm

(4)

where FSPLm = 4π fccRm,n , c is the speed of light , f c is the carrier frequency, Rm,n are the distances between UAVs and SARs, and APLm = ηLoS PLoS + ηNLoS PNLoS it depends on the environment of the specific radio.

3.2 Throughput ( ) The data transmission by the U AV n to the SAR member which placed at x m , y m is expressed as follows: R(θ m , λm ) = λm T m W m log2 ( ( )) ηLoS − 1 1 − ηNLoS 1 + ∑N p t + G 3dB − FSPLm − 2 1 + ae−b(θ m −a) n=1 h m p m + σ (5) is the where Tm is the effective time for data transmission, Wm = N ∫ Am f BW (xm ,ym )dx d y bandwidth per SAR coverage, N is the average SAR number for a given area Am , h m is the link channel gain from the UAV to the SAR, pm are the transmission power of the mth UAV and SAR pair, σ 2 is the noise power, pt is the total power utilization in the desired network, and G 3dB is the power gain of the channel from the UAV to SAR. Now, rearranging the above equation, we can find out the optimum value of the angle of elevation θm and is rewritten as follows: 1 θm = − (−2ba + ln 2b ) ( σ 2 + N h m pm − FSPLm + pt + G 3d B − ηLoS − ηNLoS + 1 ( ) a 2 N h m pm + σ 2 − FSPLm + pt − ηNLoS

(6)

3.3 Probability of Coverage for Downlink Transmission If the received power probability exceeds[ a certain threshold, it is referred to as ] coverage probability. As a result, P c = P P r,m ≥ pmin where the signal received power is P r,m for SARs for a given region Am . The coverage probability attained by the SARs in the disaster region of the proposed model may therefore be represented as follows:

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) ( P(c,m) = PLoS,m Q × C + 1 − PLoS,m Q × D [ C= [ D=

pmin + ηLoS + P L dB − G dB − pt 2 σLoS

(7)

] (8)

] pmin + ηNLoS + P L dB − G dB − pt , 2 σNLoS

(9)

where P L dB denotes the pathloss, G dB = 3dB denotes the gain of the antenna, Q denotes the function of LoS and non-line of sight (NLoS) links and additional losses are denoted by ηLoS and ηNLoS for NLoS or LoS connection between the UAV and the SAR, respectively.

4 Simulation and Result Discussion From this segment, the collaborative performance of SAR and multi-UAV for various parameters like throughput, pathloss, and probability of coverage can be examined using simulation findings. Table 2 depicts the simulation parameters. For different heights of UAV, the transmitting signals coming from the multi-UAV to the SAR are analyzed at an optimum angle of elevation to provide the maximum probability of coverage and throughput.

4.1 Pathloss Analysis The medium between the SARs and the UAV will probably be affected by the UAV A2G channel. The pathloss performance was influenced by the UAV altitudes, as shown in Fig. 2. As the UAV climbs to a greater height, the pathloss rises while the coverage range of the UAV improves. Moreover, it gives a sign that the probability of LoS reduces because of the rise in UAV height. Furthermore, results reveal that at a height of H = 200 m, the highest pathloss is calculated as 118.74 dB, while at H Table 2 Simulation parameters

Parameters

Values

Parameters

Values

a

10.6

fc

3.5 GHz

b

0.18

Tx (Transmission power)

10 W

ηLoS

1

SAR-UAV distance

100–500 m

ηNLoS

20

Bandwidth (BW)

5 MHz

σ2

− 174 dBm

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A. Khan et al. 125

Pathloss (dB)

115 105 95 85 75 65 0

50

100

150

200

250

300

350

400

450

500

SAR-UAV distance (m) UAV1 (H1) =100 m

UAV2 (H2) = 150 m

UAV3 (H3) = 200 m

Fig. 2 Pathloss versus SAR-UAV distance

= 150 m the pathloss is assessed to be 109.22 dB, and at H = 100 m, the pathloss is assessed to be 99.55 dB, because of the NLoS link, which degrades the SINR received.

4.2 Throughput From Fig. 3, it is clear that when the optimum angles of elevation change it affects the value of throughput by changing UAVs height. Also, it is observed from the figure that the value of throughput increases when the angle of elevation rises irrespective of every UAV’s height. The negligible increase in interference on the received signal-to-interference-plus-noise ratio (SINR) and fixed bandwidth consumption are responsible for this. At the optimum angles of elevation of 44.6°, the maximum value of throughput for every altitude of UAV is 420.01 Mbps, resulting in the network stability in the catastrophe region.

4.3 Probability of Coverage The probability of coverage performance versus angle of elevation of SAR for every UAV height is shown in Fig. 4. Because of the radio propagation of NLoS and LoS on the received signal, the normalized probability of coverage drops with the increase in SAR angle of elevation. Moreover, due to a reduction in communication distance and an increase in the angle of elevation between the SARs and UAV, the pattern will continue until angles of elevation roughly reach approximately 26° at UAV1 H 1 = 100 m, 36° at UAV2 H 2 = 150 m, and 46° at UAV3 H 3 = 200 m, respectively.

Angle of elevation (

m)

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44.6 44.58 44.56 44.54 44.52 44.5

415

415.5

416

416.5

417

417.5

418

418.5

419

419.5

420

Throughput (Mbps) UAV1 (H1) = 100 m

UAV2 (H2) = 150 m

UAV3 (H3) = 200 m

Fig. 3 Throughput versus optimum angle of elevation for SAR

Subsequently, the probability of coverage has increased as the SAR angle of elevation has increased because of enhanced LoS near to the multi-UAVs via the SAR distance that affects the height. Since then, the probability of coverage has increased as the SAR angle of elevation has increased because of the enhanced LoS near to the UAVs through the SAR distance that affects the angle of elevation.

Probability of Coverage

1 0.99 0.98 0.97 0.96 0.95 0.94 0

10

20

30

40

50

60

70

80

90

Angle of Elevation for SAR UAV1 (H1) = 100 m

UAV2 (H2) = 150 m

Fig. 4 Probability of coverage versus angle of elevation of SAR

UAV3 (H3) = 200 m

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5 Conclusion UAVs are seen as a viable technology for catastrophe recovery and management because of their ease of deployment and great mobility. Taking these into consideration, this article proposed a multi-UAV-based network approach for disaster management that will save lives and decrease the economic effect. With the help of multiUAV, network coverage can also be extended. The SAR angle of elevation was used to assess the model’s performance. The findings showed that increasing the angle of elevation reduces pathloss and increases the probability of coverage and throughput. In the future, research can be done on the multi-UAV and SAR team collaboration based on energy efficiency.

