Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations (EAI/Springer Innovations in Communication and Computing) 3031357507, 9783031357503

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EAI/Springer Innovations in Communication and Computing

Sam Goundar R. Anandan   Editors

Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations

EAI/Springer Innovations in Communication and Computing Series Editor Imrich Chlamtac, European Alliance for Innovation, Ghent, Belgium

The impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engineering technologies that play a key role in this process. The range of topics, focusing primarily on communications and computing engineering include, but are not limited to, wireless networks; mobile communication; design and learning; gaming; interaction; e-health and pervasive healthcare; energy management; smart grids; internet of things; cognitive radio networks; computation; cloud computing; ubiquitous connectivity, and in mode general smart living, smart cities, Internet of Things and more. The series publishes a combination of expanded papers selected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls for contribution of book titles and individual chapters, together maintain Springer’s and EAI’s high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representatives in all those walks of life affected ad contributing to the information revolution. Indexing: This series is indexed in Scopus, Ei Compendex, and zbMATH. About EAI - EAI is a grassroots member organization initiated through cooperation between businesses, public, private and government organizations to address the global challenges of Europe’s future competitiveness and link the European Research community with its counterparts around the globe. EAI reaches out to hundreds of thousands of individual subscribers on all continents and collaborates with an institutional member base including Fortune 500 companies, government organizations, and educational institutions, provide a free research and innovation platform. Through its open free membership model EAI promotes a new research and innovation culture based on collaboration, connectivity and recognition of excellence by community.

Sam Goundar • R. Anandan Editors

Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations

Editors Sam Goundar School of Science, Engineering, and Technology RMIT University Hanoi, Vietnam

R. Anandan Department of Computer Science Engineering Vels Institute of Science, Technology, and Advanced Studies (VISTAS) Pallavaram, Chennai, Tamil Nadu, India

ISSN 2522-8595     ISSN 2522-8609 (electronic) EAI/Springer Innovations in Communication and Computing ISBN 978-3-031-35750-3    ISBN 978-3-031-35751-0 (eBook) https://doi.org/10.1007/978-3-031-35751-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

It is our immense pleasure to put forth this book, Integrating Blockchain and Artificial Intelligence for Innovations in Industry 4.0. The objective of this book is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of Industry 4.0, robotic and intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross section of technical disciplines. The purpose of writing the book on Integrating Blockchain and Artificial Intelligence for Innovations in Industry 4.0 is to provide students to explore knowledge blockchain can address a variety of inefficiencies in today’s technologies, and that AI can perform similarly due to the current emphasis on data. As a result, the widespread adoption of AI and the integration of blockchain technology will bring about some interesting changes for Industry 4.0. Blockchain and Artificial Intelligence have been developing their routes for the past few years, with a little overlap in their own. In the form of data, there is an obvious link between the two technologies. However, according to a survey, the future of banks and insurance firms is primarily ordained to shift away from the relationship between blockchain and distributed AI.  Then, one of the possible harmful scenarios would be the loss of a large number of consumers, and the same settings would affect Industry 4.0. At present, organizations are not simply discussing programming, calculations, robotization, robots, and equipment, yet talking about more compound ideas like planning and delivering merchandise on request, dematerialization, and disintermediation. The Industry 4.0 will be the main significant transformation that has moved from a tech-driven state to another, maybe further developed one, and will zero in on particular foundations, including data straightforwardness, help, and interconnection. We extend our appreciation to many of our colleagues. We extend our sincere thanks to all experts for giving preparatory comments in the book that will sure motivate the reader to read the topic. We also wish to thank the reviewers who took time to review this book. v

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We are very much grateful to our family members for their patience, encouragement, and understanding. Special thanks are also due to many individuals in EAI/ Springer who have contributed their talents and efforts in bringing out this book in its present format. Suggestions and feedback to improve the text will be highly appreciated. Hanoi, Vietnam Chennai, Tamil Nadu, India

Sam Goundar R. Anandan

Acknowledgments

We are pleased to announce the publication of our book, Integrating Blockchain and Artificial Intelligence for Innovations in Industry 4.0. We appreciate the time and effort put in by everyone who reviewed a chapter for this book. For their invaluable assistance in getting this book ready for press and publication, we’d also like to thank the administrative and editorial support staff at EAI/Springer. Finally, we’d like to express our deep gratitude to everyone who contributed a chapter to this book. You have been instrumental in the creation of this book through your submission, dedication, and input. As with the writing of the book itself, assembling the Editorial Team is a laborious process. Every member of the Editorial Board serves as a volunteer in a purely honorary capacity. There is no compensation for anyone’s time. It can be difficult to recruit volunteers with specialized skills and experience, especially if they already have full-time jobs. The next step was to assemble a team of professionals who are well-versed in all aspects of blockchain and AI as they apply to Industry 4.0. The procedure for accepting, reviewing, and publishing a chapter varies from book to book and publisher to publisher. We appreciate that EAI/Springer allowed us to implement our own stringent process. We received the authors’ chapters straight from them. As a next step, the Editor conducts his own review and chooses reviewers according to their expertise and the chapter’s research topic. Chapters are ready to be submitted to EAI/Springer after they have undergone one round of peer review by more than three reviewers, as well as several rounds of revisions and reviews. The publisher then edits, reviews, and proofreads all of the chapters before they are typeset and released to the public. I really believe that anyone who reads even a single chapter of this book will come away with something useful and worthwhile. I’m hoping it will get people interested in learning more about the AI and blockchain applications that are driving Industry 4.0. Again, I’d like to offer my sincere appreciation to everyone who contributed to the creation of this book in any way.

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Contents

Application Areas, Benefits, and Research Challenges of Converging Blockchain and Machine Learning Techniques ������������������    1 A. Manimaran, Sam Goundar, D. Chandramohan, and N. Arulkumar Internet of Things and Blockchain in Healthcare: Challenges and Solutions����������������������������������������������������������������������������������������������������   17 N. Arulkumar, A. Manimaran, D. Chandramohan, and Sam Goundar A Conceptual Model for the Role of Blockchain in Overcoming Supply Chain Challenges��������������������������������������������������������������������������������   31 Jindřich Goldmann, Ahad ZareRavasan, and Seyed Mojtaba Hosseini Bamakan A Hybrid Application of Quantum Computing Methodologies to AI Techniques for Paddy Crop Leaf Disease Identification��������������������   69 A. Prema Kirubakaran and J. Midhunchakkaravarthy  Cognitive Computing for the Internet of Medical Things����������������������������   85 Latha Parthiban, T. P. Latchoumi, K. Balamurugan, K. Raja, and R. Parthiban  Blockchain-Based Privacy-Preserving Electronics Healthcare Records in Healthcare 4.0 Using Proxy Re-­Encryption ������������������������������  101 Latha Parthiban, Naresh Sammeta, A. Christina Josephine Malathi, and Betty Elizebeth Samuel A Framework for Low Energy Application Devices Using Blockchain-Enabled IoT in WSNs������������������������������������������������������������������  121 T. P. Latchoumi, Latha Parthiban, K. Balamurugan, K. Raja, J. Vijayaraj, and R. Parthiban Implementation of Real-Time Water Quality Monitoring Based on Java and Internet of Things ����������������������������������������������������������������������  133 Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Said Benkirane, and Hiba Asri ix

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 Blockchain-Based Infrastructure for Precision Agriculture������������������������  145 A. Packialatha, S. Vijitha, A. Sangeetha, and K. Seetha Lakshmi Implementation of a Distributed Electronic Voting System Using a Blockchain-Based Framework����������������������������������������������������������  163 Nisha Soms, J. Lawrance, R. Harshitha, and D. S. Dakshineshwar Blockchain-Based Organ Donation and Transplant Matching System����������������������������������������������������������������������������������������������  175 Ananya Sajwan, Soumyadeepta Das, Asnath Victy Phamila, and Kalaivani Kathirvelu A Transparent, Distributed, and Secure Crowdfunding Platform Based on Blockchain����������������������������������������������������������������������������������������  185 Vinita Tiwari, Sam Goundar, Karri Babu Ravi Teja, Basant Agarwal, and Priyanka Harjule  Certificate Authentication System Using Blockchain ����������������������������������  201 Murugan Sekar, A. Rajesh, S. Thirumal, and R. Anandan  Blockchain-Based Decentralized Student Verification Platform ����������������  213 Nisha Soms, S. Adhithyan, M. S. Lokesh, and K. Madhumitha Application of Internet of Things Systems for Aerosol Monitoring of Quarries in Morocco�����������������������������������������������������������������������������������  223 Ghizlane Fattah, Jamal Mabrouki, Fouzia Ghrissi, Mourade Azrour, and Mohamed Elouardi Blockchain Networks for Cybersecurity Using Machine-Learning Algorithms ����������������������������������������������������������������������  233 H. M. Moyeenudin, G. Bindu, and R. Anandan Blockchain of Cryptocurrency Using a Proof-of-Work-Based Consensus Algorithm with an SHA-256 Hash Algorithm to Make Secure Payments ������������������������������������������������������������������������������  243 G. Bindu, H. M. Moyeenudin, and R. Anandan An Efficient Security-Enabled Routing Protocol for Data Transmission in VANET Using Blockchain Ripple Protocol Consensus Algorithm������������������������������������������������������������������������  253 Manjunath Ramanna Lamani, P. Julian Benadit, Krishnakumar Vaithinathan, and Latha Parthiban Blockchain-Based Sinkhole Attack Detection in Wireless Sensor Network������������������������������������������������������������������������������������������������  265 D. Gaya, Latha Parthiban, and N. Nithiyanandam

Contents

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Secured Smart Manufacturing Systems Using Blockchain Technology for Industry 4.0����������������������������������������������������������������������������  281 T. P. Latchoumi, Latha Parthiban, K. Raja, K. Balamurugan, and R. Parthiban Kryptoverse: A Fully-Fledged Cryptocurrency Transfer Website Based on Web 3.0 ��������������������������������������������������������������������������������������������  295 K. Pazhanisamy, Latha Parthiban, R. Kannadasan, A. S. Anakath, and R. Parthiban  The Benefits of Combining AI and Blockchain in Enhancing Decision-­Making in Banking Industry����������������������������������������������������������  305 Artor Nuhiu and Florin Aliu Index������������������������������������������������������������������������������������������������������������������  327

About the Editors

Sam Goundar  is an International Academic having taught at 12 different universities in 10 different countries. He is the Editor-in-Chief of the International Journal of Blockchains and Cryptocurrencies (IJBC)  – Inderscience Publishers, the International Journal of Fog Computing (IJFC)  – IGI Publishers, and the International Journal of Creative Computing (IJCrC)  – Inderscience Publishers; Section Editor of the Journal of Education and Information Technologies (EAIT) – Springer; Editor-in-Chief (Emeritus) of the International Journal of Cloud Applications and Computing (IJCAC)  – IGI Publishers; Associate Editor of Tsinghua Science and Technology – IEEE Xplore; and Guest Editor of the Special Issue on Digital Banking & Financial Technology, Journal of Risk and Financial Management – MDPI. He is also on the Editorial Review Board of more than 20 high impact factor journals. Apart from Blockchains, Cryptocurrencies, e-Services, Digital Transformations, Fog Computing, Industrial Internet of Things, Enterprise Systems, Big Data, Mobile Cloud Computing, Cloud Computing, and Educational Technologies, he also researches on Management Information Systems, Technology Acceptance Model, MOOCs, Gamification in Learning, Cyber Security, Artificial Intelligence, Internet of Things, Network Intrusion Detection, Data Security Analysis, e-Sports, ICT in Climate Change, ICT Devices in the Classroom, Using Mobile Devices in Education, e-Government, and Disaster Management. He has published on all these topics. R.  Anandan  completed his UG, PG, and Doctorate in Computer Science and Engineering. He is currently working as Professor and Head at the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, Tamil Nadu, India, which is a pioneer institution in engineering. He has vast experience in corporate and all levels of academic in Computer Science and Engineering. His knowledge of interests is not limited to Artificial Intelligence Soft Computing, Machine Learning, High-­Performance Computing, Big Data Analytics, Image Processing, 3D Printing,

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and Knowledge Engineering. He associated as Member in many reputed International and National societies like International Association of Engineers (IAENG) Hong Kong; Computer Science Teacher Association (CSTA) New  York; Universal Association of Computer and Electronics Engineers, USA; Association of Computer Sciences and Information Technology (IACSIT), Singapore; Institution of Engineers, India; Computer Society of India, and Indian Society of Technical Education (I.S.T.E). He serves as Editorial Board Member/Technical Committee/ Reviewer of international journals (Springer, Thomson Reuters, SCI, Elsevier). He has published more than 130 research papers in various international journals such as Scopus, SCI, and referred journal. He has presented 75 papers at various international conferences. He has authored and edited 16 books and 20 book chapters in leading publications. Some of his leading books are Software Engineering, Advanced Java Programming, Functions of Hardware and Internet, Bigdata and Hadoop Beginners Guide for OOPS Concepts in Java Programming, Web Technology, Statistics with R Programming, Information Security, Artificial Intelligence, and A Closer look at Bigdata Analytics. He has filed ten patents of his work.

Application Areas, Benefits, and Research Challenges of Converging Blockchain and Machine Learning Techniques A. Manimaran, Sam Goundar, D. Chandramohan, and N. Arulkumar

1 Introduction Blockchain and machine learning are both recent technologies. Machine learning allows a machine to identify the pattern autonomously with self-pace of learning method. It makes robots more independent by making their own decisions without human input. It is one of the most intriguing breakthroughs because it allows machines to learn, making them similar to human brain. However, the main role of blockchain is to safeguard transactions between participants. This is an important step since it eliminates intermediaries like governments and banks who want to profit from our transactions. It will be interesting to observe how these two innovations interact. A few revolutionary ideas are visible. Blockchain and AI, already revolutionary technologies, have the potential to be even more so when integrated. Both can boost capacities and increase transparency, trust, and communication [1]. “Machine learning” is a broad phrase that encompasses a wide range of techniques, including machine learning, deep learning, and reinforcement learning. These techniques are at the heart of big data analysis. Blockchain is a natural tool A. Manimaran (*) Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, India S. Goundar School of Science, Engineering, and Technology, RMIT University, Hanoi, Vietnam D. Chandramohan Department of Computer Science & Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, India N. Arulkumar Department of Computer Science, Christ University, Bangalore, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_1

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for sharing and handling massive data from many sources through the incorporation of smart contracts (i.e. a piece of code that will systematically process in certain conditions) as a decentralized and append-only ledger system. When training and testing machine learning models, blockchain can preserve data security and stimulate data sharing. Additionally, blockchain may be used to distribute processing power, establish Internet of Things (IoT) networks, and develop online prediction models using a variety of data sources. This is especially critical for deep learning techniques, which necessitate a lot of processing power. Blockchain systems, on the other hand, create massive amounts of data from various sources, making distributed systems more difficult to monitor and govern than centralized systems. For the best blockchain mechanism designs, effective data processing and characteristics of the system forecasts are crucial. Furthermore, machine learning can help with data verification and the detection of harmful assaults and fraudulent transactions on a blockchain. Interdisciplinary research that integrates the two technologies has a lot of promise [2]. An important aspect of machine learning is its ability to generate accurate models from a vast amount of data. Data collection, organization, and verification take a significant amount of time and effort. This is where blockchain technology comes into play, since it can significantly reduce the amount of time required. Here, data transfers may be made reliably and quickly utilizing smart contracts. It would take hundreds of Terabytes of data to train a machine learning model for self-driving cars. In the past, all of the vehicle’s data, including fuel usage, driving speed, and break times, was gathered using a variety of sensors. As a last step, inspectors will verify that the data is accurate and error-free before it is sent on to be processed by data analysts. However, the use of digital signatures in smart contracts might greatly enhance the actual system. Using blockchain technology, smart contracts may be configured to convey information directly from the motorist to data analysts who will use the data to construct machine learning models, ensuring the security of the information gathered. By creating a market for data analysis, this combination of machine learning and blockchain technologies has the capacity to modify the system for a variety of different technologies. This combination can also benefit other fields like banking and marketing, which can use it to develop systems for detecting and preventing fraud. Enhanced supply chain methodologies are saving billions of dollars and it can be achieved via the use of machine learning. Machine learning (ML) has been a common topic of study and has been used in a wide range of real-world applications. Models trained on tens of thousands of data points generated by end users every day can be used to tackle a wide range of problems in the workplace and in daily life. Nevertheless, ML development is still hampered by model and data difficulties. Furthermore, malicious data contributors may cause undesired training outcomes; private data may be misused or leaked; and ML models may become out-of-date if they are not constantly updated, to name a few examples. It takes a lot of data to train an ML model. Because most of the data sets are proprietary, people who wish to train ML models on certain challenges may be unable to get their hands on them because they do not have access to them [3].

Application Areas, Benefits, and Research Challenges of Converging Blockchain…

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Unless it is continuously updated with new data sets, a machine learning model that has been trained and released will quickly become outdated. To put it another way, an unrefined ML model may not be able to produce reliable findings when new test data sets are available. However, there is no guarantee that data contributors are reliable, even if updatable data is provided. Unwanted training outcomes can be the result of erroneously labelled data being uploaded to the model by malicious data suppliers. If this is the case, the new ML model could have poor results. To improve ML development, it is necessary to improve the classic model training methods, as described above. Due to its decentralization, privacy protection, immutability, and traceability, blockchain is an emerging and rapidly evolving technology that can easily tackle these challenges, among others [4]. The rest of this article is structured in the following manner. The literature study is covered in Sect. 2, and the emphasis and overview of machine learning (ML) and blockchain are covered in Sect. 3. Sections 4 and 5 discuss in further detail about the uses of the ML and blockchain convergence as well as its breakthroughs. The study’s Sect. 6 describes the applications’ limits and recommends areas for future research, while Sect. 7 focuses on the research challenges. Sections 8 and 9 offer conclusion and Future Directions, respectively.

2 Literature Review Numerous studies are now being conducted to learn more about blockchain technology. The initial focus of cryptocurrency development is on utilizing the blockchain technology. In order to predict the price of Bitcoin, deep learning techniques are employed. Pintelas E. et al. [5] published a paper on cryptocurrency price forecasting techniques which includes some of the most extensively used and effective deep learning algorithms. They included new techniques, tactics, and alternative approaches, such as more complex prediction algorithms, advanced ensemble methods, feature engineering techniques, and additional validation measures, in order to produce more accurate results. Goel A. et  al. [6] published an article on blockchain-based machine learning innovation framework. They identified that Decentralization and enormous amounts of data are required for machine learning, and there are huge investigation capacities in the combined effort of innovation. Tanwar S. et al. [7] presented a paper to converge blockchain technology and machine learning, together with their smart applications, which have been explained in depth and an ML–BT-based architecture has been offered, an ML–BT-based data analysis system designed and implemented using proposed architecture. Numerous earlier works are compared and discussed in this article. For their solution taxonomy, they focused on aim, layer, countermeasure, and smart application aspects. Various procedures and approaches were compared in each facet of the study. Singh H. et  al. [8] proposed a model based on recent machine learning techniques that can predict when a mining node will accept and add a transaction into a

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block. Classifiers such as Bayes, Random Forest, and Multi-Layer Perceptron (MLP) with SoftMax output were examined as well as different performance metrics to best manage the unbalanced nature dataset’s were also investigated. Tian Y. et  al. [9] proposed blockchain-based machine learning framework for edge services (BML-ES) in IIoT. They developed unique smart contracts that enable multiparty engagement in edge services in order to increase data processing efficiency. Furthermore, proposed work suggested an aggregation technique for verifying and aggregating model parameters in order to assure the correctness of decision tree models. Finally, they ensured data security and prevented data privacy leaks in edge services using the SM2 public key cryptosystem. They proved that the proposed BML-ES framework was more secure, effective, and efficient, as well as appropriate to improve the accuracy of edge services in the IIoT based on simulation result. Akyildirim E. et al. [10] proposed a machine learning model for cryptocurrencies, which considered previous price data and technical indicators as model parameters, the predictability of the twelve most liquid cryptocurrencies were studied regularly and minutely. The average classification accuracy of four algorithms was constantly over 50% for all cryptocurrencies and timelines, indicating that there was some predictability of price patterns in the cryptocurrency marketplaces. Compared to logistic regression, artificial neural networks, and random forest classification methods, support vector machines showed the best and consistent results in terms of predicted accuracy.

3 Overview of Blockchain This section focuses on overview of the basic concepts, features, structure, and taxonomy of blockchain and machine learning, as well as how they work together.

3.1 What Is Blockchain? Blockchain is a kind of distributed ledger that is used to keep track of transactions between people who do not have trust in each other. There is not a single person or group that is in charge of the ledger. There is a record of these transactions on the blockchain that cannot be changed or tampered with. It is called a “blockchain ledger.” Each of them is checked by consensus mechanisms before being added to the chain [11]. Blockchain opens up new ways to work together with people who do not trust each other and to have decentralized governance in networks that are already in place. The main features of blockchain are as follows: decentralization, transparency, immutability, security, auditability, anonymity, and autonomy are some of them. Table 1 shows the comparison of three types of blockchains along with different attributes.

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Table 1  Comparison of three types of blockchains S.No Attribute 1 Who runs/manages the chain 2 Nodes need permission to access 3 Security 4 5 6

Efficiency Centralized Example

Public All miners

Consortium Selected users

No

Private One organization/ use Yes

Nearly impossible to fake Low No Bitcoin, Ethereum

Could be tampered with High Yes IBM HyperLedger

Could be tampered with High Partial Quorum

Yes

Fig. 1  Structure of the blockchain model

A blockchain, in general, is defined as a collection of blocks. Each block has four segments: information about the transaction (Bitcoin, Ethereum), the cryptographic hash of the previous block, the current block, and the timestamp. Additionally, a blockchain is a decentralized and conventional digital ledger that has been utilized to record transaction details at various times in time. Thus, an attacker is unable to recover the contents, as each block carries a cryptographic measure of the previous block. All transactions can be viewed using this method via a cryptographic hash value that is validated by each miner. It comprises blocks for each transaction and is populated with similar values throughout the ledger, as depicted in Fig.  1. The blockchain enables the sharing of a shared, secure, and confidential ledger of details. Decentralized storage is one of the sources of data in blockchain, and utilizing clever contract code, a large amount of data may be saved and connected from the previous block to the current block. Recently, decentralized databases such as Swarm, LitecoinDB, MoneroDB, SiacoinDB, Interplanetary File System (IPFS), and BigchainDB have been employed. A blockchain is simply a series of digital blocks linked together. The chain connects these data to build a distributed database, with each block containing a specified quantity of data. A node is a device that stores a complete copy of the

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blockchain’s transaction history. Multiple transactions are accumulated from nodes and distributed to every node on the network in a freshly generated block. Nodes that use the same consensus mechanism can adopt the new block and add it to the blockchain. The information from the preceding block in the chain is included in each new block. As a result, if a block is altered, all blocks before it become invalid [12]. The tactics used to establish consensus on a new block (consensus) varies depending on the type of blockchain. The topological index of a blockchain implies two important characteristics: (a) the data (in a block) is immutable; and (b) the distributed network, through consensus, enables user to interact straightforwardly with one another and download a copy of the current ledger, implying that the data in the network is constantly monitored and redundant. As a result, the blockchain is more resistant to outrage and attacks from individuals.

4 Overview of Machine Learning An overview of the fundamental principles, history, and taxonomies of ML is provided in this section. A wide range of machine learning methods for use in networking and communication systems is then presented.

4.1 What Is Machine Learning? A.L. Samuel invented the term machine learning in 1959, and it is described as “the branch of study that enables computers to learn without being explicitly programmed.” E. Tom Mitchell later improved the definition of machine learning by stating that “a computer program is said to learn from experience E with regard to some task T and some performance measure P, if its performance on T, as measured by P, increases with experience E.” Particularly, machine learning is an enticing tool for solving problems and optimizing the performance of produced systems based on data sets. As seen in Fig. 2, a typical ML framework workflow includes the training and testing processes. During the training phase, raw data is pre-processed in order to give useful data for the next step. Then, for the required tasks, the data’s features and patterns can be extracted and processed. Support Vector Machines (SVM), Hidden Markov Model (HMM), Principal Component Analysis (PCA), and other pattern recognition and feature extraction algorithms are frequently utilized. In most cases, the key phases of algorithm selection and learning patterns are carried out concurrently. Distinct learning algorithms have different patterns, as seen in Fig. 2, under the learn patterns section. For example, supervised learning employs labelled training datasets to build the model expressing the learnt relationship, whereas unsupervised learning employs unlabeled training datasets. A range of

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Fig. 2  A workflow of machine learning model construction and test

feasible results are obtained based on the unique requirements of various assignments. During the testing phase, the outcomes of the ML models are mapped to representative knowledge (e.g. a certain pattern), and insights are supplied to a dashboard or other related software components [13].

4.2 History of Machine Learning ML is a subfield of AI in which computer devices learn on their own from data and information. In this section, a brief history of ML, focusing on numerous significant discoveries and developments are described. Alan Turing developed the world famous Turing Test in 1950 to determine whether a computer has true intelligence. In this test, a computer passes if it can persuade a human that it is a human and not a machine. Arthur Samuel created the first computer learning program – the game of checkers  – in 1952. Frank Rosenblatt created the first neural network for computers, named Perceptron, in 1957, to simulate the cognitive process of the human brain. The “nearest neighbour: algorithm” was established in 1967, allowing computers to do simple pattern recognition. Gerald Dejong established the notion of ExplanationBased Learning (EBL) in 1981, in which a computer assessed training data and created a general rule that it

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may follow by eliminating irrelevant data. Deep Blue, an IBM computer, defeated world champion Garry Kasparov in a chess match in 1997. Since then, several projects and models in the field of machine learning have been widely developed. Many more ML projects were developed towards the beginning of the twenty-first century in a variety of applications and fields. GoogleBrain, for example, was a deep neural network established by Google in 2012 that focused on pattern detection in photos and videos. DeepFace, a deep neural network, established by Facebook in 2014 could recognize persons with the same accuracy as a human. Microsoft’s Toolkit, released in 2015, allowed for the distribution of ML issues across many PCs. Furthermore, AlphaGo, built by Google DeepMind, defeated a professional player of the Chinese board game. To summarize, ML technologies have evolved and undergone several alterations over a lengthy period of time. ML technologies are currently seen as a very promising answer in several fields for building intelligent machines, despite the fact that they are computationally quite expensive [14].

4.3 Taxonomy of ML Technology ML approaches are broadly categorized into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning [15]. As shown in Fig. 3, a brief overview of the four types of ML approaches are given.

Fig. 3  Machine learning algorithms

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4.3.1 Supervised Machine Learning Supervised learning algorithms are a sort of labelling learning technology that employs labelled training datasets to build a model that represents the learnt relationship between the input and then output for predicting output values. After enough training, the supervised learning algorithm can offer targets for any incoming input, compare its output to the proper, intended output, and detect faults to incrementally modify the model. There are multiple supervised learning algorithms for various applications and aims, including k-nearest neighbour, decision tree, neural network, SVM, Bayesian theory, and HMM. 4.3.2 Unsupervised Machine Learning Unsupervised learning, as opposed to supervised learning, is a type of ML technique that uses training datasets without labels. In general, the unsupervised learning algorithm seeks to classify the sample set into separate sets by examining similarities between them. Unsupervised learning algorithms are commonly utilized in clustering, anomaly detection, and data aggregation. The prominent unsupervised learning algorithms, such as k-means clustering, self-organizing maps, and anomaly detectors, have been implemented across many domains, which include handover management, fault detection, network operational configuration, and energy efficiency management. 4.3.3 Semi-Supervised Machine Learning Semi-supervised learning is a sort of learning technology in which the majority of training samples are unlabeled while just a few are tagged. Semi-supervised learning can efficiently enhance learning accuracy over unsupervised learning by combining unlabeled data with a small quantity of labelled data, while not requiring the time and costs required by supervised learning. To properly use unlabeled data, some underlying distribution of data structure assumptions, such as cluster assumption and manifold assumption, must be made. Semi-supervised learning approaches, such as Expectation Maximization (EM), co-­ training, transductive SVM, and graph-based algorithms, are widely employed in a variety of fields, including speech analysis, online content classification, and caching management, by applying diverse structure assumptions. 4.3.4 Reinforcement Learning Algorithm Reinforcement learning (RL) is a sort of learning technology that uses a system of benefit and punishment processes to train algorithms. It allows agents to learn by exploring possible options and modifying their behaviour within a specified context

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in order to optimize their long-term payoff. In other words, agents consider not just the immediate reward but also the long-term effects of their actions. RL is often represented as a Markov decision process (MDP), which includes a set of environment and agent states S, a set of agent actions A, the probability of state transition, the immediate reward, and rules. The agent interacts with the environment by experimenting with actions in order to find the sequence of state-action combinations that minimizes the predicted discounted benefit, that is, the optimum policy. The most prevalent RL algorithm is Q-learning, which uses the Q-function to learn a table that stores all state-action pairings and their long-term benefits.

5 Benefits of Converging Machine Learning and Blockchain Technologies In order to make modifications in the blockchain, any authorized user just needs to authenticate themselves. Wecanmakeblockchain technology more secure and trustworthy by using machine learning. The use of ML models in conjunction with previously specified terms and conditions can assist to assure their long-term viability. Models can assist in the collection of high-quality data from the end user. On the basis of which, award prizes to the user can be given on a constant basis. To ensure that Machine Learning (ML) models do not deviate from their designated learning path, Blockchain technologies are used to trace the hardware of other machines. The blockchain ecosystem constructs a real-time, trustworthy payment mechanism. Machine learning is complex and operates at speeds beyond the comprehension of the average human brain. Blockchain technology can assist ML in better explaining themselves. When the two are connected, it becomes easier to track ML choices on the blockchain, as all transactions are recorded. These can be implemented on the blockchain in the event of a dispute. Key features of blockchain that can benefit machine learning as shown in Fig. 4. At this time, a 51% attack on more established blockchains like Bitcoin and Ethereum seems difficult. When it comes to smaller blockchains like Tether, which has been hacked by 51% attack, ML can assist in teaching nodes about hacking characteristics and hence foresee such attacks. Detecting and preventing attacks are easier with AI- and ML-based models before they happen, since it can make choices faster than existing blockchain architecture.

5.1 Security Enhancement There is an inherent level of security in a blockchain because of the encryption. For very sensitive personal data, such as medical notes or personalized suggestions, blockchain is ideal. There is a second approach to security upgrades. While the

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Fig. 4  Key features of blockchain that can benefit machine learning

blockchain is secure at its core, applications and other layers can be susceptible. The implementation of blockchain applications will be made easier with the help of machine learning algorithms that can anticipate potential security breaches.

5.2 Managing the Data Market Place For machine learning, IT Giants such as Google, Facebook, and Amazon have access to huge amounts of data, but this data is not accessible to everyone else. With the use of a blockchain, startups and small businesses may compete with these massive corporations by having access to the same data and even artificial intelligence.

5.3 Intensifying Efforts to Reduce Energy Consumption One of the key issues facing the current world is the high energy consumption of data mining. In spite of this, Google has shown that MI is capable of handling this efficiently. They were able to reduce the amount of energy needed to cool their data centres by 40% through training the machine learning model using deep learning algorithms. Using a similar technique, the cost of mining equipment can be reduced.

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5.4 Handling Larger Data Machine learning is more effective when there is a greater amount of data. However, when data is uploaded to the blockchain, processing gets more difficult and the entire architecture grows slower. However, when ML is combined with blockchain, more data is better, because ML will make decisions on how to use the data, hence expanding the blockchain’s uses.

5.5 Crypto Contract Crypto contracts have the potential to become the backbone of commercial transactions in the not-too-distant future. They will enable unprecedented value transfer and business transactions on the blockchain in a transparent and immutable manner across wide geographic ranges. However, operationalizing crypto contracts needs a process of human/computer node verification. Financial transactions on crypto contracts will no longer require a “human touch” to verify them, as they will be automated. This would enable them to operate more quickly and efficiently. ML can leverage the data from crypto contracts to make better decisions at a faster rate than now. Additionally, the majority of crypto contracts are built in such a way that they do not collect data from the outside world. As a result, crypto contracts may not have the most up-to-date information regarding current external environment changes, such as pricing shifts or weather trends. These factors are critical for the accuracy with which crypto contracts are executed using ML, allowing for easy data collection and verification. This would improve the “smartness” of crypto contracts by enabling near-instantaneous business and value engagements.

5.6 Communication and Networking Systems Communications and networking systems are becoming more complicated because of the rapid development of information and communication technologies. This has made the infrastructures, resources, end devices, and applications in these systems much more difficult to understand and use. In addition, the amount of data and the size of the end devices may make security, privacy, service provisioning, and network management difficult. Blockchain and machine learning (ML) may be a good way to make networks more decentralized, secure, intelligent, and efficient to run and manage. This has attracted a lot of attention from both academia and industry. There is a good thing about blockchain technology on the one hand, but there is also a bad thing. Sharing training data and machine learning models can make it easier to get training data, decentralized intelligence, security, privacy, and trust in ML. However, ML will have a big impact on the development of blockchain in communication and networking systems, such as energy and resource efficiency,

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scalability, security, and privacy as well as smart contracts that are smarter than the ones that came before. However, there are still a lot of important issues and challenges that need to be resolved before the integration of blockchain and machine learning is widely used. These include how to manage resources and data mining processing, how to make the system scalable, and how to keep it safe.

6 Application Areas of Blockchain-Based Machine Learning Models 6.1 Supply Chain and Logistics All businesses face the challenge of keeping their supply chains running smoothly, and the interconnectedness of various supply chain pieces becomes more inefficient as the business grows. In the Supply Chain business, blockchain can be used to address a number of issues, including complex record-keeping and product tracking, in order to develop a less corruptible and more autonomous database replacement. Transparency of storage and delivery are made feasible by blockchain, which increases supply chain accountability and efficiency. Additionally, it handles money transactions. Supply chain difficulties in the food business are gradually being addressed by ML and Blockchain, which allow for greater accessibility and accuracy. The installation of blockchain has made it possible to track food sources and manage the financial transactions related to them.

6.2 Automation in Manufacturing Smart contracts and bitcoin blockchain-based procedures are now relied upon by businesses to offer transparency, production, security, and compliance checks as part of the manufacturing process. The predictive algorithms of machine learning are being utilized to develop flexible plans instead of typical set machine maintenance schedules. Automated product testing and quality control are becoming more commonplace.

6.3 Energy and Utilities The use of blockchain in the Energy and Utilities sector is simplifying energy trades. For example, IOTA, a corporation that deals in energy, recently deployed blockchain energy utilization in a peer-to-peer manner. Another common method for generating renewable energy is the use of smart microgrids. This is not the first time that blockchain technology has been used to enable local communities to generate, conserve, and trade energy.

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6.4 Enhanced Customer Service Customer satisfaction is a fundamental goal of any firm that serves them, and by implementing a machine learning model, or a form of machine learning framework on a Blockchain-based application, can make the service more efficient and automated for the customers.

6.5 Data Trading By incorporating machine learning models into the blockchain, companies who use blockchain for data exchange around the world can improve the speed of their service. Data trade routes are managed by machine learning models, and this is where their work comes in. Alternatively, combined technologies are used for system testing as well as for data encryption in order to protect the information.

6.6 Product Manufacturing In the current environment, the majority of large manufacturing units or organizations have begun experimenting with blockchain-based procedures in order to improve the productivity, security, visibility, and regulatory inspections of their products. Integrating machine learning algorithms can be more beneficial in terms of creating flexible maintenance strategy for machinery at specific intervals. Instead, the integration of machine learning can assist in automating the product testing and quality control processes.

6.7 Smart Cities Modern smart cities help to improve the livelihoods of their residents by using machine learning and blockchain technology. For example, smart homes may be monitored using machine learning algorithms, and device customization based on the blockchain can enhance the level of prosperity.

7 Research Challenges Machine learning faces a number of issues, including the compromise of user privacy, data that cannot be shared, manipulative and biased AI programs, hackable datasets, and a lack of data openness. Blockchain technology is evolving, and it is

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no longer exclusive to the world of cryptocurrencies. It is being adapted by numerous sectors for future research. While AI and machine learning are still working on their structural framework, which poses a trust issue, blockchain is a technology that suffers from a security concern. There will be considerable future growth in numerous application areas as a result of the combination of these technologies. This might create a new generation of machine dominance. It may also be beneficial in terms of ensuring industry transparency. Google Duplex project, in which a machine conducts automated calls on a person’s behalf, handling conflict by machines through cases, and a more scalable structure for the interaction between various IoT devices.

8 Conclusion In this study, examining the intersection between machine learning and blockchain technology is viewed. A quick introduction to machine learning and blockchain is given first. Then the work follows the uses and breakthroughs of this combination, which has been grouped into two categories: protocols that facilitate data sharing, and marketplaces that stimulate model sharing. Due to a number of research challenges, which are discussed in detail in the sections, machine learning cannot be used in blockchain-based research. Lastly, applications’ limits and potential future research opportunities are discussed. ML and blockchain could have a bright future if recent improvements in both fields can be used to create new frameworks that make it easy to share data and models.

9 Future Directions Machine learning has numerous issues, including the compromise of user privacy, the inability to exchange data, manipulative and biased AI programs, hackable datasets, and a lack of data openness. Blockchain technology is evolving, and its applications are not restricted to the world of cryptocurrency. It is being adapted for future research in a variety of domains. While blockchain is a technology that suffers from a security issue, AI and machine learning are still developing their structural frameworks, which poses a trust issue. The integration of these technologies will result in greater growth across a variety of application areas in the future. This may herald the dawn of the era of machines. Additionally, it may be beneficial in promoting industry transparency. Several projects, such as Google’s Duplex project, in which a computer conducts automated calls on behalf of a person, machines resolve conflicts through cases, and a more scalable structure for the interaction of various IoT devices.

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References 1. Shah, D., Patel, D., Adesara, J., Hingu, P., & Shah, M. (2021). Exploiting the capabilities of blockchain and machine learning in education. Augmented Human Research, 6(1), 1–14. 2. Passerat-Palmbach, J., Farnan, T., McCoy, M., Harris, J. D., Manion, S. T., Flannery, H. L., & Gleim, B. (2020, Nov). Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data. In 2020 IEEE international conference on blockchain (Blockchain) (pp. 550–555). IEEE. 3. Abou El Houda, Z., Hafid, A., & Khoukhi, L. (2020, June). BrainChain-A machine learning approach for protecting blockchain applications using SDN. In ICC 2020-2020 IEEE international conference on communications (ICC) (pp. 1–6). IEEE. 4. Pan, X., Zhong, B., Sheng, D., Yuan, X., & Wang, Y. (2022). Blockchain and deep learning technologies for construction equipment security information management. Automation in Construction, 136, 104186. 5. Pintelas, E., Livieris, I.  E., Stavroyiannis, S., Kotsilieris, T., & Pintelas, P. (2020, June). Investigating the problem of cryptocurrency price prediction: A deep learning approach. In IFIP international conference on artificial intelligence applications and innovations (pp. 99–110). Springer. 6. Goel, A., Bhushan, B., Tyagi, B., Garg, H., & Gautam, S. (2021). Blockchain and machine learning: Background, integration challenges and application areas. In Emerging technologies in data mining and information security (pp. 295–304). Springer. 7. Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., Singh, P. K., & Hong, W. C. (2019). Machine learning adoption in blockchain-based smart applications: The challenges, and a way forward. IEEE Access, 8, 474–488. 8. Singh, H. J., & Hafid, A. S. (2019, June). Prediction of transaction confirmation time in ethereum blockchain using machine learning. In International congress on blockchain and applications (pp. 126–133). Springer. 9. Tian, Y., Li, T., Xiong, J., Bhuiyan, M. Z. A., Ma, J., & Peng, C. (2021). A blockchain-based machine learning framework for edge services in IIoT. IEEE Transactions on Industrial Informatics, 18(3), 1918–1929. 10. Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research, 297(1), 3–36. 11. Ali, O., Jaradat, A., Kulakli, A., & Abuhalimeh, A. (2021). A comparative study: Blockchain technology utilization benefits, challenges and functionalities. IEEE Access, 9, 12730–12749. 12. Lim, M. K., Li, Y., Wang, C., & Tseng, M. L. (2021). A literature review of blockchain technology applications in supply chains: A comprehensive analysis of themes, methodologies and industries. Computers & Industrial Engineering, 154, 107133. 13. Shinde, P.  P., & Shah, S. (2018, August). A review of machine learning and deep learning applications. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1–6). IEEE. 14. Syeda, H. B., Syed, M., Sexton, K. W., Syed, S., Begum, S., Syed, F., et al. (2021). Role of machine learning techniques to tackle the COVID-19 crisis: Systematic review. JMIR Medical Informatics, 9(1), e23811. 15. Hakak, S., Alazab, M., Khan, S., Gadekallu, T. R., Maddikunta, P. K. R., & Khan, W. Z. (2021). An ensemble machine learning approach through effective feature extraction to classify fake news. Future Generation Computer Systems, 117, 47–58.