References 1. Khan A, Gupta SK, Assiri EI, Rashid M, Mohammed YT, Najim M, Alharbi YR (2020) Flood monitoring and warning system: Het-Sens a proposed model. In: 2020 2nd international conference on computer and information sciences (ICCIS), Jouf University, Aljouf, Sakaka, Saudi Arabia, IEEE, pp 1–6 2. Khan A, Gupta S, Gupta SK (2020) Multi-hazard disaster studies: monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. Int J Disaster Risk Reduction 47:101642 3. Gupta A, Gupta SK, Rashid M, Khan A, Manjul M (2020) Unmanned aerial vehicles integrated HetNet for smart dense urban area. Trans Emerg Telecommun Technol, pp 1–22. https://doi. org/10.1002/ett.4123 4. Alsamhi SH, Afghah F, Sahal R, Hawbani A, Al-qaness MA, Lee B, Guizani M (2021) Green internet of things using UAVs in B5G networks: a review of applications and strategies. Ad Hoc Netw, p 102505 5. Saif A, Dimyati K, Noordin KA, Shah NS, Alsamhi SH, Abdullah Q, Farah N (2021) Distributed clustering for user devices under UAV coverage area during disaster recovery. In: 2021 IEEE international conference in power engineering application (ICPEA), IEEE, pp 143–148 6. Syed F, Gupta SK, Hamood Alsamhi S, Rashid M, Liu X (2021) A survey on recent optimal techniques for securing unmanned aerial vehicles applications. Trans Emerg Telecommun Technol 32(7):e4133 7. Khan A, Gupta S, Gupta SK (2021) Unmanned aerial vehicle-enabled layered architecture based solution for disaster management. Trans Emerg Telecommun Technol p e4370. https:// doi.org/10.1002/ett.4370 8. Liu X, Li Z, Zhao N, Meng W, Gui G, Chen Y, Adachi F (2018) Transceiver design and multihop D2D for UAV IoT coverage in disasters. IEEE Internet Things J 6(2):1803–1815 9. Saif A, Dimyati K, Noordin KA, Deepak GC, Shah NSM, Abdullah Q, Mohamad M (2021) An efficient energy harvesting and optimal clustering technique for sustainable postdisaster emergency communication systems. IEEE Access 9:78188–78202 10. Alsamhi SH, Ma O, Ansari MS, Almalki FA (2019) Survey on collaborative smart drones and internet of things for improving smartness of smart cities. Ieee Access 7:128125–128152 11. Grinewitschus L, Almujahed H, Fresia M, Mueck M, Bruck G, Jung P (2021) Large device to device communication network including airborne drones for emergency scenarios. Trans Emerg Telecommun Technol 32(10):1–29. https://doi.org/10.1002/ett.4295 12. Kim S, Kim K (2021) Systematic tertiary study for consolidating further implications of unmanned aircraft system applications. J Manag Eng 37(2):03120001

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13. Saif A, Dimyati K, Noordin KA, Shah NSM, Abdullah Q, Mohamad M, Mohamad MAH, Al-Saman AM (2021) Unmanned aerial vehicle and optimal relay for extending coverage in post-disaster scenarios. arXiv preprint arXiv:2104.06037 14. Rahul AR, Sabuj SR, Akbar MS, Jo HS, Hossain MA (2021) An optimization based approach to enhance the throughput and energy efficiency for cognitive unmanned aerial vehicle networks. Wireless Netw 27(1):475–493 15. Alsamhi SH, Ma O, Ansari MS, Gupta SK (2019) Collaboration of drone and internet of public safety things in smart cities: an overview of QoS and network performance optimization. Drones 3(1):13 16. Li B, Fei Z, Zhang Y (2018) UAV communications for 5G and beyond: recent advances and future trends. IEEE Internet Things J 6(2):2241–2263 17. Nguyen MN, Nguyen LD, Duong TQ, Tuan HD (2018) Real-time optimal resource allocation for embedded UAV communication systems. IEEE Wirel Commun Lett 8(1):225–228

Photonic Crystal Drop Filter for DWDM Systems V. R. Balaji , Shanmuga Sundar Dhanabalan, T. Sridarshini, S. Robinson, M. Murugan , and Gopalkrishna Hegde

Abstract In this work, we present a channel drop filter (CDF) for dense wavelength division multiplexing (DWDM) systems designed using 2D photonic crystal square lattice (PCSL).The CDF consists of input waveguide, output drop waveguide, plusshaped resonant cavity (PSRC) and reflector. The proposed filter is designed to the resonant wavelength of 1543.9 nm. The filter is able to drop the desired center wavelength by varying the outer ring pillar radius of PSRC. The novel filter drops the wavelength with high transmission efficiency, quality factor, and spectral line width of >> 98%, 4000, and 0.5 nm. The proposed device size is 73.96 μm2 , and it is very ultra-compact and suitable for WDM systems. Keywords Photonic crystal · Drop filter · Cross talk · Quality factor

V. R. Balaji (B) School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Tamil Nadu 600127, India e-mail: [email protected] S. S. Dhanabalan Functional Materials and Microsystems Research Group, School of Engineering, RMIT University Melbourne, Victoria 3001, Australia T. Sridarshini Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, India S. Robinson Department of ECE, Mount Zion College of Engineering and Technology, Pudukkottai, India M. Murugan Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Kattankulathur 603203, India G. Hegde Centre for Biosystems Science and Engineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_45