Internet of Things and Blockchain in Healthcare: Challenges and Solutions N. Arulkumar, A. Manimaran, D. Chandramohan, and Sam Goundar

1 Introduction The Internet of Things includes applications that benefit patients and doctors in healthcare. The IoT has undoubtedly become one of the most significant intrusions into conventional company operations approaches. For instance, the advent of sensors and other edge devices and associated infrastructure has altered several companies’ activities. Considering the role of blockchain for IoT may help people understand the foundations of blockchain-based IoT businesses better. The emergence of new and creative IoT devices presents organizations with information security issues [1]. Additionally, as the number of connected devices grows exponentially with each passing year, the complexities of information security may also increase in healthcare.

N. Arulkumar (*) Department of Computer Science, Christ University, Bangalore, India A. Manimaran Department of Mathematics, School of Advanced Studies, VIT-AP University, Amaravati, Andhra Pradesh, India D. Chandramohan Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, India S. Goundar School of Science, Engineering, and Technology, RMIT University, Hanoi, Vietnam © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_2

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1.1 Security Concerns and Privacy Preservation Blockchain technology can be crucial in resolving security concerns related to IoT systems. Privacy is one of the significant obstacles to IoT applications on a big scale. Additionally, Internet of Things devices is vulnerable to Cyberattacks. Simultaneously, today’s IoT networks face scalability difficulties. Due to the growing integration of IoT devices into people’s daily lives, organizations need to address security and scalability concerns [2]. The influence of blockchain on addressing security and scalability issues in the Internet of Things reveals that it is the optimal platform for developing blockchain-based IoT firms. IoT networks can facilitate data transfers across disparate devices owned and controlled by diverse enterprises. As a result, determining the source of any data breach may be difficult in the event of cybercriminal attacks [3].

2 Literature Review on IoT and Blockchain in the Healthcare Domain The most common use of blockchain in healthcare has been to keep our critical medical data safe and secure, which is not surprising. Security in IoT network is a big concern in the healthcare business. Between 2009 and 2017, data breaches exposed over 176 million medical records [4]. Online payment information, as well as health and medical test data, were stolen by the criminals. BurstIQ’s platform makes it safe and secure for healthcare organizations to deal with large amounts of patient data. It is a worldwide health network that uses blockchain technology to link individuals and companies. It does so via an international health network service that helps people and enterprises manage, access, monetize, and get insights from health records. It contributes to the secure handling of enormous volumes of patient data. Its blockchain technology makes it possible to store, sell, share, and safely license data while following HIPAA rules [5, 6]. The company uses blockchain technology to make it easier for people to share and use medical data. It gives up-to-date information on patients’ healthcare activities. Satamraju, K. P. et al. proposed a model that blends IoT networks and a blockchain to address possible privacy and security concerns about data integrity in this study. Smart contracts need to play a big part in this integration process because they manage device authentication and access control and data. A design is also proposed for interfaces that incorporate both platforms, emphasizing their performance advantages over previous approaches. This study demonstrates how the suggested framework can be seamlessly integrated into practically any current IoT application with minimal change [7].

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Jamil Faisal et al. demonstrated a remote monitoring methodology for patients’ vital signs in hospitals using IoT-based blockchain integrity management systems [8]. The suggested system is built for constructing blockchain-based corporate applications. This technique has several advantages for patients, including a lengthy, immutable history record and worldwide access to medical information at any time and from any location. The intended and constructed system’s performance is assessed in transactions per second, latency, and resource consumption. The suggested method outperforms the standard healthcare system in terms of monitoring patient data. Frikha T. et  al. collected data from health and fitness intelligent devices connected to the planned IoT blockchain platform [9]. These devices enable us to extract a large volume of valid health data, filter, evaluate it, and save it in electronic health records (EHRs). The primary goal of this research is to use Ethereum blockchain technology to provide distributed, safe, and authorized access to this sensitive data. They developed an integrated low-power IoT blockchain platform for a healthcare application that stores and reviews electronic health records. This architecture, built on the Ethereum blockchain, comprises a web and mobile application that provides safe access to health information for patients and medical and paramedical professionals. G. Rathee et al. used blockchain technology to establish a hybrid framework for multimedia data processing in IoT healthcare [10]. This model is done by hashing each piece of data, ensuring that everyone on the blockchain network mirrors any changes or changes to the data or security breaches. The findings were compared to a traditional strategy and confirmed using better-simulated results that provided an 86% success rate against product drop ratios, falsification attacks, wormhole attacks, and probabilistic authentication scenarios due to the blockchain technology. Aujla Gagangeet Singh et al. introduced a decoupled blockchain strategy for IoT-­ enabled edge-based healthcare monitoring [11]. This method uses nearby edge devices to build separate blocks in the blockchain, making it safe to send data from sensors to edge nodes. In the next step, the data is sent and stored in the cloud by the edge nodes, which use an incremental tensor-based approach. This work contributes to reducing data duplication throughout the massive IoT healthcare network. The following is a list of the different blockchain and IoT challenges that have been identified from the literature review. • • • • • • • •

Superior data security Patient monitoring in real time Improved hospital record management Increased automation of processes The anonymity of data for added privacy Additional access control Shortage of guidelines for managing IoT devices Environment suitable for collaboration in the sharing economy

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3 Case Study: IoT and Blockchain in the Healthcare Field Sensor and intelligent chip technology has quickly improved, making them more portable and appropriate for real-time interactions with blockchain ledgers. The integration of blockchain with IoT technology in the healthcare domain has the potential to create a marketplace for services between devices. This blockchain-­ based IoT model is composed of data transactions and blocks. Here, transactions are the acts carried out by the system’s participants in the hospitals, and blocks keep track of these data transactions and ensure they occur in the correct order and are not tampered by any attacks. The following sections have the details of the IoT and blockchain model suggested for the healthcare field.

3.1 Using Hash for Blockchain A hash is a function that meets the encrypted requirements for a blockchain calculation to be done. Hashes are set in length to make it very hard for someone attempting to hack the blockchain to predict the hash length. This data is timestamped and may be hashed for reference in the future. The hash on the blockchain is generated from the preceding block’s data of the healthcare data transactions. In the blockchain-­ enabled hospital, hash functions are often used to ensure that the public ledger data is correct and cannot be changed. The technique of generating hashed text is shown in Fig. 1.

3.2 Generating Hashed Text A SHA256 hash function is a suggested mathematical function for this proposed model that compresses a numerical input value. This hash function accepts input of any length in a healthcare record, the result is always of a defined length [12]. The hash values provided by a hash function are referred to as message digests or hash values. Consider this following healthcare record as block 1 used to generate the

Fig. 1  Generating hashed text

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hashed text. The result shows that the hash value changes before and after mining the record. Date: 30.04.2022 Day: Saturday Patient Name: John Treatment: Dental Care Time: 08:00 AM Fee: USD 25 Figures 2 and 3 show the hashed text of block 1. Hashed text is different when the data is mined during the data transfer. Here, in Fig. 3, nonce value used for mining is 79,895. A nonce is an acronym for “number only used once,” and in the domain of mining, it refers to a number appended to a hashed (encrypted) block in a blockchain that, when recreated, fits the complexity level constraints [13].

3.3 Blocks in the Blockchain Model The following designs in Figs. 4, 5, and 6 are based on blockchain technology and are composed of ordered records organized in a block structure. There are five blocks in this blockchain model, numbered 1, 2, 3, 4, and 5. It is used to keep track of money in hospitals and cut down on the time and money it takes to process data. It is found that the hashed text generated in each block is unique according to the healthcare record.

Fig. 2  Block #1 before mining the healthcare record

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Fig. 3  Block #1 after mining the healthcare record

Fig. 4  Blocks 1 and 2 in the blockchain

3.4 Stages of Suggested Distributed Blockchain Model Distributed ledgers use computers called nodes and can be used to record and share hospital transactions in their electronic ledgers. They also use them to keep the ledgers in sync. Rather than centralizing data as in a typical ledger, the given distributed model is a sort of hospital database that is shared, duplicated, and synced across decentralized network participants [14–16]. Peers A, B, and C are chosen for this model. Each stage in the model has two blocks with unique hashedtext, as illustrated in Fig. 7.

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Fig. 5  Blocks 3 and 4 in the blockchain

Fig. 6  Blocks 4 and 5 in the blockchain

3.5 Token Transferring in Blockchain Tokens can be used on the blockchain to store value or make payments in hospital-­ based transactions. Cryptographic tokens are digital assets or access rights maintained by a smart contract and a distributed ledger [17, 18]. While Block 1 in Fig. 8 creates the hashed text for five transactions, Block 2 in Fig. 9 generates a new hashed text based on the previous hashed text in Block 1.

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Fig. 7  Distributed blockchain model for healthcare

Fig. 8  Block 1 with five transactions

Fig. 9  Block 2 with seven transactions

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Fig. 10  Proposed IoT model for blockchain-based healthcare

3.6 Proposed IoT Model for Blockchain-Based Hospital Network An IoT model is proposed for blockchain-based healthcare domain. It is shown in Fig. 10 and it has Things, Gateways, and Network infrastructure. Things are characterized as individually identifiable nodes, typically sensors, that communicate through a variety of various connectivity mechanisms without requiring human contact. Gateways operate as a bridge between physical objects and the cloud, providing necessary connection, security, and management. Network infrastructure has a group of equipment that includes routers, aggregators, gateways, and repeaters, as well as other devices that regulate and safeguard data flow [19, 20]. Rooms are categorized as patient room, office room, and faculty room to connect with gateways. Wi-Fi Protected Access is applied as a security protocol for computer devices connected to the Internet over a wireless connection. IPv4 addresses are assigned to the IoT devices and connected to the gateways through the Wi-Fi networks.

4 Analysis of IoT and Blockchain in the Healthcare Domain Blockchain applications in the given IoT model for healthcare provide several advantages, ranging from safe data production to distributed data management and automated data selling. The proposed IoT hospital model may handle data transactions across several devices owned and managed by the hospitals, making it

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impossible to trace the source of any data leakages in the event of a cybercriminal assault. It creates and handles a massive quantity of data due to various stakeholders’ involvement. From the Figs. 4, 5, and 6, the hashed text is given in Table 1.

4.1 Hashed Text Created Blockchain Transaction Because the blockchain’s decentralized ledger is immutable and tamper-proof, there is no need for trust between groups. By ensuring that existing records cannot be changed, blockchain technology adds an extra layer of security. Blockchain technology makes it easier for people who are allowed to see the chain and its transaction records. Blockchain technology can achieve scalability, allowing faster transaction processing and synchronization between a wide range of connected devices. Furthermore, blockchain technology can help IoT businesses cut costs for IoT gateways by not needing to use computing resources. Hashing model is used as a data compression method to extract a fixed-length bit string value from a file or text. A file is essentially a collection of data chunks. It is found that the hashed text is uniquely generated with equal length based on the previous hash value. Blockchain encryption eliminates the possibility of someone overwriting existing healthcare records. Additionally, storing healthcare records on an IoT network using blockchain offers additional protection, preventing hostile attackers from obtaining access to the network.

5 Summary The ability of blockchain technology to deflate the current healthcare spending inflation, safeguard patient data, and improve overall experience may help relieve some of the suffering associated with healthcare. The blockchain technology with IoT network model has been utilized to protect patient data and contain disease outbreaks. If is found that the hashed text in the blockchain technology is ideal for IoT network systems because it can keep a record of all patient data that is independent, decentralized, and easy to find. Additionally, although blockchain is transparent, it is also safe, concealing any person’s identity behind intricate and secure algorithms capable of maintaining the sensitivity of medical data. Because of the technology’s decentralized nature, doctors, patients, and medical practitioners may exchange the same information quickly and securely.

00009912c3a935bdbf4d95430da692bc8accd8cebcdd5f47172dade9f14c3df9

000050eeb800a81de6336517e1b85068005cdcd8574367f6130baad1f22b39ea 00008869675b4ab88c9992175ea1a70c8f8cfe7ddfb22e6ab86278111525e115

4

5

000050eeb800a81de6336517e1b85068005cdcd8574367f6130baad1f22b39ea

000004c0c1b147b38e9a0be7a170892e4881c4087b11bbc440324995bff0350c 00009912c3a935bdbf4d95430da692bc8accd8cebcdd5f47172dade9f14c3df9

3

Block Previous hash Current hash 1 00000000000000000000000000000000000000000000000000000000000000 0000baf178fc086b19377ac6f23c6113f3390d9d274378b9e49d9b6ddbd6ff34 00 2 0000baf178fc086b19377ac6f23c6113f3390d9d274378b9e49d9b6ddbd6ff34 000004c0c1b147b38e9a0be7a170892e4881c4087b11bbc440324995bff0350c

Table 1  Hashed text created

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A Conceptual Model for the Role of Blockchain in Overcoming Supply Chain Challenges Jindřich Goldmann, Ahad ZareRavasan, and Seyed Mojtaba Hosseini Bamakan

1 Introduction Due to globalization, supply chains have become, through the years, more international, causing a growing number of geographically different cooperating entities. As a consequence of increased complexity, securing the supply chain’s traceability, visibility, and efficiency is getting complicated [4, 79]. Various circumstances continuously challenge this field. A good example is the COVID-19 world pandemic, which caused massive disruptions and demonstrated how vulnerable and fragile companies are when a sudden supply chain outage occurs. During this time, global supply chains faltered in supplying goods because of demand and supply ripples, and rather than dealing with leanness and efficiency, it was talked about tension, mistrust, and misgivings. This pandemic also highlighted the importance of managing the supply chain in a sustainable approach [76]. To react to such changes appropriately and stay competitive, organizations must constantly look for diverse approaches to making interactions within the supply chain more transparent, efficient, and flexible. Current technological solutions are insufficient to fulfill this; therefore, innovative approaches and technologies, such as Blockchain, have been leveraged in recent years. The Blockchain market is expected to reach $23.3 billion by 2023 [55]. When we take a closer look at the supply chain market, the expected growth is from $145  million in 2018 to $3314  million by 2023 at a compound J. Goldmann · A. ZareRavasan (*) Department of Business Management, Masaryk University, Brno, Czech Republic e-mail: [email protected]; [email protected] S. M. Hosseini Bamakan Department of Management Sciences, Yazd University, Yazd, Iran e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_3

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annual growth rate of 87.0%. Hence, the supply chain market would have a 14.2% share of the Blockchain market by 2023 [60]. Several Blockchain-based projects related to the supply chain were introduced in practice in the last 5  years. These projects vary greatly, from projects presenting game-changing industry solutions to more solo projects focusing on solving everyday operational issues. Even though these projects have been active on the market only for a short time and Blockchain and supply chain adoption is still in its infancy, some of them have made immense progress, and it has come to a point where lessons from their activities could be drawn. As we glance at the academic side, some research discusses the appropriateness of Blockchain technology in the supply chain context and the possibilities of applications, challenges, and issues [15, 46, 52, 62, 64, 67, 69, 71, 75, 89, 90]. However, very few studies (excluding [43, 90]) focus on evaluating these projects, discovering which challenges in the supply chain could be resolved with Blockchain. This study aims primarily to fill this gap by analyzing and assessing the projects to understand the role of Blockchain in overcoming supply chain challenges. Hence, this research aims to address the following research question. RQ1: What are the supply chain challenges that Blockchain solves or assists in solving? In order to address this research question, an exploratory case study research of existing implementation cases on supply chain and Blockchain technology is conducted. This research aims to systematically assess the current scope of Blockchain and supply chain interconnection, focusing on existing implemented projects. This research reveals how Blockchain could work as a solution for supply chain challenges, and Blockchain could be encouraged to implement.

2 Related Research The supply chain is one of the most exciting sectors of Blockchain application, and some researchers have reviewed and summarized prior academic research in this domain. Most of the conducted reviews used a systematic literature review approach to identify and synthesize the academic literature. Wang et al. [90] investigated how Blockchain technology influences the supply chain sector through a systematic review of both academic literature (29 papers, from 2016 to 2018) and real projects (17 cases). They suggested that while Blockchain remains in its infancy, it gains momentum within supply chains. According to the results, the key value of Blockchain adoption in SCM was in four areas: (1) extended visibility and traceability; (2) supply chain digitalization and disintermediation; (3) improved data security; and (4) smart contracts. Kshetri [43], studying 11 cases, investigated how Blockchain affects key supply chain management objectives such as cost, quality, speed, dependability, risk reduction, sustainability, and flexibility. Later, Queiroz et al. [71] systematically reviewed 27 journal

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papers published between 2008 and 2018 about Blockchain-SCM integration. They focused on the main Blockchain applications in SCM, the major disruptions and challenges, and the domain’s future. They suggested that scholars and practitioners are not fully aware of the potential of Blockchain technology to disrupt traditional business models. Similarly, Chang and Chen [15] conducted a literature review of Blockchain and SCM research (106 papers from 2016 to 2020), focusing on issues and challenges. Four major issues, namely, traceability and transparency, stakeholder involvement and collaboration, supply chain integration and digitalization, and common frameworks on Blockchain-based platforms, were proposed as critical to the domain. Recently, Kummer et al. [46], using a systematic literature review of 22 articles published between 2018 and 2020, identified the most relevant organizational theories used in Blockchain-SCM literature. They found that Blockchain-­ SCM literature is based around six organizational theories: agency theory, information theory, institutional theory, network theory, resource-based view, and transaction cost analysis. Finally, there has been some reviews with more focus on sustainability aspects. For instance, Paliwal et al. [67], through a systematic review of academic journal papers (187 papers published from 2017 to 2020), investigated the role of Blockchain technology in sustainable SCM. They used the What, Who, Where, When, How, and Why (5W + 1H) pattern to formulate research objectives and questions. Their results revealed that traceability and transparency were the key benefits of applying Blockchain in SCM domain. Some other research followed more descriptive and qualitative approaches. Lim et al. [52], for instance, used descriptive and content analysis to review 106 publications related to Blockchain-based supply chains between 2017 and 2020 inclusive. They examined (1) publications per year, leading journals and countries; (2) supply chain themes; (3) research methodologies; (4) illustration types; and (5) industries addressed. They observed a growing interest in applying Blockchain technology to supply chain operations. More recently, due to the growing number of publications in Blockchain and SCM, there has been more interest in computational review methods such as bibliometric analysis. For instance, Pournader et al. [69] reviewed current academic and industrial frontiers on Blockchain application in the supply chain, logistics, and transport management. They conducted a systematic review of the literature (48 papers from 2015 to 2019) and found four main clusters in the co-citation analysis, namely Technology, Trust, Trade, and Traceability/Transparency. Musigmann et al. [64] provided a bibliometric analysis (citation network analysis and a co-citation analysis) of 613 academic supply chain research articles. They classified the existing literature into five different research clusters, including theoretical sense making, conceptualizing and testing Blockchain applications, framing Blockchain into the supply chain, the technical design of Blockchain applications for real-world supply chain applications, and the role of Blockchain within digital supply chains. Finally, Moosavi [62] conducted bibliometric and network analysis on 769 academic papers between 2010 and 2019. They determined the key authors, significant studies, and the collaboration patterns. Using citation and co-citation analysis, key supply chain areas that Blockchain could contribute are supply chain management,

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finance, logistics, and security. Furthermore, they observed that the Internet of Things (IoT) and smart contracts are the leading emerging technologies. They found that Blockchain could enhance transparency, traceability, efficiency, and information security in supply chain management; however, empirical research is scarce in this field. Finally, they proposed that implementing Blockchain in the real-world supply chain is a considerable future research opportunity. Overall, all mentioned Blockchain and SCM integration reviews provide valuable insights to the community. However, excluding the review of Wang et al. [90] and Kshetri [43] that examined real projects and presented their findings based on real-world cases, others primarily have focused on academic publications, missing the practical aspects.

3 Supply Chain Challenges Fernie [24] suggests critical supply chain challenges in the retail industry as increasing on-shelf availability and replenishment, RFID, covering multiple channels, or planning skills. However, these challenges are operationally focused on more generalized challenges like e-commerce and sustainability. In contrast to e-commerce, sustainability is a challenge pervasive across all industries. The necessity of following the sustainable and green trend of the last decade is an aspect mentioned by several scholars, and this topic has been extensively studied. Since 1995, there have been 198 literature reviews on supply chain sustainability published. Half of the reviews used a triple bottom line perspective, evaluating sustainability from an environmental, social, and economic perspective [56]. This extent shows evidence of incorporating this aspect into the list of challenges. It is labeled in Table 1 as C3: Sustainability.

Table 1  A practical classification of SCM challenges SCM challenges C1: Enhanced collaboration

Definition (sample challenges) This category encompasses challenges related to the systems’ ability to be interconnected to other systems and comply with the standards (lack of interoperability, low level of standardization). C2: Transparency This category comprises challenges related, for example, to the ability to trace the item’s origin or gain more downstream and upstream information that might be further utilized in other fields (visibility, traceability, and complexity). C3: It refers to if and how Blockchain assists to help with challenges related to Sustainability sustainability, such as if it provides scalable solutions for the multi-tier environment (scalability). C4: Risks This category includes various risks that supply chain entities encounter mitigation (unforeseen events, security). C5: Efficiency This category includes performance measures of the supply chain (time, cost).

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Balan et al. [3] examined the challenges and opportunities of the supply chain of globally operating companies. Among the challenges mentioned the most by chief consultants, executives, and academics were technological challenges, supply chain network complexity, and limited data. Other challenges include cultural mindset and trust, which are crucial for information sharing and collaboration. These are labeled as C1: Enhanced collaboration and C3: Transparency in Table 1. Trkman [87] proposed strategic insights, business redesign, supply chain risks, supply chain frameworks and standards, performance measurement, and IS support as critical issues. The first two difficulties are somewhat related to an individual’s strategic decisions and, therefore, not applicable to this study. The rest of the challenges could be categorized as C1: Enhanced collaboration and C4: Risks mitigation. According to Jensen et al. [39], global supply chains have suffered from three main challenges for decades. The first one relates to substantial administrative costs related to processing various documents. The second challenge is associated with delays and uncertainty within global shipping. The last challenge mentioned is risk and security, labeled as C4: Risks mitigation. Looking at the challenges from the marketing perspective, Flint [25] identified the following challenges. The first critical challenge is understanding what customers value within supply chains across the globe. It might be complicated to understand company preferences when organizations operate in different national cultures, regional business norms, economic situations, and regulatory environments. The second challenge relates to understanding changes and customer value perceptions occurring in different environments. It is not straightforward to comprehend when these changes occur and how they affect supply chain customers’ preferences. The third challenge relates to value delivery in a world of uncertainty. The last challenge is about commitment to address these difficulties. The key mentioned challenges are labeled in Table  1 as C1: Enhanced collaboration and C4: Risks mitigation. Companies within a supply chain communicate with each other regularly. This communication might be simple or advanced. Advanced communication usually involves interconnecting two databases through inter-organizational information systems [33]. Interoperability is crucial in any software solution because it enables us to reach the full potential of an implemented system by working together with other software. Nadia Hewett, Blockchain and Digital Currency Project Lead at the World Economic Forum, stated that the challenge of interoperability is not only a technology problem but even more so a problem in terms of governance, data ownership, and commercial business models. It is addressed as C1: Enhanced collaboration. Another challenge of SCM is transparency and visibility. Visibility plays an important role internally in both upstream and downstream operations, and organizations must ensure visibility to provide transparency. Transparency is vital for downstream operations to enable aspects like traceability used, for instance, to ascertain Provenance. Organizations can disclose various information ranges, like supplier information, environmental footprint, supply chain cost, supplier workplace safety compliance. Sodhi and Tang [77] mention that gaining visibility might bring benefits such as better management of supply chain risks, reduction of

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reputational damages, or improvement of supply chain efficiency addressed as C3: Transparency. Finally, supply chain efficiency in terms of speed and cost is mentioned in some prior research, addressed here as C5: Efficiency [6, 54, 96]. To summarize, it is evident that the supply chain itself offers numerous possibilities for adopting new technologies, which might satisfy the current needs. Moreover, Blockchain technology can make the industry more efficient and effective, and adopting these technologies might cause new business models to arise. Based on the above information, key challenges were summarized in Table 1, which will be used for projects’ evaluation later on.

4 Research Method A qualitative approach is exploited in this research to gain a better and deeper understanding of the particular phenomenon. It comprises data collected from diverse sources, data evaluation, analysis of conducted evaluation, and presentation of the outcomes [94]. A case study is one of the types of qualitative research used to explore a phenomenon in a particular context. Multiple data collection methods might be applied, and cases are described in depth. It can be further differentiated between single and multiple case studies. As Yin [94] stated, multiple case studies (adopted in this research) are more beneficial since the cases might be compared mutually. Data collection uses various sources such as articles, annual reports, conference recordings, testimonies, white papers, case studies, audio/video interviews, speeches, books, magazines, web content, and non-conventional sources such as tweets and other social media posts. Considering the speed at which Blockchain is being evolved, these non-conventional sources might be highly beneficial, mainly because they can react to occurring events and report about them. Required data were collected in the second and third quarters of 2021.

4.1 Cases Collection The first step of data collection was to find appropriate projects. The search involved the following prerequisites. A project must implement a Blockchain-based platform. Information about the project must be accessible publicly through the Internet, and it must be either an academic, non-commercial, or commercial project. Most of the projects were adopted from a study conducted by Gonczol et  al. [27]. These projects were reviewed and updated accordingly. Some projects went bankrupt, terminated, or their status has advanced, for example, from pilot to production. For the rest of the projects, a search on websites focusing on reporting Blockchain news

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using keywords “Blockchain” and “supply chain” was used. Specifically, these are websites, which inform about crypto news or Blockchain for enterprises. A good example is “ledgerinsights.com” or “coindesk.com.” The next stage of data collection involved deciding which information to monitor. The decision was made to retain all the data fields from the study conducted by Gonczol et  al. [27]. It had been closely analyzed, and there is no reason to stop monitoring any of these data fields, as each is beneficial. It includes name, foundation year, focus area, business field, industry/academic, platform, status, and access rights (see Table 13 in Appendix).

4.2 Case Selection As mentioned earlier, this study primarily focuses on projects that operate in practice. Therefore, academic projects were excluded from further research [Criterion#1]. Another set of projects, which would not be further studied are projects with the state Proof-of-Concept [Criterion#2]. This study does not aim to prove the feasibility of a technical solution. The release date might or might not be a good criterion for excluding a project. It is evident that different projects progress at different speeds. In some cases, this might signal something, but generally said, this would be used as a guideline, not a strict criterion. Finally, there exist some projects, which are not publicly active. They do not report their activities as much as other projects for unknown reasons. Studying these projects in detail would be very inefficient, and therefore, these projects are excluded due to the unavailability of information [Criterion#3]. Table 14 in Appendix shows the evaluation results of projects based on the criteria.

5 Findings This section presents more detailed information on selected projects, and the analysis is conducted. For each project case, the following information is described: project’s history, technical background, industry overview (if available), competition/ similar projects (if available), and project’s impact on supply chain challenges. Even though some projects have been live for years, evaluating their impact might still be complicated based on the available information. In such cases, a description of the impact is left out. Besides, there is not an exact structure concerning the analysis of a project. Each project is described uniquely based on the available information.

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5.1 Case 1: TradeLens TradeLens is a proprietary project developed mutually by Maersk and IBM. It was launched in 2018, and since then, it has proliferated. In November 2020, it included more than 175 organizations and ten ocean carriers. At that time, it encompassed data from more than 600 ports and terminals, tracked 34 million container voyages, and processed 1.6 billion transport events. Concerning the technical background, TradeLens is a permissioned platform that enables the network participants to view, edit, and take advantage of shipping data. Only those entities relevant to a specific shipment have access to that shipment’s data. Permissions are assigned according to their roles and based on the permissions matrix. The platform provides data streams and ensures that they are secure and that they can be trusted [86]. At the time of a release, this project struggled mainly with the reluctance of other organizations to join the platform. This disinclination to enter has been caused by pragmatic concerns since Maersk was an industry leader. It would be unrealistic to assume a collaboration in a competitive environment where certain members could benefit from the platform more than the others. Nevertheless, it seems that they have succeeded, at least with welcoming other partners on the platform. In the last 3 years, many organizations have joined the platform. Among the first pilot partners were, for example, Canada Border Agency and Hong Kong terminals. In August 2018, TradeLens started with 92 clients, and within the 2-year horizon, this number almost doubled [32]. Not only how many organizations but also who joins the platform is also essential. Recently, CMA CGM and MSC have connected to the platforms. Marc Bourdon, CMA CGM senior vice president of commercial agencies network, said: “Digitization is a cornerstone of the CMA CGM Group’s strategy aimed at providing an end-to-end solution tailored to our customers’ needs. An industry-wide collaboration like this is truly unprecedented” [11]. The most important word from this comment is that TradeLens is an industry solution, not an isolated one. The platform’s success highly depends on the willingness of the industry’s participants and the overall acceptance of such a platform. It is essential to mention that when most shipping organizations join the platform, which has happened in November 2020, then it might not mean that this platform will become an industry standard. The current development indicates that multiple systems, even multiple Blockchains, might co-exist next to each other. A significant number of large shipping companies are already part of multiple Blockchain platforms. A good example is CMA CGM and Hapag-Lloyd, which have joined all three major platforms  – TradeLens, GSBN, and DCSA.  Digital Container Shipping Association (DCSA) attempts to develop data standards in shipping lines through a general approach. This is very crucial for the industry as different logistics providers use different systems, and most of them are based on different data standards. This has caused digital landscape fragmentation, and it has always been a challenge to integrate and combine data from different logistic participants [40]. Accordingly, as the Senior Vice President of CMA CGM Marc Bourdon highlighted, “Only by working together and agreeing to a shared set of standards and

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Table 2  TradeLens – summary of SCM challenges and approaches SCM challenges Approach C1: Enhanced TradeLens has joined the United Nations Centre for Trade Facilitation and collaboration Electronic Business (UN/CEFACT) and shaped its processes according to CEFACT standards and objectives. Moreover, it offers Application Programming Interfaces (APIs) based on CEFACT principles [48]. C2: Auditable, trusted, and real-time updated history is enabled because of Transparency Blockchain’s architecture. Therefore, it provides higher visibility across the shipping industry [48]. C3: More effective utilization of assets was reported as well as the elimination of Sustainability paper documents [85]. C4: Risks It was not directly addressed, but it can be assumed that digitizing the mitigation documents will most likely mitigate the risks typical to the physical documents. The conventional systems, based on the large amount of paperwork, creates opportunities for fraud at multiple stages. C5: Efficiency Real-time information sharing through many logistics participants. There is no need to manually request data from each entity or establish complex and vulnerable peer-to-peer interconnections between different systems since the information is available in one platform [32].

goals are we able to enact the digital transformation that is now touching nearly every part of the global shipping industry” [30]. Another challenge that many partners are addressing is efficiency. When the cargo data is transparent and enables seamless, real-time information sharing at different stages by different entities in the global supply chain, it will increase efficiency for global trade flows [48]. According to Henrik Wretensjo, Global Vertical Head of Chemicals – Maersk, digitizing global supply chains with TradeLens enhances visibility and sustainability. Concerning visibility, he refers to TradeLens real-time data update, which enables predicting and acting with agility, making better decisions, and utilizing the data to a greater extent. In his view, digitizing paper documents enhances sustainability since it helps with efficiently utilizing assets, in his words, “using the assets we have in a far more efficient way instead of producing new ones” [85]. In conclusion, members recognize enhanced collaboration between supply chain nodes as the main driving force for implementation. Even though involved companies are not entirely relying on TradeLens, the overall number of organizations and especially the representation of large and significant players piloting the platform show that they have started to realize strong use case of this industry solution (Table 2).

5.2 Case 2: Everledger It might be complicated to prove the authenticity of a luxury product, especially in today’s world, where counterfeit products are frequently seen. This problem is the main challenge that Everledger aims to solve. Usually, luxury products are trusted

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based on paper-based certification, which is susceptible to fraud. Suppose there is a low degree of visibility along the supply chains, supported only by these insufficient certifications. In that case, it is likely that these frauds, which generate substantial financial losses, will continue to happen, and therefore, Everledger brings a different approach to proving the product’s authenticity. Everledger could be described as a database that protects the value of an item by storing its authentic information among industry participants. It integrates the supply chain onto the same digital network creating a single version of the truth for all parties involved in the diamond trade. It also shared records visible across the industry participants. Participants interact with the platform through API, and permissioned access can be assigned to banks, insurers, law enforcement organizations, and online marketplaces. The first and the most widespread use case is a solution for proving diamonds’ authenticity, and it works as follows. First, diamonds are precisely measured, and secondly, a digital footprint of a diamond is created. Major certification houses then recognize and verify data uploaded on the Blockchain. In this manner, Everledger actively works with insurance companies, banks, and international criminal police organizations to mitigate fraudulent behavior. Diamonds are under strict certification requirements within the processes that ensure ethical sourcing. It also reduces counterfeits as the platform checks that each diamond has its unique report, which cannot be used for a different diamond. However, the diamond industry is just one of the many deployment examples. Generally, Everledger aims to create a digital space for storing valuable assets [29]. In August 2020, Everledger implemented a solution for JD.com, which should increase trust and transparency during a diamond purchase process. On this project, these two organizations also cooperated with the Gemological Institute of America (GIA), a certification house in gemology, which provides grading characteristics of a diamond. This collaboration enables JD.com customers to gain more comprehensive information of their product regarding transparency and authenticity and push the e-commerce experience to a higher level. Besides diamonds, Everledger is very active in the alcohol industry. Counterfeit alcohol accounts globally for around $3 billion loss per year. However, it is hardly quantifiable since it may also affect other factors such as brand reputation. Nowadays, it is not an exception to see QR codes on products that we daily buy, but what is not common is that this product would have a unique ID referring to the specific substance in it, in our case, wine. Everledger uses NFC and RFID tags to authenticate the bottle’s provenance, enabling customers to be aware of its production and distribution processes [82]. Further, Everledger cooperates on merino wool tracking solution with Australian Wool Innovation (AWI), an organization which protects and represents wool producers. Especially, it aims to provide a customer with information regarding the origin and processing of wool [23]. The last cooperation that Everledger leads and is relatively recent is the monitoring of electric vehicle batteries. Ultimate objectives are to create a digital identity of each battery in order to enhance the chance of a battery being repurposed, enable circular recovery of battery materials, and generate less waste.

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Table 3  Everledger – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration C2: Transparency

Approach Not addressed.

Blockchain assists with making supply chain processes more transparent. Blockchain stores provenance records of diamonds and aims to provide indisputable information of their origin [22, 51, 82]. C3: Sustainability The organization encourages ethical sourcing, low carbon economy, and keeps records of certified and verified products. Aspects of sustainability are verifiable and thus more meaningful [51]. C4: Risks Risks related to the tampered origin of products might be suppressed. mitigation Blockchain keeps records of digital identities, which can, to a certain level, help with the elimination of counterfeit products [22]. C5: Efficiency Not addressed.

To summarize, Everledger has progressed in creating digital products’ identities and thus reaching a higher level of authenticity. However, it has to be critically noted that significant challenges still exist unsolved. The solution does not address the discrepancy between the physical and the digital world. What if a forged product with a tampered history was uploaded on the Blockchain? One must still trust the third party, which issued the product’s digital identity in these cases. There is progress in that sense that one knows who the issuer of such a product is; therefore, it is possible to trace back the history and take action (Table 3).

5.3 Case 3: Provenance Provenance is similar to Everledger and engages in three fields: supply chain, commerce, and social and environmental good. It develops a solution for joining these three fields together. It originally started with the ambition to focus on the food industry and try to de-commoditize the products to differentiate them with price or quality and other factors such as the product’s carbon footprint or more transparent supply chain. According to the founders, this type of differentiation will become more popular in the future [21]. One of Provenance’s projects is cooperating with Princes, specifically, its subsidiary Napolina, a fresh tomato producer. It aims to provide comprehensive information about tomatoes such as where they grew, where they are processed, which parties were involved and how they got to the shelves of supermarkets. This information is enabled by scanning a QR code on a product package. Provenance’s representatives mention trust enhancement and increased transparency as their primary impulses for implementation. However, it is crucial to ask how they can assure credibility when disclosing information about their processes and products. In this respect, Provenance provides so-called Proof Points, which act as evidence of certification or a statement. Consumers, therefore, do not trust only the organization, which issued the QR code but also the issuer of certification [9].

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Table 4  Provenance – summary of SCM challenges and approaches SCM challenges Approach C1: Enhanced Not addressed. collaboration C2: Transparency It is pretty questionable in this case. Product-related information is more accessible and verifiable as well. However, it does not necessarily make the companies’ supply chains more transparent since the company does not disclose operational data (e.g., when and where was an ingredient processed), but only available products’ information such as certificates [2, 70]. C3: Sustainability It contributes to sustainability by disclosing information related to environmental and social aspects such as better work conditions or carbon footprint. This makes sustainability to be more tangible [2, 9, 70]. C4: Risks Not addressed. mitigation C5: Efficiency Not addressed.

Another industry besides food, which is worth mentioning, is the beauty industry. In 2018, more than 120  billion units of cosmetics packaging were produced globally, but only about 30% could be recycled. Estimates are that if refillable containers were used, around 70% of carbon emissions of the entire beauty industry could be eliminated [70]. Provenance provides its services to Cult Beauty, an online international beauty business. They help reshape their business processes to empower clients with proven data and insights. Similar to previous cases, proof-of-­ points are published next to the products to quickly check the product’s history [70]. To conclude, Provenance provides information related to the journey of products, audits, and certifications and keeps this information up-to-date. On the other hand, it does not track and trace the history of individual products. It only serves as an enabling tool for disclosing information that already exists. Based on the available sources, it does not seem to be a strong use case for Blockchain technology, and Blockchain does not reach its full potential in this application. Blockchain implementation, in this case, only aims to ensure that certifications are verifiable and can be trusted. However, this also has significant limitations as well (Table 4).

5.4 Case 4: CargoX CargoX engages in digital transfer solutions for documents such as Bills of Lading, transfer documents, certificates, and others. In February 2018, the company raised $7 million in 7 min through the initial coin offering of its Ethereum-based CXO Tokens, which are used as a payment method for services provided by CargoX. The solution enables tracking the origin of a document and authenticates it. It is pretty similar to TradeLens, but it is notably smaller in some ways. In June 2020, CargoX had 3000 registered users, including shipping lines, freight forwarders, and shippers. The main driving force of CargoX is to get rid of paper documents, which are

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constantly being sent around the world. This could have saved billions of dollars, but the maritime industry still primarily relies on paper documents due to cooperation issues. It is about 400 million documents sent annually. It not only costs a lot and is inefficient, but organizations incur the risks of unintended manipulation with documents [34]. This platform can be used for any type of transport. It originally started with container transportation, but it can be used anywhere where documents are exchanged. Primarily beneficial and efficient, it is for trading since the owner of the goods can be modified while cargo is still on “water” by changing the owner of the Bill of Lading flexibly. CargoX itself does not store any documents but unique hashes. Users store documents by themselves through the web interface provided by CargoX [34] that valuates users’ data ownership. In September 2018, CargoX launched a Blockchain-based Bill of Lading into production. Bill of Lading is one of the key documents in the shipping industry. It serves for documenting the goods carried, as evidence of a contract, and as proof-­ of-­ownership. This launch was preceded by the live pilot during which a Bill of Lading was processed in minutes and at the cost of $15 compared to the traditional process that takes days and costs up to $100 [92]. Among the first adopters were G2 Ocean, a joint venture of two of the world’s largest open hatch ship-owning companies Gearbulk and Grieg Maritime Group, and Manuchar NV, an emerging market-­ focused trading and distribution company [7]. In June 2020, G2 Ocean expanded the implementation of the Bill of Lading to new trade lanes and destinations. This platform gave G2 Ocean’s customers complete visibility over who has the original e-bill in their custody at all times. Other benefits include eliminating courier-related expenses for paper documents and the speed it takes to distribute digital documents between those involved in the supply chain [88]. In October 2020, Contour, a global trade finance network, started a partnership with CargoX to digitize the Bill of Lading. Carl Wegner, CEO of Contour, commented: “Transforming trade finance cannot be achieved by a single company acting by itself – collaboration is central to building a trade finance network that is truly global.” Furthermore, he is convinced that the solution will streamline the processes and create an ecosystem of technology providers [13]. To summarize, CargoX delivers and implements its product to several significant customers. However, based on the feedback, it was evident that the transformation would take 5–10  years until the digitized form of documents became a standard (Table 5).