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1 Introduction With growing demand for the need of high capacity, high-speed, and high bandwidth communication system, optical communication provides a suitable solution in the form of dense wavelength division multiplexing (DWDM) system. DWDM is one of the optical multiplexing technologies which effectively increases the bandwidth than the normal optical fiber system. Out of the components required for DWDM system, optical filter and demultiplexer play an important role. In this paper, we have demonstrated an optical filter using photonic crystals (PC), which is an emerging technique in optical communication for photonic integrated circuits (PIC) [1]. Photonic crystals can be defined as the arrangement of dielectric materials in a periodic manner, such that there is a variation of dielectric constant. Photonic crystals can be devised manually based on the requirement and application. Various optical communication components such as splitters [2], filters [3], circulators [4], routers [5], analog-to-digital converters [6], laser [7], demultiplexer [8], logic gates [9] etc. To design components based on PC, we exploit the property of photonic bandgap (PBG) or forbidden bandgap of photonic crystal. This is similar to the bandgap in semiconductor materials. Similar to the semiconductor materials where it can control the flow of electrons inside them, photonic crystals can control and manipulate the flow of photons in them. As we have the process of doping in semiconductors, we employ the process of inducing defects to alter the PBG of photonic crystals. We have designed photonic crystal drop filters based on the same phenomenon. In the literature survey [10–22], it is observed that optical filters were designed with different shapes using square and triangular lattice and many existing works reported with high transmission efficiency, and Q factor. Still short falls such as uniform channel spacing, uniform spectral line width, compact size device, high Q factor, and high transmission efficiency are not addressed. The proposed work is designed in novel plus-shaped cavity, and the design provides the flexible tuning for the DWDM filter. The filter provides the high transmission efficiency, high-quality factor, and ultra-compact size, and it can easily integrated for photonic integrated circuits (PIC). Our paper is organized in such a way that Sect. 2 discusses the structural parameter of proposed design. The schematic view of the plus signed channel drop filter is described in Sect. 3. Section 4 discusses the modeling and working principle of proposed channel drop filter. Simulation and discussion are analyzed in Sect. 5. Section 6 concludes the paper.

2 Structural Parameters The 2D square lattice photonic crystal (PC) is used in the proposed design. The artificial lattice is designed with high refractive index silicon rods (3.47), and the rods are implanted on the air. The silicon rods are used in the design for fabrication

Photonic Crystal Drop Filter for DWDM Systems Table 1 Structural parameter for the design

485

Structural parameters

Size/Unit

Period of square lattice (A)

570 nm

Height of dielectric rods (h)

I300 nm

Radius of non-defect rods (R)

130 nm

Dielectric constant of rods (e)

11.56

Refractive index (n) of Si at 1550 nm

3.46

Index difference (Δ)

2.46

Refractive index (n) of air

1

Rod profile

Step profile

Number of rods in X side

15

Number of rods in Z side

15

Size of proposed device ΔX × 73.96 μm2 ΔZ TE PBG for lattice before defects

0.28517 ≤ a/λ ≤ 0.41749

TE PBG in terms of wavelength 1365 nm–1998 nm

feasibility, and it is a better choice for guided optics. The detailed structural parameter of the proposed design is given in Table 1. Figure 1 shows the PBG for the lattice before introducing the defects. TE1 PBG range varies from 1438 to 2104 nm. The large bandgap obtained at the 135 nm is shown in Fig. 2. Figure 3 shows the bandgap diagram for normalized frequency with lattice constant. The frequency shifted to higher end for the increased value of lattice constant, optimized value is 600 nm.

3 Plus Signed Resonant Cavity-Based Channel Drop Filter The schematic view of the proposed channel drop filter is shown in Fig. 4. The filter is designed with bus waveguide, drop waveguide, and plus signed resonant cavity (PSRC) and reflector. The plus-shaped resonant cavity (PSRC) behaves as a resonator and a coupler between input bus waveguide and drop waveguide. The PSRR acts as a coupling element to couple the resonant frequency to couple the signal to drop waveguide. The PSRR plays an important role in DWDM systems for selecting the mode from multiplexed mode from single mode fiber (SMF). The design consists of two ports: port X and port Y. The wide wavelength range (many modes) optical spectrum is launched in the bus waveguide, PSRC resonant only one mode, and complete transfer to drop waveguide for the particular mode to port Y. The reflectors 1 and 2 are placed in the right end of the bus and left end of the drop waveguide.

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Fig. 1 PBG for 10 ×10 PC square without defects

Fig. 2 Bandgap map for PBG versus radius of rod (R)

4 Proposed Channel Drop Filter Figure 5 shows the proposed plus sign resonant cavity-based drop filter for DWDM systems. The filter design consists of plus signed resonant cavity (PSRC), input bus waveguide, and output drop wave guide. The square lattice PC is created with 15 × 15 array of rods (250 rods). The plus (+) signed resonant cavity introduced in 2D square lattice PC by removing four rods in horizontal and other four rods in vertical

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Fig. 3 Bandgap map for PBG versus period (A)

Fig. 4 Schematic view of drop filter

direction and one center rod is removed at the center of the cavity. The input bus waveguide is created by removing six rods in horizontal direction, and the output waveguide is created by deleting eight rods in the lattice. Figure 6 shows the 3D view of PSRC-based drop filter, with chip size of 8.6 μm × 8.6 μm. The PSRC micro-cavity acts as a coupling region for drop waveguide, the coupling region acts as reflection mirrors, and the resonant frequency in PSRC couples to drop waveguide via constructive interference. The scatter rod is placed at the corner with the radius of 80 nm, and the radius of scatter rod varied from 70 to 90 nm, after several iterations. The scatter rod used to reduce the scattering loss and back reflection loss helps to improve the transmission efficiency. The performance parameters of CDF are listed in Table 2.

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Fig. 5 Proposed channel drop filter Fig. 6 3D view of square lattice

Table 2 Performance parameter of CDF Δλ (nm)

TE (%)

1543.9

0.4

100

3859.75

125

80

1544.3

0.4

93

3860.75

127

80

1548.1

0.42

97

3685.9

129

80

1552.3

0.41

98

3786

131.5

80

λ0 (nm)

Q factor

Outer rod radius

Scatter rod radius

TE Transmission efficiency, λ0 Resonant wavelength Δλ Spectral linewidth.

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489

5 Performance Parameter Finite difference time domain (FDTD) algorithm is used to study the propagation of light inside the waveguides and arbitrary geometry [23]. The FDTD algorithm is very powerful to solve the Maxwell equation. The Gaussian pulse with center wavelength of 1550 nm is launched in the input port X. The power monitor is placed in the end of the port Y, and it measures the normalized transmission spectrum. The normalized transmission spectrum is measured with the following equation [23]. ∫ ) ( 0.5 real p( f )monitor .dS T( f ) = Source Power

(1)

T ( f ) is normalized transmission with function frequency, p( f ) is pointing vector, and dS is a surface integral to determine best time value. The meshing size of the structure is Δz = Δx = a/20 which is equal to Δz = Δx = 28.5 nm, based on the lattice constant a = 570 nm. The 2D FDTD is used for simulation purpose as it reduces the computation time without compromising the accuracy rather than 3D simulations. The electric field distributions of PSRC at ON resonance at the wavelength of λ = 1543.9 nm are shown in Fig. 7 and at OFF resonance are depicted in Fig. 10, respectively. The transmission spectrum at the outer rod radius 125 nm and 127 nm is shown in Figs. 8 and 9, respectively.