5.5 Case 5: Modum Modum is a startup company that combines technologies to provide a supply chain solution underpinned by Blockchain. The core product of this company is a device that measures data during shipments of sensitive goods. Modum has raised in total $13.3 million in funding, which is quite a lot compared to other projects studied in this paper, for example, Provenance ($1.2 million). In May 2018, Modum announced

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Table 5  CargoX – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration C2: Transparency

Approach Collaboration is enabled using a digitized version of the bill of lading with an independent solution [7, 12, 13]. One knows to whom the document belongs and where it is. It is traceable and trackable in real-time, and thus, it enhances transparency [7, 13]. C3: Sustainability It provides a way of eliminating paper documents. In that sense, it positively affects sustainability. There are fewer risks related to delays or losses with the digitized version of the bill of lading [7, 12]. C4: Risks The digitization and secure transfer of the bill of lading reduce the time mitigation required to transfer documents and improve communication [7, 13]. C5: Efficiency Not addressed.

a partnership with the Swiss post to monitor drugs during transportation. It is considered a more demanding process than transporting other goods since there are many regulations for such transport, which must be satisfied [17]. The idea behind this project is that there are certain products, such as medicine, which must be transported in a specific temperature range and for a specific time. If there is no way to prove it, one must use the particular vehicle equipped with the cooling system. Nevertheless, what if medicine would be transported in a standard car and the carrier could prove that all the measured variables were in an allowed range? Modum provides a way of proving this. Their device constantly monitors various variables during transport and records them on the Blockchain. As soon as the receiver gets the shipment, the QR code of the device is scanned, and it is immediately recognized if the shipment was carried by regulations or not [61]. Blockchain provides a more transparent, neutral, and robust architecture than centralized databases. Besides that, couriers can dynamically set the temperature range in an application and position the sensor into the box. At that moment, a smart contract is created. After receiving a package, it checks the recorded data with the ranges set in an application. If everything goes fine, the smart contract is validated, and eventually, payment is made. The benefit of uploading data directly to the Blockchain guarantees that this data cannot be modified. Health centers and other purchasers do not have to trust the courier or Modum but only the smart contract. Modum is Blockchain agnostic and tries to remain independent in that sense. Initially, it started with Ethereum, but afterward, it also integrated Hyperledger Fabric. At the end of 2017, Modum started cooperating with SAP, one of the biggest ERP providers worldwide, and SAP is considering integrating Modum’s solution into its standard logistics processes. This made it an attractive extension to other SAP solutions for logistics, such as SAP Global Track and Trace [17]. To summarize, Modum presents a strong use case of utilizing Blockchain. Their product is based on the coordination of different technologies, and it enables them to carry out specific processes in a far more effective way and with fewer requirements on resources. Although there are quite a few reports and feedback from its clients, some references and partners such as Swiss post or SAP are significant (Table 6).

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Table 6  Modum – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration C2: Transparency

C3: Sustainability C4: Risks mitigation C5: Efficiency

Approach Not addressed. At any time, the parameters of a shipment equipped with sensor are known, and they are shared between relevant parties, who cannot tamper with these recordings [17, 61, 74]. Not addressed. Risks related to the demanding transport of certain goods (e.g., inappropriate temperature) are eliminated [61, 74]. Not addressed.

5.6 Case 6: Chronicled Chronicled provides and manages MediLedger, a consortium implementing a Blockchain network for the healthcare and life science industry, established in 2017. It is designated for permissioned parties, either drug manufacturers, wholesalers, dispensers, or logistics providers. The MedilLdger network was developed with the following objectives: (a) prove the feasibility of Blockchain as a tool for complying with strict Drug Supply Chain Security Act (DSCSA) regulations; (b) comply with transaction throughput, speed, and cost as predetermined by stakeholders; (c) enable tracking of prescription medicines; (d) comply with data privacy requirements; (e) validate the authenticity of product identifiers; (f) provide open system architecture and be established on business rules and industry standards; and (g) become the interoperable system for the pharmaceutical supply chain [58]. DSCSA was introduced in 2013 by US Food Drug Administration (FDA), to control the counterfeit drug market. In order to do so, supply chain participants must share information, and the industry must become entirely interoperable. This act is planned with partial deadlines over 10 years horizon. MediLedger is one initiative that addresses these requirements set by DSCSA [1]. Taking a look at the progress of MediLedger, we might observe that the project advances as DSCSA introduces a new set of regulations. According to the first deadline of the act, which came into force in 2019, all drugs returned and resold to wholesalers must be verified and proved authentic. This accounts for more than 2% of all drugs or 60 million units, which might seem like a small percentage, but it accounts for almost $7 billion a year [1]. The process of drug verification can last up to 48 h and involves contacting a manufacturer and tracing the drug’s serial number. Using MediLedger, this process can be done in less than a second. This stage is already working and applied in production. The next stage involves tracing and tracking each box of drugs, and this has to be finished by 2023 when the next set of regulations comes into force [78]. Further, Chronicled cooperates closely with SAP, and MediLedger has been integrated into the SAP Information Collaboration Hub for Life Sciences. This combined solution is used by organizations responsible for 94% of the US pharma trade. It is the first step toward interoperability and improves safe access to medicines [47].

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Table 7  Chronicled – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration

Approach One of the DSCSA’s objectives is to enhance the interoperability of this industry. This solution provides a standardized communication method, and participants are forced by the law to cooperate more intensively [1, 47]. C2: Transparency Each medicine producer has its prefix assigned, which identifies its products, and it enables to monitor in real time the genuineness of a medicine. Altogether, it makes the industry more transparent [1]. C3: Sustainability Not addressed. C4: Risks Counterfeit medications might be eliminated because of regular checks mitigation during the return and buying process, which DSCSA requires. Besides, it helps to mitigate the mistakes that occurred during the contracting and chargeback operations [1, 47]. C5: Efficiency Not addressed.

To conclude, MediLedger’s consortium presents well Blockchain’s application-­ driven purely by the regulation on the state level. Once again, we might observe that Blockchain is only one solution component. In this case, it aims to help secure the drug distribution primarily. Chronicled also develops a non-consortium Blockchain application, which is not yet excessively implemented. Blockchain is harnessed as a neutral enforcer of data correctness in both cases. It creates a more efficient environment between trading partners (Table 7).

5.7 Case 7: OriginTrail OriginTrail is an open-source middleware developed by Trace Labs, aiming to improve standardization of data exchange, interconnectivity, and data integrity. The company offers three different ways of providing its product to its clients [37]. They cooperate directly with clients, educate members of Trade Alliance about the implementation, or work with partners who implement the protocol to their clients. Further, OriginTrail cooperates with Oracle on the deployment of various projects. Jens Lusebrink, Oracle Principal Business Consultant, comments that they focus intensively on the meaningfulness and appropriateness of Blockchain implementation. They work on optimization of current solutions rather than entirely replacing them. Together with OriginTrail, they developed an efficient solution for making payments to farmers in Slovenia [28]. Farmers use network operating systems (NOS) for uploading data about milk volume and laboratory test results. This solution combines OriginTrail decentralized knowledge graph for milk traceability and data integrity and Oracle Blockchain for smart contract framework and business automation. In the next stage, a price to be paid for milk is calculated based on the uploaded data, and with the help of a smart contract, these rates are confirmed. NOS is further connected to the Oracle Blockchain Platform and an arbitrary ERP system. This interconnection enables the complete automation of the payment process. The figure below presents a high-level overview of this process [84].

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Table 8  OriginTrail – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration

Approach It enables more connections between isolated systems, which would otherwise be too costly and disorganized. Thus, it contributes to making a more collaborative environment [10, 28]. C2: Transparency It assists with reaching higher transparency within a network of many suppliers as it aggregates data from various sources and makes them trackable and traceable [10, 28]. C3: Sustainability It enhances sustainability by offering a very scalable middleware that does not compromise with the new interconnections [10]. C4: Risks Not addressed. mitigation C5: Efficiency Not addressed.

Another worth-mentioning project is cooperation on a project initiated by the Swiss Federal Railway, which aims to monitor parts and components used in the railway industry across Europe and connect these components to the provenance of steel. If a particular part deteriorates, they can quickly locate all other parts of a specific material and react accordingly. During the starting phase, the biggest problem was to unify the communication as organizations used to name the same things differently. This hitch was solved by implementing GS1 standards for standardization in the supply chain [37]. The next big challenge was the standardization of data exchange. Interconnectivity looks easy at first glance when it is desired to connect only a few ERP systems. However, when one exceeds a certain number of interconnections, then it becomes intractable and self-destructive. Therefore, they decided to look for other ways of establishing interconnectivity and encountered EPCIS for components life cycle. This foundation lies in establishing middleware, which standardizes data exchange [66]. The essential characteristic of this EPCIS repository is decentralization, and therefore, the following aspects must have been addressed. Data integrity is enabled through Blockchain verification, and a fingerprint of the data is anonymously stored on the Ethereum Blockchain. Organizations are identified through identity-based access control, and each organization has specific permissions. It enables the development of scalable solutions with more robust architectures [66]. Taking everything into account, OriginTrail cooperates with very significant partners such as Oracle or Swiss Federal Railways, and it has successfully managed to deploy its solutions to these companies (Table 8).

5.8 Case 8: IBM Food Trust In 2016, Walmart teamed up with IBM to develop a Blockchain-based system, which would monitor all the products sold by Walmart, called IBM Food Trust. The main objective of such a solution was to address the safety and quality of products and provide a system that would react effectively to urgent situations such as food

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fraud or poisoning. The system can be used by authorized users, starting at the beginning of the chain with producers, followed by distributors and grocery stores, and finally ending with customers [35]. IBM Food Trust stores data of the farm origin, batch number, factory and processing data, expiration dates, storage temperatures, or shipping details. IBM Food Trust’s Blockchain (Hyperledger Fabric) is also compatible and interoperable with Ethereum. It can be deployed to different architectures, either on-premise or a preferred cloud provider. In order to prove the feasibility and efficiency, Walmart executed two proof-of-concept projects in their largest sales areas, one in the United States, the other in China. First, tracking the origin of mangoes lasted for 6 days, 18 h, and 26 min using the former capabilities, and with a new platform, it took 2.2 s. The second proof-of-concept in China dealt with the increase of meat credibility by uploading certificates of authenticity to the Blockchain [57]. This solution is not the first attempt that would address these problems. Earlier, Walmart has tried several different systems. However, they always encountered scalability issues since all systems were centralized and incapable of handling such a high number of miscellaneous supply chain entities. With IBM Food Trust, users can view complete history and current location in seconds with access to end-to-end data [68]. The solution is divided into four modules: (a) Trace – used to provide provenance of a product. (b) Fresh insights – enable visibility gain into inventory, compare metrics, inefficiencies identification. (c) Certifications  – used for information management, ensure authenticity. (d) Data entry and access – sharing data with any network participant [68]. Looking at the platform’s progress, in August 2017, Walmart added Nestle and Unilever to launch IBM Food Trust. Later on, it had announced that each fresh vegetable supplier would be required to join the system during 2019. However, as the current development indicates, they are not in that state yet [16]. In an interview, Paul Lightfoot, president and founder of BrightFarm, justified why they have decided on Blockchain and what it brings to them. The primary driver for the implementation was their customer Walmart, who initiated the process and invited them to join the platform. Concerning the benefits, it enables them to identify the position of a product quickly, and in the event of a recall or an unexpected situation, they can immediately take action. Thanks to available data, the platform helps them trace performance indicators, which they later use to optimize yields, allocate resources, and deliver better insights to processes. Furthermore, he perceives it as an additional safeguard for food safety [10]. In conclusion, IBM Food Trust is a network with many well-known companies that have joined the platform to secure their processes in case of a sudden misfortune and provide their customers with deeper insights into their products. Out of the scope of this project, both IBM and Walmart have several other Blockchain initiatives, and it can be stated that they are one of the largest pioneers of this technology in the world (Table 9).

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Table 9  IBM Food Trust – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration

Approach It increases collaboration because supply chain entities exchange information, and new data streams are established. On the other hand, Walmart pressures its suppliers to use the system [57]. C2: Transparency The current solution increases transparency as it was the primary objective of initiating such a solution [36, 68]. However, only a fraction of the products is integrated into the system at this time. C3: Sustainability aspects are improved as customers have more insights and Sustainability information. On the other hand, customers still need to trust the retailer and its suppliers because they are the ones who upload the data on the Blockchain [57]. C4: Risks It is perceived as a tool to decrease risks. If sudden food contamination mitigation occurs, they do not have to withdraw the products from circulation since their origin can be traced in real time [36, 68]. C5: Efficiency The two proofs-of-concept demonstrated a clear improvement in tracing the origins of food [36, 68].

5.9 Case 9: VeChain VeChain is slightly different from the previous projects. It originally started to operate on Ethereum public Blockchain, and in 2018, it transitioned to its own Blockchain. As a part of the re-branding process, Vechain renamed the original VEN token to VeChainThor (VET). VeChain provides Blockchain as a Service, and it is closely integrated with Internet-of-Things devices. The leading solution delivered is called Toolchain, an enterprise Blockchain as a service platform. It has already been deployed to a diverse and wide range of applications, such as Avery Dennison, one of the largest global packaging solution providers. This cooperation aims to integrate the Toolchain into Avery Dennison’s solutions and help enterprises enable transparency and digital transformation. Unfortunately, there is yet little information regarding the deployment to final customers. However, the partner is a significant company without a doubt [49]. The next project is participation in medical data management platform for the Mediterranean Hospital of Cyprus. The application’s core is that patients possess an NFC card, which serves as a digital healthcare passport. They identify themselves with this card as they enter the hospital, and relevant medical records are associated with their passport. To remain objective, this is undoubtedly not revolutionary or innovative in any way, as centralized systems can easily accomplish this. However, Blockchain is not used in this case to store medical records but to manage them. It is used to log authorizations to access medical records data and gain analytical insights for optimizing patients’ treatment by storing timestamps of their actions. This solution results in patients’ complete control of who can access and view their records [80].

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Table 10  VeChain – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration C2: Transparency C3: Sustainability

C4: Risks mitigation C5: Efficiency

Approach Not addressed. Feedback from deployed projects in China (e.g., Walmart, Tea) indicates increased transparency [73, 80]. Sustainability aspects are very similar to Food Trust. Customers have more profound insights into the food distribution, and the overall chain is more transparent, which might lead, for example, to accept only authenticated and certified suppliers. Nevertheless, once again, it depends highly on the genuineness of data, which is uploaded on the Blockchain [73]. VerifyCar application might reduce risks related to various frauds because, besides a physical identity, a digital identity exists, which cannot be tampered with [73, 80]. Not addressed.

VeChain solution reduces communication costs, enables new information links, and provides full coverage of the supply chains with no “dead ends.” What is quite innovative compared to other projects is self-certification. VeChain outlined that the platform could integrate an inspection mechanism to show no discrepancies between the physical and digital world [73]. This innovation would help companies quickly identify substandard products. Apart from these applications, ToolChain is further used, for example, for tea traceability. It assists in uniquely tracing the life cycle of tea and providing these insights to customers. To summarize, VeChain is utilized, and most of the partnerships in this time were predominantly in China. Even though it is implemented by huge companies such as Walmart China, the actual number of products tracked is relatively small, and there is much space for extension (Table 10).

5.10 Case 10: Dltledgers Dltledgers was founded in 2017, and it is a Blockchain-based platform that was developed with the following objectives: (a) digitize trade with all the areas that are related to it, such as banks, shipping, logistics, and insurance; (b) reduce delays in cross-border transactions; (c) increase finance operations efficiency; (d) reduce trade cycle time and manual errors; and (e) reduce manual processes, physical documents, and different communication channels [20]. In 2020, it had around 4000 clients, cooperated with 48 banks, and the platform processed more than $3 billion in trade finance [63]. It primarily focuses on commodities such as agribusiness, energy, metals, and minerals. The company provides Blockchain as a service, and it is unique because of its close integration of supply chain processes with financial institutions. The first use case of Dltledgers is cooperation with Cargill, provider of food, agriculture, financial and industrial products. They could digitize cross-border trade execution, comprising 27 steps between five parties. Based on the company reports,

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Table 11  Dltledgers – summary of SCM challenges and approaches SCM challenges C1: Enhanced collaboration C2: Transparency C3: Sustainability C4: Risks mitigation C5: Efficiency

Approach It enabled interconnectivity between parties’ systems, which in the past relied predominantly on paper documents [65]. The visibility was increased in a multi-tier supply chain because of the architecture of Blockchain [19]. Not addressed. The platform will likely help to reduce fraud and related risks [93]. It was reported that the solution might reduce financing costs by around 20% [19, 72, 93].

the trade was executed in 5  days compared to 30. Further, they cooperated with Vertiv. It was required to automate service contract management between parties. They established end-to-end service management and certification tracking on the Blockchain. As reported, it improved satisfaction and reduced cost disputes and time spent on reconciliation. The deployment for Rio Tinto involved tracking aluminum material from the mine to the customer using the data from existing ERP systems [19]. The other use case is Schneider Electric, where Dltledgers was applied to increase visibility in a chain composed of several tiers without disrupting existing ERP systems. This is a substantial benefit since Blockchain provides a less costly framework for tracking and authentication than current ERP systems, usually priced based on the users. In this multi-tier environment, it would be too expensive. All these cases value multi-tier extended supply chain visibility predominantly, and they perceive transparency as a key differentiator [19]. Agrocorp, an agriculture business trader, is another Dltledgers’ partner. In April 2020, they conducted the first shipment worth $12 million with six trading partners Cargill, Rabobank North America, Rabobank Singapore, shipowner Amarante, shipping agent Transmarine, and agri-commodity trader Agrocorp International. The trade took 5 days compared to a month with traditional trading processes. Abhinav Vijay, Sustainability Manager, Agrocorp International commented, “We have been engaging in digital trade execution using Blockchain for over 18 months now and have been able to increase efficiency internally and externally” [50]. Overall, they reported saving 12–20% on trade financing costs [72]. In conclusion, Dltledgers has, for the time being, proved itself useful in diverse deployment cases. The platform profits from close interconnectedness with financial institutions. The following months will show if the transition to Corda Blockchain was the right step or not (Table 11).

6 Conceptual Model The results indicate that Blockchain applications positively endorse standardization. Some of the studied projects deliver solutions that aim to transform the traditional methods within the entire industry. With the adoption of such solutions, it is required that the parties agree on and promote the same standards. The good

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examples are TradeLens, CargoX, or Chronicled. Some applications already cooperate with several different partners and clients worldwide. The most significant application is TradeLens that has already established hundreds of new interconnections based on the recognized standards. As Marc Bourdon, CMA CGM senior vice president, commented, “An industry-wide collaboration like this is truly unprecedented.” Consequently, it is relatively evident that accepting the same standards and cooperation of these subjects on one platform will increase collaboration. A similar development related to promoting and adopting the standards could be seen as well by Chronicled or IBM Food Trust. Furthermore, it was demonstrated how Blockchain assists with digitizing certain documents through neutral and non-proprietary systems. As some cases addressed (e.g., OriginTrail, TradeLens), it can handle more entities than the centralized systems and the fact that cases are industry-oriented endorses interoperability. Other cases, such as IBM Food Trust, have reported the establishment of new data streams and end-to-end connectivity. All these factors mentioned positively affect and enhance collaboration between supply chain parties. Accordingly, proposition1 is suggested as below: Proposition1: Blockchain positively assists supply chain collaboration. All of the cases have strengthened the conclusions of the existing studies toward transparency. Frequently, transparency aspects were reported to be the primary drivers for embracing Blockchain technology. It is mainly due to the distributed feature of Blockchain technology. This finding is not ground-breaking in any sense, but the research’s contribution lies in providing more tangible transparency aspects, and it outlines how it is accomplished. Cases such as IBM Food Trust, Everledger, or VeChain introduced how they achieve traceability and its benefits. Kai Gindner (Horváth Partners) indicates, “We are now able to offer our customers end-to-end connectivity of ecosystem partners, complete supply chain network transparency, and reliable product traceability.” Nonetheless, it was also mentioned that the number of products traced on Blockchain when studying the cases is relatively insignificant compared to all of the products sold by their clients. Therefore, it signals that organizations gradually get hands-on experience with the technology. Each of the cases increases transparency in a slightly different way. However, it is always enabled because of the very nature of Blockchain. It allows each authorized party to view the status of a transaction, and the parties must reach a consensus so that it is ensured that they work with the same data set in real time. All these characteristics enable creating end-to-end supply chain visibility, and thus, it provides and increases transparency. Accordingly, proposition2 is suggested as below: Proposition2: Blockchain assists in increasing transparency within supply chains. The cases showed evidence of the Blockchain’s assistance in providing sustainable solutions. Specifically, more effective utilization of assets and the elimination of paper documents were addressed. In addition, Blockchain helps deliver more verifiable and quantifiable data that makes sustainability aspects more justified and legitimate. Two cases, Everledger and Provenance, have mentioned aspects related to ethical sourcing. However, this is instead a decision of an individual project than

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Blockchain ability. IBM Food Trust and VeChain disclose information about the production conditions and origin of ingredients, enabling customers to have deeper insights and make more conscious and information-based decisions. For example, Henrik Wretensjo (Maersk) commented that it enables “using the assets we have in a far more efficient way instead of producing new ones.” Besides, Lauren Roman, Metals Minerals Ecosystem lead at Everledger, added, “This will support establishing a low-carbon economy, in a manner consistent with the UN Sustainable Development Goals.” Based on the information, scalability was identified as a critical aspect as well. For instance, TradeLens, Chronicled, and OriginTrail provide solutions that do not compromise the incoming workload. It must be added that the platforms are not deployed to their full potential, most likely, so it might be more relevant to assess this after a few years. To conclude, several studied platforms disclose information, which is somehow related to sustainability, and therefore, it makes it more meaningful and quantifiable. Accordingly, proposition3 is suggested as below: Proposition3: Blockchain assists in providing sustainable supply chain solutions. Concerning the risks, several cases have addressed somehow positive impact in this respect. Most of the benefits stem from digitizing existing paper-based processes susceptible to various risks such as delay, loss, or unauthorized manipulation. TradeLens and CargoX represent good examples of digitizing traditional shipping documents. Everledger ensures the authenticity of diamonds by the provision of verifiable information exposed only by certified parties. As JD.com representative described, it provides “a more credible and assured online diamond purchase.” IBM Food Trust and VeChain focus intensely on securing the safety of food products. Paul Lightfoot, president and founder of BrightFarms, expressed that they can react much faster and mitigate the consequences of an unexpected event. Chronicled, with its MediLedger platform, assists in fighting forged drugs that might be potentially dangerous for customers. Finally, Modum ensures compliance with required shipping variables so that transport data can be trusted. The researchers partially corroborated the role of Blockchain in mitigating risks. The study has observed similar effects, and it provided concrete examples of what risks are being suppressed. The existence of the digital and shared identity of a specific asset was reported by several cases to be the most significant factor for risk mitigation. Besides, the positive impact is a consequence of a Blockchain’s constructive role in establishing transparency and collaboration. Accordingly, proposition4 is suggested as below: Proposition4: Blockchain assists in mitigating supply chain risks. It was demonstrated how some of the implemented Blockchain-based supply chain solutions improved process efficiency. As an illustration, IBM Food Trust reported a concrete example of significant time saving during the tracking of mangoes’ origin, and CargoX described the proceeding of a Bill of Lading that resulted in the processing in a matter of seconds and the final cost of $15 compared to the traditional process that takes days and costs up to $100. Within TradeLens case,

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Fig. 1  The proposed conceptual model

several partners such as Mark Wootton (Yilport Holding), Nguyen Xuan Ky (CMIT), and Ted Muttiah (SAGT) promised a considerable efficiency increase for global trade flows. Besides, Modum has presented a more efficient drug transportation method by the decreased necessity to use refrigerated delivery vehicles. Overall, increased efficiency is primarily caused because information resides in one platform, which requires fewer complex and vulnerable peer-to-peer connections to be established, resulting in faster tracing. The need to send physical documents or request data manually was also reported to be mitigated. Nevertheless, all these efficiency improvements were accomplished because new data streams and end-to-­ end connections were created, and participants were willing to participate. Accordingly, proposition5 is suggested as below: Proposition5: Blockchain increases the efficiency of supply chain processes. Figure 1 presents the proposed conceptual model based on the raised propositions. Table 12 presents the summary of the evaluation of all the project cases. A more detailed discussion of the outcomes is introduced in the following section.

7 Discussion and Implications From the findings of this research, it has been observed that Blockchain is used differently in the studied cases. For instance, Everledger, Provenance, and VeChain often focus on more expensive and luxury goods, and Blockchain is utilized to store the digital identities of these goods. The concentration on luxury goods was also reported by [44]. Besides these companies, there exist others such as Arianee, which

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Table 12  Evaluation of studied projects Projects Case 1: TradeLens Case 2: Everledger Case 3: Provenance Case 4: CargoX Case 5: Modum Case 6: Chronicled Case 7: OriginTrail Case 8: IBM Food Trust Case 9: VeChain Case 10: Dltledgers

C1: Enhanced collaboration x

x

C2: Transparency x

C3: Sustainability x

C4: Risks mitigation x

x

x

x

x

x

x

x

C5: Efficiency x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

were not studied in detail but concentrate practically on the same areas. The typical objectives are to provide Provenance, mitigate fraud, and possibly enhance the marketing presentation of the company. It also targets more intensively the B2C market rather than B2B.  Others, such as TradeLens, CargoX, Dltledgers, focus on the secure transfer and management of critical documents related to the supply chain, and it centers on cooperation between B2B supply chain entities. Applications deployed to retail are developed by Food Trust, Chronicled, VeChain, and partially also OriginTrail. They focus mainly on tracking, tracing, and verifying products. Quite a specific application is Modum since Blockchain is deployed closely with other technologies, and in this case, it is utilized to guarantee the safe delivery of demanding goods. All of the projects centered on the cooperation between supply chain entities such as Food Trust, TradeLens, CargoX, or Chronicled have chosen different adoption methods. TradeLens uses the brand and credit of Maersk and tries to explain the benefits of the platform to others and practically persuade others to join the platform. Instead, Walmart and its Food Trust application force its suppliers to start using the system actively. Chronicled and its MediLedger platform offers a solution to comply with the upcoming regulations, and participating members join the platform because of that. Finally, CargoX is in a position with no significant industry power. It offers its solution with no particular interest of other third parties. Furthermore, studied cases were initiated by various organizations or founders. The involvement of large enterprises, as well as newly emerged startup companies, can

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be observed. Naturally, large organizations such as Maersk (TradeLens initiator) and Walmart utilize their market coverage to expand the platforms. On the other hand, startups are generally much more flexible and innovative. Despite their size and resources, companies such as Modum or Everledger are not lagging behind, and they were able to deploy significant Blockchain applications as well. This study provided different Blockchain applications for supply chain transparency in the analyzed cases. A substantial majority of the cases somehow addressed this aspect. Nevertheless, it must be mentioned that many projects (e.g., Food Trust, VeChain, Provenance) have proven this only on a small scale compared to the total range of their products. It signals that the adoption is still in the early stages and that companies are still acquainting with this technology. Nevertheless, the observed positive impact of Blockchain on supply chain transparency is in line with prior research [8, 26, 41, 81, 97]. The disintermediation aspects of Blockchain were not uniformly described in the literature. Even though most authors expressed promising impacts [5, 14, 15, 18, 90, 91, 95], Tönnissen and Teuteberg [83] argued such a view. This research’s outcomes strongly incline this argument. Practitioners have not reported the scenario when a company would replace its existing system and rely purely on a new Blockchain-­ based system. On the other hand, it was observed that Blockchain plays the role of a particular superstructure or an additional layer on top of existing information systems. Thus, Blockchain’s potential to replace existing information technology intermediaries was not observed within the studied cases. It may be too early, and it might be changing as the adoption progresses. Further, several authors have addressed the positive effect of sustainability [42, 53, 75]. Based on our results, they were too optimistic about the potential impact. This study has shown a partial positive impact but less than some studies. It has been observed that it makes sustainability aspects more meaningful as it can provide related data. However, the claim that it helps better assure human rights or fair work practices [75] is questionable. Scalability, a rather technical aspect of sustainability, is, based on the results, a solid virtue of Blockchain technology that was not extensively discussed in the literature review. Cases that aim to create colossal industry solutions (e.g., TradeLens, Chronicled, OriginTrail) have proven to deliver highly scalable systems, at least for the time being. Concerning performance and efficiency, the adoption can lead both to enhancements and decreases in supply chain performance [31, 43]. The cases in this study addressed efficiency increase predominantly as they frequently reported time savings while performing some processes. It was also demonstrated that organizations utilize Blockchain in order to mitigate delays, losses, counterfeits and to be able to react more quickly to unexpected events. This is accomplished chiefly by Blockchain’s ability to store records securely. In the review, Irannezhad [38], Kumar et al. [45], and Min [59] reported a positive impact, and this research’s outcomes are primarily aligned with their conclusions.

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57

There are several distinguishing factors between the studied deployments. Some cases such as Chronicled, TradeLens, Cargox aim to change old and inefficient industry processes like sending Bills of Lading or monitoring the chargebacks of drugs. Blockchain is perceived in those cases as an enabler of highly scalable solutions, trust endorsers and primarily targets B2B cooperation. On the contrary, cases such as Everledger or Provenance present something that would instead differentiate the assets stored on their platforms from the competition, ensure the provenance and provide more information to the end customer. The cases also differ in the type of assets they are recording on the Blockchain. Most of them are agnostic in that sense and do not differentiate between the assets (e.g., OriginTrail, VeChain, and Dltledgers). Others predominantly store digital hashes of documents (e.g., TradeLens and CargoX). Provenance stores¨products’ certificates. Chronicled stores drug-related information. Modum stores the temperature data, and finally, IBM Food Trust stores animal and plant products. Regarding the adoption aspects, several practitioners have addressed analogous barriers. Matt Sample has expressed that the companies have commercial interests, and it is challenging to explain the benefits. Provenance and CargoX have also addressed educating people about this technology. Ruedi Reisdorf (CargoX) added that the adoption is exceptionally time-consuming as supply chains comprise many entities. For example, the transfer to a digitized version of a shipping document might take 5–10 years, as Arne Strømmen (G2 Ocean) states. Besides, representatives of industry solutions (e.g., TradeLens, Chronicled) have concurred that the long-term prosperity of the platforms depends highly upon the participation of all industry partners. The overwhelming majority of the cases provides platforms for identified parties. Thus, this diminishes the decentralized character of Blockchain slightly, and the platforms are more centralized than, for example, the Ethereum protocol, which might be considered as a drawback. However, representatives would not perceive it as a potential threat.

7.1 Implications for Research This research has brought interesting findings regarding Blockchain-based projects and reported how they help overcome supply chain challenges. Nevertheless, due to the scope of this research, certain areas such as technical aspects of platforms are not profoundly covered intentionally. It was described that different cases use different business models for adoption, either managed by a consortium, driven by regulation, or pushed by a private company. On several occasions, practitioners have addressed the complexity of implementing industry solutions. The pros and cons of Blockchain adoption in the supply chain and its impacts are not yet thoroughly known. Although this research examined the project cases relatively closely, individual solutions could have been studied separately and in more detail. A good example is Chronicled and its MediLedger network that aims to monitor drug

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circulation in the USA.  A centralized case exists in Europe (The European Regulatory System for Medicines) as well, and a comparison between these two different systems with the same objectives might be beneficial. Furthermore, Everledger, Provenance, or IBM Food Trust partially target final customers. It is unclear if customers value the fact that the information resides on the Blockchain rather than on a centralized database and if they understand the difference. Provenance representatives also mentioned this as they expressed the need to educate people in this regard. However, this is not related to the supply chain but rather to marketing. Finally, the developed conceptual model can be tested using empirical and quantitative data.

7.2 Implications for Practice This study presents empirical evidence that business owners might use as a decisive criterion for Blockchain embracement. A sample of ten cases was introduced where Blockchain is incredibly disruptive and suitable. Thus, it can be used as a guideline on this strategic and tactical management level while making future information system investment decisions. Furthermore, these companies do not have to develop owned Blockchains. However, they might evaluate whether joining the existing platforms is reasonable. It was described for whom these solutions are designated, and therefore, practitioners might draw inspiration from this and decide if joining such a platform would make sense. Besides, developers might consider the challenges mentioned in this research and adjust the development process accordingly. It is improbable that there will be only one platform. Instead, having different platforms in the market is more plausible. Then, there would be a need for interoperability, and the way to give that interoperability is to have standards. It calls for joint activities and consortia encompassing all stakeholders to set the industry standards. The R3CEV consortium of 92 major financial institutions is a good example of this in the finance sector, formed in 2015 to respond to the threat/opportunity of Blockchain technology. It aims to develop standards and protocols for Blockchain-­based financial services. Global Shipping Business Network (GSBN) is an early attempt in the supply chain sector. This platform will establish a digital baseline that aims to connect all stakeholders, including carriers, terminal operators, customs agencies, shippers, and logistics service providers, to enable collaborative innovation and digital transformation in the supply chain. The other implication refers to the necessity to undergo proofs-of-concept. The literature and experience from practice have reached a level when the usability of Blockchain is well covered and confirmed. This paper has strengthened the

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conclusions and presented an updated state of the projects on several counts. Therefore, the necessity to conduct proofs-of-concept is undoubtedly lower than in the past, and business owners might move forward directly to pilots and production deployments. Last but not least, bringing all relevant parties together and educating people about the adoption of new technologies is the key to the mass adoption of Blockchain technology. There must be incentives for all network members to contribute to the platform. They all must have their data ownership for the network to run sustainably.

8 Limitations and Future Research This study suffers from some limitations. Firstly, this study looks at the Blockchain from a broad perspective, and it does not distinguish between different technological types of DLTs. It is therefore proposed that other studies might concentrate only on applications developed on Hyperledger Fabric, for instance. Secondly, the case studies differ in business models and objectives, and Blockchain is incorporated differently into the products. Thus, it might be misleading to evaluate the role of Blockchain on particular challenges, as it might not be caused by Blockchain per se but by the inappropriate application. Thirdly, the sources primarily utilized in case studies analysis are often unilaterally oriented, which might cause the results to be less objective. The sources such as articles are often published on sites, which concentrate on Blockchain or crypto. Therefore, their perspective might be less critical. These sites report very well about new partnerships or milestones. However, with a contribution of Blockchain’s hype, they might tend to present Blockchain applications in a slightly better shape than they are. Fourthly, some information was gathered from company reports, which inform their references. It might be somewhat subjective, as they like to present themselves nicely. On the other hand, they often cited their customers, making it less subjective. Besides, due to the scope of this research and the lack of proper access to experts in the field, we could not conduct a quantitative analysis to test and validate the proposed conceptual model that could be followed in future research. Finally, we did not aim to focus on the technical aspects of the examined platforms and how integrating Blockchain with other technologies such as IoT, artificial intelligence, and other emerging technologies can help tackle supply chain challenges. This could be examined further in future research.