Fig. 7 Electric field distribution of PSRC at ON resonance (λ1 = 1543.9 nm)

490

Fig. 8 Transmission spectrum for PSRC outer radius 125 nm

Fig. 9 Transmission spectrum for PSRC outer radius 127 nm

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Fig. 10 Electric field distribution of PSRC at OFF resonance

The fabrication feasibility and rod tolerance for the drop filter are considered, to match simulation output with real-time systems. The drop filter drops the desired wavelength by changing the outer rod radius in PSRC and its simple method to get the desired response. The 2 nm change in outer rod radius in the design will shift the 0.4 nm shift wavelength with the reference wavelength. The fabrication tolerance allowed for the proposed design is ± 0.5 nm to drop the wavelength without shift in the reference wavelength. The functional parameters of the CDF filter are compared to the existing PC filter, which are given in Table.3. The proposed results show that PSRC demultiplexer works better than reported papers. The PIC can be fabricated using techniques like optical lithography [24], direct UV laser writing [25], multi-exposure holography [26], focused ion beam lithography [27], and electron beam lithography [28]. The proposed 2D rod-based PC can be fabricated by electron beam lithography that is able to control in sub-nm size in vertical etching.

6 Conclusion In this work, a CDF designed with novel PSRC is proposed. The PSRC is used in the model along with scatter rods and reflector. The filter provides the high Q factor due to cancelation of multimode inside the cavity, and it traps only one defect

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Table 3 Comparison of type of lattice, number of output ports, transmission efficiency, Q factor, footprint, and channel spacing of the proposed filter with reported filter Reference no.

Lattice constant (nm)

Spectral line width

T.E (%) (Min/Max)

Q factor (Max)

Footprint (μm)2

10

540

NA

92.7/99.5

172.5–312.7

122.47

11

630

NA

95

1290

211.1

12

NA

NA

100

228. 57

152.76

13

797

0.8 nm

99

1937

432

14

549

1 nm

76/100

1550

NA

15

600

NA

NA

NA

NA

17

623

4.5

95/100

344

NA

19

200

NA

92/100

206.5

152.76

Proposed

570

0.4

99

4000

73.96

NA Not applicable, T.E Transmission efficiency

mode inside the drop waveguide. The filter acts as tunable demultiplexer to drop the desired resonance wavelength with adjusting the outer radii rod. The filter work can be extended further with splitting the PSRC into three ultra-resonant cavities to drop three wavelengths with one PSRC, and this idea drastically reduces the footprint.

References 1. Akahane Y (2003) High-Q photonic nanocavity in a two-dimensional photonic crystal. Nature 425(6961):944–947 2. Shi S (2004) Dispersion-based beam splitter in photonic crystals. Opt Lett 29(6):617–619 3. Sundar DS (2018) High-efficiency filters for photonic integrated networks: a brief analysis. Laser Phys 28(11):116203 4. Sundar DS (2019) Compact four-port circulator based on 2D photonic crystals with a 90° rotation of the light wave for photonic integrated circuits applications. Laser Phys 29(6):066201 5. Sathyadevaki R (2018) Photonic crystal based routers for photonic integrated on chip networks: a brief analysis. Opt Quant Electron 50(11):1–15 6. Tavousi A (2018) Optical-analog-to-digital conversion based on successive-like approximations in octagonal-shape photonic crystal ring resonators. Superlattices Microstruct 114:23–31 7. Altug H (2006) Ultrafast photonic crystal nanocavity laser. Nat Phys 2(7):484–488 8. Balaji VR (2021) A novel hybrid channel DWDM demultiplexer using two dimensional photonic crystals meeting ITU Standards. Silicon, 1–12 9. Sivaranjani R (2020) Photonic crystal based all-optical half adder: a brief analysis. Laser Phys 30(11):116205 10. Chhipa MK (2017) Improved dropping efficiency in two-dimensional photonic crystalbased channel drop filter for coarse wavelength division multiplexing application. Opt Eng 56(1):015107 11. Seifouri M (2018) Ultra-high-Q optical filter based on photonic crystal ring resonator. Photon Netw Commun 35(2):225–230 12. Rashki Z (2018) Novel design for photonic crystal ring resonators based optical channel drop filter. J Optoelectron Nanostruct 3(3):59–78

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13. Shaverdi A (2018) Quality factor enhancement of optical channel drop filters based on photonic crystal ring resonators. Int J Opt Photonics 12(2):129–136 14. Tavousi A (2018) Realization of a multichannel drop filter using an ISO-centric all-circular photonic crystal ring resonator. Photon Nanostruct-Fundam Appl 31:52–59 15. Rafiee E (2018) Realization of tunable optical channel drop filter based on photonic crystal octagonal shaped structure. Optik 171:798–802 16. Rajasekar R (2019) Nano-channel drop filter using photonic crystal ring resonator for dense wavelength division multiplexing systems. J Nanoelectron Optoelectron 14(6):753–758 17. Alipour-Banaei (2018) Channel drop filter based on photonic crystal ring resonator. Optica Applicata 48(4):1–6 18. Ren H (2006) Ptohonic crystal channel drop filter with a wavelength-selective reflection microcavity. Opt Express 14:2446–2458 19. Rashki Z (2017) Novel design of optical channel drop filters based on two-dimensional photonic crystal ring resonators. Optics Commun 395:231–235 20. Foroughifar A (2021) Design and analysis of a novel four-channel optical filter using ring resonators and line defects in photonic crystal microstructure. Opt Quant Electron 53(2):1–12 21. Naghizade S (2019) Loss-less elliptical channel drop filter for WDM applications. J Opt Commun 40(4):379–384 22. Soma S (2019) Tunable optical add/drop filter for CWDM systems using photonic crystal ring resonator. J Electron Mater 48(11):7460–7464 23. Joannopoulos JD, Villeneuve PR, Fan S (1997) Photonic crystals. Solid State Commun 102(2– 3):65 24. Scrimgeour J (2006) Three-dimensional optical lithography for photonic microstructures. Adv Mater 18(12):1557–1560 25. Deubel M (2004) Direct laser writing of three-dimensional photonic-crystal templates for telecommunications. Nat Mater 3(7):444–447 26. Dwivedi A (2008) Formation of all fourteen Bravais lattices of three-dimensional photonic crystal structures by a dual beam multiple-exposure holographic technique. Appl Opt 47(12):1973–1980 27. Cabrini S (2005) Focused ion beam lithography for two dimensional array structures for photonic applications. Microelectron Eng 78:11–15 28. Ruan Y (2007) Fabrication of high-Q chalcogenide photonic crystal resonators by e-beam lithography. Appl Phys Lett 90(7):071102