Fr8 Gemalto Guardtime HSX Helo et al. Insurwave Intel JD Chain Lockheed Martin and Guardtime Federal MediLedger Minehub Modum Morpheus.Network NextPakk

11 12 13 14 15 16 17 18

19 20 21 22 23

Name AgroBlockIoT Alibaba Ambrosus Aquachain Bext360 CargoCoin CargoX DltLedgers Entrust Everledger

ID 1 2 3 4 5 6 7 8 9 10

Anti-counterfeit Traceability Not applicable Transparency

2018 2018 2016

Focus area Traceability Traceability Visibility Traceability Authenticity Transparency Generic Unknown Unknown Traceability, authenticity Transparency Traceability Anti-counterfeit Traceability Transparency Traceability Traceability Traceability

2017

2018 2017 2016 2018 2015 2013 Unknown 2016

Foundation year 2018 2018 2016 Unknown 2017 2017 2017 Unknown Unknown 2015

Table 13  List of Blockchain-based supply chain projects

Appendix

Generic Trade, finance Transport

Transport Pharma Pharma Generic Shipping Generic Generic Mining, manufacturing Pharma

Business field Food Food Food, pharma Food Generic Shipping Shipping Unknown Unknown Generic

Industry Industry Industry

Industry

Industry Industry Industry Academic Industry Industry Industry Industry

Industry Industry Industry Industry Academic Industry Industry Industry Unknown Unknown Industry

Unknown Permissioned Unknown Permissionless Unknown Agnostic Unknown Permissioned

Access rights Permissioned Permissioned Hybrid Unknown Unknown Unknown Unknown Unknown Unknown Permissioned

Production Permissioned

PoC Pilot Production PoC Pilot PoC Unknown Production

Status Pilot Production Production PoC Pilot PoC Production Unknown Unknown Production

Hyperledger Production Permissioned Ethereum Production Permissionless Stellar PoC Unknown

Ethereum

Agnostic Ethereum Unknown Ethereum Unknown Ethereum Unknown Hyperledger

Platform Hyperledger Hyperledger Ethereum Agnostic Ethereum Ethereum Ethereum Unknown Unknown Hyperledger

60 J. Goldmann et al.

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

OpenPort OriginTrail PDV Peer Ledger PharmaTrace pixelplex POMS Provenance Riddle & Code ShipChain Skuchain SKYFChain SmartLog SyncFab Traceability of Wood TradeLens TrustChain ubirch Vechain Walmart Waltonchain Yojee

2018 2017 2018 2014 2019 2014 2019 2016 2015 2017 2014 2015 2018 2013 2018 2018 2018 2014 2015 2018 2018 2019

Traceability Generic Traceability Traceability Traceability Authenticity Anti-counterfeit Traceability Not applicable Traceability Generic Not applicable Traceability Transparency Traceability Generic Trust management Authenticity Traceability Traceability Generic Generic

Shipping Generic Transport Generic Pharma Generic Generic Generic Generic Generic Generic Generic Generic Manufacturing Wood Shipping Generic Generic Generic Food Generic Generic

Industry Industry Academic Industry Industry Industry Academic Industry Industry Industry Industry Industry Industry Industry Academic Industry Academic Industry Industry Industry Industry Industry

Ethereum Ethereum Unknown Hyperledger Hyperledger Unknown Ethereum Ethereum Unknown Ethereum Agnostic Hyperledger Hyperledger Ethereum Ethereum Hyperledger Hyperledger Agnostic Ethereum Hyperledger Agnostic Agnostic

Production Production PoC Production PoC Production PoC Production Production Production Production PoC Pilot Pilot Pilot Production PoC Production Production Production Pilot Production

Hybrid Permissionless Permissioned Unknown Permissioned Hybrid Hybrid Hybrid Unknown Permissionless Permissioned Hybrid Permissioned Unknown Permissionless Permissioned Permissioned Permissioned Hybrid Permissioned Unknown Hybrid

A Conceptual Model for the Role of Blockchain in Overcoming Supply Chain Challenges 61

Table 14  Projects evaluation based on the assessment criteria ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Sum

Project name AgroBlockIoT Alibaba Ambrosus Aquachain Bext360 CargoCoin CargoX DltLedgers Entrust Everledger Fr8 Gemalto GuardtimeHSX Heloetal. Insurwave Intel JDChain LockheedMartin MediLedger Minehub Modum Morpheus.Network NextPakk OpenPort OriginTrail PDV PeerLedger PharmaTrace pixelplex POMS Provenance RiddleandCode ShipChain Skuchain SKYFChain SmartLog SyncFab TraceabilityofWood TradeLens TrustChain ubirch Vechain IBMFoodTrust Waltonchain Yojee

Criterion#1 X X X X X X X

Criterion#2 X X X X

X X X X X X X X X

X X X X

X X X X X X X X X X X X X X X

Criterion#3 X X

Studied X X

X X

X X

X

X

X X

X X

X

X

X X

X X

10

10

X

X X X X X X X X X X

X X X X

X X X X X X X

37

24

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A Hybrid Application of Quantum Computing Methodologies to AI Techniques for Paddy Crop Leaf Disease Identification A. Prema Kirubakaran and J. Midhunchakkaravarthy

1 Introduction Agriculture is the backbone of every country. Crop cultivation varies from region to region depending on the climatic conditions. A plant comes across various types of diseases from the day it is sown. The disease that causes the damage is due to various reasons like climatic changes, natural disasters, human and animal interventions, pesticides. An experienced farmer finds out the diseases easily but this type of manual identification cannot be done always since there are a lot of variations in the pesticides used and diseases are also of various types. Few diseases do not have any visible symptoms that the farmers find difficult to recognize. To overcome the loss incurred due to non-identification of the disease, technology gave a solution to identify the defects caused in a plant. A disease in a plant can be identified using Image processing techniques, but as the future of the technology relies on quantum computing, the idea of switching over to quantum computing emerged from the previous work of identifying the flaws in an oil pipeline using image processing based on mathematical morphological operators, through unsupervised learning techniques. The digital image processing technique was the major tool used to achieve the result. Image analysis and classification have to be improved according to the A. P. Kirubakaran (*) Department of Computer Science, Lincoln University, Kota Bharu, Malaysia e-mail: [email protected] J. Midhunchakkaravarthy Faculty of Computer Science and Multimedia, Lincoln University College, Kota Bharu, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_4

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development in technology. The results with digital image processing were successful, the same concept can be applied here, but as the future is going to march with quantum computing, this research work is used as a platform to introduce quantum image processing in detecting leaf diseases. The existing research work on the image processing of leaves with AI techniques and Machine Learning did not achieve better results. So, the idea of moving on with quantum computing emerged, and this paved the way to switch over from digital image processing to quantum image processing with quantum computations. Quantum computing is a type of computation that utilizes the combined properties of quantum states derived from various output samples to determine the superposition, nosiness, and quandary to perform calculations. The study of quantum computing is a subfield of QIS (quantum information science). Feynman proposed the concept of quantum computing in 1982 [1] and after continuous discussions, it was agreed that quantum computing shows improved computational efficiency. The repeated implementation achievements by many prodigies have eventually resulted in the understanding of quantum computing that emerged from technical issues with the basic concepts of qubits manipulation [2]. NISQ (Noisy Intermediate scale quantum), the recent epoch of quantum computing, has shown effective results in the field of bit manipulation [3]. The conversion of classical data to quantum states to maximize the Gaps between the mapped classes as per the Hilbert space concept helps to distinguish between holes and cracks [4]. This method helps to recognize the image efficiently. Images of a leaf are not only classified and recognized; QIP helps to determine the properties to represent, manipulate, compress, and address other issues related to quantum images. Though higher efficiency is achieved, manipulating an image sometimes leads to state collapsing as quantum states follow the non-cloning principles. In case an image has to be manipulated without collapsing, mathematical morphological operators can be implemented to achieve better results. The disease in a leaf image develops clusters during image classification, cloning occurs, but by applying the MMOs (mathematical morphological operator), the image is diluted and converted for further image analysis. The leaf images undergo filtering to obtain a clear image for analysis. Filtering techniques follow many methods like Gaussian and Kalman filters. The basic mean filters were replaced by a Gaussian filter that provides gentle smoothing and edges are preserved when compared to a normal mean filter. So, when an image is converted into bits, Gaussian played a vital role in producing a clear and gentle image for image classification and analysis. Since this research work involves quantum computing with states calculation, the Kalman filter is recommended more than the Gaussian filter. Predicted states and noisy measurements are two sources of information that are combined to produce optimal, unbiased estimates of system states. Kalman filter also helps to evaluate both known and unknown variables based on the system’s control models and multiple generated values through the image filtrations.

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2 Literature Survey Understanding the conversion of bits to qubits through physics was explained by R.P. Feynman in his “Simulating Physics with Computers” [1]. The concept of why quantum computers are illustrated by C.R. Chang in his “The second quantum revolution with quantum computers [2], the basic idea of why to opt quantum computers is described briefly. Conversion from bits to qubits by using NISQ techniques is briefed by J.  Preskill in quantum computing in the NISQ era and beyond [3]. Comparison of quantum computing with machine learning was understood through the research work “Quantum embedding for machine learning,” by S.  Lloyd, M. Schuld, A. Ijaz, J. Izaac, and N. Killoran [4]. Image recognition through quantum computers was studied through the work of A.Y. Vlasov in his work “Quantum computers and image recognition” [5]. Bit storing techniques are learned through “Storing, Processing, and retrieving an image using quantum mechanics” [6]. Compression of the image is understood through “Image compression and entanglement,” by J.I. Latorre [7]. Image compression is explained in detail from “A flexible representation of quantum images for polynomial preparation, image compression, and processing operations” by P.Q. Le, Phuc K F Hirota [8]. Image digital representation with qubits transformation is discussed in “NEQR a novel enhanced quantum representation of digital images,” by Y.  Zhang, K.  Lu, Y.  Gao, and M.  Wang [9]. Log-polar coordinate representations were studied through “FLPI representation of quantum images for log-polar coordinate,” by M. Wang, K. Lu, Y. Zhang, and X. Wang in proceedings of the 5th international conference on DIP in 2013. Log-polar image descriptions were understood by “A novel quantum representation for log-polar images,” by Y.  Zhang, K.  Lu, Y.  Gao, and K.  Xu, vol.12, no.9 pp. 3103–3126 published in 2013. Y. Ruan, H. Chen, Z. Liu, and J. Tan illustrated the quantum image processing’s high-performance retrieval techniques in “Quantum information processing,” published in 2016. Edge detection usage in quantum images was illustrated by X. W. Yao, H. Wang, Z. Lioa et al. in “Quantum Image processing and its application to edge detection.”

3 Types of Paddy Diseases False smut, sheath blight, sheath rot, bacterial leaf blight (BLB), neck blast, and brown spot are the six different types of diseases found in a paddy leaf. Brown planthopper (BPH), Stemborer, and Hispa are the three pests (Figs. 1, 2, 3, 4, 5, and 6).

72 Fig. 1  False smut

Fig. 2  Sheath blight

Fig. 3  Sheath rot

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A Hybrid Application of Quantum Computing Methodologies to AI Techniques… Fig. 4  Bacterial leaf blight

Fig. 5  Neck blast

Fig. 6  Brown spot

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Fig. 7  Paddy field images from Namakkal

4 Leaf Disease Identification Using Quantum Computing Quantum image processing mainly focuses on the quantum image format, now the leaf image to detect the disease needs to be converted into qubits (Fig. 7). The above image shows the planting of rice crop first phase, images are real images taken from the paddy fields: now as this crop grows, pictures will be taken and evaluated for the prediction of diseases occurring in the leaf. In the previous research work, digital images of the oil pipelines were converted into bits using Raster graphics image analysis concepts. Raster image concept was opted because when an image is photographed, it gets into the compilation of pixel or tiny dots format that contains unique color and fine tones that puts images together for classification and analysis. In this research work qubits are opted because of their characteristics; an image is converted to a qubit only if it has a spot; if there are no spots, qubits cannot be formed, thus it helps to identify leaves with spots. After a spot is identified image is converted into qubits and it undergoes the techniques to distinguish between the different colored spots in the leaf. These spots are stored as qubits to get processed as QIPs (quantum image processing) as these qubits are stored as “superposition states in which they can be stored both as 1’s and 0’s at the same time that enables them to perform multiple calculations at the same time”.

5 Conversion of Leaf Images for Prediction Images for prediction and analysis from the digital camera were transformed to bits; now, to get better accuracy, bit images are transferred to qubit images and processed accordingly. Quantum image format is the core topic of QIP. The three major quantum image formats are Qubit Lattice, RealKet, and FRQI.

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Image Acquisition This research work describes the framework of the disease detection and classification system, which includes the step-by-step process of training and testing of sample images. The training starts from image acquisition, which is either captured through a digital camera or taken from available datasets. To achieve better results, the image captured should be perfect, without any noise. Noise should be removed and the image must be resized. Images with defects are separated by image segmentation techniques. Then in feature extraction, different types of attributes obtained from diseased leaves are acquired and prepared to enter them into the classification stage. In the classifier’s testing stage, by using training experience, the condition of the image is classified as defective or not defective. Training of these pictures emerges as composition elements called pixels. Image Preprocessing Image preprocessing is used to refine the quality of the image. Image enhancement, noise removal, cropping of the image, resizing, and smoothing are the techniques that are performed through image preprocessing. All the techniques are applied to one image; out of these, any one or two will be evaluated according to the image condition and requirements. The filtering and enhancement of the image are performed based on the conversion techniques defined in the color space conversions. The leaf image taken for evaluation should be cropped exactly, as most of the leaf taken for consideration is with 70% defect and only 30% is without defect. This is focused on the concept named ROI (Region of Interest) wherever the defect in the leaf is more, that area which is mostly defective will be focused on first to enhance the image. The leaf disease discovery along with the classification is performed using various data samples taken as pictures from different fields around Tamil Nadu. These images are resized to scale the image up to 128X128 pixels to obtain a clear and vivid image to identify the defects. In this image, classification of defective regions are identified using the concepts of HSV (Hue, Saturation, and value). This is done to bring the images under the RGB concept of HIS (Hue, Saturation and Intensity) color space transformation where the H component is retained and S, I are dropped as they don’t carry further information. Noise removal should be given more importance as it helps to eliminate unnecessary changes in the variations of image color, brightness, as it makes the image to appear blurred. Image Segmentation This is a process of dividing an image into multiple parts, this helps to identify the defective images efficiently. Partitioning of images into foreground and background is performed through image thresholding and helps to identify color contrast in the images. Mathematical Morphological Operators MMO is used to compare the value of each pixel with its neighboring pixels to identify the defective image. The basic and simplest methods in MMO are erosion

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and dilation. Erosion eliminates pixels on object margins, whereas dilation helps to add pixels to the borders of objects in an image. After fixing up these two methods, images should be formatted using the quantum image format techniques.

6 Quantum Image Formats As classical computers work with ones and zeroes to head through the operations, Quantum bits or Qubits are used in Quantum computers. Quantum computers also use Zeroes and Ones, but Quantum computers also have a third state called a “superposition” that allows representing a one or zero simultaneously. These superposition concepts help to identify the exact defective position in the leaf images. Quantum image format is the main part of quantum computing. Qubit lattice gives the format for the image values scanned for defect identification, this was proposed by Venegas Andrea [6, 7]. In which, it is stated that the frequency value(color) of the light wave can be mapped to the probability amplitude of a qubit, then the pixel value of ith row and the jth column can be stored in the amplitude angle. Image processing is widely used in our day-to-day life, where facial recognition, biometrics, vehicle automation are some of the applications that are growing fast. In quantum information science, QIP is an emerging field that illustrates the edge detection concepts in identifying the defective leaf image. Zhang et al. proposed in 2014 a new quantum image edge detection [3, 4] extraction algorithm (QSobel) based on the FRQI (flexible representation of quantum images) which is compared at par with the classical edge extraction algorithm Sobel. O(n2)O(n2) for an FRQI with a size of 2nX2nX2NX2N gives a better speedup when compared to classical-­ based edge algorithm [3]. FRQI (flexible representation of quantum images) and NEQR (novel enhanced quantum representation) are used to encode the images in quantum states, once encoded quantum algorithms like QSobel [3] is applied to achieve the result. The objective of FRQI is to provide a quantum representation of images that allows obtaining a good encoding of converting classical data into quantum states and the consequent use of operators that are derived from the image processing operations.

7 Quantum Image Operation Images for evaluation combine erosion and dilation operations that are used to find the quantum image local feature point frame extraction. This is achieved by implementing novice quantum binary image morphology operation methods that

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are briefed in the mathematical morphological operator. This proposed method is used to extract quantum images, quantum denoising, object recognition like seal recognition, which improves the strategies of quantum image processing, and this will help to achieve advanced morphology processing technique development in QIP. In an electronic computer set, operation tasks take time for computation. To improve the efficiency, quantum computation and image processing are combined, which also combines the concept of multiple quantum logic gates and quantum image storage, quantum loading scheme, and Boyer search algorithm to propose a new quantum-based image processing algorithm to detect the defects in paddy leaf images. Quantum erosion and dilation algorithm are used for identifying the basic operations of erosion and dilation. Image processing achieves higher efficiency through the application of these algorithms. Erosion and Dilation In any image processing, whether it is classical or quantum-based computing, erosion and dilation methods are applied. Erosion helps to remove the pixels on object boundaries, where it shrinks the objects in the foreground. Holes in the foreground are enlarged; since the holes are enlarged, the structure of the image looks larger, and therefore it is easy for evaluation. To identify the defects, quantum erosion algorithm is applied. Thus, the operations are performed by combining both erosion and dilation by using a small structure called structuring element. The structuring element is a matrix that finds the pixel in the image under process and defines the neighboring pixel used in the process (Fig. 8). To find the faults, the leaves are enlarged under the dilation concept using dilation quantum algorithm. This algorithm helps to enlarge the pictures to identify the defects from ROI (Region of Interest). ROI is identified using the portion of the defective image that must be filtered to obtain the result. ROI is represented as a binary mask image, where pixels inside ROI are assigned a value of 1 and outside pixels are assigned a value of 0 (Fig. 9). The above picture shows the defect in the leaf, these defects should be identified at an early stage using the proposed algorithm that will help to give proper pesticide Fig. 8  Erosion of paddy leaf identified using color chart manually

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Fig. 9  Dilation of the paddy defective leaf

to improve the yield of the crop. The implementation of image processing through erosion and dilation using mathematical morphological operators helps to identify the defects, the existing methodology is only for classical computation, and this proposed methodology is defined through the quantum computing application using quantum image processing techniques. Instead of evaluating bits, the conversion bits to qubits are performed and the images are assessed to rectify the defects. Qubit is obtained by identifying the defective part then light or magnetic fields are used to create superposition, entanglement, and other related properties to source the identification of damaged parts in the paddy leaf. Quantum Image Operation Image operations are generally classified as geometric transformation, color transformation, and the third one is a complicated operation with the operations of compression and retrieval. One bit can be encoded to one qubit, as it can hold more information when compared to a bit using the superdense coding technique. Geometric Transformation A geometric position of an image is very important to identify the positions of the image to be processed. These geometric positions help in changing the spatial position of the pixels in an image, which is used to change image rotation or change the image position for zooming in and out. The recognized operation includes rotation, top or bottom swapping, left or right swapping, interchange of the coordinates of the selected image with the operations of erosion and dilation [12], image magnification [13, 14], and the translation of image (both cyclic and overall) [15]. The classical bits should be transferred to qubits the formats can be defined by FRQI methods, where the bits can be identified both as black and white or color.

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Color Transformation It refers to the change in the color format; suppose the image color varies while processing, FRQI method is adapted to obtain efficient color transformation of the images using single-qubit operations. This operation helps in obtaining a massive result in the color transformation of the defective image for further evaluation when compared to classical bit transformation methods. The usage of synthetic and Lena used in classical images are derived here for simulations of the images while using FRQI techniques. Position-related operations are laid as a foundation in determining the color transformation of the images. Complicated Image Transfers Preprocessing is required in a classical image for the compression, retrieval, recognition, segmentation of an image in classical computers; these are termed as a complex way of handling images. As the image size is in large volume, compression of images gives a better solution when it comes to the storage space of these images in any device. Compression of images in quantum computing is performed by reducing the number of gates when the states of a quantum image are prepared. Many authors have described image compression in quantum computing [10, 11, 16, 17]. Compression is done by simplifying the Boolean expressions given in the quantum gates. Afterimage compression retrieval of the image should be achieved. Image retrieval in quantum computing is of two types: the first one is retrieving information from classical computers, and the other one is retrieving information from quantum states. According to the probability distribution stage, original quantum image is retrieved. To perform this quantum image, content should be obtained first. The quantum algorithm represented by Schutz hold illustrates how to find simple patterns in black and white images [18]. Image taken for evaluation should undergo all these transformations and then the mathematical operators are applied to identify the defects by using the erosion and dilation quantum algorithms. Though there are lots of mathematical operators, morphology is mostly preferred as it processes images based on shapes. This method applies the structuring element to the given input image that creates an output image of the same size as the input image. The value of each pixel in this morphological operation is based on the comparison of the corresponding pixel in the input image as compared with its neighbors. Implementation This research work can be implemented in two methods using Matlab in basic applications or Qiskit for accurate image results (Fig. 10).

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Fig. 10  Image showing the conversion of bits to qubits through erosion and dilation algorithms

Qiskit is suggested as it gives very clear values on image analysis and classification. By using the FRQI states, quantum images are represented as

I (θ ) = 12 n22 n − 1 ∑ i = 0 (cos θi 0 〉 + sin θi 1〉 ) ⊗ i



 i   0, 2  , i  0,1, , 22 n  1





(1) (2)

Erosion and Dilation Algorithm The first primary step in classical image processing is erosion and dilation. Basic image structures are extracted through these algorithms and they play a vital role in most of the image processing techniques to extract the geometric shapes, which are fed into higher-level algorithms for defect identification and recognition. An improved algorithm to recognize the quantum method is proposed to realize dilation and erosion in image processing. Qubits are used to code the defective bits’ location by applying quantum operators. Designing the algorithm depends on the nature of the qubits position. If the bit positions are in clusters, they should be trained under any unsupervised k-means

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Fig. 11  Image showing bacterial streak disease after dilation process

cluster algorithm to remove the tangles and should be proceeded for the evaluation of defective bits position. The fundamental steps in any image processing method are erosion and dilation, which help to extract many basic features. It is important because it extracts the basic geometric shapes that are fed into higher algorithms to identify and recognize the objects. In this research work, an improved quantum methodology is implemented to understand the dilation and erosion applications in the processing of an image. Information retrieved is stored as quantum bits. These bits are used to code the location and other information of each pixel and quantum operators are applied, this is also achieved through quantum gate implementation. Qubits result in accurate identification of defective leaf images. Though there are advantages, their natural representation leads to challenges in designing quantum algorithms; this research work is targeted to solve this problem and enhance the productivity rate (Fig. 11). Conclusion Quantum image processing gives better results when compared to classical image techniques. As many problems occurred during the conversion of bits to qubits, the application of erosion and dilation using MMOs helped to obtain a refined image that helped to identify the defects in the leaf images. Sharpening and smoothing of the image was a challenging task when compared to classical images; maybe in the future, by applying various possible techniques, this can be achieved without any hardships. This method of applying quantum image processing techniques was very

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challenging, but the quest to learn more about the algorithms of QIP and QiSkit helped to solve the issues in a better way. As a budding trial, this research was taken over. The sample dataset is fed as real datasets from the paddy fields across Tamil Nadu. As the crop grows, this research work also gets developed by capturing real images. The sample dataset is fed in already with QiSkit, where it is compared with the defective image, to achieve better results.

References 1. Feynman, R.  P. (1982). Simulating physics with computers. International Journal of Theoretical Physics, 21(6–7), 467–488. View at: Publisher Site | Google Scholar. 2. Chang, C.  R. (2020). The second quantum revolution with quantum computers. AAPPS Bulletin, 30(1), 9–22. View at: Google Scholar. 3. Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. View at: Publisher Site | Google Scholar. 4. Lloyd, S., Schuld, M., Ijaz, A., Izaac, J., & Killoran, N. (2020). Quantum embeddings for machine learning, https://arxiv.org/abs/2001.03622 5. Vlasov, A.  Y. (1997). Quantum computations and images recognition, https://arxiv.org/abs/ quant-­ph/9703010. View at Google Scholar 6. Venegas-Andraca, S., & Bose, S. (2003, Aug). Storing, processing, and retrieving an image using quantum mechanics. In Proceedings of quantum information and computation (Vol. 5105). International Society for Optics and Photonics, Orlando, FL, USA. View at: Publisher Site | Google Scholar. 7. Venegas-Andraca, S., & Elías, S. (2005). Discrete quantum walks and quantum image processing, University of Oxford, Oxford, UK, Ph.D. thesis. View at: Google Scholar. 8. Latorre, J.  I. (2005). Image compression and entanglement, https://arxiv.org/abs/quant­ph/0510031. View at Google Scholar. 9. Le, P. Q., Phuc, Q., Hirota, K. F., & Hirota, K. (2011). A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quantum Information Processing, 10(1), 63–84. View at: Publisher Site | Google Scholar. 10. Zhang, Y., Lu, K., Gao, Y., & Wang, M. (2013). NEQR: A novel enhanced quantum representation of digital images. Quantum Information Processing, 12(8), 2833–2860. View at: Publisher Site| Google Scholar. 11. Wang, M., Lu, K., Zhang, Y., & Wang, X. (2013, July). FLPI: Representation of quantum images for log-polar coordinate. In Proceedings of 5th international conference on digital image processing (ICDIP 2013) (Vol. 8878). International Society for Optics and Photonics, Beijing, China. View at: Publisher Site | Google Scholar. 12. Iliyasu, S., Mao, X., Chen, L., & Xue, Y. (2013). Quantum digital image processing algorithms based on quantum measurement. Optik, 124(23), 6386–6390. View at: Publisher Site | Google Scholar. 13. Zhou, R.-G., Chang, Z.-B., Fan, P., Li, W., & Huan, T.-T. (2015). Quantum image morphology processing based on quantum set operation. International Journal of Theoretical Physics, 54(6), 1974–1986. View at: Publisher Site | Google Scholar. 14. Jiang, N., Wang, J., & Mu, Y. (2015). Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio. Quantum Information Processing, 14(11), 4001–4026. View at: Publisher Site | Google Scholar.

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15. Wang, J., Jiang, N., & Wang, L. (2015). Quantum image translation. Quantum Information Processing, 14(5), 1589–1604. View at: Publisher Site | Google Scholar. 16. Li, H.-S., Qingxin, Z., Lan, S., Shen, C.-Y., Zhou, R., & Mo, J. (2013). Image storage, retrieval, compression and segmentation in a quantum system. Quantum Information Processing, 12(6), 2269–2290. View at: Publisher Site | Google Scholar. 17. Li, H.-S., Zhu, Q., Zhou, R.-G., Li, M.-C., Song, L., & Ian, H. (2014). Multidimensional color image storage, retrieval, and compression based on quantum amplitudes and phases. Information Sciences, 273, 212–232. View at: Publisher Site | Google Scholar. 18. Schützhold, R. (2003). Pattern recognition on a quantum computer. Physical Review A, 67(6), Article ID 062311. View at: Publisher Site | Google Scholar.

Cognitive Computing for the Internet of Medical Things Latha Parthiban, T. P. Latchoumi, K. Balamurugan, K. Raja, and R. Parthiban

1 Introduction Professionals and scientists are worried about the high number of losses in the building industry. Construction workers are the most vulnerable to industrial accidents and illnesses [1–3]. Notwithstanding that the Occupational Safety and Health Authority (OSHA) has established and mandated Safety Procedures and Services. Over the last decade, the number of deaths, injuries, and non-lethal illnesses in the construction industry has remained stable. Since the building industry has such a high rate of fatal and non-fatal injuries, businesses are still looking for new ways to improve safety [4]. Since construction is a transient and competitive industry, organizations respond to rapid changes by store, capture, and disseminate new injury prevention techniques efficiently [5]. As a result, emerging innovations could be a candidate for safety advancement. The technology has

L. Parthiban Department of Computer Science, Pondicherry University, Pondicherry, Tamil Nadu, India T. P. Latchoumi · K. Raja Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tami Nadu, India e-mail: [email protected] K. Balamurugan Department of Mechanical Engineering, VFSTR (Deemed to be University), Guntur, Andhra Pradesh, India R. Parthiban (*) Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_5

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undeniably improved construction procedures, its application to customized construction safety monitoring has yet to be thoroughly investigated. The building and construction industry, despite being one of the most influential, faces a variety of challenges. Because of the complex, transitory, and dangerous nature of construction activities, for example, safety management is especially difficult. According to figures, the building industry causes more than 60,000 fatal accidents per year around the world. Staff and their families suffer a great deal as a result of these accidents, resulting in increased annual losses. High efficiency and low productivity rates, like low safety results, are a universal challenge in the construction industry [6]. This latest paradigm places a strong emphasis on sustainability. To address Industry 4.0’s biggest challenge not just in ecological but also in public sustainability is encouraged. New CPS should be converted into employee-centred human architectures that ensure protection, well-being, and comfort to achieve the best possible operational environment and output tariff. The growth of the wearable market plays a role of a key marker of worldwide achievement, allowing for the development of smarter devices by integrating sensors and actuators. On the one side, advancements like Bluetooth Low Energy (BLE) will take the direction of IoT standardization as a result of the need for a further flexible alternative. And it is leading an ongoing industrial revolution in which diffusion capability will play a key role in the current approval of BLE web topology. Meanwhile, accelerometers start an IoT wave that enables gestural devices to interact with humans and machines more effectively.

2 Related Works Literature has shown that to meet production and efficiency goals, employees often prioritize safety methodologies [7]. Furthermore, evidence suggests that when productivity pressures are high, risk-taking behaviour increases [8]. As a result, preventing such accidents necessitates measures that benefit both protection and quality, and productivity. To achieve this goal, the research presented centred on a national safety problem currently confronting transportation agencies, taking quality, effectiveness, and other safety factors into account. When road and bridge staff perform bridge maintenance on the bridge, fall safety is a problem [9]. Rails to momentarily raise the barrier height when operating. These staff rely heavily on existing railings for fall safety at lower levels when performing these tasks [10]. Several transportation agencies, including the North Carolina Department of Transportation, are working to reduce the danger of falls (NCDOT). Figure 1 shows how it pioneered the use of Fall Security Supplementary Devices (FSSDs). Fall safety devices can be added to existing guard. The architecture and operational features of these fall defence systems vary. NCDOT conducts an initial examination into more than 50 of these FSSD as part of its groundbreaking efforts and agreed to pursue four options for widespread adoption [11, 12].

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Fig. 1  A low-height guardrail mounted with FSSD

In terms of medical aspects, [13] proposes a systematic scheme to prevent nosocomial infection, which includes sensors and bracelets with accelerometers. Other intelligent bands for muscle movement identification and physiologic signals have also been suggested to help patients with obstructive sleep apnoea and to diagnose Parkinson’s disease. Furthermore, accelerometer-based systems are often used in rehabilitation. When it comes to accelerometer-based applications, there is a lot of research into gesture and motor recognition. A laptop or even a Smartphone can be used for this. In [14], an accelerometer for movement monitor was presented for detecting deteriorating balance, and finally, it is used to detect certain predefined actions in youthful and aged adults. The authors also create and introduce an integrated method to estimate pedestrian walking positions in [15]. Industrial accelerometers are used to calculate trunk and longitudinal flexion angle in a lab setting in [15], and this research is in a real-world setting. The studies looked at data from commercial accelerometers and use cell phone sensors to detect construction worker behaviour. The authors of [16] investigate the use of accelerometers in assessing a variety of professionals. It has not yet been considered for industrial settings, where it could play a significant role in the emerging Industry 4.0. The majority of traditional methods for calculating security competence capacity are labour-intensive and based on slanted assessment [17–19]. These approaches depend on the manual processing of vast volumes of electronic data; as an effect, information is gathered rarely (e.g. monthly) and in the event of an accident [20]. These methods are costly, prone to loss of data, and result in data sets that are too limited to efficiently manage work. Although it overtakes the weaknesses of manual processing, automated security monitoring is perhaps the most efficient method for

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reliable as well as consistent tracking of safety performance across construction projects.

3 Fall Protection for Highway and Bridge Workers While such studies have been done on falling mostly in the construction sector as a whole, a little has been done specifically on drops between roads and bridge employees. Nevertheless, road and bridge employees, like those in the specific building industry, sustain a disproportionately large number of fall-related accidents. Falls are especially dangerous for roads and bridge staff who work at heights, such as those on bridge decks. Each year, for example, approximately 3000 fall-related accidents on roads and bridges staff are registered. Furthermore, research shows that even more than 80% of fatalities happen while on-the-deck bridge projects are in progress. When operating at peaks or on endplates, there are many options for preventing falls. The least successful solution, according to the structure of drop security measures as seen in Fig. 2, with the use of admin safety controls, which depend on staff to follow accepted safety methods and processes (e.g. maintaining a safe distance from incompliant guardrail). Sadly, between roads and bridge staff, it is the most popular fall safety method. One of the most powerful strategies, on the other hand, is to eliminate the risk of falling. Another way to implement this was to construct bridge safety barriers that provide adequate security, obviating any kind of additional security measures.

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Fig. 2  Hierarchy of fall protection controls

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4 OperaBLE: The Proposed Wearable This work examines a wearable device designed to meet IoT requirements in manufacturing technologies. While unique hardware is suggested in this research to introduce a testable watch, OperaBLE’s significant contributions are in its accessibility and, as a result, in the proposed algorithms. Different distinctions have been employed for the production of OperaBLE, and the first is the efficiency for Industry 4.0 technologies, as well as the second, is its long-term activity. As a result, in the event of a confrontation, proper performance takes precedence over energy consumption. OperaBLE’s main challenge is to ensure that its features can be implemented in just about any lightweight IoT system with such a high level of reliability. Due to decreased micro-controller functioning frequency that characterizes some small IoT devices, and also the successful handling of sensors and communication units, which guarantees to provide as much energy, we designed an Inertial Measurement Unit, but have a 3-axis accelerometer built into the micro-­controller framework (IMU). This latest panel has 10° of freedom, including a 3-axis accelerometer, 3-axis gyroscopes, 3-axis magnetometer, and a temperature sensor. Although we only use a gyroscope, it allows the use of additional sensors for any further analysis. A pulse detector has been included in the model to monitor operations’ heartbeat and detect potentially dangerous conditions. Finally, we modelled houses utilizing three-­ dimensional computer technologies as the prototype’s covering, with the form seen in Fig. 3a, b. The functionality of the OperaBLE model is indeed one of the key

Fig. 3  OperaBLE, prototype used for experimentation. (a) Exterior design: OperaBLE with pulse sensor. (b) Interior design: OperaBLE with accelerometer

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characteristics, and that is why the features to be built must be checked under various conditions. Our scenario laboratory used two different OperaBLE models (seen in Fig. 3) to evaluate the two methods built separately. OperaBLE is a hybrid of decreased and low-cost prototyping board with a designing algorithm that is based on the ability to adapt, low wattage activity, and sample rate, depending on these properties. A. Movement Characterization One of the experiment’s major accomplishments is the creation of a method for motion detection using only a sampling rate of about 10 Hz, which is up to 10 times smaller than that used in traditional experiments. Such reality offers significant benefits in terms of more effective battery use, with the following contributions were illustrated in the following phrase: 1. The motion characterization methodology can be used in a variety of devices, including those that are regulated by limited frequency micro-controllers, that use less power than large processing mechanisms. As a result, the algorithm’s ability to analyse direct acceleration data and classify individual body language while using the fewest resources possible allows it to be implemented in compact and cost-effective devices, leading to more efficient usage of resources. 2. Other than being effective in improving characterization with a smaller sample per action, the technique themselves necessitates a low measurement operating frequency. Drop samples are not an integral energy-saving feature of OperaBLE, and it does help to reduce the amount of data collected, which reduces the amount of power required by accelerometers. The use of BLE digital technology allows for the optimization of the communications service module energy demand. The activity characterization methods process assumes that even if the content was acquired is irrelevant to OperaBLE (that also preprocesses actual displacement information), no data is sent to the processing point. BLE allows for periods of sleep among data transmissions with minimal power usage, and so as a result of the proposed preprocessing stage, the quantities of information encapsulated and transmitted via BLE are significantly decreased.

5 Results and Discussion Each study’s key findings are discussed, along with the wearable computing devices and technologies that are considered to become the most effective for safety management tracking and development. A. Construction Safety and Health Hazards The construction sector has a greater mortality rate than that of the national median for all sectors in this group. Over the years, the number of injuries and illnesses on building sites has risen. Falling from a height caused by unsafe support

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beams or staircase used by the workers, repetitive stress wounds, heatstroke, or heat exhaustion as a result of body temp increasing to hazardous levels, and being hit by moving machinery operating in close vicinity to employees are only a few of the possible safety and health risks for construction professionals. Dangers to safety and security, and also the observable indicators connected with them. For effective implementation of wearable technology again for compilation and evaluation of the measurements needed for such reduction of dangers, the corresponding sections of the observable safety performance measures are indeed given (Fig. 4). Figure 5 also shows the devices and sensors used to detect the most frequent construction-related protection and environmental hazards. The average sensitivity, specificity, and accuracy are ±1.30, ±1.52 and ±1.04, respectively. With both the support of NCDOT management, collaboration among roads and bridges staff was sought in preparing for field tests. Provided that the use of FSSDs for highway construction is a relatively recent concept introduced by NCDOT, only 2 of 14 units in North Carolina have used them before. As a result, 6 personnel from out of the 2 phases were selected to take part in 96 field tests as a result of the frequent test. When implementing one of its 4 FSSDs indicated in Fig. 4 (i.e. six employees, four activities, four FSSDs, 96 field experiments), each of the workers performed four of most related techniques (i.e. unloading, deployment, and disassembling). Moreover, while the participants were familiar with FSSDs in particular, they might have no previous knowledge with particular FSSDs investigated in this review. The researchers had only previous knowledge of a custom-designed FSSD produced by NCDOT contractors, which will not be included in this study. This removed any questions about validity that may have harmed the study’s findings due to prior knowledge. The staff who participated in this research were always in good health and ranging in age from 30 to 60 years. The employees had between 6 and 20 years of professional experience in road and bridge infrastructure evaluation, renovation, and reconstruction. After the employees are informed of the costs involved with both the field experiments, they signed the document consent form indicating their readiness to participate in the research. The parts that follow explain the field and laboratory

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experimental design in the light of the four methods that have been completed as related to the research initiative. B. Loading and Unloading Tasks Whenever the FSSDs were implemented in operation, the loading and unloading of FSSDs become two processes that must be done regularly. Staff would have to put the FSSDs over to a vehicle, transfer themselves to the bridge site, then offload them to preparing for both the scheduled work activities, for example. Employees must also refill the FSSDs onto the vehicle and offload these in a container yard at the beginning of each working hour until the work shift activities were completed. Field tests included simulations of FSSD loading or unloading. To reduce the possibility of a traffic-related event, the simulation has been carried out in a container yard wherever necessary. The contributing employee was given a proper summary of portable sensors before starting one of the simulated field activities but was made to wear the device according to the maker’s guidelines. After that, the employee was told to sit and relax on a bench beside the vehicle to get his pulse rate back to normal. Throughout this period, the researchers put the FSSDs at such a range of 6.07 m (50 ft) from either vehicle, as seen in Fig. 6. Following the schedule, the involved employee was associated with loading the FSSDs onto a vehicle, as is customary in bridge planning. It was necessary to put eight reports as part of the mission. After loading was over, the employee had been given enough place to relax before their pulse rate stabilized. The employee was again given the responsibility of offloading the previously filled FSSDs again from the vehicle and arranging the components as shown in Fig. 7. The proposal replicated the conventional method of positioning FSSDs across a bridge’s fence in readiness for its construction. Each one of the staff is treated the same way for every one of the FSSDs throughout the present study. After installation is performed, the employee has been given another seated break to allow the pulse rate to stabilize. Following the break, the employee removed the fall safety pillars, which included softening, raising, and repositioning the posts

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Fig. 6  Placement of the FSSDs before the loading task

Fig. 7  Placement of the FSSDs after the unloading task

mostly on the bridge. The process was replicated for each of the staff who participated. C. LoMoCA: Low-Frequency Movement Characterization Algorithm The protocols carried out by LoMoCA, with process diagram is given in Fig. 8. OperaBLE’s methodology is separated into preprocessing and non-wearable computer deployment. In terms of the smart interface, we created a battery-saving code that keeps the system in a sleep state when there is no noticeable activity. As OperaBLE detects the start of a new activity, the subroutine gathers information from IMU’s combined accelerometer and gyroscope and stores it in the system till the activity is completed. This moving trigger activity is caused by a threshold that compares the previous and present samples in real time to see if there is a difference of more than 1 m/s2 (acceleration) and 1 rad/s (angular acceleration). The computer goes into sleep mode when waiting for such a major acceleration and angular velocity throughout an instant to decide the conclusion of each action. The collection of information is fully achieved when no activity is detected throughout this period. LoMoCA Experiments. To test as many of the variations as possible, this work defines five baseline motions relevant to economic activities. In each case, a diagram with related angles as well as a trajectory interpretation is shown in Fig.  9. The so-called lever, valve activation, and assembling motions (motions considered to reflect standard manufacturing activities for checking the algorithms with such a

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Fig. 8  Processing stages of LoMoCA

progressive complexity rise) were categorized into two parts: the first one involves information demand and notifications, and the other includes data demand and alerts. To keep the number of stimuli minimal and thus impede the character development process, both gestures were registered as shorter gestures. Here is a more in-depth description: The 1-2-3 series is replicated 3 times with the hammer, which uses mostly two axes and just a single rotating direction. The OperaBLE XY plane was used to conduct this movement. It faces an additional challenge as a result of centrifugal acceleration, which causes a Z-axis difference, which obstructs the recognition process. Four times the 1-2-3 series is performed. Assembly: consisting of link tasks requiring six axes (most complicated motion), which would be useful for determining LoMoCA reliability and distinguishing between identical gestures. It’s 1-2-3 in the right order. Information request: This is a unique activity that allows operations to test samples with devices

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using a simple three-tap process mostly on the device’s layer. It does not make use of displays, buttons, or other complicated devices. Three times the series 1-2-3 is replicated. The OperaBLE consumption curves throughout hammer activity, with the phases, mobility, and idle established. OperaBLE accurately represented the community in this portrayal using only three Degrees of Freedom (DOF) (acceleration data).

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It was also kept in a sleep state pre- and post-characterizations (mobility stage) to aid inside the classification of the various consumption phases, with a 1-second idleness phase afterward to guarantee that mobility information gathering was complete. When it came to valve open and assembling actions, the spending time on each action was around 3 seconds. In these circumstances, OperaBLE stayed in a sleep state after 2 seconds because the sleep period coincides with both the duty cycle flow velocity. The amount of time spent characterizing the movements is an important issue. OperaBLE utilizes a maximum of 0.011 mAh during the 6-second measuring interval set in this research (assembly movement). This suggests that their prototypes will run for a minimum of 15.70 hours with 6 DOF utilizing this working process (worst case). The entire disastrous situation yields a lifetime of 10.27 hrs by splitting the 105 mAh capacity of the batteries by average usage of 10.22 mA (motions are consistently characterized). The longevity attained is ideal, even though it is based upon measurement data. The longevity attained is ideal, despite the model that is based on real measurements. Even if the device’s lifetime was reduced by 30%, it will still last a regular workday without any energy constraints. Aside from the time being spent characterizing every activity, this work proposes a set of objectives as well as a decision-making mechanism depending on the number of DOF to be examined to conserve quite so much energy as feasible. As mentioned in earlier sections, OperaBLE’s first objective is stability to satisfy Industry 4.0 criteria. Furthermore, for the procedures, durability is also important because there are two primary categories of energy usage: the running rate of a device’s microcomputer (upon which work concentrates) as well as the sensor modules linked to a gadget. The correction factor outlined in AHRA was employed ten times using three different test personnel during the trial. Everyone was kept asking to fill out a questionnaire stating either they felt tired or comfortable. Figure 10 depicts the proportion of

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statistics correctness for every group of ten observations, with tired and relaxed groups underlined for every operator. Comparison of performance measures over training and testing on dataset is illustrated in Fig. 11a, b. The performance is based on both loss and accuracy. Figure 12 illustrates the calculated risk could also help classify the participants to identify those who are likely to develop security on working condition.