Evaluation of Modulation Methods for SOA-Based All-Optical Logic Structure at 40 Gbps V. Sasikala, K. Chitra, and A. Sivasubramanian

Abstract The speed of electrical circuits is the major hurdle of concern in highspeed communication. To overcome conventional computing limitations, the electrical components are replaced by photonic components. All-optical switching characteristics are used to construct various logic gates output using SOA nonlinear effects. This article analyzes the switching characteristics of all-optical logic gates in terms of extinction ratio, eye diagram, and BER using different modulation formats. Tailoring the important parameter of SOA proves the feasibility of the analysis and provides optimum performance. Keywords Nonlinear optical effects · All-optical logic · Semiconductor optical amplifier nonlinearities · Switching characteristics · Modulation formats

1 Introduction All-optical logic gates are implemented and executed in linear and nonlinear methods. Nonlinear optical effects fall into two different categories. Light propagates in a single channel causing a self-induced nonlinear effect whereas the other nonlinear effects are arisen due to interactions among the channels in dense systems. Self-induced nonlinear effects are exhibited primarily by a self-phase modulation (SPM), in which the optical signal propagates through the channel and is modulated by its phase. Nonlinear effects in Dense Wavelength-Division Multiplexing (DWDM) channel are categorized into four-wave mixing (FWM), cross-gain modulation (XGM), and cross-phase modulation (XPM). More number of channels are mixed and transfer a portion of energy from one signal to another is called FWM. The gain saturation is V. Sasikala (B) Associate Professor, Sri Sairam Engineering College, VIT University, Chennai Campus, Chennai, India e-mail: [email protected] K. Chitra · A. Sivasubramanian School of Electronics Engineering, VIT University, Chennai Campus, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_46

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achieved by the XGM effect in the active region of the nonlinear component and turn, provides wavelength conversion. In XPM, the phase of each channel is modified by the power of the co-propagating channels [1]. In this chapter, we realized DWDM system uses different modulation formats on the transmitter side. The nonlinearity is generated using SOA nonlinear principles. The switching characteristics in the DWDM environment are analyzed using an eye diagram and quality factor. Introduction to nonlinear effects in the DWDM system is presented in Sect. 1. Section 2 discusses the theory and nonlinear effects of SOA. The various modulation formats in DWDM optical systems using return to zero (RZ), carrier suppressed return to zero (CSRZ), non-return to zero (NRZ), differential phase shift keying (DPSK), and duo binary modulation format are presented in Sect. 3. Section 4 discusses the effects of various modulation formats, and finally, the summary is made in Sect. 5.

2 Semiconductor Optical Amplifier and Nonlinear Effects Semiconductor optical amplifier is having a gain medium in its structure and has a very similar construction to the laser. It differs from the laser by its anti-reflection coating at the end facets. Similar to laser, the SOA has a p–n junction layer that is forward biased during the operation. It results in the spontaneous emission of light and provides optical gain for light propagation. It also exhibits nonlinear characteristics and generates nonlinearity in the active gain medium when the signal power is high. All-optical switches or gates are designed at ultra-high speed by properly choosing structural parameters to obtain the nonlinear effects in SOA [2, 3]. In the active region of amplification, the material gain coefficient g (N) (per unit length) is directly proportional to the carrier (electrons and holes) density N. This relationship can be derived in Eq. (1) as g(N ) =

dg (N − Ntran ) − k(λ − λ p )2 = S(N − Ntran ) − k(λ − λp)2 dN

(1)

where dg is the differential gain. N is the linear gain model. The value dg defines the slope of the dependence of the material gain coefficient g (N). The parameter N tran is the transparent carrier density. The variable k determines the curvature of spectral gain, λ is the wavelength, and λp represents the peak gain wavelength. The SOA nonlinear effects are XGM, XPM, and FWM. All three SOA nonlinear effects comprise at least one SOA device associated with another optical device including optical filter, Mach–Zehnder interferometers (MZI), nonlinear fiber, or another SOA device to realize the all-optical logic gates. Realizing a simple structure using SOA followed by the optical filtering technique is limited in high-speed processing due to slower gain. It is also limited by phase recovery characteristics [4, 5]. The alternate solution to overcome this limitation is using nonlinear interferometric-based structures. However, these structures require more nonlinear

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devices that increase complexity and are very prone to polarization effects. Thus, another efficient method is designed in earlier work [8] to realize the required structures using a minimal number of nonlinear components that operate at a higher bit rate. There are more critical parameters in understanding the theory of SOAs and their nonlinear effects. Some of the important parameters are material gain coefficient, carrier lifetime in spontaneous emission, internal loss, and the fraction of total spontaneous emissions to the signal along with its further amplification by an SOA. The performance of an SOA is measured in terms of gain parameter, gain bandwidth, saturation level of output power, noise figure margin, gain Ripple, and switching time of SOA [6, 7]. A good optical amplifier should preferably have larger bandwidth, especially for optical communication systems. The 3 dB bandwidth of a bulk SOA is about 45 nm, and it can exceed 60 nm for quantum well SOA. The simulation tool used in all-optical switching is OptSim, an advanced tool for optical structure simulation. It evaluates the performance through various estimation algorithms. There are two modes of simulation based on the requirement of the analysis. In block mode, the simulation engine represents the signal data representing time in a block of data. In sample mode, the simulation engine simulates the signal data passed between the components representing time step at a time.

3 Effects of Various Modulation Formats To realize an all-optical logical inverter, SOA is arranged in such a way that it provides more logical outputs in a single integrated structure. All the logic gate operations are obtained by third-order nonlinear effects between the applied pump and probe wavelength of 1551.5 nm and 1550 nm, respectively. The optical filter is adjusted to a specific spectral component of either blue or red-shifted wavelength components. All-optical logic of different gates is realized in the proposed structure using XGM and FWM nonlinearities. The interferometer-based nonlinear effect is not used in the design as it is having a drawback of more nonlinear devices and also makes the system bulkier and more complex. Figure 1 shows the SOA-based all-optical structure to produce logical output using the concept of all-optical computing. The structure consists of a PRBS generator to produce data in the desired format. It is followed by the all-optical signal generation unit, SOA, and optical filter. The BER tester and multiplot component in the OptSim tool are connected to monitor the output. The signal generation using different modulation formats is already designed in previous research works using compound component blocks at 2.5 Gbps [9, 10]. In this work, the analysis is made on different formats at 40 Gbps. Each block commonly includes the CW laser source, driver unit in RZ/NRZ, and external Mach–Zehnder modulator. The NRZ signals are generated directly using the available driver unit in the block mode component. The RZ signals are generated with raised cosine shape for the given duty cycle. In the carrier-suppressed RZ modulation format, the RZ optical signal is phase modulated.

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Fig. 1 SOA-based all-optical logics structure using different modulation methods

This phase modulation induces a phase shift among the two adjacent input bits and alters the spectrum such that the central peak of the carrier frequency is suppressed. The standard DPSK signals are obtained by splitting the optical source input and modulating the two signal components using in-phase and quadrature-phase data. The quadrature signal phase is tuned properly by the biasing technique to obtain the required bias. The important design parameters like probe and pump signal power, wavelength, and rise time/fall time are tailored to bring the optimum performance.

4 Results and Discussion The comparison on the extinction ratio, quality factor, and BER of the system is made for NRZ, DPSK, RZ, CSRZ, and Duo Binary (DB) formats. The results in Fig. 2 show that among all the formats, DPSK and Duo Binary formats have a good quality factor as well as the extinction ratio. Comparing between these formats, the better performance is demonstrated by duo binary, followed by DPSK. In comparison with all the modulation formats, the overall performance is shown by DB and the best results are obtained by associating NRZ-Duo Binary format. The results are also tabulated in Table 1.

5 Conclusion All-optical computing is a highly demanded technological field that replaces conventional computing in electronics by its equivalent all-optical principles. The nonlinearity in SOA is playing a major role in computing applications. In this article, we presented all-optical logic gate structures using different modulation formats. The nonlinearity is generated using one of the SOA nonlinear principles. The switching characteristics in the DWDM environment are analyzed for 40 Gbps using an eye

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(a) NRZ

(c) CSRZ

499

(b) RZ

(d) Duo Binary

Fig. 2 Outputs and eye diagram for different modulation methods

diagram and quality factor. This analysis is done exclusively for NRZ, DPSK, RZ, CSRZ, and Duo Binary signals. The eye diagrams, extinction ratio, Q factor, and BER comparison shows that NRZ-Duo Binary performs much better than the other modulation formats. It is also proved that it produces wider eye-opening as well as lesser BER along with a good extinction ratio. This analysis may be extended for an ultra-high data rate by tailoring the other parameters in the design. All-optical computing is really an inspiring field in research that brings fluent innovations in ultra-high-speed applications.

500 Table 1 SOA logic gate comparison based on modulation formats

V. Sasikala et al. Modulation method Q factor Extinction ratio in Jitter in ns dB RZ

6.49

13.23

0.00614

NRZ

7

15.66

0.0021

CSRZ

12

23.11

0.00636

CRZ

11.96

14.43

0.00489

DPSK

14.91

17.98

0.0037

Duo binary

16.42

18.22

0.0265

NRZ-duo binary

16.97

25.38

0.0166

References 1. Sasikala V, Chitra K (2018) All optical switching and associated technologies: a review. J Opt 47(3):307–317 2. Amiri IS, Rashed ANZ, Mohamed AENA, Aboelazm MB, Yupapin P (2019) Nonlinear effects with semiconductor optical amplifiers. J Opt Commun 3. Hamaoka F, Okamoto S, Nakamura M, Matsushita A, Kisaka Y (2018) Adaptive compensation for SOA-induced nonlinear distortion with training-based estimation of SOA device parameters. In: 2018 European conference on optical communication (ECOC) . IEEE, pp 1–3 4. Sasikala V, Chitra K (2018) Effects of cross-phase modulation and four-wave mixing in DWDM optical systems using RZ and NRZ signal. In: optical and microwave technologies, pp. 53–63. Springer, Singapore 5. Kaur A, Sheetal A, Miglani R (2017) Impact of optical modulation formats on 10 G/2.5 G asymmetric XG-PON system. Optik 149:351–358 6. Kotb A, Guo C (2021) 100 Gb/s all-optical multifunctional AND, NOR, XOR, OR, XNOR, and NAND logic gates in a single compact scheme based on semiconductor optical amplifiers. Opt Laser Technol 137:106828 7. Kotb A, Guo C (2020) All-optical multifunctional AND, NOR, and XNOR logic gates using semiconductor optical amplifiers. Phys Scr 95(8):085506 8. Sasikala V, Chitra K (2020) Performance analysis of multilogic all-optical structure based on nonlinear signal processing in SOA. J Opt 49(2):208–215 9. Wang J, Sun Q, Sun J (2009) All-optical 40 Gbit/s CSRZ-DPSK logic XOR gate and format conversion using four-wave mixing. Opt Express 17(15):12555–12563 10. Mao YY, Wu CQ, Sheng XZ, Liu B, Ullah R, Tian F (2017) Multi-channel NRZ/RZ-DPSK to CSRZ-DPSK format conversion based on nonlinear polarization rotation of SOA. Chin Phys Lett 34(10):104201

Tunable U-Band Multiwavelength Brillouin-Raman Fiber Laser with Double Brillouin Frequency Spacing in a Full Open Cavity Salah Abdo, Amer Abdulghani, A. W. Al-Alimi, N. A. Cholan, M. A. Mahdi, and Y. G. Shee Abstract A tunable U-band multiwavelength Brillouin-Raman random fiber laser with double Brillouin frequency spacing is demonstrated in a full open cavity utilizing 11 km dispersion compensating fiber (DCF). In this setup, in order to increase the generated Brillouin Stokes lines, optimizing Brillouin and Raman pumps powers with their corresponding wavelengths is necessary. At Brillouin pump wavelength of 1640 nm, with optimized Brillouin and Raman pumps powers, a high number of output channels with high optical signal-to-noise ratio (OSNR) have been attained. In this case, a total of 250 Brillouin Stokes lines of which 152 channels within 3 dB peak power variation are obtained across 26 nm bandwidth at Brillouin and Raman pump powers of 11.8 dBm and 1200mW, respectively. In addition, all the 3 dB-flattened Brillouin Stokes lines (BSLs) exhibit an average OSNR of 29 dB. The laser has a tuning range from 1633 to 1640 nm, translated into 7 nm restricted by the tunable laser source (TLS) maximum wavelength, which can be extended by utilizing TLS with a higher maximum wavelength. Keywords MWFL · Brillouin scattering · Raman scattering · Rayleigh scattering · U-band fiber laser · Random fiber laser