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6 Conclusion This work addresses how wearable devices can be used for tailored safety management surveillance and trends. A thorough examination of the characteristics of wearable devices is offered, as well as the associated safety metrics that are assumed to be effective in forecasting safety quality and leadership strategies. According to the study, wearable technologies are utilized in a range of industries to improve safety and productivity, but just a few applications have been found in the building industry 4.0. Given that the many industries where wearables techniques have been widely implemented are not increased industries, such as building, there seems to be an immediate need to preserve the status quo in respect of wearables application in building projects. It’s past time for construction firms and professionals to adopt these new and emerging technologies to significantly improve safety management. It identified wearable tech possibilities for gathering and tracking several measures linked to frequent building site accidents and deaths. According to the findings of this study, the detectors and major mechanisms in existent wearable devices used for other industries can indeed be designed to measure and analyse a wide range of safety performance measures in the building. Wearables with numerous sensor types, as well as multimodal sensors that may gather information from a data area of the body were also proposed for use in the building. Device manufacturers should focus their efforts on figuring out how to extract meaning from many sensors integrated into the portable sensors, to provide a holistic perspective as to how the programme works or performs across many sensors and devices. This finding of the study can even be utilized to incorporate various wearable sensors and technologies into construction-specific smart technology. Models for individual safety monitoring could be evaluated. Future research on the topic may include selecting potential wearable technology among the commonly available ones that could be used in industry, or building models for

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construction-specific wearables depending on the results of this study and conducting an experimental test to confirm their efficacy. More techniques are constantly being designed to improve OperaBLEs’ versatility and monitor the surrounding environs of operators, guaranteeing worker safety. A BLE meshes network is currently being constructed for assessing a comprehensive BLE-based CPS because BLE technology enhances flexibility and opens up opportunities for complex datasets. The findings are encouraging, indicating that more research is needed to assess durability and improved productivity in real-world Industry 4.0 scenarios.

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16. Krey, M., Schlatter, U., Mahadevan, D.  A., Derungs, K., & Oehler, J.  K. (2020). Wearable technology in healthcare. Journal of Advances in Information Technology, 11(3), 172–180. 17. Almusawi, H. A., Durugbo, C. M., & Bugawa, A. M. (2021). Innovation in physical education: Teachers’ perspectives on readiness for wearable technology integration. Computers & Education, 167, 104185. 18. Awolusi, I., Nnaji, C., & Okpala, I. (2020, Nov). Success factors for the implementation of wearable sensing devices for safety and health monitoring in construction. In Construction research congress 2020: Computer applications (pp. 1213–1222). American Society of Civil Engineers. 19. Morcos, M. W., Teeter, M. G., Somerville, L. E., & Lanting, B. (2020). Correlation between hip osteoarthritis and the level of physical activity as measured by wearable technology and patient-reported questionnaires. Journal of Orthopedics, 20, 236–239. 20. Blount, D.  S., McDonough, D.  J., & Gao, Z. (2021). Effect of wearable technology-based physical activity interventions on breast cancer survivors’ physiological, cognitive, and emotional outcomes: A systematic review. Journal of Clinical Medicine, 10(9), 2015.

Blockchain-Based Privacy-Preserving Electronics Healthcare Records in Healthcare 4.0 Using Proxy Re-­Encryption Latha Parthiban, Naresh Sammeta, A. Christina Josephine Malathi, and Betty Elizebeth Samuel

1 Introduction In the current circumstances, patient data and privacy are important. This data is stored in cloud which needs to be protected. Doctors need data to evaluate patients effectively, and many pharmaceutical companies utilize this data for research [1]. This data, together with the doctor’s advice, is required for a patient to decide about his health and life. Furthermore, they must maintain track of their medical advice and treatments, as they will need this information to confer with a variety of specialists before making a decision on their continuing treatment. This condition needs [2] the efficient online interchange of patient data, so that the designated person may access it instantly. At the same time, the patient should have ownership of his data, and only the patient should have access to medical records. Their privacy should never be affected.

L. Parthiban (*) Department of Computer Science, Pondicherry University, Pondicherry, Tamil Nadu, India e-mail: [email protected] N. Sammeta Department of Computer Science & Engineering, R.M.K College of Engineering and Technology, Chennai, India A. C. J. Malathi SENSE, VIT University, Vellore, Tamil Nadu, India e-mail: [email protected] B. E. Samuel College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_6

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Patient records are now preserved on paper or electronically. The precision and trustworthiness of this patient data vary. Some people keep their whole medical record, while others do not. E-data collection occurs across multiple healthcare companies, putting patients’ privacy at risk. While we cannot analyze healthcare 4.0 providers’ motivations, a large-scale cyber-attack might reveal the personal information of many people. The only objective of collecting patient data [3] is to expand the patient’s accessibility to various agents in the healthcare ecosystem. In this work, we want to establish an ecosystem that combines all of the components, improves accessibility, and effectively gathers data while also preserving patient privacy and guaranteeing that patient data is never subject to a botnet. We use blockchain [4], a distributed data storage method that avoids single points of failure, to achieve our aim. To guarantee system compliance, each patient must consent to the sharing of their data for both primary care and research purposes. For the system described in the paper, the following security properties are required: data integrity, authenticity, availability, and data confidentiality. In the context of our system’s functioning, the properties are as follows: Data Integrity: users of the system may be certain that their data has not been tampered with either in transit or at rest. Authenticity: Users may be certain that the data they got originated from the sender who claimed to be the sender. Data Availability: Data may be accessible at any time and from any place. Confidentiality: It is not possible to divulge information to unauthorized persons. It is not possible to divulge information to unauthorized persons. We leverage consortium blockchain to develop a distributed system that combines current EHRs using Hyperledger Fabric [5, 6]. The proxy re-encryption [7] is a software agent on the cloud server. Before storing data on a cloud server, it checks its validity and integrity. The paper is ordered as follows. Section 2 discusses the relevant works. Section 3 provides an overview of blockchain technology, while Sect. 4 provides a system model. Section 5 describes the proposed algorithms. Section 6 summarizes the results of the performance analysis. Finally, Sect. 7 provides conclusions and future work.

2 Related Works This section reviews the most current cutting-edge blockchain-enabled healthcare 4.0 solutions, with a focus on safe medical data transmission. These current systems have resulted in an inadequately digitized complex, with paper records still being used at several levels in hospitals [8]. In 2015, Anthem insurance business computers were hacked and 79 million patient records were taken [9]. In 2017, cyberattacks on the US Department of Health harmed around 2.6 million patient data [10]. It also

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targeted FedEx, and over 300,000 server workstations in over 150 countries were attacked, making it the “biggest ransomware pandemic in history” [11, 12]. Sandro et al. [13] developed a system to govern shared data, and the design mitigated data security risks. A decentralized personal data management system was described by X. Zheng and colleagues [14]. It can ensure that people own and control their data. X. Zheng et al. [15] created a conceptual architecture for safely and transparently exchanging personal health information. A decentralized “MedRec” system on blockchain has been proposed to handle EHR [16]. MedRec has contributed to the advancement of data economics. It also provides enormous amounts of data to academics while giving patients and clinicians the option of disclosing or not disclosing information. Mikula and Jacobsen [17] created a blockchain-powered system. Xue et  al. [18] developed a medical data sharing architecture based on blockchain technology. The technology solves the problem of confirming, maintaining, and synchronizing medical data across several medical institutions by enhancing the consensus process. However, since the system lacks the ability of a machine learning algorithm, it has certain limitations in data storage. Cao et al. [19] demonstrated a safe cloud-based health solution. Xia et al. [20] developed an innovative blockchain system. Guo et al. [21] have provided an attribute-based signature strategy with several authorities to ensure the effectiveness of blockchain-encapsulated EHRs. Several schemes used cloud and blockchain technologies to improve the security of EHR sharing. MeDShare, a blockchain-­based solution with reduced data privacy problems, has been proposed by Xia et al. [22]. The two systems have cloud vulnerabilities since they continue to rely on the cloud for assistance.

3 Blockchain Overview Blockchain [23] is a decentralized ledger in which data is stored in blocks that are sequentially sorted and linked by a cryptographic hash function and can be public or private. Public blockchains are permissionless, open to anybody, and entirely decentralized. Private blockchains are administered by a single entity. Public blockchains need proof of work to achieve consensus because they must survive the Sybil attack, but permissioned blockchains may use less computationally expensive alternatives [24]. Figure 1 shows a simplified representation of the blockchain. To accomplish our work, we deployed Hyperledger Fabric, one of the Hyperledger Project’s multiple blockchains. The organizational structure of Hyperledger Fabric is detailed here. A. Organizational Structure of Hyperledger

1. Membership Service Provider (MSP): This node performs the function of a certificate authority. 2. Client Node: This node is meant for the end user and contains the middleware code. 3. Peer Nodes: This network’s backbone is divided into two categories:

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Fig. 1  Blockchain architecture



(a) Committing peer: This node updates the ledger transactions. (b) Endorsing peer: This node is responsible for running the chaincode and validating transactions.



4. Ordering Node: These nodes are not equipped with chaincodes and do not keep track of the ledger. Unlike the MSP nodes, this node need not be present in every organization, but it must be administered by at least one of them. 5. Certification Authority (CA): A certification authority (CA) is responsible for issuing certificates to all network members. The CA has digitally signed these certificates. Fabric CA is a built-in CA component provided by HLF. It keeps track of participants’ digital identities in the form of X.509 certificates. Each company has its node that serves as a certifying authority. 6. Organizations: A controlled collection of blockchain network participants is referred to as an organization. A Membership Service Provider (MSP) is used by every organization to manage its members. Peers, users, and clients make up an organization. 7. Channel: Multiple ledgers may be shared among network participants (organizations) in HLF to perform private and secret transactions. These ledgers, or private communication subnets, are referred to as channels. Every transaction, whether it is adding an asset or querying the ledger, takes place on a channel, and each participant must be verified and permitted to trade on it. 8. Chaincode: In an HLF network, a smart contract is referred to as a chaincode. It is a self-executing logic that contains the rules for certain network transactions. All peers on a channel who want to participate must install Chaincode. Client-side apps may be used to activate chaincode by authorized peers. If chaincode transactions are verified, they are added to the shared ledger and change the status of the world. For chaincode development, HLF offers a variety of programming languages.









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9. Ordering Service: A public blockchain’s consensus process is probabilistic, which ensures ledger consistency, but leaves it susceptible to divergent ledgers (sometimes called forks). HLF avoids forks by using deterministic consensus mechanisms. The ordering service is built utilizing Apache Kafka and RAFT in this solution. Kafka is a communications queue with a leader and follower node architecture that is Crash Fault-tolerant. The leader node replicates transactions to the following nodes. If a leader is killed, one of the followers takes his place, guaranteeing fault tolerance. RAFT uses a “leader and follower” architecture, in which a leader node is chosen (per channel) and the followers copy the leader’s choices. RAFT ordering services are simpler to set up and operate than Kafka-based ordering services since they do not need any extra services (like zookeeper) to maintain the cluster. Another advantage of RAFT is its dynamic leader architecture, which means that if the leader node fails, the remainder of the cluster picks a new leader instead of waiting for the leader to resume.

B. EHR Using Blockchain We provide the proposed framework’s privacy and security analysis to establish that the above-mentioned privacy and security qualities are assured for the following categories of data: EHR data, metadata, cryptographic keys, and user credentials. Integrity and Validity of Data  To maintain data integrity and authenticity, we must verify that users are confident that data was not tampered with in transit or at rest by an adversary and that the data was delivered by the stated sender. When users upload EHR data to the Cloud Server, during the time the data are stored on the Cloud Server (from the time they were uploaded by a user until the expiration of the corresponding permissions), and when the data are downloaded from the Cloud Server, the integrity and authenticity of the EHR data may be jeopardized. When a user or the Cloud Server issues a transaction and the peer nodes execute consensus protocol to update the ledger, the metadata’s integrity and authenticity may be jeopardized. When patient P changes or queries his record on the ledger, when caregivers check their rights on the ledger, when Cloud Server queries the nodes, and when the system is functioning and the nodes must maintain the ledger, the integrity and validity of the system information might be jeopardized. Throughout the lifespan of the private keys, dangers to the integrity and validity of the cryptographic keys exist. Patients and caregivers digitally sign all transactions with their associated private keys to secure the validity of the healthcare 4.0 data and metadata given by the users. Metadata is saved on the blockchain to ensure the validity and integrity of healthcare data. The hash of the associated healthcare-data file submitted to the cloud server, as well as information about the person who uploaded the data file, make up metadata. The answers from the peer nodes are signed when the Cloud Server or users query the ledger, ensuring data authenticity and integrity. The integrity and authenticity of the healthcare data and system metadata are guaranteed by the correctness and unforgeability of the digital signature algorithm, as well as

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the properties of a secure cryptographic hash function because we assume that the nodes and users can securely manage the secret keys and credentials generated during enrolment. The employed consensus protocol assures an atomic broadcast and its properties: validity, agreement, integrity, and total order, which protects the integrity of a patient’s data kept on the blockchain. Patients’ and independent carers’ private keys are saved on a smart card or a mobile phone that is secured by a pin code that is only known by the owner. As a result, no opponent may access or tamper with the keys when they are at rest. The secure clinical infrastructure is where caregivers keep their keys. Only after two-factor authentication can the keys be accessible (e.g., a badge of the medical doctor and his password, received at the registration). If a user’s private keys are lost or compromised by an attacker, a membership service will sign new keys and certificates based on the users’ individually identifying information. Availability  In an ideal world, the system would ensure that data could be accessible at any time and from any location. However, with our system, we demand data availability by the patient’s access-control policy. If the Cloud Server is down, the data was lost or never uploaded to the Cloud Server, there is no appropriate authorization, and a user does not have the necessary keys to decrypt the data, the availability of EHR data may be jeopardized. If the peer node network is unavailable, metadata availability cannot be assured. If a user’s credentials and/or smart card are lost, the availability of cryptographic keys is jeopardized. By the permissions set by the patients, the EHRs are kept on a HIPAA-compliant fault-tolerant cloud storage. As a result, when the permissions recorded on the ledger expire, the data is destroyed. We further suppose that the patient may express his consents/access-control policy via the Web portal by defining permissions and providing their keys, respectively. The qualities of the atomic broadcast, as well as the availability and trustworthiness of Web apps, ensure the availability of system information through the blockchain (UI). The availability of healthcare data is likewise contingent on cryptographic keys being available. If a key pair used for data encryption is lost, the patient may view the permission history, upload material encrypted using the doctor’s fresh new public key, and adjust permissions accordingly. If a patient forgets his or her login credentials, they may be recovered using an identity management system at the hospital or hospitals where the patient is enrolled. This may now be done through the hospital administrator Web portal in our prototype solution. Confidentiality  When it is impossible to disclose information to an unauthorized person, confidentiality is secured. From the minute the data is transmitted from the local database of the medical institution/caregiver/patient to the cloud server, the confidentiality of the healthcare data may be jeopardized. When data is in transit, that is, when it is uploaded/downloaded by a solution user, and when it is at rest, that is, when it is kept on a cloud server or the ledger, the confidentiality of the data might be compromised. If the content of the ledger is leaked to an unauthorized user, the metadata’s secrecy may be jeopardized. If the user’s smart card and

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credentials are hacked, the cryptographic keys’ secrecy may be jeopardized. The confidentiality of healthcare data is initially secured in our system by using HIPAA-­ compliant cloud storage, which restricts access to the data to those with the appropriate rights. Second, the security aspects of the asymmetric encryption technique used to encrypt the patient’s data assure confidentiality. To preserve the security of the system information, the system must ensure that caregiver C is only aware of the rights that have been granted to C (and nothing about the permissions specified by P for any other users). The chaincode implementation in our system ensures that C may only query the permissions that pertain to him. The pin code of the smart card or the phone, which is known only to the owner, protects the secrecy of the doctor’s private key. If the doctor’s private key is compromised, the patient may request that the data be deleted by adding appropriate permissions. The doctor will need to create a new key pair, while the patient will need to adjust permissions and upload data encrypted using the doctor’s newly created public key. Unlinkability  In our system, unlinkability is defined as the inability of any unauthorized user who has never been allowed by the patient to link the system information to the associated patient’s identification. This characteristic is based on our identity management strategy. When a patient enters the hospital, he is assigned a pseudonym. As a result, the identity and the pseudonym can only be linked by the doctor with whom the patient has ever exchanged data. Cloud services, as well as nodes-participants in the blockchain network (hospitals) that do not have access to patients’ data, are unable to connect the patient’s identity and the pseudonym, and therefore the system metadata. We cannot make a person “forget” stuff he previously had legal access to. We can only restrict the linking of the patients identified with the data after a user is unauthorized. The period of the permissions is taken into consideration when querying the ledger: if the permission has expired, no additional information will be attached to the identity. Linking the identity to the blockchain record is impossible unless there is a collaboration between the blockchain nodes (kept by the hospitals) and the carers (and the system design assumes that collusion is not feasible between the caregivers and the blockchain nodes).

4 System Model We used a permissioned blockchain architecture of Hyperledger Fabric in the suggested model in Fig. 2, where a membership service provider looks after all network participants and issues keys and certificates. At the time of enrolment, the functions of entities are specified. Endorsers, orderers, and committers are all roles that hospitals may play. A single channel is used to enroll patients, hospitals, and researchers [25]. As a result, everyone has access to the ledger. When a patient visits the hospital, a record is made and kept in the cloud. Metadata is also saved on the blockchain at the same time.

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Fig. 2  Proposed system architecture

We divided the data into two groups to make this easier: (1) sensitive and (2) non-sensitive data. Sensitive data is personal information that may expose a patient’s identity, while non-sensitive data is comprehensive diagnostic information that cannot be used to identify a patient. This categorization aids us in reducing the amount of time and resources needed to communicate e-health data [26–29]. A member Organization’s end user issues a transaction request on a channel using a client-side application that connects to an authorized peer. 1. The transaction invocation request is broadcast by the client application to the endorsing peer. 2. The Endorser peer verifies the member’s Certificate information and sends the transaction request to others for validation. The Chaincode (i.e., smart contract) is then executed, and the endorsement replies are returned to the client. As part of the endorsement response, the endorser peer delivers transaction approval or rejection. 3. If the transaction is accepted, the client delivers it to the orderer peer, who will appropriately order it and add it to a block. 4. The orderer node organizes the transaction appropriately and adds it to the block. The block is also sent by the orderer node to anchor nodes (peers) of the HLF network participating member organizations who are permitted in the specified channel. 5. When the anchor peer gets the new block from the orderer node, they broadcast it to all peers in their organization, bringing all ledgers up-to-date.

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Data Storage The following measures are taken to store healthcare 4.0 data: • When the patient comes to the hospital for a consultation, a record containing their identity will be produced. • Sensitive data need to be encrypted. • The hospital will now submit a transaction proposal to the endorsers to start a transaction. • Endorsers will simulate chaincode and prepare a read/write set before sending. • The starting peer will assemble a sufficient number of supported proposals under the endorsement rules, then submit them to the ordering service. • At this point, the ordering service will group the transactions and deliver them to the committed peers. • The transaction will be validated by committing peers, who will then commit the block to the ledger. • The patient’s public key will be used to encrypt the IDs in the metadata. The remaining data will be kept in its current state. Data Access • Patient: Using their private key, patients get immediate access to their data on the cloud. The ledger may also be accessed by the patients as needed. • Researchers will join the channel to have access to the data. They may then undertake statistical analysis using the ledger. Researchers may use the request key transaction to access detailed data by submitting a request. Patients who want to contribute detailed information may use the send Key transaction to submit the non-sensitive data’s private key. The transaction will be approved and ordered in the same way as in Fig. 3. The requester will issue a proxy re-encryption request to a randomly chosen endorser to get sensitive data. This request will be carried out off the main chain.

5 Proposed Algorithms Five chaincodes were put in our prototype system to conduct business logic. Each chaincode has several programming functions that read and write the ledger state while also including all of the business logic. Before being created in a real system, each chaincode must get consensus from all of the member hospitals. The doctor registration function takes (DoctorID, Name, HospitalID, Specialization) as input and sends the request to the system through the API. When a client requests an API, the node server looks for an endpoint matching method in the “app.js” file. This function calls blockchain Transaction Processing Facility (TPF) defined in “network.js” after collecting all. To register a doctor as a network participant, all of the functions in this file are TPF and use the Hyperledger composer NodeSDK functions. It also produces an identification card for the doctor, which is

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Fig. 3  Transaction flow

saved in the identity wallet, when the doctor has been registered. The doctor registration function is summarized in Algorithm 1. Algorithm 1  Chaincode on Doctor Registration. Input: DoctorID, Name, HospitalID, Specialization Output: Registered the Doctor function AddDoctor(contains data params) if(doctor is not registered) then check his profile in Global Practioners data if(License exists) then Adding Doctor to the hospital system return DoctorID else Abort the Session end if end if end function

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Algorithm 2  Chaincode on Patient Registration Input: PatientName,IDProof, Address Output: generates PatientID as a participant function AddPatient(contains data params) if(Patient is not registered) then Enter his profile in HIS data after getting the privileges from CA return PatientID else Abort the Session end if end function

On the patient side, the registration function uses (PatientName, IDProof, Address) as input and sends the request using the system’s API. When a client requests an API, the node server looks in the app.js file for an endpoint matching method. This function calls the blockchain transaction processing function (TPF) defined in “network.js” after collecting all arguments. All of the functions in this file are TPFs, and they use Hyperledger composer NodeSDK functions to register the patient as a network member. It also develops an identification card for the patient, which is saved in the identity wallet, when the patient is registered. The patient registration function is summarized in Algorithm 2. Algorithm 3  Chaincode on Creating EHR data by the Doctor Input: DoctorID, PatientID, PatientKeys, HospitalID Output: Encrypted file URL function DoctorCreateEHR(contains data params to create) check his Patient profile in HIS data if(PatientID is not found) then through Admin create new PatientID end if if(Registered Patient) then get the keys from the patient then upload the encrypted file in HIS return file URL end if end function

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The Doctor function creates EHR data by entering (DoctorID, PatientID, PatientKeys, HospitalID) as input and submitting the request over the system’s API. When the doctor uses the API, the node server looks in the app.js file for the endpoint matching function. This function calls the blockchain transaction processing function (TPF) defined in “network.js” after collecting all arguments. All of the functions in this file are TPFs that use Hyperledger composer NodeSDK functions to get patient data. If the data is not discovered in the network, the patient will be registered and a new PatientID will be issued. If the patient is already registered, the doctor will encrypt the file and save the metadata information with the generated URL. The Doctor Creating EHR function is described in Algorithm 3. Algorithm 4  Chaincode on Doctor retrieving the Patient Data Input: DoctorID, Patient ID, HospitalID Output: Display the Patient PHR data items function ViewingPatientRecord(PatientID) checking the PatientID in global blockchain while(PatientID) if(Doctor has role to view) then get the Timestamp, DoctorID, HospitalID retrieve metadata of EHR and URL of EHR else if(Emergency purpose for Doctor) check the role to view the data get the Timestamp, DoctorID, HospitalID retrieve metadata of EHR and URL of EHR else Access Denied end end function

For accessing EHR data created by the Doctor function enter the (DoctorID, PatientID, HospitalID) as input and send the request over the system’s API to obtain EHR data. When the doctor uses the API, the node server looks in the app.js file for the endpoint matching function. This function calls the blockchain transaction processing function (TPF) defined in “network.js” after collecting all arguments. All of the functions in this file are TPFs that use Hyperledger composer NodeSDK functions to get patient data. If a doctor can examine a patient’s EHR data, they will get the EHR metadata along with the URL address where the data is kept in the network. Whether it is for an emergency, the admin will check to see if the doctor has any access rights to the data; if he has, they will receive the metadata information along with the created URL; otherwise, access will be refused. The Doctor obtaining EHR function is detailed in Algorithm 4.

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Algorithm 5  Chaincode on Updating the Patient Record Input: DoctorID, PatientID, PatientKeys, HospitalID Output: Encrypted file URL function UpdatingPatientRecord(contains data params to update) if(RegisteredDoctor in HospitalID) then if(PatientID && PatientName) then get the keys and update the patients record and get the URL return Succeded else return failed end if else Abort the Session end function

The Doctor function inputs (DoctorID, PatientID, PatientKeys, HospitalID) as input and requests the API in the system to update EHR data. When the doctor uses the API, the node server looks in the app.js file for the endpoint matching function. This function calls the blockchain transaction processing function (TPF) defined in “network.js” after collecting all. All of the functions in this file are TPFs that use Hyperledger composer NodeSDK functions to get patient data. If a doctor can edit a patient’s EHR data, the admin will compare the DoctorID to the HospitalID on file. The doctor will update the patient records information as metadata and store the information along with the URL after receiving clearance from the patient with sharing of their keys, or else the doctor’s access to the EHR records would be prohibited if they do not have any privileges to view the records. Algorithm 5 explores more into the role of updating the patient record.

6 Performance Analysis To produce the client workload, we utilize Huawei Technologies’ Caliper benchmarking tool [30]. We can deploy tested operations to the blockchain using Caliper. Caliper is a client-side application that broadcasts transactions over the Fabric channel. It listens for block notifications from peers to verify blockchain transaction approvals and adds a completion timestamp to such transactions. It uses transaction timestamps to determine transaction throughput and latency. The simulation PCs have the following configurations: Intel(R) Core(TM) i5-8265U CPU @ 2.70 GHz, 8 GB of RAM processor.

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Fig. 4  Experimental setup

The experimental setup we employed in all of our tests is shown in Fig. 4. Each experiment transmits transactions at a pace ranging from 85 to 8500 transactions per second, which was the maximum capacity for the client nodes we employed in our tests. As a result, the blockchain network is subjected to a transaction load resulting from all of the clients’ transactions. Each client transmits transactions at a certain pace, then pauses for 3 seconds before starting the following round. Three rounds of the experiment are carried out. The average of the throughput and latencies is determined after the third cycle. Total transactions on the blockchain network are limited to 78,000. For the length of the experiment, we additionally keep track of the CPU and memory use on the peers. The workloads listed below were employed. • Write workload: This is done in Fabric by invoking the invoke function from inside the chaincode. Write creates a transaction. • Read workload: It consists of reading transactions that read values from the key-­ value store inside the chaincode for randomly chosen keys. Reads do not result in a blockchain transaction that is confirmed. The latency and throughput metrics for all four workloads are shown in Fig. 5a, b. On the blockchain network, a maximum load of 3600 tx/sec was created with four clients, each producing 600 tx/sec. The findings demonstrate that read throughput grows linearly throughout the whole range of transaction rates for the provided range [31, 32]. We describe the results of tests on the blockchain network to represent big consortiums.

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6.1 Scaling of Chaincode Chaincodes are business logic that must be executed as part of a blockchain application [33] to update the data (key-value state) stored there. When there are 100 chaincodes in the system, for example, we employ a 100 transaction per second input send rate, with each request sent to a single chaincode. The peer runs the chaincode and performs the invoke function when each chaincode is called within the same time frame, utilizing resources on the peer nodes. When a single chaincode is installed, the throughput in Fig. 6a, b closely equals the system throughput up to 3246 tx/sec. When the number of chaincodes reaches 2000 and the transaction rate exceeds 3300 transactions per second, the latencies skyrocket and the throughput plummets. With this approach, we were able to deploy a maximum of 2000 chaincodes, but only 1943 of them could be properly activated at the same time. At this point, increasing the chaincode count caused transactions to fail and the RPC connection between client and peers to time out.

6.2 Scaling by Peers The consortium’s peers are administered by the consortium’s participants. Given that each firm is a participant in a bigger consortium, each of them should preferably operate with minimum of one peer and the impact of huge consortium sizes on system performance in this collection of tests is noted. We compared latency and throughput for four different consortia sizes: four, eight, twelve, and sixteen. The endorsement policy is structured such that for a transaction to be included on the blockchain, it must be endorsed by all peers in the consortium on a single channel. The throughput and latencies for transactions with varied transmit rates up to 3400 tx/sec are shown in Fig. 7a, b. With default configuration settings specified, the biggest consortium option offers the lowest throughput. For higher throughput rates of 3400, the disparity widens substantially, with the 16-peer arrangement achieving around half the throughput of the 4-peer setup.

7 Conclusion Sensitive information should not be entrusted to other parties, since it is open to a variety of assaults and exploitation. As a result, consumers should be able to own and govern their data without exposing security. Our technology accomplishes this by segregating a patient’s sensitive and non-sensitive data, allowing healthcare 4.0 data to be shared efficiently. The model effectively employs the proxy re-encryption approach to transfer sensitive patient data without disclosing the patient’s private key. The proposed blockchain technology was evaluated using a structured

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experimental method. As a part of future work, the framework will be extended to share audio and video data that can be employed with the stakeholders to achieve secure medical data transmission.

References 1. Walsh, T., & Miaoulis, W. (2014). Privacy and security audits of electronic health information. Journal of AHIMA, 85(3), 54–59. 2. Greenhalgh, T., Hinder, S., Stramer, K., Bratan, T., & Russell, J. (2010). Adoption, non-­ adoption, and abandonment of a personal electronic health record: A case study of Health Space. BMJ, 341, c5814. 3. Van der Linden, H., Kalra, D., Hasman, A., & Talmon, J. (2009). Inter organizational future proof EHR systems: A review of the security and privacy related issues. International Journal of Medical Informatics, 78(3), 141–160. 4. Nakamoto, S. (2008, Dec). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/ bitcoin.pdf 5. Hyperledger Fabric. (2017). Hyperledger Fabric. https://www.hyperledger.org/ projects/fabric 6. Linux Foundation. (2017). Linux Foundation Hyperledger project. https://www. hyperledger.org/ 7. Ateniese, G., Fu, K., Green, M., & Hohenberger, S. (2006). Improved proxy re-encryption schemes with applications to secure distributed storage. ACM Transactions on Information and System Security (TISSEC), 9(1), 1–30. 8. Chen, L., Lee, W. K., Chang, C., Choo, K. K., & Zhang, N. (2019). Blockchain based searchable encryption for electronic health record sharing. Future Generation Computer Systems, 95, 420–429. 9. Akpan, N. (2016). Has health care hacking become an epidemic? [Online]. Available: https:// www.pbs.org/newshour/science/has-­health-­care-­hacking-­become-­an-­epidemic 10. U.S.  Department of Health. (2017). Breaches affecting 500 or more individuals [Online]. Available: https://www.hhs.gov/hipaa/for-­professionals/breach-­notification/breach-­reporting/ index.html 11. Smart, W. (2018). Lessons learned review of the WannaCry Ransomware Cyber Attack. [Online]. Available: https://www.england.nhs.uk/wp-­content/uploads/2018/02/lessons-­ learned-­review-­wannacryransomware-­cyber-­attack-­cio-­review.pdf 12. Morse, A. (2018). Investigation: WannaCry cyber-attack and the NHS [Online]. Available: https://www.nao.org.uk/wp-­content/uploads/2017/10/Investigation-­WannaCry-­cyber-­attack-­ and-­the-­NHS. pdf 13. Amofa, S., Sifah, E. B., Agyekum, K. O., Abla, S., Xia, Q., Gee, J. C., & Gao, J. B. (2018). A blockchain-based architecture framework for secure sharing of personal health data. 2018 IEEE 20th international conference on e-health networking, applications and services (Healthcom). Ostrava. 14. Zyskind, G., Nathan, O., & Pentland, A. (2015). Decentralizing privacy: using blockchain to protect personal data. 2015 IEEE security and privacy workshops, San Jose, USA, pp. 180–184. 15. Zheng, X., Mukkamala, R.  R., Vatrapu, R., & Ordieres-Mere, J. (2018). Blockchain-based personal health data sharing system using cloud storage. 2018 IEEE 20th international conference on e-health networking, applications and services (Healthcom). Ostrava. 16. Ekblaw, A., Azaria, A., Halamka, J. D., & Lippman, A. (2016). A case study for blockchain in Healthcare: "MedRec" prototype for electronic health records and medical research data. The 2016 IEEE of international conference on open and Big Data, Iscataway, USA, pp. 25–30.

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17. Mikula, T., & Jacobsen, R.  H. (2018). Identity and access management with blockchain in electronic healthcare records. 2018 21st Euromicro conference on digital system design (DSD). Prague, pp. 699–706. 18. Xue, T.  F., Fu, Q.  C., Wang, C., & Wang, X.  Y. (2017). A medical data sharing model via blockchain. Acta Automatica Sinica, 43(9), 1555–1562. 19. Cao, S., Zhang, G. X., Liu, P. F., Zhang, X. S., & Neri, F. (2019). Cloudassisted secure eHealth systems for tamper-proofing EHR via blockchain. Information Sciences, 485, 427–440. 20. Xia, Q., Sifah, E. B., Asamoah, K. O., Gao, J., Du, X. J., & Guizani, M. (2017). MeDShare: Trustless medical data sharing among cloud service providers via blockchain. IEEE Access, 5, 14757–14767. 21. Guo, R., Shi, H., Zhao, Q., & Zheng, D. (2018). Secure attribute-based signature scheme with multiple authorities for Blockchain in electronic health records systems. IEEE Access, 6, 11676–11686. 22. Xia, Q., Sifah, E. B., Smahi, A., Amofa, S., & Zhang, X. S. (2017). BBDS:Blockchainbased data sharing for electronic medical records in cloud environments. Information, 8(44), 1–16. 23. Yuan, Y., & Wang, F.  Y. (2016). Blockchain: The state of the art and future trends. Acta Automatica Sinica, 42(4), 481–494. 24. Sousa, J., Bessani, A., & Vukolic, M. (2018., no. Section 4). A byzantine Fault-Tolerant ordering service for the hyperledger fabric blockchain platform. In Proceedings - 48th annual IEEE/ IFIP international conference on dependable systems and networks, DSN 2018 (pp. 51–58). 25. Liu, J., Li, X., Ye, L., Zhang, H., Du, X., & Guizani, M. (2018). BPDS: A blockchain based privacy-preserving data sharing for electronic medical records. 2018 IEEE global communications conference (GLOBECOM). Abu Dhabi, United Arab Emirates. 26. Chen, Y., Ding, S., Zheng, X., Zheng, H. D., & Yang, S. L. (2019). Blockchain based medical records secure storage and medical service framework. Journal of Medical Systems. https:// doi.org/10.1007/s10916-­018-­1121-­4 27. Chen, L. X., Lee, W. K., Chang, C. C., Choo, K. R., & Zhang, N. (2019). Blockchain based searchable encryption for electronic health record sharing. Future Generation Computer Systems, 95, 420–429. 28. Shen, B., Guo, J., & Yang, Y. (2019). MedChain: Efficient healthcare data sharing via blockchain. Applied Sciences, 9(6), 1207. 29. Androulaki, E., et al. (2018). Hyperledger Fabric: A distributed operating system for permissioned blockchains 30. Huawei Technologies. (2017). Caliper: A Blockchain benchmark framework. https://github. com/Huawei-­OSG/caliper/ 31. Level DB. (2018). Level DB database. http://leveldb.org/ 32. Couch DB. (2018). Couch DB database. http://couchdb.apache.org/ 33. Rodrigues, J. P. C., Torre, I. D. L., Fernandez, G., & Coronado, M. L. (2013). Analysis of the security and privacy requirements of cloud-based electronic health records systems. Journal of Medical Internet Research, 15(8), 418–426.

A Framework for Low Energy Application Devices Using Blockchain-Enabled IoT in WSNs T. P. Latchoumi, Latha Parthiban, K. Balamurugan, K. Raja, J. Vijayaraj, and R. Parthiban

1 Introduction Communication must have historically evolved from Human-to-Human interactions (H2H) [1]. IoT is the collection of websites that can be considered a generic network with internal borders and considerations for standardized communication formats and interoperability [2]. IoT encompasses components across all domains, including connectivity, IT, information gathering, and merging. Personality is found in real and digital objects; the characteristics of the digital personality use intuitive platforms and are reliably organized in a data structure. Many IoT accelerators

T. P. Latchoumi · K. Raja Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India e-mail: [email protected] L. Parthiban Department of Computer Science, Pondicherry University, Pondicherry, Tamil Nadu, India e-mail: [email protected] K. Balamurugan Department of Mechanical Engineering, VFSTR (Deemed to be University), Guntur, Andhra Pradesh, India J. Vijayaraj Artificial and Data Science Department, SRM Easwari Engineering College, Ramapuram Campus, Chennai, Tamil Nadu, India e-mail: [email protected] R. Parthiban (*) Department of Chemical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_7

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focus on wireless communications. Enormous Machine-Type Communication (MTC) services are developed with the frequency of 5G standards which can address the large rise in integrated mobile networking that optimizes the issues [3]. Different types of communications can be used to address future IoT needs. Economic management technologies, smart communities, green monitoring, smart meters, and transportation employ the usage of LR-WPAN. Blockchain technology has been around for less than a decade, and it may still be in its infancy. However, blockchain technology faces several challenges in the fields of technology, business, education, and regulatory services. As WSN comprises many sensory nodes, storing the data in a central place leads to attacks which are overcome by blockchain by decentralizing the data from sensors.