S. Abdo · A. Abdulghani · M. A. Mahdi Research Centre of Excellence for Wireless and Photonics Network (WiPNET), Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia A. W. Al-Alimi · N. A. Cholan Department of Electronic Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, 86400 Johor, Malaysia Y. G. Shee (B) Centre for Photonics and Advanced Materials Research, Universiti Tunku Abdul Rahman, Sungai Long Campus, Jalan Sungai Long, Bandar Sungai Long, Cheras, 43000 Kajang, Selangor Darul Ehsan, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_47

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1 Introduction Multiwavelength fiber lasers have gained the attention of researchers for their potential applications in many areas, including phased-array antennas [1], optical sensors [2, 3], spectroscopy [4], microwave photonics [5], and wavelength-divisionmultiplexing (WDM) [6]. Multiwavelength laser sources exhibit many features, including more compact size, low cost, broad wavelength range, and less energy consumption [7] which make these sources an efficient alternative to be deployed instead of using multiple laser diodes sources in WDM system [8]. Multiwavelength fiber laser (MWFL) can be generated utilizing different nonlinear effects in optical fibers, such as nonlinear polarization rotation (NPR) [9], stimulated Brillouin scattering [10], four-wave-mixing [11], hybrid stimulated Brillouin-Raman scattering [12]. Multiwavelength fiber laser sources are currently in huge demand as they are employed in DWDM and Next Generation Optical Networks (NG-PONs) to meet the high demand in internet traffic and telecommunications. Therefore, it is necessary to explore and work in a new window other than the C-band (1530–1565 nm), and L-band (1565–1625 nm) to the untapped low-loss optical communication bands such as the ultra-long (U-band) window (1625 to 1675 nm). Such approach is conceivable with the availability of optical amplifiers that cover the U-band [14] and is more convenient as it will use the same fiber deployments, which is the most expensive in any optical network deployments. Recently, many multiwavelength laser sources have been reported in the U-band utilizing semiconductor optical amplifier (SOA) as again medium. Muridan et al. [13] reported the generation of up 20 lasing lines within 5 dB peak power variation and 0.78 wavelength spacing in the near U-band region (≈ 1628–1643.6 nm) utilizing SOA and Lyot filter with polarization rotation controlling scheme in a ring cavity. The other demonstration was reported by Qureshi et al. in [15] utilizing a semiconductor-based booster optical amplifier (BOA) as a gain medium with MachZender interferometer in a dual ring fiber structure where 30 lasing lines of about 100 GHz spacing were obtained across ≈ 23 nm wavelength span in the far L-band and near U-band region. Nonetheless, the aforementioned laser sources exhibit poor spectral flatness and limited output channels both in the near U-band region below 1645 nm. Moreover, demonstrating U-band multiwavelength lasing in an all fiber configuration utilizing stimulated Brillouin and Raman scattering in a DCF as a nonlinear gain medium has been reported in [16]. About 132 channels were generated with an OSNR of 26.6 dB at 2.26 W Raman pump power which is relatively high power. In this study, a tunable U-band multiwavelength fiber laser has been proposed and experimentally demonstrated based on a hybrid Brillouin-Raman source. A single Raman pump is solely utilized to produce a tunable multiwavelength fiber laser with a high number of channels and considerable high OSNR. To the best of our knowledge, this research is considered the first work in MWFL based on Brillouin-Raman source

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Fig. 1 Configuration of the U-band multiwavelength BRFL using full open cavity

with tunable laser output operating in the U-band region with the highest OSNR and number of generated channels to date.

2 Experimental Setup The structure of the proposed U-Band MW-BRFL, as depicted in Fig. 1, consists of two pumping sources, the Raman pump unit (RPU) that provides the Raman signal at 1550 nm with a maximum power of 3W, and an external tunable laser source from 1455 to 1640 nm with 200 kHz linewidth and 11.8 dBm maximum power that provides the Brillouin pump signal. It also incorporates two optical circulators to direct the pumping signals into an 11 km long DCF acting as a gain medium with an effective area of 20 μm2 , an insertion loss of around 7.3 dB, and a nonlinear coefficient of about 7. 3 W−1 km−1 . As shown in Fig. 1, the Brillouin pump signal is fed into the DCF through port 1 of the Circulator 2, which also provides isolation to protect the TLS from back-reflected power. The Circulator 1 is used to inject the Raman pump into the laser cavity to provide distributed Raman amplification across the DCF and allow the extract of the output signal from the laser and fed it into the OSA for analysis.

3 Results and Discussions As illustrated in Fig. 1, the SBS effect begins once the injected Brillouin pump power exceeds the Brillouin threshold which stimulates the generation of a new frequency downshifted signal in the backward direction called a Stokes line or Brillouin Stokes (BSL) that is shifted by around ~ 10 GHz (0.08 nm), which is the typical Brillouin shift in silica-based fibers (λBS1 = λBP + 0.08 nm). This signal then undergoes a Raman amplification along the DCF and initiates the generation of another shifted Stokes line (BS2) in the forward direction (λBS2 = λBS1 + 0.08). The second-order signal (λBS2