2 Related Works Multiple Radio Access Networks (RANs) is the component of any portable communications network, which uses a radio accessibility system. This connects gadgets, including smartphones, desktops, and other wireless equipment, to the Crucial Networks (CNs). According to this standard, smartphones and other wireless gadgets are referred to as User Equipment (UE), Terminal Equipment (TE), Mobile Stations (MS), and so on. RAN capacity is usually provided through the silicon device which is present within all major grids and its mainstream devices [4]. The main objectives are cloud homogeneity and maintaining operational improvements, interference with avoidance, and optimizing capacity [5]. Even though some authors refer to non-registered frequency networks called homogeneous networks, wireless variability usually refers to authorized bandwidth channel systems. Additionally, whenever wireless cable technologies are combined, hybrid networks are created [6]. For example, the basic fiber-optic broadband transmission infrastructure includes some relatively small wireless subdomains. The creation of MAC and the techniques of networking layers in the modern infrastructures of dispersal bandwidth lead to power minimization. In addition, interoperability refers to the integration between multiple wireless networks [7]. Telecommunications are usually programmed alternately in both cases, making it possible to modulate information using multiple algorithms. Supports Zigbee and Wireless Zero Power connectivity needs IEEE 11073 requirements and Collaborative Conversation (CC) for a data connectivity methodology. Collaborative interaction improves workforce expertise and generally drives better results. On the other hand, larger access connections make use of the underlying disseminating element, in which several processes are also assessed and sent to a single text [8, 9]. TDMA sequence is the mechanism to access shared network channels. Optimized Long Range (LoRa) with NB-IoT methods are designed for mobile innovations adapted to a particular need of M2M and IoT gadgets [10]. However, most of these IoT gadgets, particularly those used in more intelligent cities and industries, do not

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require similar performance and capacity as consumers’ mobile phones. They demand the long-term stability of regular LTE mobile telephone connections [11]. The Global System for Mobile Association (GSMA) created a variety of Low Power Wide Area Networks (LPWAN) guidelines in early 2015 which will assist networks and carriers to satisfy their particular price, availability, and energy usage criteria with IoT operations. LPWA technologies rose into popularity being preferable for IoT solutions [12]. Similarly, its LoR association has established support for other new LPWAN technologies, providing alternative cellular connectivity for IoT workloads that require minimal throughput and latency. A variety of innovative IoT gadgets and applications are designed. NB-IoT increases the power consumption of gadgets, system throughput, and frequency saving. Due to the wide variety of scenarios, a lifetime of more than 10  years could be provided. Broad regional scope, deeper interiors, and micro gadget sophistication are key requirements for single physical level communications and paths. These NB-IoT circuits should be equivalent in price to early GSM/GPRS components. GSM is a worldwide cellular telecommunications network. GPRS is a packet-based information transfer and storage technology based on a GSM network. Another primary distinction between GPRS and GSM is that GSM is a network-changing technology, while GPRS is a packet-changing technology. GSM could simply send information over SMS and could be “truly accessible” and “volume-loaded.” GPRS has obvious advantages in the incumbent and the support of information providers, as well as the ability to make more effective use of mobile connectivity connection resources when compared to GSM.  It is well suited to the occasional transmission of acyclic information, small amounts of information, and the occasional transmission of high-level information. Fast communication speed; fast capacity utilization; fast connection period; consistent availability; support for IP connection and X.25 protocols; low costs are some of the technological advantages of GPRS. GPRS has several benefits, including inexpensive communication fees, fast information transfer, and high efficiency. GPRS is a connected information transfer system that can run numerous gadgets at the same time. Industry 4.0 [13] company incorporated a specific item surface to gather site research readings, such as knowledge, utilizing Bluetooth Low Energy (BLE), which is a mobile local region connectivity innovation engineered and advertised by its Bluetooth Special Interest Groups (Bluetooth SIG).The authors in [14] analyzed emerging uses in medicine, wellness, safety, and the residential media industry. This is not compatible with traditional Bluetooth and had minimal interoperability; however, it does support BR/EDR.  The Bluetooth Classical wireless, commonly known as Bluetooth Base Percentage Data Rate (BR/EDR), is a reduced broadcaster that transmits information across 79 bands within an unregulated 2.4  GHz commercial, academic, and medicinal frequency spectrum. Bluetooth Classical is primarily used to provide wireless music transmission which has become the default wireless technology underpinning wireless devices, headphones, and multimedia devices. It supports point-to-point devices. Information transmission capabilities, such as portable printers, are now possible with a RAN

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which is made to run on less electricity [15, 16]. The Bluetooth LE transmitter transmits information across 40 streams to on 2.4 GHz unregulated ISM frequencies region, giving manufacturers a lot of freedom to create devices that match the specific networking needs of the industry. Bluetooth LE offers diverse communication architecture, including spot, broadcast, and more recently, mesh, allowing Bluetooth for reliability, the huge network of devices. Although Bluetooth LE is originally developed for gadget connectivity, this is currently widely used as a gadget placement solution to meet the growing requirement of interior location applications. Bluetooth LE also provides Bluetooth Directions location and, ultimately, high distance measurements, in addition to the basic appearance and proximity features [17, 18]. In contrast to traditional Bluetooth, Bluetooth Low Power is designed to use less energy and expenditure while retaining comparable communication distance. The reduced wireless voltage is supported by the operating platforms of smartphones such as Windows 10, Android, Linux, BlackBerry, iOS, macOS, Windows 8, and Windows Phone. However, there are additional LoRa technologies under consideration. Simultaneous connections LTE/5G LTE are connected using 4G and 5G wireless telecommunications standards for portable gadgets, including mobile phones, tablets, and portable routers. VoLTE (Voices through Long-Term Evolutionary) is compatible to 4G and 5G LTE smartphones. VoLTE sends the audio in a “phase shift,” which significantly improves the call clarity, volumes, and speed of the information. WLAN is the wireless computing system that connects two or multiple computers through wireless communications that can establish the LAN inside a constrained region including a house, schools, computer lab, university, and company buildings. This allows customers to travel across a region while still related to its connection. Any WLAN may also permit access to the rest of its network via a gateway [19]. The most widely deployed computing networks in the world are WLAN, frequently referred to as Wi-Fi, which is a Wi-Fi Association brand. It is utilized by residential and smaller business networking to connect laptops, scanners, cell phones, Web TVs, and games gadgets online via a wireless gateway. Customers can use transportable mobile gadgets and can browse the web via spots offered by computers in eateries, tea stores, motels, museums, and airplanes. Wireless cell phones are widely used for personal use in modern literature. Blockchain can be described as a decentralized method by which any type of data (including, but not limited to, financial transactions, securities, or asset orders) can be permanently recorded in an encrypted and irreversible ledger [20]. The first major use of blockchain technology came from the development of Bitcoin. This is a digital encrypted currency, launched in 2009. The proposed work suggests physical integration connectivity with a group architecture which varies depending on previous studies in several ways.

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3 Proposed System The proposed hybrid network combines the LR-WPAN terminals with LPWAN, being wireless network architecture around the world that brings together short-­ range plug-in devices. LPWANs, originally designed mainly for M2M computers, but also IoT networking, are less expensive and use less electricity than typical cellular channels. The sensors support a greater number of network-related gadgets over a long wireless distance. Assuming speeds of around 200 Kbps, LPWANs could handle message lengths ranging from 10 to 1000 gigabytes. The extensive distance of any LPWAN can range from 2 to 1000  km, depending on the architecture. Numerous WLAN terminals are connected directly to a central accessible station, similar to Wi-Fi. Along with the updated hardware that supports both NB-IoT and IEEE 802.15.4g. Figure 1 presents the proposed novel integrated network structure. Each FFD gadget can function as a network coordinate, providing synchronizing capabilities with additional units, including directors. Clustering is arranged hierarchically, with single CH and multiple EDs connecting behind them. The overall routing of frequency data packets is reduced, and the consumption of cable network resources improves, thus the overall latency decreases. Layer 1 (L1) CHs not only provide communication but also transmit messages. When compared to the previous LR-WPAN, the improved topology reduces the

Fig. 1  Subsystem topologies networks infrastructure

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overall quantity of connected gateways. The merged channel provides a technique that translates one IP addressing field onto others through changing networking location metadata within the package. IP headers are being routed via a particular congestion forwarding mechanism. Whenever any networking is switched off, its downstream online services providers are switched. Although the address field of its channel would never be routed, the approach is used effectively to prevent this requirement and must give new addresses to all servers. Given the context of running out of the IPv4 network, it has been increasingly important and a necessary method to save global addressing capacity. The World Wide Web IPs of any gateway can be used through its complete personal system.

3.1 Control Services for the Backbone WSN Control functions are supplementary functions that facilitate the control and analysis of program activities. It includes systems created which ensure compliance with the performance standards of commercial applications. • Solutions for Entries The applicants’ facilities are the resources required by the individual programs. Software functions provide outputs into software architecture and actuation-like offering, which operates systems components. When carrying out relevant software operations, several different components are incorporated. • Modeling of Proximal Services The programs should become aware of real relationship of each network within the overall framework for any region under consideration. For any smart architecture situation, networks must be adapted to a tower design. In contrast, the frame relay has little information about the components of its cable network. As a result of mitigating the same problem because the address space is geographically agnostic, resource identification based on geography must be developed. They would preferably preserve information linkage among each datatype based on geographical placement which includes their source as well as other supplied capabilities. The centralized geolocation mapping system architecture keeps track of the names within any centralized register based on branch architecture containing names. Those things that must be addressed within such intelligent infrastructural systems may be depicted using any hierarchy organization; therefore, preserving such branch architecture becomes reasonable. • Logistics The majority of creations are economic but also essential to safety, the preparations should be done appropriately to meet these requirements. In part, they have

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created new tools that facilitate system administration and oversight. Let us start by defining the condition updating application that delivers sensors measurements, but also every datatype state into one centralized register. In particular, every node offers another function that can be used to determine whether a particular node is present. In addition, each node keeps track of its state, but also updates things, if necessary. • Services of Proxies These applications, but also infrastructure installations, are capabilities of the Common Offer Acceptance Portal (COAP) that cannot be accessed through conventional Internet interfaces. As a result, they created another gateway application that provides relaxing communication through Hyper Text Transfer Protocol (HTTP) that uses the COAP server throughout the order that provides those capabilities on our Internet. Designers provide relaxing administration protocols that could be used to build the required applications based on this platform. Such programs are easily transferable between many machines, but also smartphones, owing to their compliance using common Internet standards.

3.2 Control Services for the WSN for Occupants’ Identification The work could implement complicated methods, enabling customer detection, monitoring, and identifying, researchers employ the basic method within software that can recognize a person through two aspects: • Bluetooth Our initial method toward identifying, but also monitoring, any person is using leverage detecting characteristics. Wireless research sensors allow users to locate and monitor wireless-capable gadgets within close vicinity. The scanner’s algorithms search seeking Peripherals tells the computer when one is found. They can create one connection between distinct MAC identifiers and particular individuals in that manner. Using one 5 dBi antennae, this scanner’s bandwidth enables researchers to detect objects within a couple of hundred yards from each detector. • NFC An NFC microchip may trigger any contactless payment at any range up to 7 cm. Touching any NFC cards over its sensors is all that takes to communicate the smart scanner, which sends information at a top range of 424 kbps. NFC is intrinsically secured, given very limited distance frequency communications. Users may send information through these gateways using 1 of 2 techniques: direct USB port link or Transceiver communications (Table 1; Fig. 2).

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Table 1  Clusters identities with CH locations Cluster E F G H

Level 01 02 02 01

L1-bits 001 010 001 010

L2-bits 0000 0001 0001 0000

ID 0x50 0x51 0x31 0x50

CH address 0x3000 0x5100 0x3100 0x5000

Parent group – 0x50 0x30 –

Some ED address E1: 0x5101 E5: 0x5105 F3: 0x3003 F4: 0x3004 G2: 0x5002 G6: 0x501A H6: 0x3106 H9: 0x3019

9 8 7 6

MC CE0 Level

5

RC CE0 Level

MC CE1 Level2

RC CE1 Level2

4

MC IEEE 802.15.4 RC IEEE 802.15.42

3 2 1 0

0

100

500

1000

Fig. 2  Periodic batteries deterioration

4 Results and Discussions Any networking terminating (NT) device (formerly known as networking terminating devices) links patient’s information rather than telephone hardware with company’s connection that enters business structure. This NT gadget connects the regional circuit onto termination apparatus (TE) and terminal adapter (TA) devices. NT1 is a type of networking terminating device employed for case events having ISDN Base Rates. As seen from the results, CE1 is high, while CE0 is the low, as shown in Tables 2 and 3. The impacts from these Dynamic Link Selection (DLS) is an equipment that connects any laptop rather than any telephone network, so that the order can offer modern Digital Subscription Lines (DSL) services allowing Web access. It is also known as DSL speed. Algorithms are then investigated (Figs. 3 and 4). Figures 5

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Table 2  Setting up selected NB-IoT connection Parameter Extended timer Active timer CE levels

CE0 level CE1 level UL transmission block time Windows per eDRX stage

Value 13.6 days 1.50 min UL: 3 repetitions

DL: 5 repetitions

UL: 25 repetitions 9–4 mins

Table 3 Electricity utilization in NB-IoT

State 1. Idle 2. Transmission 3. Reception 4. PSM

DL: 65 repetitions

Power consumption (mW) 20 754 198 16

18 16 14 12

Predefined CE0 level

10

8

Random CE0 level

6 4 2 0

0

Fig. 3  Energy deterioration

5

10

20

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100 90 80 70 Predefined CE0 level

60 50

Random CE0 level

40 30 20 10 0

0

100

500

1000

Fig. 4  Frequency contradiction on throughput 10

9 8 7

DLS off - Random

6

DLS off Predefined

5

DLS on - Random2 DLS on Predefined3

4 3 2 1 0

0

100

(a) 500

1000

Fig. 5  DLS off methods

and 6 are operational consequences from DLS algorithms. The communications channel is crucial toward implementing any Intelligent Town idea. 3G (third generations), LTE, Wi-Fi (Bluetooth frequency), WiMAX (global connectivity enabling microwaves access), Bluetooth, CATV (cord video), and satellites telecommunications are examples of current interaction technologies used within Intelligent Town infrastructures. The major goal is to link various kinds of items (detectors including IoTs) that may assist to make resident lives easier and healthier. Telecommunication solutions within the same household domains, for instance, interconnect telephones

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10 (a) 9

DLS off - Random

8 7

DLS off Predefined

6

DLS on - Random2

5

DLS on Predefined3

4 3 2 1 0

0

100

(B) 500

1000

Fig. 6  Rechargeable lifespan and transmission failure frequency

hardware via an online network. Internet and telecommunications technologies are merged within this government-industry to achieve a stronger management structure. Communications technology may be employed within the healthcare industry to integrate medical information, medicine, and also patient’s geolocation at any distant place, resulting in any Intelligent Healthcare network.

5 Conclusions This research analyzes common problems with mobile communications systems, including scaling, that is addressed by combining LR-WPANs with newer LPWANs. Integrating blockchain with WSN decentralizes the sensor data, thereby improving security and trust. Such wireless convergence also paves the way for the integration of 5G format with the current infrastructure. To reduce electricity consumption and to improve the overall lifespan of energy with CHs, another DLS algorithm for uninformed research is proposed and validated.

References 1. Jaiswal, K., & Anand, V. (2021). A survey on IoT-based healthcare system: Potential applications, issues, and challenges. In Advances in biomedical engineering and technology (pp. 459–471). Springer. 2. Landaluce, H., Arjona, L., Perales, A., Falcone, F., Angulo, I., & Muralter, F. (2020). A review of IoT sensing applications and challenges using RFID and wireless sensor networks. Sensors, 20(9), 2495.

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3. Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Al Kurdi, B., & Akour, I. A. (2021). IoT for smart cities: Machine learning approaches in smart healthcare—A review. Future Internet, 13(8), 218. 4. Gulati, K., Boddu, R. S., Kapila, D., Bangalore, S. L., Chandnani, N., & Saravanan, G. (2021). A review paper on wireless sensor network techniques in the Internet of Things (IoT). Materials Today: Proceedings, 51, 161–165. 5. Ezhilarasi, T. P., Dilip, G., Latchoumi, T. P., & Balamurugan, K. (2020). UIP—A smart web application to manage network environments. In Proceedings of the third international conference on computational intelligence and informatics (pp. 97–108). Springer. 6. Menon, P.  C., Rani, B.  K., Kumar, K., & Gupta, V. (2021). An effective OS–DPLL design for reducing power dissipation in an IoT application. Journal of Ambient Intelligence and Humanized Computing, 1–9. https://doi.org/10.1007/s12652-­021-­03016-­z 7. Ullo, S. L., & Sinha, G. R. (2020). Advances in smart environment monitoring systems using IoT and sensors. Sensors, 20(11), 3113. 8. Hajjaji, Y., Boulila, W., Farah, I. R., Romdhani, I., & Hussain, A. (2021). Big data and IoT-­ based applications in smart environments: A systematic review. Computer Science Review, 39, 100318. 9. Sun, W., Tang, M., Zhang, L., Huo, Z., & Shu, L. (2020). A survey of using swarm intelligence algorithms in IoT. Sensors, 20(5), 1420. 10. Kris, D., Nakas, C., Vomvas, D., & Koulouras, G. (2020). Applications of wireless sensor networks: An up-to-date survey. Applied System Innovation, 3(1), 14. 11. Chéour, R., Khriji, S., Götz, M., Abid, M., & Kanoun, O. (2020). Accurate dynamic voltage and frequency scaling measurement for low-power microcontrollers in wireless sensor networks. Microelectronics Journal, 105, 104874. 12. Baraneetharan, E. (2020). Role of machine learning algorithms intrusion detection in WSNs: A survey. Journal of Information Technology, 2(03), 161–173. 13. Petrol, R., Loscrí, V., & Mitton, N. (2014). Towards a smart city based on cloud of things. In Proceedings of the 2014 ACM international workshop on Wireless and mobile technologies for smart cities  – WiMobCity ’14 (pp.  61–66). ACM Press. https://doi. org/10.1145/2633661.2633667 14. Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, Series B, 30, 205–247. 15. Shafer, G. (1976). A mathematical theory of evidence. Princeton University Press. 16. Ali, T., Dutta, P., & Boruah, H. (2012). A new combination rule for conflict problem of Dempster-Shafer evidence theory. International Journal of Energy, Information and Communications, 3, 35–40. 17. Yoon, S., Ryu, C., & Suh, D. H. (2014). A novel way of BPA calculation for context inference using sensor signals. International Journal of Smart Home, 8, 1–8. https://doi.org/10.14257/ ijsh.2014.8.1.01 18. Sekaran, K., Rajakumar, R., Dinesh, K., Rajkumar, Y., Latchoumi, T. P., Kadry, S., & Lim, S. (2020). An energy-efficient cluster head selection in wireless sensor network using a grey wolf optimization algorithm. Telkomnika, 18(6), 2822–2833. 19. Latchoumi, T.  P., & Parthiban, L. (2022). Quasi oppositional dragonfly algorithm for load balancing in a cloud computing environment. Wireless Personal Communications, 122(3), 2639–2656. 20. Gupta, V. (2017). A brief history of blockchain. https://hbr.org/2017/02/a-­brief-­history-­of-­ blockchain. Accessed May 4, 2021.

Implementation of Real-Time Water Quality Monitoring Based on Java and Internet of Things Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Said Benkirane, and Hiba Asri

1 Introduction Water is vital to well-being, as it significantly influences and determines the overall wellness and healthy lifestyle of people. It constitutes a valuable natural substance required for the basic daily functioning of individuals, including nutrition, respiration, navigation, drainage, and reproduction, among others. Furthermore, it constitutes a framework for survival and a basic factor in the establishment of human livelihood [1]. Typically, the task of conducting water quality assessments necessitates carrying out water samples immediately to the appropriate laboratory where they will be examined and tested from day to day. Yet, such a common procedure is extremely hard to check, notably in cases where the actual location of the water source is very distant from the testing site. On the other hand, the rollout of the so-called Internet

M. Azrour (*) IDMS Team, Department of Computer Science, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University, Meknes, Morocco e-mail: [email protected] J. Mabrouki Laboratory of Spectroscopy, Molecular Modelling, Materials, Nanomaterial, Water and Environment, CERNE2D, Mohammed V, University in Rabat, Faculty of Science, Rabat, Morocco A. Guezzaz · S. Benkirane Technology Higher School, Cadi Ayyad University, Marrakesh, Morocco H. Asri LIMA Laboratory, Computer Sciences Department, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_8

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of Things (IoT) has been influencing completely ordinary activities [2–6]. Henceforth, it has extensively been incorporated in a variety of different sectors, including transportation [7–9], smart city [10–12], health [13–15], environment [16–18], and so on [6, 19–26]. The area of water surveillance is officially touched too, although some innovative solutions have been developed. As a result, there are many innovative proposals to check, supervise, and control the water quality. Thanks to various specific sensors and devices, both water scientists and controllers have the ability to gather numerous water metrics in real time. Although there are systems that provide the captured data to users over a range of network-based technologies, the remainders are restricted to displaying the collected information on a simple local screen. Typically, by using captors for detecting and gathering of the water quality related parameters, the water administrators can check the process of water handling, considering the current situation of the water. Meanwhile, an innovative outlook totally distinct from previous methodologies has been delivered by the Internet of Things (IoT): this new approach is presented through a system which may be interconnected to the network and which may incorporate smart and intelligent policies. Accordingly, it is possible to display the relevant water properties and to interact within that system in real time. Nevertheless, the development of a core server is required in order to achieve the synchronization of various users’ transactions. On the other hand, an embedded solution [27–29] is a stand-alone system which combines two independent components: electronics and computing. Such a system is designed to carry out a well-defined mission while processing any input received from its surroundings. Essentially, the embedded system is an important area of research as it provides the opportunity to schedule and personalize the device based on the requirements of the user. Moreover, it provides real-time, online, and remote application solutions. Hence, it enjoys a very strong role in the development of Web-based embedded solutions. In current research work, an Embedded Web Server for Water Quality Monitoring was developed. The developed webserver is in charge of collecting and displaying the water metrics through charts and graphics. Hence, it allows water administrators to interface in real time with the implemented system. The remaining part of this chapter is structured as following. In the second section, we summarize some recent related works. The third section describes the proposed system step by step. In the fourth section, the achieved outcomes and discussions are highlighted. Finally, the final section concludes this chapter.

2 Literature Survey For the purpose of monitoring a household from anywhere using an interface that can be accessed from any navigator, Albuquerque et al. [30] designed an embedded webserver application known as “MyHOME.” The designed system is based on the

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Arduino card and enables users to manage equipment, supervise the status of the building, and personalize the functionalities. In view of the major problem which is caused by air quality on all beings, Senthilkumar et al. [31] developed a new intelligent embedded system for monitoring the air pollution. The system is constructed on the bases of the Internet of Things technologies. Especially, it is micro-processor-based hardware taking the advantages of IoT techniques. The conceived system is made ready to collect the measurements of various gases and other physical metrics such as humidity and temperature. In the farming field, Snoussi et al. [32] designed an embedded web server for real-time monitoring of greenhouses in 2017. Authors conceived a quite basic greenhouse monitoring and control system prototype which is based on the Internet of Things approach. Moreover, they employed some of the newly emerged web technologies such as JavaScript frameworks in order to extend the web application more extensively. The designed web server enables farmers to input some environmental details about the greenhouse and then personalize the climate requirements according to the needs of the corps. In addition, Almeida et al. [33] developed an embedded server to automatically control the irrigation of the corpses based on the Internet of Things. Such a solution was developed to provide farmers with the possibility to monitor crops. Actuators and sensors that are installed in the field are capable of exchanging data with an embedded server. The system is based on the Intel Edison device as hardware and MQTT as networking protocol. Besides, to interact with the overall system, the embedded server is provided with a web interface which can be accessed via a web browser. On the industrial area, the contemporary manufacturing industry development demands an industrial independent controlling system. Consequently, Telagam et al. [34] suggested a smart sensor network for real-time sensing of temperature and humidity in the industry. This proposed system is based on IoT and utilizes the so-called virtual instrumentation server. After measured actual values, the outcomes are displayed on a specific web page that the administrator may access after inputting their username and password.

3 System Overview In order to implement a new water quality embedded system, various materials and software were integrated. Besides, many codes were implemented through different programming languages (Java, Arduino, PHP, etc.). The global architecture of the system can be visualized in Fig. 1. As one may observe, the architecture is made up of multiple components, which include the sensor module, the actuator module, the wireless module, the server applications, and the client applications. On the server side, the functionalities and operation are mainly based on the two most significant applications, namely, the web application and the database.

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Fig. 1  The architecture of designed system

3.1 Sensors The designing of multiple detection systems for water quality monitoring worked with various sensors, where each sensor helped in collecting the required data and performing some analysis tasks within the system. In order to sense the common water parameters, the sensing system should be deposited on the stream, river, reservoir, or other water source continually. When monitoring the water parameters, several external conditions such as environment and temperature influence the measurement. Subsequently, they should be considered. According to the initial experiments, certain metrics are increasing abnormally. The system design with multiple sensors was made as a prototype and was thoroughly evaluated at the laboratory to ensure that the system is functioning effectively. The sensor module consists of physical devices which allow the data gathering from the surrounding environments in which the system is situated (Fig.  2). Basically, sensors are able to react to some kind of specific physical and chemical trigger such as light, noise, moisture, temperature, movement, pH value, and conductivity. As demonstrated in Fig.  2, the sensor module within the designed system consists of four sensors, specifically a temperature sensor, a pH sensor, a conductivity sensor, and a turbidity sensor. Consequently, they enable our system to measure four values and send to the server via the wireless module in real time. Moreover, in cases in which a user wishes to record other parameters, they can connect additional sensors to the system.

3.2 Arduino Arduino consists of an electronic programming device which has its own processing unit and its own internal memory. In this board, it is possible to plug in several sensors such as humidity, temperature, light, or vibration sensors. Besides, actuators

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Fig. 2  Connected sensors and actuator with Arduino

can usually be linked to this circuit board. For the system designed in this chapter, the Arduino UNO was used for connecting the sensors and actuators with the system (Fig. 2). There is also a Wi-Fi module connected to Arduino in order to interconnect both the sensor and actuator modules with the server.

3.3 Database The purpose of the majority of information systems on the market consists in analyzing data and generating knowledge which can be useful for making decisions. To achieve this goal, it is necessary for the system to have the ability to handle the data, contextualizing it as well as delivering summary and analytical techniques. To this end, a specific database is designed. In addition, the databases may be structured in various manners and can therefore adopt multiple structures. Nevertheless, the most frequent structure is that of the relational one. Hence, within the present architecture, the MYSQL database was used, in which the data is arranged in three tables. The first table is named measure; it is designed to store the measured results. The

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second table is labeled user; it holds the user’s information such as login and password. Finally, the third table is entitled location. It is used to record information about the sites where the system is established.

3.4 Java Application The user application is programmed under the Java language using the graphical interface based on the swing API. Besides, it is designed according to a client– server framework. The user can access this application by using connected laptops to the Internet. The designed application has as basic roles to display measured water parameters either in real time or historical ones. By using this application, the user will be able to easily see the results of the measurement by means of a graphical representation of the gathered water metrics or by displaying statistics on the water quality.

3.5 Communication Protocols The adoption of a network protocol is mandatory as the proposed architecture is based on the classical client–server model, which means that the client opens a connection to make a request and then waits to receive a response from the server. For these reasons, the Hyper Text Transfer Protocol (HTTP) is selected. HTTP is a standard application layer protocol for the transmission of hypermedia files, such as HTML, XML, and JSON It was designed for the communication between the browsers and the web servers; however, it could be used for different applications as an alternative (Fig. 3).

4 Results and Discussion During the implementation of the new system, all necessary programming features were accomplished and then the validation experiment tests were conducted. As previously discussed, the overall focus of this system is to provide to the final user the capability to supervise and monitor the measurement parameters of the water in real time. Furthermore, the system enables instantaneous control of remotely installed actuators. In order to gain access to the online application, users have to prove their authenticity. Typically, the user has to input their username and password on the login page. With this authentication procedure, it is possible to identify legitimate users from non-authorized ones. Such distinction holds a great deal of trust and privacy

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Fig. 3  Architecture layers of proposed system

for users and their private information, because only the profile owner is supposed to have the valid login credentials. In fact, the communication efficiency between the diverse user requests and the actioner module has been verified under real-world conditions. For this purpose, the user can get access using different systems, including a laptop, a personal computer, or tablet. Laboratory experiments have been performed over an extended time period to ensure that the system performs as expected. The findings demonstrate that the various parameters for detecting water parameters yield clear results than the manual or conventional system calibration (Figs. 4 and 5). The usage of designed for sensing water quality allow us to reach the fixed goals adequately. Furthermore, the implementation of a smart system on the programming of the microcontroller supports the precision of the decision on the results. On the home page of the web application (Fig. 4), a dashboard of live water metrics is displayed. Thus, the user can visualize four parameters measured in real time. The four parameters are temperature, turbidity, alkalinity (pH), and conductivity. Due to their importance in defining the quality of the water, the application is focused on only these four parameters. Nevertheless, it is possible to upgrade this application with other parameters. Advanced charts are employed here to support users in viewing the real time measured values and to provide a nice design and look to the application.

5 Conclusion In this chapter, an embedded server system is effectively introduced and evaluated. The proposed system is designed using IoT-based technology; it enables remote and real-time monitoring and controlling of water quality. The system consists of sensors that can sense water parameters, including temperature, turbidity, conductivity, and

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Fig. 4  Dashboard of real-time water measurement

Fig. 5  The variation of pH during a 24 h

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pH. It can be also connected to different actuators. In addition, the operator can view the captured data through a developed application at any location. Therefore, he can visualize the data as numerical values or specific charts. Furthermore, the implementation of a smart system on the programming of the microcontroller supports the precision of the decision on the results.

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Blockchain-Based Infrastructure for Precision Agriculture A. Packialatha, S. Vijitha, A. Sangeetha, and K. Seetha Lakshmi

1 Introduction In agriculture, distributed ledgers and smart contracts, which are part of blockchain technology, have the potential to eliminate counterfeits in agri-food production chains, resulting in healthy and balanced products being made available in the market, establishing trust between industry leaders, and empowering a better quality of life on a global scale [12]. Consumer demand for digital services suited to their unique needs develops as the human population grows (Fig. 1). According to Sharma et  al. [18], blockchain technology can be used for agriculture and farm management software to improvise farm financial performance and meet the growing demand for food. The agriculture industry must undergo technological change in order to: • Offer higher quality foods technology solutions to meet evolving customer expectations, while also satisfying the need of a growing population. • Reduce your carbon impact with the help of eco-friendly agriculture strategies. • Lower the cost of agriculture’s supply chain. A. Packialatha (*) · S. Vijitha Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India e-mail: [email protected]; [email protected] A. Sangeetha RMK College of Engineering and Technology, Chennai, Tamil Nadu, India e-mail: [email protected] K. Seetha Lakshmi Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_9

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Fig. 1  Demand for information about food

Fig. 2  Precision agriculture system

• Strictly cohere to sanitary and phytosanitary guidelines. • Help small farms, individual farmers, and food producers maintain profitable agricultural and agribusiness operations and make more money. Sensing with IoT [17], agribusiness organisations, topographical intelligence systems, and yield managing software package, as well as data management, in-season decision-making, and grid specimen technologies, are allowing agricultural businesses to improve their nourishment creation and stock chain organisation. Consumption of food is amplified with new issues, such as fake goods, which represent a threat to nutrition stock systems on several stages. Due to inefficiency as well as lack of transparency, agronomists and customers are at a drawback [16]. Finally, blockchain farming and distributed ledger technology (DLT) have the potential to raise the efficacy, clarity, and assurance in the cultivated source chain (Fig. 2). Blockchain for cultivated source chain can authorise entire marketplace participants by constructing reliable connections.

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GIS yield maps 1

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Stress detection algorithm (water, nutrient, weed, insect) 2

Precision agricultural application cycle

Vegetation 5

Prescription plan (water, fertilizer, herbicide, pesticide) 3

VRT (tractor) 4

Fig. 3  Application cycle of precision agriculture

Precision agriculture is defined as “the use of cutting-edge resource technology to access and examine multiple resources with big dimensional and progressive determination for decision-making and crop production operations.” Figure 3 shows a precision agricultural cycle. GIS, or geographic information system; VRT, or variable rate technology, is a technology used in agriculture to increase output [11] and sustainability. It is becoming increasingly crucial to use data and information to raise production and sustainability. Data and communiqué technology improvise the efficacy for collecting the resources, stowage, scrutiny, and convention in agriculture [25]. It permits agrarian experts and cultivating societies to simply attain up-to-date date and make better decisions in their daily farming operations [7]. Sensors are mounted on specialised stands for practice in technical research, breeding, prototype growth, ecological observing, and accuracy cultivation, enabling for proper sensor functioning to capture phenotypical characteristics of floras or other biotic substances. Implementing blockchain technology in agriculture presents a number of challenges, as well as potential solutions: Cultivation source chain supervision is an added difficulty than other source chains, while agronomic output is affected by uncontrollable factors, including weather, pests, and illnesses. Financial transactions are slower and human labour is frequently required in the agricultural supply chain due to the lack of traceability. Additionally, imitations might appear at any point in the source chain, posing a threat to all shareholders in the organisation, as well as governments and consumers.

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1.1 Why Is Using Blockchain Technology a Better Approach for Precision Agriculture? The quality of the seed, the crop growth, and even the path a plant takes after leaving the farm can all be tracked using blockchain technology. With the help of this information, supply chains might be more transparent and problems involving unethical and illegal production could be diminished.

2 Precision Farming Components 2.1 Internet of Things (IoT) The connectivity of IoT interacts with items equipped with sensors. It allows physical objects to be detected and operated from afar, bridging the gap between the actual creation and virtual systems. Pumps, sheds, tractors, weather stations, and computers are just a few of the equipment that may be remotely supervised and measured in physical period. Smart wearables and linked gadgets, as well as automated machinery and autonomous cars, have all been made possible by the Internet of Things [8]. Despite its rarity, precision agriculture is one of the most essential IoT applications. These new technology advancements to improve harvests might save lives as our globe approaches a food catastrophe. Natural circumstances must be predicted and responded to as fast as possible in order for farming to be efficient and profitable [8]. In Fig. 4, the Internet of Things is driving this application, which is a new approach in farming. Precision agriculture and data collecting quality may both benefit from IoT technology. The Internet of Things is used to collect data from sensors that detect various factors such as soil moisture and humidity and to remotely monitor them via developed mobile applications to take decisions [2]. Merits and Demerits Improvising flexibility of processes stands the major benefits of engaging IoT in cultivation [2]. Agronomists can quickly acknowledge to any substantial variation in meteorological conditions, moistness, air value, or the health of any yield or soil in the field using instantaneous monitoring and prediction systems. It reduces the amount of human labour and labour and helps save time and effort. Lack of infrastructure is one of the most significant challenges that farmers encounter when integrating IoT in agriculture [9]. Farmers can appreciate the IoT technique, but they are unable to obtain assistance from it as there is no proper network structure. Farms are located in rural places with limited access to the Internet. Unemployment has increased. Concerns regarding privacy have intensified in recent years [19].

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Fig. 4  Internet of Things (IoT)

2.2 Grid Specimen The method of gathering tiny samples at similar intervals throughout a certain area is known as grid specimen. Soil samples are collected in a grid system at consistently spaced locations. Grid dimensions vary from one to five acres, with minor grids producing the best results. For each one-acre grid cell, collect at least five subsamples [13]. For grid cells among 2.5 and 5 acres, gather 8–10 subsamples. In Fig. 5, soil sampling aids precision agricultural producers in developing management zones and prescription maps, improving the accuracy of rate and placement of essential inputs (primarily fertilisers and lime to adjust pH [1]). When collecting soil samples, producers and managers frequently employ Grid or Zone Sampling methodologies. Grid sampling indicates the distribution of nutrients over a field. We acquire a better knowledge of the nutrients available by collecting more soil samples from a field [13]. We can be sure that the fertiliser expenditures are being used wisely if we know what nutrients are accessible. In areas with high nutrient levels, a grid specimen reduces fertiliser overapplication. Grid sampling enables fertiliser augmentation in areas where nutrient levels are low. A fertiliser programme with grid soil sampling and variable rate application of lime, gypsum, and fertilisers gains control and security [20]. Merits and Demerits Measures the nutrients left in your field after harvesting. It shows you which nutrients in a field’s soil are insufficient or abundant [1]. Assists in establishing the most

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Fig. 5  Grid specimen

effective fertiliser method for growing or maintaining production the next year. Grids are appealing because they are simple to use and give better spatial data than whole-field soil sample [13]. Grid sampling has a number of drawbacks, the first of which is an improved chance of influence. If the bulk of grid specimen fall on a single soil type, for example, the grid miniature might diminish the underlying variety in the ground [20]. Grid sampling can be inefficient as well.

2.3 Topographical Information System (TIS) In agriculture, TIS is all about assessing the land, plotting field data on a map, and putting that data to good use. Precision farming, which is based on TIS, allows agronomists to make informed choices and speculate in order to maximise the value of each acre, while reducing ecological effect [5]. Satellites, aircraft, drones, and sensors, to mention a few devices, are used in geospatial technology in agriculture. These technologies are utilised to make images and correlate to graphs and resources [5]. As a consequence (Fig. 6), a map with information is obtained about crop location and health, geography, soil type, fertiliser, and other factors. A topographical

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151 Georeferenced data collection

Field records Raw data Leaf analysis Soil analysis Yield Fertilizer use

Agronomy Database System

Analytical laboratory

Data analysis

Analytical reports

Query

Geographic Information System

Base maps Roads Drains Rivers Creeks Satellite imagery

Spatial analysis

Estate maps

Fig. 6  Topographical information system (TIS)

information system (TIS) is a tool for collecting and analysing geographic data to aid in agricultural development. In Li, S., TIS can analyse soil data and determine which yields should be planted and how to maintain soil nutrition current so that plants get the greatest benefit. TIS in agriculture allows for better land resource management, which leads to increased production and lower costs for farmers. Merits and Demerits It has the ability to improve organisational cohesion. TIS would therefore combine software, technology, and resources in order to collect, analyse, accomplish, and display the entire forms of spatially connected information. TIS would also allow users to examine, query, analyse, visualise, and understand the resources in a number of formats, such as earths, maps, graphs, and collected information, to show connections, trends, and patterns. The goal of a Topological data System is to help people by answering their questions and rectify issues by analysing data in a fashion that is easy to understand and distribute [5]. TIS technology is frequently regarded as high-cost software. It also necessitates a significant quantity of data for various purposes, and the more data supplied, the better. When property agents interpret the TIS chart or the technologist’s plan around the TIS useful outlines, TIS layers may result in expensive blunders [5]. There might be difficulties in starting or continuing attempts to fully integrate the TIS, but there could also be considerable benefits to look forward to.

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2.4 In-Season Decision-Making Farmers routinely make management decisions that affect their financial line, from hybrid selection to planter changes. The need of competent decision-making rises when margins shrink owing to low crop prices. Farmers must utilise all of the tools at their disposal to make the best judgments possible in every aspect of their operation in order to be successful [19]. Growers making educated decisions during the growing season rather than at the end of the year is the next frontier for precision agriculture. In Fig. 7, farmers’ decision-making is ongoing throughout the growing season as they observe what is going on in their fields. That is why it is critical that maps and information are available on in-cab displays, cell phones, and tablets throughout the season [15]. Farmers do not make choices alone, which is why it is crucial to consult with reliable experts. Merits and Demerits Recognise that every decision you make for your crop has a monetary worth and an environmental impact as the first step towards confident decision-making. Then, using today’s high precision technology, link precise data points to construct a solid foundation of facts to back up those conclusions [19]. It is critical to employ the correct precision agriculture technologies to lay a foundation of reliable data since poor data cannot lead to excellent decisions. Reliability, availability, and usability are the three key components that underpin agronomic decision-making [19]. Because approaches are continuously being refined, it is vital to get expert advice before making any costly decisions. It should be regarded as a long-term investment because the initial capital expenses are likely to be significant. Gathering enough data to fully implement the technology might take several years [15]. Fig. 7 In-season decision-making

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2.5 Sensor Technology in Precision Agriculture In precision agriculture [22], sensor technologies are utilised for yield mapping and prediction, soil sensing, fertiliser and pesticide application, irrigation management, and other applications. Sensors are commonly employed in precision agriculture to determine accurate targets and crop demands so that locally variable chemical dosages may be applied. Agriculture sensors are sensors used in smart agriculture. These sensors give data that allows agronomists to analyse and improve their yields by responding to environmental modifications. The agriculture business uses these sensors in meteorological stations, drones, and robots. GPS satellite signals are employed by position sensors [21] to identify latitude, longitude, and altitude to within a few feet, which helps agronomists to analyse and enhance yields, and also to familiarise with altering ecological situations in precision agriculture. At least three satellites are essential to triangulate a location. Precision agriculture is strongly reliant on pinpoint accuracy. GPS integrated circuits, such as the NJR NJG1157PCD-TE1, are used to locate the detectors [4]. In Fig. 8, optical detectors use light to detect soil quality. The detector monitors distinct incidences of light reflectivity in the near-infrared, mid-infrared, and polarised light ranges. Vehicles, airy devices such as drones, and even satellites can be outfitted with sensors. Only two examples of variables that can be combined and managed are ophthalmic sensor data on soil reflectance and plant colour. Optical sensors may be used to assess clay, organic matter, and soil moisture levels. Electrochemical sensors provide critical data for precision agriculture, such as soil pH and nutrient levels. In Wachowiak et al. [23], sensor electrodes are used to detect certain ions in the soil. Soil chemical data is now gathered, analysed, and mapped using detectors mounted on specially manufactured “sleds.” Mechanical sensors are used to assess soil compaction, often known as “mechanical resistance.” The detectors employ a probe that goes into the soil and records resistive forces using load cells or strain gauges. On large tractors, a same sort of technology is handled to predict the dragging needs for ground-engaging equipment. Tensiometers,

Fig. 8 Fundamental components of optical sensors (hundreds of photodetectors and photodiodes)

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Fig. 9  Honeywell force sensor

such as the Honeywell FSG15N1A, are highly useful for irrigation since they measure the force produced by the roots in water absorption (Fig. 9). Airflow. The air permeability of the soil is measured using sensors. Measurements might be made at certain places or while moving. The targeted outcome is the burden necessary to force a predefined quantity of air obsessed by the ground at a particular depth. Compaction, structure, type of the soil, and moisture level all provide unique distinguishing characteristics in the soil. Agronomic meteorological conditions are self-sufficient equipment strategically positioned across agricultural areas. In Wahabzada et al. [24], these stations are equipped with a range of sensors tailored to the crops and climate of the area. Air warmth, ground warmth at several intensities, rainstorm, leaf moisture, chlorophyll, humidity, dew point warmth, wind control, relative dampness, sun radiation, and atmospheric pressure are all monitored and recorded at predefined intervals. At specified intervals, this data is gathered and wirelessly transferred to a central data recorder. Because of their mobility and low cost, meteorological conditions are alluring to farmhouses of all dimensions. Sensing technologies produce actionable data that may be analysed and deployed as needed to boost agricultural productivity, while reducing environmental impact. Here are a few examples of how this data is used in precision farming. Harvest Monitoring and Analysing methodology are installed on yield harvesting vehicles such as syndicates and corn gleaner. Crop mass yield is calculated using period, remoteness, or global position, all of which are sustained and taped to beyond 30 cm. Yield Mapping makes use of dimensional synchronised information from harvesting equipment’s GPS sensors. Yield maps are made by putting yield monitoring data and locations together. Weed Mapping currently builds maps based on operator input and interpretation, which are then quickly tagged with a GPS retriever and information jack. Slang incidences can be compared to harvest atlases, fertiliser maps, and spray maps, to name a few. As visual recognition technologies advance, manual input will be swapped with computerised, graphic devices fitted on working equipment. Benefits from Sensor Technology Sensor integration has the potential to boost farm output, save expenses, and enhance working conditions. Precision farming results in healthier cattle, more sustainable

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Fig. 10  Weather monitoring solution and agriculture factors

farming operations, and more efficient production, in addition to enhanced performance. Sensor technologies provide secure data collecting, as well as a committed group is dedicated to better weather monitoring and other agricultural factors included in the following Fig. 10.

2.6 Data Management in Precision Agriculture Farmers can also benefit from data by allowing them to investigate how various modifications to procedures, equipment, or other variables may affect profitability. Precision agriculture (PA) [6] service providers use agricultural data to generate suggestions for particular fields and give information about them. The key to making data valuable to a farm is putting it in a spot where it can be utilised to create information (i.e. “actioned”). In Fulton et al. [3], the data’s worth will be maximised if it can be shared with trusted advisers and others who can evaluate it and produce recommendations and fresh insights into the farm operation. Farmers can also benefit from data by allowing them to investigate how various modifications to procedures, equipment, or other variables may affect profitability. Precision agriculture (PA) service providers use agricultural data to generate suggestions for particular fields and give information about them. They use analytical tools to analyse farm data layers and deliver field suggestions, production estimates, soil moisture status, harvestability predictions, and other insights [10]. Figure  1 depicts the process a farm goes through when submitting data to an agriculture technology provider (ATP), which analyses the data and provides it to the farm for decision-making purposes. Figure 11 shows how vital it is to explicitly describe a data management plan in order to make the data valuable.

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Fig. 11  Flow of data to provide information for farm decisions

The Big Data Flow A farmer will use ground and equipment sensors, drones, and other devices to upload agricultural and personal data. Agricultural Technology Provider (ATP) will compile farmer data, mix it with other relevant data, and evaluate it using algorithms. Based on the data obtained, the ATP then provides the farmer with a personalised solution or advice. The farmer may then make agronomic, economic, and farm management decisions on their farm based on the ATP’s suggestions. Digital Agriculture Today, digital agriculture is advancing as the sector creates services and technology that enable wireless data exchanges and data analyses. In Moreira [14], the expansion of precision agricultural tactics, prescriptive agriculture, and the trendy issue of big data and how it may enhance the agriculture industry are all part of the digital agriculture concept. Precision agricultural technology will help farmers, retailers, and custom applicators to improve nutrient management in terms of location, timing, and development of new data layers for assessing performance, which is a common thread running through this process. Most significantly, these geographical data layers will guide new policies that include allowances for sustainability and environmental nutrient management.

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Data Sharing As organisations and consultants provide PA services and the usage of digital technology on the farm, the ability to easily communicate farm data is becoming more crucial. If a clear method for storing and organising data has not been specified in a digital strategy, sharing agricultural data might be problematic. There are various methods for exchanging data effectively; here are two to explore as part of your farm’s digital strategy: 1. Develop a system for sharing files both on and off the farm so that farm data may be utilised to capture fresh insights and learnings. 2. Store data in an easy-to-copy and/or share format with trustworthy advisers and PA services. Data Storage The correct storage and preservation of data is an important aspect of efficiently utilising data. To guarantee that there is a backup that can be retrieved in any circumstance, data should be saved in both on-farm and off-farm storage locations (i.e. “in the cloud”). In Kountios et al. [10], Cloud solutions should be password-­ protected and use any available cyber security features, while on-farm data storage should be kept in a closed, fireproof safe. At the very least, organise data by year, then crop, field, or farm, as applicable to the business. This will make it easier for you to find what you are looking for. Keep an original copy of data on-farm and off-­ farm so that you have a backup. The “raw” data acquired by the in-cab display or gadget on agricultural machinery is referred to as an original copy. (Data that has been translated – for example, by uploading it to a farm management software platform – is no longer considered raw.) Having a backup of the original copy allows you to return to this data file at any time. Make sure data is accessible from a convenient location (the cloud, or a phone, tablet, or desktop computer, etc.). If you are utilising offline tools, be sure they will automatically sync whenever you have access to the Internet. • Use safe passwords to protect data and never disclose it without authorisation. • Create a method for digitising any data that was collected or written by hand. Potential Benefits of Sharing Data and Storage Include • Making better use of data acquired more rapidly • Ensuring that judgments are based on a single source of data to avoid misunderstandings or incorrect interpretations • Reducing the amount of redundant data collected • Generating fresh insights • Having access to data so you can double-check your findings (i.e. confirm results) • Using greater datasets, scientists and researchers may conduct high-quality studies that have never been done before, leading to breakthrough discoveries for agriculture and the public benefit

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2.7 Precision Methods Used in Agriculture Precision methods (Table 1), the process of detecting, measuring, and reacting to numerous inter- and intra-field variability inputs, are at the heart of the farm management concept known as farming. • Reduction in cultivation costs brought on by site-specific crop management techniques • Enhanced input production efficiency as a result of site-specific input management

Table 1  Comparison between all components in precision agriculture Internet of Things (IOT) The performance of the workforce and the effectiveness of equipment may all be tracked using sensor data IoT helps to regulate agricultural methods

Topographical information Grid sampling system (TIS) Using grid It involves sampling to examining the take more soil land, samples from displaying a field field data on a map, and using that data to make decisions

In-season decision-­ making Making decisions in relation to selecting the right crop

Understanding the nutrients that are available is improved

Five key components are hardware, software, data, people, and methods

Drones, IoT systems for smart farming, and connected weather stations can all be used to collect this data

Identifying the variety of nutrient concentrations across a specific area is the goal of this method

Used to lower costs and improve functional performance

Sensor technology Key data needed for precision agriculture is provided by electrochemical sensors: pH and amounts of soil nutrients

Data management It is employed to create efficient technology to process the enormous amounts of data produced by precision farming production and study

The result depends on environmental and soil factors

Specific ions in the soil are detected by sensor electrodes to function

The level of crop appropriateness will be provided by the suitability analysis, and the user can then make a decision with that information

Enhanced sensitivity during data collection, nearly lossless transmission, and ongoing, real-time analysis

It assists them in examining the potential effects of various adjustments to procedures, tools, or other elements on profitability Farm data is used by service providers to create recommendations for and provide information about specific fields

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• Reduced application of nutrients, particularly nitrogen fertiliser, reduces nitrate in groundwater and nitrous oxide in the atmosphere. Reduced soil and environmental pollution • Chemicals are reduced using variable rate application technology. • Decreased irrigation water application, which reduces nutrient levels and deep percolations • Reducing water body erosion, runoff, and sedimentation

3 Results and Discussion A topographical information system (TIS) is a tool for collecting and analysing geographic data (in Fig. 12) to aid in agricultural development. TIS can analyse soil data and determine which yields should be planted and how to maintain soil nutrition current so that plants get the greatest benefit. TIS in agriculture allows for better land resource management, which leads to increased production and cheaper costs for farmers. In Sensor technology in Precision Agriculture, GPS satellite signals are employed by position sensors to identify latitude, longitude, and altitude (Fig. 13) to within a few feet, which helps agronomists to analyse and enhance yields and also familiarise with altering ecological situations in precision agriculture.

Fig. 12  TIS tool – collecting and analysing geographic data

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Fig. 13  Sensor technology in PA, GPS satellite to identify and alter ecological situations in precision agriculture

4 Conclusion Precision agriculture has expanded to satisfy rising global food demand by utilising technology which is easier and less expensive to gather and use data, adjust to altering ecological circumstances, and make the maximum efficient use of resources. Smaller farms may now profit from these technologies as well, thanks to features incorporated into smart phones, related software, and smaller-sized gear. Furthermore, these technologies are helping to solve problems that are not limited to farms, such as pollution, global warming, and conservation. Increased use of autonomous farm vehicles, as well as upgraded cellular data communication and collecting from keener, minor Remotely Piloted Aircraft are expected to be among the next precision agricultural breakthroughs. These lesser vehicles can track the position of agricultural apparatus as well as crop and soil conditions, allowing farmers to optimise machine maintenance and repair. Industrial manufacturing process advances will stay to invent their methods into agriculture.

References 1. Ackerson, J. P. (2018). Soil sampling guidelines. https://www.extension.purdue.edu/extmedia/ AY/AY-­368-­w.pdf 2. Ahmed, N., De, D., & Hussain, I. (2018). Internet of Things (IoT) for smart precision agriculture and farming in rural areas. IEEE Internet of Things Journal, 5(6), 4890–4899. 3. Fulton, J., Port, K., Lindsey, L., Shearer, S., Darr, M., & Luck, J. (2017). Digital agriculture tools to support soybean production: Final report to the united soybean board (5).

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Implementation of a Distributed Electronic Voting System Using a Blockchain-Based Framework Nisha Soms, J. Lawrance, R. Harshitha, and D. S. Dakshineshwar

1 Introduction Democratic voting is a fundamental right of every citizen wherein every country of the world has a preferred way to cast votes for their people. The voting ranges from paper – seal-based voting – to postal votes with a common concern of security and fraudulent votes to conducting body’s bias. As the technology is advancing at a faster pace, digitizing voting is considered a necessity that is minimum in efforts but maximum in benefits. E-voting has been considered a solution for the concern of security, transparency, and integrity of an election, irrespective of the level of governance. The main objective is to design a simple Blockchain-based e-voting system for easily viewing the votes casted as well as verifying their integrity.

1.1 Conventional Voting Systems Traditional e-voting systems are found to be expensive, high cost of time, and reduced voter anonymity and voter satisfaction, as the work done by the members are unknown and hidden from the public. As the election system is democratic, so should be the voting system. N. Soms (*) Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India J. Lawrance · R. Harshitha · D. S. Dakshineshwar Department of Computer Science and Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_10

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1.2 Blockchain Blockchain is a distributed database that comprises blocks of encrypted information in it, which is added to when a specific function or task is done. The blocks are accessed by all the stakeholders of the block. Mining is a term vastly used in the Blockchain which is said to be the addition of blocks into a Blockchain. A Blockchain system is said to differ from a typical database by the structuring of the data, a Blockchain contains data that are in chunks which has blocks that forms a chain of blocks, whereas database forms a table structure. This data structure introduces its decentralized nature, which is said to be a nature of data where no authority is controlling it, and it is available for all to view, based on certain conditions.

2 Related Works Harsha V. Patil et al. described in their paper pertaining to the transparency of the blockchain technology that can make the election process easier. This paper involves the discussion of usage of Blockchain know-how in designing the voting scheme that can address transparency and nature of distribution [1]. Praveen M. Dhulavvagol et al. analyzed proof-of-concept, decentralized application over Blockchain technology, and also performed the studies on Ethereum clients such as Geth and Parity, which gave a conclusion on how to leverage the Ethereum platform for performance [2]. Nurul Hanis Abd Rasid had put forth the idea that the most popular consensus used in Blockchain technology is Proof of Work (PoW) as it adopts the Ethereum. It showed the pairing of ZK-SNARK and EIGamal in implementing Blockchain supporting e-voting scheme and is further stressed with the pairing between the cryptographic protocol and algorithms leading to cryptographic advancements [3]. Yamuna Rosasooria et al. discussed a student representative e-voting system that uses Solidity to build a Blockchain interface, providing the outcomes as error-free. Usage of Solidity was found in the analysis of designing an e-voting system [4]. Patricia Baudier et al. had provided an insight into whether e-voting peace causes influx of more citizens and reduces conflicts. This survey made us analyze the framing of salient features for the voting system with maximum effort on the societal aspects as stated above [5]. Koç et al. [6] had studied a voting system based on smart contracts. It is deployed on the Ethereum Blockchain, saving the owner of the contract as “chairman” and defining structures for voters and candidates. Chairman initializes the voting process and gives out voting rights to individuals based on their Ethereum wallet codes. Voters contact the smart contract through a transaction in the Ethereum wallet to vote for their candidate. The smart contract checks if they have already voted, and if not, distributed a vote to their favored candidate. Current winning candidate is returned after each vote. The function for the winning candidate can also be called

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off once the election is over. Voting rights are given based on Ethereum wallet codes. Voters contact the smart contract through a transaction on the Ethereum wallet to cast votes. Smart contract counts votes and returns the winning candidate. Ayed [7] had proposed a voting solution that employed a distinct Blockchain for the candidates present. Voters log into the voting system and valid users were granted a right to vote. Voters are then guided into a ballot where they chose a candidate and cast their vote. Voter’s identification number, full name, and the hash of the previous vote were encrypted and added to the Blockchain of the desired candidate. The hashed vote was linked to the previously cast vote on the Blockchain. Sven Heiberg et al. [8] studied the usage of smart contracts by verifying it with various attributes such as number of voters, transparency, verification and found that the results were said to be complex. Freya Sheer Hardwick et  al. [9] discussed Blockchain as a tool to perform the voting process. King-Hang Wang et al. [10] presented a detailed review of electronic voting, by studying the parameters that are devised and also reviewed some of the real-world voting systems to get a practical knowledge on the attributes and issues that exist. Yi Liu and Qi Wang [11] discussed voting process as decentralized and fully open without the usage of any third party. The system offered extensions that helped in access control permission in Blockchain. Damien MacNamara et  al. [12] discussed “Just-Like-Paper” (JLP). They protracted the classification of voting efficiency and recognized a universal ten-step process encompassing all possible selective steps spanning the 26 machines calculated. The study on the protocol that can be used for payment using Blockchain was brought forward and presented by Pavel Tarasov and Hitesh Tewar [13]. Rachid Anane et al. [14] emphasized the complexity of e-voting system containing security issues which is vulnerable due to the fact that any person can access it. The paper concludes the field of e-voting by presenting a case study on the design and implementation of an e-voting prototype system, and by providing guidance on the selection and deployment of relevant mechanisms to address issues of security, privacy, and accountability. The paper also highlights some fundamental issues for further investigation, which could inform future research in the field.

3 Proposed System We proposed a system that provides a real-world voting scenario that makes people vote for a candidate and test the new attributes which get added with the usage of Blockchain. 1. Registration: During the registration phase, the essential data about the vote caster will be collected and be stored along with their photo which will be used for biometric verification. Once the registration is over, a public-key pair will be generated and will be stored securely.

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2. Authentication: Before the voter casts the vote, they need to authenticate themselves using the biometric security system available in the polling booth. Once the authentication is over, they will be allowed to cast the vote. The proposed work ensures a secured, decentralized, and distributed voting system without the use of rewarding while mining and, at the same time, is manageable by any organization who undertakes the election process. It also uses asymmetric public-key cryptography for the storing and retrieving of votes, thereby ensuring more security. The advantages of our proposed system are: • Security: One of the most important factors of voting is security. Voting systems are currently quite vulnerable to hacking. Violent actors can penetrate the system and change the outcome if there aren’t strong security measures in place. Blockchain is useful in this situation. A seemingly impenetrable system could be introduced, thanks to technology. Once voting is complete, every vote can be verified to ensure that it was cast correctly. Without Blockchain, this would require a central authority to oversee the process. This raises a lot of concerns about how much of these central bodies can be trusted. However, there is no need for a central authority that might be corrupt or subject to error, thanks to Blockchain and its decentralized ledger system. • Transparency: Votes can be counted and recorded on an unchangeable public ledger utilizing Blockchain technology. This implies that they are visible to anyone and can be counted while being tracked. Everyone will be able to verify the legitimacy of the voting by viewing real-time records of the amount of votes cast, creating a transparent and secure voting system. • Anonymity: People yearn for seclusion while voting and do not always want others to know who or what they voted for. Blockchain allows for anonymity when voting. As with transactions on the Blockchain, voters can use their private keys to keep themselves anonymous. They can then vote in the system without being anxious about others comprehending how they voted.

4 System Design 4.1 System 1 . The client will create and maintain a socket connection with the server. 2. Each instance a voter casts a vote, the socket connection will disconnect itself and connect back to the server for the next vote. 3. Once the socket connection is established, the server will send a public key generated by means of RSA key generation algorithm, which will be used to encrypt the vote data before being sent to the server.

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4. Each socket connection can be uniquely identified using a socket-id assigned to it whenever the connection is established. 5. Once the data is received on the server side, the data will be decoded via the private key generated by means of RSA key generation algorithm. 6. The decrypted data will be encrypted again using the user’s public key which was generated using the Elliptic curve cryptographic key generation algorithm during registration. 7. Then the encrypted data will be stored up in the Blockchain along with the user details, which will be stored in a smart contract. Figure 1 depicts the overall framework deployed for ensured steps of secured communication during the voting process.

Fig. 1  System overview

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Fig. 2  System architecture

Figure 2 depicts the system architecture that involves Blockchain for an authenticated and secured voting method.

4.2 Client 1. Service workers are deployed in the client side for initiating socket connection if there is no active connection found. 2. Once the connection gets established with the server, the server will send a public key which will be stored securely in the client side for later use. 3. To cast vote, the user should pass through the authentication service by using the credentials provided to them during registration. 4. After passing the authentication service, the user will be presented with a screen in which they can view the candidate and will be allowed to cast their vote. 5. Once the vote is cast, the vote data will be encrypted using the public key available via the public-key provider. 6. Then the encrypted data will be sent via the socket connection securely to the server. All steps of processing at client side are represented in Fig. 3.

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Fig. 3  Client architecture

4.3 Server The server will establish a socket connection with itself once the client comes online and requests for the connection. Figure 4 depicts the functionalities that take place at the server side. 1. When the socket connection gets established, the server will send a public key generated using RSA key generation to the client and stores the private key in a key-value pair data structure with socket-id as the key and private key as the value such that the private key can be uniquely identified for each socket connection. 2. The encrypted vote data will be sent by the client to the server, which will be decrypted using the appropriate private key. 3. The decrypted vote data value then is verified using checksum mechanism. 4. Then the vote data will be encrypted using the public key of the user which was generated during the registration phase using Elliptic curve cryptographic methods.

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Fig. 4  Server architecture

5. The encrypted vote data will be added to the Blockchain along with the smart contracts which contain all the details of the voter with the help of web3.js library. 6. The server will disconnect the socket connection from the client, and will wait for the next connection request  – the server will perform these processes repeatedly. All steps of processing at server side are represented in Fig. 4.

5 Implementation This application is developed using NodeJS and Ethereum Blockchain network. The Blockchain network is set up on top of a NodeJS server so that other running instances of server can interact with the Blockchain for storing and retrieving data from the Blockchain network with the use of smart contracts.

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This application is based on client/server model for interactions with each other in a secured way, using various cryptographic algorithms such as RSA and ECC encryption algorithms. When the user logs in to the system, an RSA key-pair will be generated using the RSA encryption/decryption library called node-forge. This is implemented as shown in Fig. 5. Once the voter casts their vote, the vote data will be encrypted, as shown in Fig. 6, using the RSA public key generated earlier. When the vote data reaches the backend, the data will be decrypted using the voter’s private key, which has been stored in the session of the user, as shown in Fig. 7. After the above steps, the ECC key-pair will be generated for the user and the vote data will be signed and encrypted using that key-pair. Figure 8 illustrates the code snippet for decryption. The resultant data is then forwarded to the Blockchain server (Fig. 8). Each data that must be stored in the Blockchain should be a collection of vote data, the ID of the user, and the location coordinates in which the vote has been cast. Using this information, a smart contract will be deployed onto the Blockchain. The significant part of coding are depicted in Fig. 9. In this proposed system, smart contract acts as an origin for data storage and retrieval. A smart contract can be defined as a program or a transaction protocol

Fig. 5  RSA key-pair generation

Fig. 6  RSA encryption

Fig. 7  RSA decryption

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Fig. 8  ECC key-pair generation

Fig. 9  Adding block to Blockchain

which can automatically execute, control, document legally relevant events and actions according to an agreement (Fig. 10). Figure 10 showcases the addition of a block in blockchain which in this context are votes casted per individual.

6 Conclusion Making the voting process transparent, elegant, and quicker is the need of the moment in current government elections, as corruption is increasing during the time of elections. Hence using disruptive technologies such as Blockchain and other technologies make the entire process easier as a boon for combating corruption.

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Fig. 10  Smart contract for deploying vote data

Hence this need is addressed in our work in a smaller level, and it can be further customized according to the level of election and the hierarchy of government.

7 Future Work The primary goal is to strengthen Blockchain technology’s resilience to the “double spending” problem, which translates to “double voting” for electronic voting systems. To overcome such fatal situations, we believe an effectual model to ascertain responsible provenance for e-voting systems will be crucial. Also, an end-to-end verifiable e-voting scheme should be achieved. The work to achieve this is underway in the form of an additional provenance layer to aid the existing Blockchain-­ based infrastructure.

References 1. Patil, H.  V., Rathi, K.  G., & Tribhuwan, M.  V. (2018). A study on decentralized e-voting system using blockchain technology. International Research Journal of Engineering and Technology, 5(11), 48–53. 2. Dhulavvagol, P. M., Bhajantri, V. H., & Totad, S. G. (2020). Blockchain ethereum clients performance analysis considering e-voting application. Procedia Computer Science, 167, 2506.

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3. Abd Rasid, N.  H. (2020). Blockchain technology in e- voting: Comparative study. OK Blockchain Centre. 4. Rosasooria, Y., Mahamad, A.  K., Saon, S., Isa, M.  A. M., Yamaguchi, S., & Ahmadon, M.  A. (2020). E-voting on blockchain using solidity language. In 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE. 5. Baudier, P., Kondrateva, G., Ammi, C., & Seulliet, E. (2021). Peace engineering: The contribution of blockchain systems to the e-voting process. Technological Forecasting and Social Change, 162, 120397. 6. Yavuz, E., Koç, A. K., Çabuk, U. C., & Dalkılıç, G. (2018). Towards secure e-voting using ethereum blockchain. In 2018 6th International Symposium on Digital Forensic and Security (ISDFS). IEEE. 7. Ayed, A.  B. (2017). A conceptual secure blockchain-based electronic voting system. International Journal of Network Security & Its Applications, 9, 1–9. 8. Heiberg, S., Kubjas, I., Siim, J., & Willemson, J. (2018). On trade-offs of applying block chains for electronic voting bulletin boards. IACR. 9. Hardwick, F.  S., Gioulis, A., Akram, R.  N., & Markantonakis, K. (2018). E-voting with blockchain: An e-voting protocol with decentralization and voter privacy. In 2018 IEEE international conference on Internet of Things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData). IEEE. 10. Wang, K.-H., Mondal, S. K., Chan, K., & Xie, X. (2017). A review of contemporary e-­voting: Requirements, technology, systems and usability. Data Science and Pattern Recognition, 1(1), 31–47. 11. Liu, Y., & Wang, Q. (2010). An e-voting protocol based on blockchain. ACM. 12. MacNamara, D., Paul Gibson, J., & Oakley, K. (2014). The ideal voting interface: Classifying usability. Journal of eDemocracy and Open Government, 6(2), 182–196. 13. Tarasov, P., & Tewar, H. (2017). The future of e-voting. IADIS International Journal on Computer Science and Information Systems, 12(2), 148–165. ISSN: 1646-3692. 14. Anane, R., Freeland, R., & Theodoropoulos, G. (2007). E-voting requirements and implementation. In The 9th IEEE CEC/EEE 2007 (pp. 382–392). IEEE.

Blockchain-Based Organ Donation and Transplant Matching System Ananya Sajwan, Soumyadeepta Das, Asnath Victy Phamila, and Kalaivani Kathirvelu

1 Introduction The objective of this study is to propose and develop a decentralized application for streamlining the organ donation and transplant matching process with the help of blockchain technology. The application aims to connect donors to patients, with hospitals acting as mediators and automate the process using smart contracts. The existing systems lack an organized platform and do not have security and traceability and are not fast. Organ donation and transplant matching takes place manually in the country which is due to the dire state of the system, and this leads to the loss of lives. Ethereum blockchain technology can be used to add traceability, security along with immutability of data and functionality with proper authentication for the two-organ donation process between donors and recipients. It will act as a much-­ needed, fast, decentralized and secure platform to regularize organ donation by connecting hospitals, donors and recipients. One of the crucial factors in organ transplants is timely access and speed, due to the lack of which most deaths occur. Due to this instant and automated matching of organ transplants, it could be more efficient and faster, with the potential of saving countless lives, which are lost only due to lack of timely access. The application with its high potential for scalability due to the use of blockchain technology can also aid users during the harsh A. Sajwan · S. Das · A. V. Phamila School of Computer Science Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India e-mail: [email protected] K. Kathirvelu (*) Department of Computer Science and Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_11

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pandemic of COVID-19 by providing them with fast and verified access to plasma donations for therapy, essential medications and medical services. The current situation due to COVID-19 is dire and users are unable to procure verified or approved leads of organ, blood or plasma donations along with other important medications. Our application can bridge the gap and provide a secure and traceable manner to access the above with the help of Ethereum blockchain. As our problem statement requires an approach which is implicitly secure and traceable without human aid, it also requires high scalability and efficiency, as decision must be made in a matter of seconds, hence we have chosen Ethereum blockchain as our approach for solving this real-world issue.

2 Literature Review Alandjani et al. [1] displayed the use of blockchain integration with IoT devices for the purpose of tracking any transaction related to organ donation with no compromise in security of data, preventing any risk of fraud via intact data integrity for the purpose of medico-legal needs. The study analyses a scheme that is blockchain based for allowing auditable medical transactions in order to prevent organ trafficking or tracking of verified organ recipients or donors which could be applied to modern smart health devices. Lakshminarayanan et al. [2] studied the mitigation of lack of dynamic updates on blood usage and information of blood trails in detail, including from donation to consumption via a blockchain-based blood management system. The implemented system, using the Hyperledger Fabric framework, brought higher transparency to the process of blood donation by blood trail tracking and helping curb unnecessary wastage of blood via a unified platform aimed to aid in exchange of blood and its derivatives between blood banks. Jiang et  al. [3] proposed a unique system of healthcare information exchange (HIE), using blockchain technology. As the data is huge and expanding day by day, it requires higher scalability as well as security without any compromise in efficiency. Thus, the research has its focus on analysis of various requirements to share health data via different sources. On the basis of the analysis, the study used two loosely coupled different blockchains for handling various types of health data. It also combines on-chain verification and off-chain storage for satisfying the needs of authentic ability and privacy. It also proposes two fairness-based packing algorithms for improvement of the system throughput and fairness among various users in a joint manner. Ananth et al. [4] focused on implementing and adding to the existing system by National Health Insurance Administration (NHIA), Taiwan called national medical referral (NMR) system. By elimination of any current restrictions in the NHIA’s NMR system, the study proposed a flexible, scalable and blockchain framework that utilizes the referral data on NHIA’s NMR system for building a medical referral service that is alliance-based, thereby connecting various healthcare facilities. It uses a blockchain-enabled framework which can integrate referral data of a patient

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obtained via NHIA’s NMR with data of EMR (electronic medical record) and EHR (electronic health record) of community clinics and hospitals to establish a proper medical referral service for clinics, patients, hospitals, etc. and improve trust in transaction security. This is made possible via a blockchain-based personal health records decentralized app (DApp) for patients to receive their EHR and EMR data. Kim et al. [5] proposed a blockchain-based blood cold chain system design. The application of blockchain in this use case of the blockchain technologies is to generate traceability by generating a single record for each point. The system proposes blood transactions between medical institutions to cope with suddenly increased demands of a specific region or a medical institution in emergency situations. The team proposes the design for requesting and responding to the blood from the surrounding medical institutions and helping stable blood supply even in special situations. Kitsantas et al. [6] discussed the concepts and application of blockchain technology in different domains such as finance, business, healthcare and governance through smart contracts which can help in keeping tabs between the different users involved in the particular sector. Jamil et al. [7] applied the concepts of blockchain in creating a platform for drug management in a hospital using Hyperledger technology, demonstrating the security of smart contracts in the platform.

3 Modules The application is composed of five different modules, namely, the blockchain environment, smart contracts, server side (NodeJS server), client side (React application) and the MongoDB database. These modules are critical in the overall structure and functioning of the application. The proposed application is built using the technology stack, with Ethereum blockchain: Ethereum blockchain technology is used to provide security and maintain a distributed tracking system. MongoDB database: MongoDB is used as the database for the application’s general public and non-sensitive details. React & NodeJS: React and NodeJS are used for the frontend and backend of the application, respectively. Ganache: Developer tool to recreate blockchain environment and test smart contracts. Metamask: Browser extension for accessing Ethereum-enabled distributed applications, or “DApps” in browser. Solidity: Solidity is described as high-level object-oriented language used for the implementation of smart contracts. Remix IDE: Web-based IDE to create, compile and transfer smart contracts in Solidity. The application is a website powered by NodeJS in the backend, React in the frontend, MongoDB as the database and is integrated with Ethereum blockchain for adding traceability and security. A test blockchain environment is set up using Ganache and Metamask to support the testing of the application. The proposed application contains several features to aid in the process, as is visible in the functional architecture in Fig. 1.

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Fig. 1  Functional architecture

The features of the application being, Donor signup – Organ Donors can sign up from their devices filling in their details, after which they are prompted to visit a nearby hospital to get approved. Donor verification by hospital – Once a donor is registered, the hospital needs to verify them in person using the donor’s public key. Once verified, the donor can access their profile and the system stores and registers the donor as active or verified. Mettler [8] discussed and suggested in his study that blockchain can be disruptive in the health sector, providing an efficient method to track patient records, medical drugs and supply chain. Similarly, the application consists of Donor login and dashboard – the Organ Donor can view their details as well as the details of the matched person once they are matched, access their EMR, along with other specific details, on their dashboard. Hospital login – Hospitals can approve donors, register recipients, view list of recipients and match recipients to the approved donors for transplants, along with easy access of the users’ EMR. Donor–patient matching – Patients can be matched to the right donors based on blood type and organ/blood requirement. The matched patient and organ donor are then reflected on the portal. Once matched, the donor can view details of the hospital and recipient on their own dashboard too. EMR records – Security of EMRs are guaranteed in the blockchain and transferred and accessed via IPFS. El Rifai et al. [9] discussed the deployment aspect of medical records in blockchain and avoiding any bottlenecks faced in the same. EMRs can be accessed using the unique public keys of the patients or the donors on the platform. The main modules involved in the study of the proposed application include the following: • Blockchain environment: Ethereum blockchain, which allows greater functionalities of development and automation using smart contracts.

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Fig. 2  Donor and recipient data structures

Fig. 3  Technical architecture

• Smart contracts: The Piece of code and data structures, as described in Fig. 2 for donors and recipients; are present inside the blockchain that will carry out the functions and also hold the data inside the blockchain. • Server side (NodeJS server): The server side of the application which will tie the database and blockchain with the application. • Client side ( React app): The frontend will be made using React for faster rendering and modularization of code. • MongoDB database: For storing fast access public data for the platform. (For general and common data). The technical modules and relations involved in the application are visually represented in Fig. 3 as the technical architecture.

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4 Methodology The system has several different modules coming together to be integrated to implement it successfully. The basic idea in the implementation is to create a web application with React as frontend and NodeJS as backend and integrate it with the Ethereum blockchain environment. Rupa et al. [10] designed a similar application for Remix Ethereum blockchain for maintenance of sensitive data records by employing a distributed application (DApp) technique. A test environment is set up to emulate the real blockchain environment for application testing purposes. The Ganache server allows us to create test public keys to be used for transactions in the application. Each donor/patient on the application is assigned a unique public key in the blockchain which makes it easier to identify during transactions. All transactions are executed and tracked with the help of Metamask, which allows users to access their Ethereum wallet to interact with the blockchain via a web extension. This blockchain technology helps eliminate any inefficiencies and reduces cost in the healthcare sector as also found by Oflaz [11] while investigating the blockchain framework and smart contracts. The flowchart of the web application is depicted in Fig. 4, beginning with the registration of a donor or recipient and thereby entering their medical data. Parallelly, the hospital can login for patient verification and update necessary details of the patient and/or organs harvested and available for transplant. The application then conducts donor-patient matching and then approval of transaction is done with data secured using Ethereum. The information is updated and the Electronic Medical Record can be then securely downloaded. The major processes involved in the application creation are detailed as follows:

Fig. 4  Flowchart of the web application

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1. Setting up of the blockchain environment – this is achieved by setting up the Ganache server which produces Ethereum public keys for testing the application. Metamask is also used for confirming transactions in the environment. 2. The database for hospitals was set up in MongoDB containing basic hospital details like name of the hospital, city, etc. 3. The blockchain code is written on the Remix Ethereum IDE using Solidity programming language. 4. The website is designed keeping the ease of users in mind and React and NodeJS are used to create the frontend and backend of the application. 5. The testing of the application was done for donor registration and hospital listing display for the donor to visit and get verified. 6. Testing of application was done for hospital login and verification of donor details using the donor’s unique public key, with Metamask confirming and executing the transaction after which donor details are stored in blockchain. Verification was done by logging into the Donor Login – this is possible only after the hospital verifies the donor using the unique public key. 7. Recipient registration is also tested, which is done from the Hospital’s dashboard –Metamask confirms transaction and details are stored in blockchain. 8. Further testing is done by cross-verifying EMR records access by submitting the user’s public key and confirming Metamask transaction. 9. The main function “Transplant Matching” which is available on the Hospital Dashboard is also tested  – the list of recipients is available and selecting “Match” option for a particular recipient, the application searches for a donor match on the basis of blood type and organ and displays the match successfully. 10. Upon login using “Donor Login,” we see that matched recipient and hospital basic details are available verifying that the application is working efficiently. The implemented system can be easily scaled up and down and can be accessible to all in a secure manner with the help of blockchain. Hafid et al. [12] discussed the scalability of Ethereum blockchain in their study through a performance-based analysis of protocols, indicating the advantages of blockchain scalability. The web application is fast, responsive and has easy to understand user interface to aid users in connecting patients to donors swiftly and securely. Such an application can prove to be a gamechanger for the Indian healthcare system, through the use of blockchain, as discussed similarly by Shukla et al. [13].

5 Results and Discussions The application provides a smooth interface, as shown in Fig. 5, for users to interact with the platform and carry out the process of organ matching in a hassle-free manner. The application results show that integration with Ethereum blockchain adds an

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Fig. 5  Snapshot of the prototype application created

extra layer of security to patient data and all transactions are tracked thus, leaving no room for fraud in any case. With such E-health blockchain-based systems currently only existing in research, the proposed application stands apart as it is accessible to the common man and can easily be used for fast and efficient donor–patient organ matching. Such an application can prove to be necessary in current times when time is a factor in saving lives. It also provides a tamper-proof security of patient data and also helps in reducing the risk of organ trafficking.

6 Conclusion The application “EtherealOrgan” which is a blockchain-based organ donation and transplant matching system is a much-needed product to connect donors to recipients in a fast, secure and traceable manner, with hospitals acting as mediators in the process. It eliminates the hassle and paperwork involved in the manual process and securely stores data in the blockchain. The application helps reduce the role of middlemen that could lead to organ trafficking thus aiding in the welfare of the patients and donors as well. The application can also be extensively used during the current COVID-19 pandemic situation to aid people in getting timely access to organ/blood/plasma donations, important medications and treatment in an efficient and fast manner with high security so that verified leads are provided and fraud cannot occur.

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The application could prove to be a gamechanger in the healthcare industry by using secure Ethereum blockchain technology to provide an E-healthcare solution to the dire state of organ donation and transplant matching in the country. With easy scalability and high efficiency, it can securely connect patients to donors in a fast manner, thus saving the loss of countless lives and bridging the gap between supply and demand in the industry.

References 1. Alandjani, G. (2019). Blockchain based auditable medical transaction scheme for organ transplant services. 3C Tecnología, 2019, 41–63. https://doi.org/10.17993/3ctecno.specialissue3 2. Lakshminarayanan, S., et  al. (2020). Implementation of blockchain based blood donation framework. In Computational intelligence in data science. ICCIDS 2020 (IFIP Advances in Information and Communication Technology) (Vol. 578). Springer. 3. Jiang, S., et  al. (2018). BlocHIE: A blockchain-based platform for healthcare information exchange. In IEEE international conference on smart computing (SMARTCOMP) (pp. 49–56). IEEE. 4. Ananth, C., et al. (2018). A secured healthcare system using private blockchain technology. Journal of Engineering Technology, 6(2), 42–54. 5. Kim, S., et al. (2018). Design of an innovative blood cold chain management system using blockchain technologies. ICIC Express Letters, 9(10), 1067–1073. 6. Kitsantas, T., et al. (2019). A review of blockchain technology and its applications in the business environment. In International conference on enterprise, systems, accounting, logistics & management, Corfu, Greece, 166–178. 7. Jamil, F., et  al. (2018). A novel medical blockchain model for drug supply chain integrity management in a smart hospital. Electronics, 8, 505. 8. Mettler, M. (2016). Blockchain technology in healthcare: The revolution starts here. In IEEE 18th international conference on e-health networking, 2016. IEEE. 9. El Rifai, O., et al. (2020). Blockchain-based personal health records for patients’ empowerment. In F. Dalpiaz, J. Zdravkovic, & P. Loucopoulos (Eds.), Research challenges in information science (RCIS 2020. Lecture Notes in Business Information Processing) (Vol. 385). Springer. 10. Rupa, C., et al. (2021). Industry 5.0: Ethereum blockchain technology based DApp smart contract. Mathematical Biosciences and Engineering, 18(5), 7010–7027. 11. Oflaz, N. K. (2019). Using smart contracts via blockchain technology for effective cost management in health services. In U. Hacioglu (Ed.), Blockchain economics and financial market innovation (Contributions to Economics). Springer. 12. Hafid, A., et  al. (2020). Scaling blockchains: A comprehensive survey. IEEE Access, 8, 125244–125262. 13. Shukla, G.  R., et  al. (2021). Leveraging blockchain technology for Indian healthcare system: An assessment using value-focused thinking approach. The Journal of High Technology Management Research, 32(2), 2021.