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= λBP + 2 (0.08 nm)) produced in the forward direction inducing the generation of third-order Stokes and so on. This cascaded generation of Brillouin Stokes is maintained till the Raman gain is not sufficient to satisfy the threshold condition of the following order Brillouin Stokes signal, thereby stop the generation of the next Brillouin Stokes. Meanwhile, the generated Stokes signals undergo successive reflections inside the laser cavity due to the Rayleigh scattering, that acts as a virtually distributed feedback mirror. The odd-order Brillouin Stokes signals propagating in the forward direction due to elastic Rayleigh scattering are slightly amplified, referring to the dependency of the gain on the Raman pumping scheme direction [7] in contrary to the even-order Stokes that experience saturation and continue to grow, resulting in high power variation between the even and odd Stokes propagating in the forward direction relative to the Brillouin pump and hence achieving double wavelength spacing. The effect of varying RPU power on spectral evolution is investigated when the BP wavelength is set at 1640 nm, and its power is maintained at 11.8 dBm. The setup output corresponding to a gradual increase in RP power is illustrated in Fig. 2. It is found that the first-order Stokes line can be produced when the RP power slightly exceeds 800mW. When the RP power is increased to 850mW, the amplified Brillouin Stokes lines (BSLs) cannot overcome the cavity self-excited modes resulting in a chaotic output spectrum. Alternatively, when the RP power is further increased to 900mW, the cavity self-excited modes are overcome and a stable multiwavelength lasing spectrum is obtained. Continuing to increase the Raman pump power, more stable Stokes lines are produced. The measured free spectral range (FSR) between adjacent channels is 0.168 nm, which corresponds to double Brillouin wavelength spacing. Next, we studied the effect of the RP power on the spectrally flattened SLCs and the average OSNR. The number of generated channels is calculated by including all BSLs that fall within spectral power variation of no more than 3 dB. In accordance, that same approach is followed when estimating OSNR and multiwavelength bandwidth (MW-BW). The injected RP power, BP power, and wavelength are optimized to attain the highest number of flattened channels and OSNR. The whole measurements are achieved by integrating an 11 km length DCF as a hybrid gain medium in the structure. The effects of Raman pump power on the number of the outputs 3 dB-flattened channels and the corresponding average OSNR are manifested in Fig. 3. First, the investigation of the effect of Raman pump power on the number of output channels is carried out by varying the RPU power from 900 to 1300mW. The BP power is maintained at 11.8 m, and the BP wavelength is fixed at 1640 nm. It is observed from the results that the total number of the channels raises with increasing the RP power from 900 to 1200mW. This is due to the fact that the increment of the pump power, influences the Raman gain associated with the SBS effect. As a result, more BS lines are created as more energy is transferred from the RPU pump to the signal. However, continuing to increase the RPU power over the 1200mW, the number of channels decreases gradually due to Brillouin Stokes lines saturation.

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Fig. 2 Spectra output of MBRFLs at different Raman pump powers. BP wavelength and power are 1640 nm and 11.8 dBm, respectively

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Fig. 3 Variation of flattened SLC and OSNR with RP power. The BP power and wavelength are maintained at 11.8 dBm, and 1640 nm, respectively

Next, to study the effect of the RPP on the average OSNR for the 3 dB-flattened channels, the input parameters, including BP wavelength and the BP power, are set at 11.8 dBm and 1640 nm, respectively. The RPU power is varied from 900 to 1300 mW. The average OSNR variation with RPU power is manifested in Fig. 4. It is observed that the average OSNR is increased gradually with the increments of the RPU power from 900 to 1200 mW. When RPP are 900, 1000, 1100, and 1200 mW, the average OSNRs achieved are 26.5, 27, 28, and 29.1 dB, respectively. This is attributed to the fact that the increase in the Raman gain causes the suppression of the self-lasing that causes the reduction of the OSNR. However, once the Raman power increased further to 1250 and 1300 mW, the OSNR decreased gradually because of the spectral broadening effect of the lasing lines. The optimum spectrum resulted when the RPU power set at 1200 mW where a total of 250 Stokes lines, 152 of them are within 3 dB spectral flatness and 29 dB average OSNR as shown in Fig. 5. As such, the lasing performance outperforms the one that was achieved in [16], where the incorporation 2.4 km DCF through BWP has resulted in 136 channels with 26 dB OSNR at 2.26 W and 5 mW RP power and BP power, respectively. Hence, this is the best reported performance for BRFL operating at the U-band spectral window to date. Finally, the spectrum tunability of the laser output as a function of Brillouin pumping wavelength was investigated as manifested in Fig. 5. This was achieved by setting the BP and RPU powers at 11.8 dBm and 1200 mW, respectively. The laser source exhibits a tuning range of 7 nm from 1633 to 1640 nm. However, the wide Raman gain profile with peak gain at around 1650 nm allows the extension of the tuning range of this BRFL further when utilizing TLS with a longer wavelength as a Brillouin pump.

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Fig. 4 Spectrum of the MW-BRFL with 11 km length of DCF and inset is the magnified view of the generated BSLs. (RPU power = 1200 mW, BP power = 11.8 dBm, BP wavelength = 1640 nm)

Fig. 5 The tunability of the multiwavelength BRFL, RP, and BP powers are 1200 mW and 11.8 dBm, respectively

4 Conclusion We have demonstrated a tunable U-band multiwavelength BRFL with double Brillouin frequency spacing (~ 20 GHz) in a full open cavity structure. The total number

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of generated channels and the corresponding OSNR showed strong dependence on different parameters, such as Brillouin pump power, Raman pump power, and their corresponding wavelengths. By utilizing an 11 km DCF as a gain medium, a total of 152 channels are achieved within 3 dB peak power variation spanning 26 nm bandwidth from 1655.7 nm to 1681.7 nm with an average OSNR of 29 dB. The achieved tuning range of the laser output is from 1633 to 1640 nm that can be further extended by employing TLS of a longer maximum wavelength. In conclusion, such tunable multiwavelength BRFL with simple architecture has potential applications in various fields, such as optical communications, methane sensing, and microwave photonic filters. Acknowledgements This work is supported by Universiti Tunku Abdul Rahman Research Fund (UTARRF) IPSR/RMC/UTARRF/2020-C2/S04.

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Correction to: Maturity Level Detection of Strawberries: A Deep Color Learning-Based Futuristic Approach T. K. Ameetha Junaina , R. Kumudham , B. Ebenezer Abishek , and Shakir Mohammed

Correction to: Chapter “Maturity Level Detection of Strawberries: A Deep Color Learning-Based Futuristic Approach” in: N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_13 In the original version of this chapter, the following correction has been incorporated: In chapter 13 second and third authors’ names and affiliations were incorrectly published. It has now been corrected with thier respective ORCIDs. The correction chapter and the book have been updated with the changes.

The updated version of this chapter can be found at https://doi.org/10.1007/978-981-19-9748-8_13

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Subhashini et al. (eds.), Futuristic Communication and Network Technologies, Lecture Notes in Electrical Engineering 995, https://doi.org/10.1007/978-981-19-9748-8_48

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