A Transparent, Distributed, and Secure Crowdfunding Platform Based on Blockchain Vinita Tiwari, Sam Goundar, Karri Babu Ravi Teja, Basant Agarwal, and Priyanka Harjule

1 Introduction Crowdfunding refers to pooling of funds for a new venture or a project especially via the Internet. The geographical dispersion of the entrepreneurs and investors necessitates a mutually trustworthy, safe, and reliable environment for such transactions. Since the investors are not collocated with the entrepreneurs, the primary challenge is to convince each other that the funds allocated for a specific purpose are utilized on that project itself. In addition to this, minimum intervention from the political or financial institutions and a decentralized fund management not only encourages the investors to respond overwhelmingly for such investments but also helps the entrepreneurs to go beyond the geographical boundaries. Since, V. Tiwari (*) Department of Electronics and Communication Engineering, Indian Institute of Information Technology Kota (IIIT Kota), MNIT Campus, Jaipur, India e-mail: [email protected] S. Goundar School of Science, Engineering, and Technology, RMIT University, Hanoi, Vietnam K. B. R. Teja Department of Electrical and Electronics Engineering, BITS Pilani, Pilani Campus, Pilani, Jhunjhunu, India B. Agarwal Department of Computer Science and Engineering, Indian Institute of Information Technology Kota (IIIT Kota), MNIT Campus, Jaipur, India P. Harjule Department of Mathematics, Indian Institute of Information Technology Kota (IIIT Kota), MNIT Campus, Jaipur, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_12

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crowdfunding eliminates the mediation from banking or other non-banking financial institutions a key challenge is the trust. Trust could be built only when there is transparency and transparency is not a one-time process. It is a recurrent step where the necessary information has to be shared with all the interested parties in the process and in a real-time environment. Such attempts boost the confidence of the investors and can help raise more funds. According to the World Bank report, investments through crowdfunding platforms are expected to reach $100 billion by 2025 [25]. As per this report, there are around 350 million households which make crowdfunding-based small investments in community businesses [25]. Another report from US Small Business Administration (SBA) predicts that there will be a remarkable growth in the number of individual crowdfunding campaigns from year 2018 to 2020, that is, from 178,500 to 216,230, respectively [21]. These reports and data emphasize the possible opportunities for growth in the area of crowdfunding and a need to develop a reliable, transparent, and safe environment for crowdfunding. Crowdfunding opens opportunities for those entrepreneurs who have limited or no options in traditional funding methods. Traditional funding is the one in which a business idea/project is presented before a bank, other financial institutions, or venture capitalists, and sometimes, governments as well. In each of these cases, there are limitations. Banks and financial institutions lend only to established businesses after assessing their net worth and security. Hence new business proposals from individuals without proper finances or assets have very less probability to acquire funds from them. Venture capitalists invest primarily for high returns and claim equity, therefore, limiting it only to highly profitable business. Government backing for new business is always burdened either by regulations and laws or neglected due to lack of interest in the idea itself. These limitations are alleviated by crowdfunding. There are several advantages of crowdfunding [9, 28]. It provides equal opportunities for every entrepreneur to reach out to investors, since the contributions are made in small sums from a large group of investors, the risk involved is minimum. The idea/concept is discussed among several peers and thus the concept/idea gets improved as well. It helps fetch customers from across the globe. Therefore, crowdfunding, when unleashed in its full power, can propel economies across the globe. Internet serves as the primary workhorse for crowdfunding. Although, due to its omnipresence and universal acceptance, the Internet has been able to sustain and support crowdfunding, it has a few drawbacks. In the current form, Internet-based transactions are centralized (stored on a server), restricted based on the laws and regulations of the nation in which the investor or the fundraiser resides, prone to data theft or corruption since the data is available on a single source or a few sources, maintenance cost is high and with a possible downtime and maintenance issues. All these issues can pose serious challenges for crowdfunding, where trust and transparency are of paramount importance. Hence, it is necessary to adapt a new platform which could resolve all of these, or at least most of these, issues. Blockchain technology is a disruptive technology; it solves most of the problems posed by conventional online processes [27]. First it is a decentralized technology.

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Therefore making it free from a single entity’s control, robust from network outages, low cost, and high efficiency system [24]. Along with these, it also solves the problem of the transaction charges involved in conventional currency-based trading, if cryptocurrency is used. Since the framework of a blockchain technology is based on a public ledger, it adds transparency, data security, and tamper-proof record keeping. Public record keeping on distributed systems, making it ubiquitous and hence free from any sanctions or censorship [17, 23]. Our work uses a blockchain-based approach for crowdfunding. Blockchain serves as the backbone of democratized investor contributions, making it more accessible, secure, and flexible for entrepreneurs and investors. Due to its immutability and security, it can be used for data storage, financial transactions, real estate, asset management, and much more. In 2019, the Telangana government proposed to set up India’s first blockchain district in Hyderabad (India). Their project is to establish a conducive ecosystem for blockchain [20]. Blockchain has various advantages over traditional database, namely, immutability, reliability, decentralized nodes, fast transactions with low cost, and security using SHA-256 encryption. Implementation of blockchain technology in crowdfunding provides more reliable transactions [5, 13, 15]. The rest of this chapter is organized as follows: Section 2 focuses on how the transactions are performed in a traditional blockchain technology. In Sect. 3, Ethereum, an improvised version developed on the blockchain technology, is outlined. In Sect. 4, FUNDSMART, the proposed platform for crowdfunding, is presented. Section 5 looks at the smart contract at the core of the crowdfunding platform. Conclusion is provided in Sect. 6.

2 Transaction Using Traditional Way Versus Blockchain This section provides an overview of traditional transactions versus blockchain-­ based transactions. The process of blockchain-based transactions is presented clearly, and its advantages over the traditional approach is highlighted. A traditional funding approach is depicted in Fig. 1. In traditional ways, to perform any transaction, a contributor has to go through several processes/steps which

Lawyers, Brokers & Insurers Contributor

Fig. 1  Traditional way of transaction

The person with the Idea

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involve tedious paperwork, that could not only be time-consuming but also chaotic and vulnerable to human errors. In addition to this, there are high chances of breaching trust, which may result in a loss of a considerable amount of money in the process. Besides that, there is high possibility of fraud, in which the person or firm for whom the money has been granted could have diverted the funds elsewhere. This lack of accountability has been a severe setback for unestablished individuals/firms to gain investments. Whereas, in a blockchain using smart contracts [16], the need for third party is eliminated, as shown in Fig. 2. Smart contract is a code committed on a blockchain through which transaction of money, property, or shares can be done in an easy and conflict-free way, while avoiding any third-party involvement. After committing on blockchain, smart contract works like a self-operating computer program that automatically executes when specific conditions are met and they run exactly as programmed without any possibility of censorship, downtime, fraud, or third-party interference [12]. Blockchain is a useful technology for various applications such as finance, IoT, and healthcare management [3, 22]. Ethereum and Bitcoin are the two most popular cryptocurrencies in the world by market capitalization. Although they both have several similarities, there are stark differences too. Bitcoin (BTC) is a cryptocurrency. It is the first cryptocurrency and has the largest market capitalization. By the end of 2019, the total estimated value of Bitcoin is around $130 and $25 billion worth of Bitcoins are traded every day [8]. Bitcoin, in a nutshell, is an alternative to paper currency and serves as electronic cash for transactions. The primary advantage being, there is no centralized agency like a bank or a government to impose any restrictions. While Ethereum, which is also developed on the concept of blockchain, is not just a cryptocurrency but also allows its users to develop what is called as dapps (decentralized applications). Through the concept of dapps and the power of programming, users can implement what is called as “smart contracts”, which are nothing but pieces of code which implement a predefined set of rules and take necessary and prompt actions without human interventions. Therefore, we choose Ethereum as a platform for crowdfunding. The next section elaborates on the operation and functioning of Ethereum.

Code is Law Contributor

Fig. 2  Transaction with blockchain

The person with the Idea

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3 Ethereum Blockchain Ethereum is an example of blockchain technology (a popular blockchain platform). It is a public, decentralized platform for computations. Simply put, it is an interconnection of several computers (referred to as nodes) across the globe which maintain a copy of code and data [14]. The large number of nodes available with the copy of data makes it impossible for anyone to censor, stop, or destroy the data [26]. Hence making it a platform for decentralized applications. It not only serves the purpose of financial transactions but is also very useful for other non-financial transactions. Ethereum, unlike Bitcoin, is not just a cryptocurrency, but beyond it. Ethereum incorporates several features to overcome the limitations of scripting language used in Bitcoin, for example, lack of Turing completeness, lack of state, value blindness, and blockchain blindness [4]. As mentioned by Buterin [4] in his seminal work on Ethereum, it is one such system which has “scalability, standardization, featurecompleteness, ease of development and interoperability offered by these different paradigms all at the same time”. Ethereum achieves these features on the foundation of blockchain with the power of a Turing complete programming language [7]. This Turing complete programming language simplifies the process of writing smart contracts, which are nothing but a set of rules that govern the transactions among the accounts [2]. One need not be a part of an Ethereum network to be a part of it. It can be done by MetaMask [6]. MetaMask, according to Antonopoulos and Wood [1], is browser extension, which lets you connect Infura (another Ethereum node) and lets you run smart contracts on that. It also acts as your Ethereum wallet which contains ether (money) and lets you do transaction through any dapps of your choice. A list of a few commonly used terms and their meaning in Ethereum are explained below. Ether (ETH): It is an intrinsic currency in the world of Ethereum. Wei: It is the smallest indivisible sub-denomination of Ether (1 Ether = 1018 Wei). Gas: A prescribed cost for performing a transaction (this is only a number). Gas Price: The value of each unit of gas is called gas price. Usually denoted in Wei. Fixed by the miner. Gas Limit: The maximum amount of gas a transaction is allowed to consume. Fixed by the account while initiating a transaction. Account: Any object in the Ethereum network is an Account. It has a public address and a private key. There are types of accounts: Externally Owned Accounts (EOA) and Contract Accounts (usually referred to as contracts also). Contract accounts are those where the “rules” of a contract are mentioned in the form of a code. The primary difference between an EOA and a Contract Account is that the latter does not have a private key. Smart Contracts: These are set of rules which govern the contract. They are written using high level programming language. EVM: Ethereum Virtual Machine is a powerful Turing Compatible 256 Byte virtual machine.

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Fig. 3  Gas usage in blockchain

Fig. 4  Non-refundable gas in blockchain

The following example explains the operation of Ethereum. Figure  3 shows a basic transaction from a sender to a receiver on Ethereum. Here the sender initiates a transaction with a STARTGAS of 250 and as a part of the transactions done to reach the receiver, 50 and 30 gas is spent. Therefore, at the end of the transaction, which is after the request has reached the receiver, the remaining 170 gas is sent back to the sender. The fee collected by operator-1 and 2 is basically a charge for using their computation power. The miners can choose which transaction to process and which to decline based on the gas and gas price available. Therefore, any transaction where the sender is ready to spend more gas shall be completed fast. Figure 4 shows a failed transaction where the sender has initiated a transaction with insufficient gas in this case. In this case also, the STARTGAS is 250, but the operation-1 and 2 has exhausted 120 and 130 is consumed and after that there is no gas left. Hence the transaction could not be completed, and the transaction is reverted back. However, since the miners have already used their resources for computation their reward of gas is not revoked. An example is presented here to understand the concept of pricing in terms of gas on an Ethereum network. Suppose we set gas limit = 50,000 and gas price = 20 wei. Then transaction fees will be 50,000  ×  20  =  1,000,000,000,000,000  wei  =  0.001 Ether that is about 12–15 which is very less than traditional transaction fees [10]. Hence, using Ethereum, one can develop and deploy a secure, cost efficient, decentralized, and reliable platform for crowdfunding. This is presented in Sect. 4.

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4 FUNDSMART In this section, we present a crowdfunding platform developed on the basis of Ethereum blockchain. FUNDSMART is designed to cater to the demands of both the fundraiser (one who is presenting an idea for raising capital, also referred to as the “idea person”) and the contributor (the one who is making an investment). The operational details for each of these perspectives are explained in detail in the subsequent subsections. FUNDSMART is an online crowdfunding platform using blockchain for secured and public transactions. The main objective behind this is to develop a secure, easy to use, reliable platform for crowdfunding transactions between the contributors and the fundraisers.

4.1 Contributor Functionalities A contributor (Fig. 5) can search for an idea based on the category such as art, comics, music, design, gaming, and real estate or directly by the name of a particular project. If the contributor likes the idea, then he/she can contribute some capital. For each project shared on FUNDSMART, there is some minimal amount required for contribution specified by the fundraiser. Contributions are accepted only if the contribution is above this minimum value. To contribute, the contributor requires to enter a private key which they can get easily from MetaMask. MetaMask is a browser extension that works as e-wallet for ether (money).

4.2 Public–Private Keys Ethereum is built on public-key cryptography, which uses a pair of public keys which are known publicly and essential for identification and a private key which is meant to be private and essential for encryption and authentication [1]. Every

Fig. 5  Contributor’s flow chart

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Fig. 6  Public–private keys

transaction on Ethereum blockchain is being signed by the private key that we provide before transaction and is verified by the public key. Public key is derived from private key using an algorithm, as shown in Fig. 6. At a high level, to generate a public key, first a private key is converted into its binary representation and then identifying bits with value 1. Summing an exponentially multiplied generator variable to arrive at final public key. This cryptography algorithm used to generate key pairs is very easy one way but very hard the other way around, so it is very difficult to reverse the algorithm to obtain private key from public key. It is like doing multiplication on paper where we can multiply two numbers easily but if we are given the product then it is difficult to identify numbers. Blockchain wallets like MetaMask automatically generate private key for the user which grants them full ownership of the fund on a given address. When a transaction takes place, the software signs the transaction with your private key (without actually disclosing it), which gives authority to transfer the funds on the address you are sending to. If the provided key is incorrect then the transaction will be aborted.

4.3 Fundraiser Functionalities This section has basically two functionalities: (a) Pitching a new idea that requires funding (Fig. 7). (b) If your idea gets desired funding, then manager/fundraiser has to submit spending request to spend the fund (Fig. 8). To submit a new idea, one has to give all details about it like the title (unique), description, summary, funding requirements and time span in which you require funding, and also the minimum contribution one has to make, etc. All these details will be stored in blockchain in the form of a transaction. In the second section, if your idea has been denied funding then the manager cannot withdraw money without any permission. Because there can be a chance of fraud, or they will use all this money for some other purpose. For that, we have the spending request, approve request, and finalize request system (Fig. 9).

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Blockchain New Idea

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Required amount has been funded

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Above 50% contributor approve request No

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Fig. 7  Fundraiser’s flow chart

Fig. 8  Sending spending request with vendor’s account address

4.4 Spending Request As shown in the Fig. 8, if manager wants to withdraw some funds, then first, he has to create a request in which he will specify the address of the vendor/person’s address to whom they want to transfer the funds to. Because blockchain is public, hence all requests will be shown on the website.

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Fig. 9  Spending request approval

4.5 Approve Request As shown in Fig. 9, when a spending request is submitted, contributors who contributed to the idea will approve request, if they wish. For that, we have a voting system in which every contributor’s vote will have some weightage according to the funds he or she has invested. For example, if a contributor has contributed $20 in a project that requires $100, then their vote weightage will be 20%. It requires more than 50% approval to finalize the request. If the manager gets more than 50% vote, then this will create a transaction on blockchain and then the manager can finalize the request and send ether (money) to the vendor’s address. This spending request, approve request, and finalize request system will give contributors a safer and more reliable environment. Because when a contributor invests some money then that money worth of ethers will be debited from the contributors e-wallet and will be stored on the blockchain. Now, when a manager will submit a spending request, then they will give the address of a vendor’s account which will be verified on the blockchain, and contributors will decide by a voting system that the request should be approved or not and all these steps will be stored in the blockchain as transactions.

5 The Smart Contract The smart contract embedded in the FUNDSMART crowdfunding platform is to ensure transparency and trust. It will assure contributors that they are getting the fair return on their investments. This transparent return on investments system based on the smart contract will attract more investors and will safeguard crowdfunding

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success. Since these returns are computed by the smart contract and not the fundraiser, investors are certain that they will be paid what they are due. The smart contracts will also take into consideration the investments at a granular level and execute accordingly. For example, the fundraiser, for a particular project, might indicate that the returns are based on the amount you invest. This fundraiser project needs $5000 to materialize. Therefore, those that contribute $1000 will get 20% returns, contributing $500 will get 10% returns, $1500 will get 30% returns, and so on, providing a granular level of investments and returns. Again, considering this particular project and the lifetime of the project, and the total returns from the project will all be under the control of the smart contract. It might be a successful project and make a lot of profit, and the profits will be returned to the investors accordingly or it might eventually fail. But since all the stakeholders (fundraiser and investors) are part of the blockchain peer-to-peer network, they are all able to see the smart contracts (transparency/trust). Smart contracts enable us to eliminate the “third party”, simply because we no longer trust a third party for investment given the number of scams and pyramid schemes. Smart contracts ensure that a transaction is only executed when all the required conditions are met. Whereas a human being can execute transactions regardless of the requirements. The embedded smart contracts would attract more investors to the FUNDSMART crowdfunding platform ventures as there would be consensus (all investors and fundraisers need to agree on the direction of the project) and immutability of transactions (eliminating fraudulent transactions). In terms of crowdfunding projects that result in a product being prototyped, tested, and the manufactured product to be sold, the smart contract will control everything from raw materials, production, distribution, sales, and accounting. It will automate payments back to the investors and fundraisers and once again all transparent transactions. The concept is not new. It has been applied for all Initial Coin Offering (ICO) start-ups for new cryptocurrencies and also for new products. Traditional crowdfunding platforms have an element of distrust, as the investors are not guaranteed a return on their investments. The investments fail, the fundraiser absconds with the money, or the fundraiser does not report the actual success (money made) from the project. Smart contracts in our FUNDSMART crowdfunding platform offer a safeguard against this distrust. It puts a safety measure in place and secures the contributor’s investment. Projects that do not attract enough investments to materialize, investors are again assured that their investment is safe and will be returned to them as it is under the control of the smart contract, not the fundraiser or the platform owner. In addition to the above, smart contract will also: automate the investment, returns, allocation, and withdrawal of funds; remove the untrusted third party; increase credibility of the crowdfunding platform through security and privacy (encryption); secure data storage (distributed data storage); reliability (smart contracts versus error prone humans); consensus (everyone agrees before any decisions are made); fast and efficient (automated). Ethereum is already recognized as a decentralized, open-source blockchain with advanced smart contract functionalities. This is the main reason for our proposed FUNDSMART crowdfunding platform to be deployed

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on Ethereum. In terms of any conflicts arising among the contributors, fundraisers, between contributors, between fundraisers, or between contributors and fundraisers, the smart contract will have the adjudicating vote. Human fundraisers and contributors we feel are biased, have vested interests, not smart contracts. For us, the smart contract is the law (code is the law). According to Jacynycz et  al. [11], the Smart Contract protocol proposed by Szabo [19] allows the possibility of self-executing contractual clauses, providing more security than traditional contracts with fewer costs. Ethereum’s smart contracts offer the possibility of developing decentralized autonomous applications such as cryptocurrencies, financing platforms, or social networks, to name a few.

5.1 The Smart Contract Design The smart contract design, as depicted in Fig. 10, for our FUNDSMART crowdfunding platform has been adapted from Shegakula Nagaraj [18]. The smart contract controls the whole platform and links different modules and components within the platform. All the rules, regulations, decision-making for transactions, payments, computation of payments, basically the whole business model, is coded into the smart contract. This enables the FUNDSMART crowdfunding platform to control the flow of business logic, work independently, in a peer-to-peer network.

Fig. 10  The smart contract design. (Adapted from Shegakula Nagaraj [18])

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The smart contract is the core of the FUNDSMART crowdfunding platform that interfaces with the web platform that users (fundraisers and contributors) interact with. According to Shegakula Nagaraj [18], “the root of the platform is CampaignRegistry contract, which is created only once and contains all the campaigns reference that are created in the platform. When a new campaign contract is created its contract address and contract creation timestamp is stored in the Campaign registry contract. The crowdfunding platform fetches all the contract references, stored in a struct datatype, from the Ethereum network when the app is loaded and builds the landing page with campaign tiles”. The logic of the application is embedded in the smart contract implemented using the Ethereum network. As such, any Ethereum user can run and interact with this smart contract. The contract is fully autonomous in order to minimize dependencies. This implies that once it is deployed over the blockchain, its creator (or anyone) cannot modify its behaviour or access the stored funds.

6 Conclusion Crowdfunding is the way to attract investors towards one’s venture and how investors can be assured of safe and reliable transaction of funds. In this chapter, a blockchain-­based framework is proposed that improves the security and reliability of the crowdfunding transaction. It is due to the reason that the control in blockchain-­ based technology is decentralized, it effectively addresses the monopoly over the funds and improves the accountability of the one who is making the investments, that ensures the security. The proposed model shows how a transaction is verified on blockchain using public–private key; in addition, the proposed blockchain-based secure model provides a crowdfunding environment which sharply decreases the possibility of fraud and increases the number investors. All transactions that have ever occurred on the network are recorded by the blockchain technology with public visibility that allows communication between two untrusted parties without involving any trusted third party and this ensures its security and low cost. In the future, we would like to explore more sophisticated methods to ensure the security and privacy in transactions in blockchain-based framework.

References 1. Antonopoulos, A. M., & Wood, G. (2018). Mastering Ethereum: Building smart contracts and DApps. O’Reilly Media. 2. Atzei, N., Bartoletti, M., & Cimoli, T. (2016). A survey of attacks on Ethereum smart contracts. IACR Cryptology ePrint Archive, 2016, 1007.

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24. Wang, Z., et al. (2020). Blockchain-based certificate transparency and revocation transparency. IEEE Transactions on Dependable and Secure Computing, 19, 681–697. 25. World Bank. (2019). Crowdfunding’s potential for the developing world [online]. https://www. infodev.org/infodev-­files/wb-­crowdfundingreport-­v12.pdf. Accessed 29 January 2019. 26. Wright, A., & De Filippi, P. (2015). Decentralized blockchain technology and the rise of lexcryptographia. Available at SSRN 2580664. 27. Wu, D., et  al. (2019). Equilibrium analysis of bitcoin block withholding attack: A generalized model. Reliability Engineering & System Safety, 185, 318–328. https://doi.org/10.1016/j. ress.2018.12.026 28. Zhu, H., & Zhou, Z. Z. (2016). Analysis and outlook of applications of blockchain technology to equity crowdfunding in China. Financial Innovation, 2, 29.

Certificate Authentication System Using Blockchain Murugan Sekar, A. Rajesh, S. Thirumal, and R. Anandan

1 Introduction Data security is a major concern in this digital world. Blockchain features decentralized, peer-to-peer (P2P) and unalterable information that has a huge ability for a variety of purposes. It can be described as a distributed ledger technology that has a frequently appending public ledger. A group of blocks interconnect between them to form a blockchain and every block contains or follows information about all latest transactions once completed it was kept for good permanently on a blockchain network that is secured and immutable too. When a general agreement is achieved between various nodes, the details of the transaction are appended to a block that previously contains information for multiple transactions. The hash or numeric value of the appropriate last connection is found in every block. All blocks are interconnected and together these blocks will be forming a blockchain. Information is distributed because it is distributed to various nodes (distributed data management). As a result, the database is jointly maintained by these nodes. The block will not be validated in the blockchain until it is validated by several users. Also, the originality of the block cannot be changed freely. For example, blockchain-based smart contracts can create reliable systems to dispel doubts about the authenticity of the information. Blockchain is different from traditional databases in that the blockchain is decentralized as a result of there being no intercessor or negotiator, and it’s confidential. Figure 1 represents the evolution of different versions of the blockchain from 2005 to 2022. M. Sekar (*) · A. Rajesh · S. Thirumal · R. Anandan Department of Computer Science and Engineering, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_13

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Fig. 1  Evolution of blockchain versions

Ethereum smart contracts have been introduced in early 2005. From then, the blockchain technology has been enhanced to blockchain 2.0. Problems related to digital wallet and decentralized transactions have been encountered in cryptocurrency; to solve these, blockchain 1.0 was primarily developed. Blockchain 2.0 focuses on worldwide decentralization and is used to send or receive assets through smart contracts and the emerging Bitcoin alternatives created a lot of value. The open-source and decentralized platform named Ethereum had good completeness and support for a variety of enormous applications. Ethereum played a major role in building a variety of smart contracts and decentralized independent organizations [1]. If bitcoin was considered as a world payment system then the worldwide computing system will be Ethereum. Android was an open-source software introduced by Google; similarly, Ethereum was also an open-source platform introduced for blockchain. Developers can form applications with the infrastructure provided by the Ethereum platform. Ethereum and its developers are developing and maintaining their infrastructure. The Ethereum has a list of key features: (1) incorruptibility: no third party can change the data; (2) security: avoids errors caused by personal factors since entities maintain decentralized applications instead of individuals; (3) permanence: even a private laptop or server failure cannot affect the properties of the blockchain. In the early 1990s, Nick Szabo initially proposed smart contracts. A smart contract that allows computers to perform a group of transactions was demonstrated by him. As blockchain became well liked, more and more attention is received by smart contracts. The key feature of the blockchain-based platform Ethereum includes smart contracts which were introduced into the technological world in 2015. A smart contract can be defined as “a digital contract written in ASCII text file and executed by a computer, incorporating the underlying engine, blockchain’s anti-forgery mechanism” [2]. Smart contracts can be deployed by employing the Ethereum platform. Developers can specify any instructions in the smart contract if needed; develop numerous types of applications, as well as one contract can interact with another contract; data saving; and ether transactions. In addition to preventing contract meddling, they are present at all nodes of the network. With the relevant tasks performed by machines and services provided by Ethereum, the manual blunder could be minimized to overcome quarrels over these contracts. In the electoral system [3] smart contracts are principally employed. During the academic career, students achieve enormous educational accomplishments or satisfy particular requirements which are documented and it plays a character in evaluating the skills, ability, personality, etc., of the individual who owns it.

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In the existing digital world, everything is stored in a digital format. Today, all graduation badges and credits contain data that can be simply forged lawlessly. Therefore, there exists a great want for an accurate methodology that will ensure the originality of information in these certificates, which suggests or confirms that the document comes from a trusted and licensed source and that it has not been tampered with. Numerous methods have been formulated to safeguard the electronic certificates of educational centers and accumulate their confidentiality. Similarly, the land certificate possession system in the Asian nation continues to be registered involving human intervention. The system has boundaries and ambiguity which will be explored by idle entities. Some limitations are the physical certificate can be lost. To resolve that, a land and asset certificate possession registering tool can be constructed using Hyperledger Fabric blockchain technology [4]. Blockchain was a key tool for safeguarding information, and when merged with different hashing algorithms, it was a robust methodology for securing information. This methodology aims to cut back on the issues that arise within the manual system. Blockchain technology is employed to scale back the rate of faux certificates and make sure that the safety, validity, and authenticity of educational certificates would be enhanced. In recent days, information technology has developed like a storm, so the need for data protection is increased. Graduates, during the continuation of their studies or while looking for work, need different certifications to interview. However, they often realize that they have lost their academic credentials and honors. Applying for a reissue of a hard copy will be long as a result of certificates square measure provided by completely different organizations and offline application is also necessary. On the other hand, requesting a soft copy or digital or electronic certificate saves time and individual efforts. By giving details for identity authentication, graduates can easily claim any certificate. However, owing to this convenience, fake diplomas, licenses, and certificates are in vogue. As a result, educational institutions and companies were not able to immediately authenticate the received documents. To solve this drawback, a blockchain-based certificate authentication methodology was formulated. The data is placed in various nodes, and if someone wants to alter a certain piece of internal data requires other nodes to alter it at the same time. Therefore, the system has extreme reliability.

2 Existing System The academic certificate proves that the possessor has achieved certain educational qualifications or consummated certain requirements. These certificates or documents are utilized in three separate processes including issuance, sharing, and authentication [5]. It was a headache for the students and academic management to store these documents either in traditional paper format or digital format. And at the same time, it was a difficult and time-consuming process for employers during employee certificate verification tasks. Several articles recently highlighted the cases of university faux diploma certificates in 2020  in many countries [6]. The

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research presents that more than 30% of degrees are lawlessly attained [7]. Nowadays, some universities pay over two million greenbacks a year for reviewing validation requests [8]. In certificate authentication method using Quick Response code [9], the paper certificates are protected using encryption algorithm but it can be easily decrypted, and on Unique Smart card verification method [10], students are provided with a smart card of the magnetic strip which contains the student details and certificate parameter and a Unique Identification number which will be encrypted using Blowfish algorithm before transmission. The above-discussed authentication methods using Quick Response code or Unique Smart Card for authentication of academic certificates do not ensure security. Since they can be easily manipulated, they are not reliable. Our work focuses on resolving these drawbacks in certification validation system.

3 Objective The prime intention of this proposed system is to overcome the forgery certificate issues in the education sector by enabling the emerging blockchain technology, which ensures un-changingness and openly confirmable records, these attributes of blockchain are employed to produce the digital certificate that is anti-pirated and straightforward to authenticate. The encrypted data is obtained from digital certificate using hashing algorithm. The Merkle tree is formed and subsequent Merkle root is calculated until a single Merkle root is obtained. Then the single Merkle root data is stored in a blockchain and can be retrieved when needed. During verification stage, once again Merkle root will be generated and compared with the retrieved Merkle root from blockchain. If they are not equal, then we can confirm the integrity of certificate is compromised. In the end, academic documents like university certificates and transcripts can be more secure, reliable, and deliverable. This enhances the transparency and prevents any user from faking his qualification.

4 Related Work Tuti et al. [11] implemented an innovative blockchain-based effective solution for validating certificates in educational systems utilizing the unique benefits of blockchain, a decentralized encryption system. The various results of previously executed research are focused through the library study method. Namasu et  al. [12] proposed a privacy-preserving technique for creating and maintaining health records that also provides an interface between the user and the nearby health centers. Medical certificates are used to take advantage of financial benefits such as tax, insurance claims, and litigation. The advantage of the proposed scheme is to ensure confidentiality by setting rules in smart contracts.

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Garba et  al. [13] implemented a sinewy and extensible domain authentication methodology that records a group of devoted Certificate Authority each related to the particular entity at the network in the blockchain. So, every Certificate Authority initially validates if it is devoted to executing the actual issuance process. The advantage of this method is it would need lesser storage space and lesser bandwidth to authorize certificates than various systems. The disadvantage is it needs to download the entire block header every time and Power of Work (PoW) requires high energy consumption. Gayathri et al. [14] developed a document verification system using a chaotic algorithm that generates a numeric representation for the certificate. But the main disadvantage is the majority of chaotic encryption algorithms will be employing floating calculations which results in inefficient and complex implementation compared to the traditional ciphers [15]. Wang et  al. [16] designed a system to prevent signing fraudulent certificates. Here, the Certificate Authority validated certificates and their updated annulment information of a web server are processed in blockchain, and verifier accepts only published and not revoked. This paper provides various countermeasures for compromised public key pairs which serve as an advantage for this system. The disadvantages of this system are it requires larger storage, has more validation delay, and leads to higher incentive cost. Bahrami et al. [17] proposed a devoted and tinker-proof certificate authentication system that enhances the confidentiality and heftiness features of the system by eliminating problems caused by a single entity and so the dependence on any single party is reduced. It leveraged permissioned blockchain-based, sealed smart contracts for the authentication process. The advantage is it provided transparency. The disadvantage is permissioned blockchain has a high risk of collusion and overriding of consensus. Afrianto et al. [18] developed a secured document storage system for job training institutes based on the open-source blockchain platform employing smart contracts to generate data. The InterPlanetary File System (IPFS) can be utilized to save documents in an Ethereum infrastructure. The advantage here is Metamask is used to store data which enhances security. The disadvantages are IPFS consumes a lot of bandwidth which is not appreciated by the metered internet user. IPFS employs transport encryption instead of content encryption. IPFS makes sure that the data sent from one node to another is confidential. But, anyone in the system can download and view that data if they have CID. Budiono and Karopoulos et al. [19], designed a Covid-19 result certificate handling system which is an open and non-permissioned blockchain that is executed via Ethereum Virtual Machine. This can be done since blockchain employs a decentralized network to save its information with a confidential and common agreement mechanism [20]. But it is a costly affair and offers limited block size.

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5 Proposed Method In this proposed system at first, the hardcopy certificate data are digitalized and uploaded into the system by the issuer. The issuer can also upload multiple numbers of certificates. A hash code is generated for all the quarters of the certificate using the SHA3-512 algorithm which will produce a 128 characters length hash value and then the Merkle tree is formed using those hash values. In comparison with similar data structures, the Merkle trees take up only a little disc space and can be used to confirm the integrity of data efficiently. The root of the Merkle tree was added to the append-only blockchain network as a new block and a digital certificate will be issued to the qualifier or owner by the issuer Institution or University. When the verifier initiates the verification step, the system checks if the digital certificate matches the data present on the blockchain network and returns the appropriate output to the verifier. Figure 2 represents the execution flow of our methodology over the period of time. Types of modules: Issuer: Data digitalization Algorithm: Hash Code Creation, Merkle Tree Formation, Block Creation Verifier: Digital Certificate Authentication

Fig. 2  Flowchart of certificate generation and authentication

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Considering that many previous papers achieved similar or lower transition speed and security, we have ensured the drawbacks are overcome with this reliable prototype. Web Portal can validate certificates rapidly. Our developed Android application can run on offline mode also.

6 Method of Implementation 6.1 Data Digitalization Here the academic certificates are initially digitalized by converting them into digital or electronic certificates. The academic certificates issued by the institution or issuer are digitalized for every individual qualifying. In this module, the institution or issuer will upload the academic certificates like 10th marksheet, 12th mark sheet, university certificates, public certificates, sports certificates, and so on. These records need to be verified by the respective sector before uploading to the interface because blockchain was immutable. Academic certificates for an entire academic year batch can be stored in a database or they can be uploaded to the system directly. After getting digitalized, certificates for individuals will be issued.

6.2 Hash Code Creation Hash algorithms convert a digital message into a brief message digest for applications such as digital signatures. Any amendment within the original message results in an amendment to the digest, making it easier for you to detect intentional or unintentional changes to the original one. The SHA3-512 algorithm was employed to produce the numeric value of the academic record. SHA3-512 comes under the scope of the SHA-3 cryptographic hash family. SHA3 is considered more secure than SHA-2 for the same hash length. SHA3-512 provides more cryptographic strength than SHA-256 for the same hash length. SHA3-512 hash algorithm was compared with SHA1, Message Direct (MD5), Rijndael algorithm, and so on. And based on the evaluation SHA3-512 has more security than other traditional hashing algorithms and has a hash length of 128 characters.

6.3 Merkle Tree Formation Merkle tree was a simple data structure that ensures proper mapping of huger data blocks as a connected tree or linked list. In Bitcoin and other cryptocurrencies, Merkle trees are used to encrypt blockchain data more productively and

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Fig. 3  Merkle tree formation

confidentially. They can also be called “binary hash trees”. By repeatedly hashing pairs of nodes until only one hash remains (this hash is called the root hash or Merkle root), Merkle tree can be produced. They are built from the down up, from hashes of single appropriate certificates. Each transaction is hashed, then every pair of transactions is merged and hashed along, and then on till there’s one single hash for the complete block. (If there is an odd range of transactions, a transaction is duplicated and its hash is merged by itself.) The Merkle trees are used in distributed systems for efficient data verification because it enables validation of a particular transaction without having the entire full blockchain. The root of the Merkle tree will be added to the Ethereum blockchain network. A smart contract is written to generate and validate the digital certificate instantly by comparing the value of the root hash, this contract is then deployed using Ganache, Ethereum local blockchain. Figure 3 provides an example of extracting Merkle root with the help of encrypted hash from four certificates.

6.4 Block Creation Here every certificate will be created as a block. The data structure of the block acts as a container. For all blocks, a hash code will generate and get interconnected securely. During block creation, the count can be increased as per our requirements. The prime advantage of this unit is users are allowed to share the hash code with someone if needed.

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6.5 Digital Certificate Authentication The digital certificate will be submitted by the owner to verifier for pursuing higher education or for employment hunt. If there is an art of fraud done by modifying data in this digital certificate by the owner it will also change the Merkle root. The system checks if the root hash value of the Digital certificate matches the data present on the blockchain using smart contracts and triggers appropriate output to the verifier. We have also implemented the verification for the validity of the certificate.

7 Comparison and Evaluation Metrics To compare the code complexity of SHA3_512 Merkle tree and MD5 Chaotic Algorithm, we use metric analyzer. Figure  4 is the Method Metrics Graph of SHA3_512 Merkle tree. Figure 5 is the Method Metrics Graph of MD5 Chaotic Algorithm. Cogc is the Cognitive Complexity ev(G) is the Essential Cyclometric Complexity iv(G) is the Module Design Complexity Metric v(G) is the Cyclometric Complexity Table 1 provides a comparison of system performance of SHA3_512 Merkle tree with MD5 Chaotic Algorithm.

Fig. 4  Method metric plot for SHA3 512 Merkle tree

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Fig. 5  Method metric plot for MD5 Chaotic Algorithm Table 1  SHA3_512 Merkle tree vs MD5 Chaotic Algorithm CPU usage RAM usage Gradle build Checksum time

SHA3_512 Merkle tree Initial: 31% Average: 6% Initial: 124 MB Average: 75.1 MB 15 s 34 ms

MD5 Chaotic Algorithm Initial: 36% Average: 9% Initial: 131 MB Average: 81.1 MB 19 s 77 ms

8 Conclusion Certificates are a chunk of paper that’s regularly issued in an elite way by a scholarly institution to graduates. Once the student moves to another institution or employment, certificate verification becomes difficult. It is possible to forge digital or paper certificates in ways that are difficult for users to detect. The importance of blockchain technology in the education sector is not distinctive from what has been done within the commercial sector. The espousal of blockchain in the world of education shall be employed for considerable improvements in the education sector. In this work, we offered a blockchain-based innovative solution to the certificate fraudulence problem. Data security is achieved by employing the immutability parameter of blockchain. The proposed methodology makes use of distributed processing to make it nearly hard for sensitive information to be reformed or feigned. This prevents the probability of fraud or forgery acts and can enhance privacy and security through smart contracts. The smart contract authenticates the academic

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certificate document with the help of hash identity generated. The developed application allows us to generate and validate the certificate in a secured and user-friendly manner.

References 1. He, B. (2017). An empirical study of online shopping using blockchain technology. Department of Distribution Management, Takming University of Science and Technology, Taiwan, R.O.C. 2. Lin, X. (2017). Semi-centralized blockchain smart contracts: Centralized verification and smart computing under chains in the Ethereum blockchain. Department of Information Engineering, National Taiwan University, Taiwan, R.O.C. 3. Shi, Y. (2017). Secure storage service of electronic ballot system based on block chain algorithm. Department of Computer Science, Tsing Hua University, Taiwan, R.O.C. 4. Syawaludin, A. R. S., & Munir, R. (2021). Registration of land and building certificate ownership using blockchain technology. In 2021 International Conference on ICT for Smart Society (ICISS) (pp. 1–7). IEEE. https://doi.org/10.1109/ICISS53185.2021.9533191 5. Grech, A., & Camilleri, A.  F. (2017). Blockchain in education. Publications Office of the European Union. 6. Palma, L. M., Vigil, M. A. G., Pereira, F. L., & Martina, J. E. (2019). Blockcahin and smart contracts for higher education registry in Brazil. International Journal of Network Management, 29, 1–21. 7. Attewell, P., & Domina, T. (2011). Educational imposters and fake degrees. Research in Social Stratification and Mobility, 29(1), 57–69. 8. Bajwa, N.  K. (2018). Modelling and simulation of blockchain based education system [Ph.D. dissertation]. Concordia University. 9. Mayowa, O., & Jinmisayo, A. (2021). Design and implementation of a certificate verification system using quick response (QR) code. LAUTECH Journal of Computing and Informatics (LAUJCI), 2(1), 35–40. ISSN: 2714-4194. 10. Lingampalli, J.  R., & Namdeo, V. (2021). Unique smart card verification system for validating university degree certificates. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICI-CCS) (pp. 1574–1578). IEEE. https://doi.org/10.1109/ ICICCS51141.2021.9432360 11. Nurhaeni, T., Handayani, I., Budiarty, F., Apriani, D., & Sunarya, P. A. (2020). Adoption of upcoming blockchain revolution in higher education: It’s potential in validating certificates. In 2020 Fifth International Conference on Informatics and Computing (ICIC) (pp.  1–5). IEEE. https://doi.org/10.1109/ICIC50835.2020.9288605 12. Namasudra, S., Sharma, P., Crespo, R.  G., & Shanmuganathan, V. (2022). Blockchain-­ based medical certificate generation and verification for IoT-based healthcare systems. IEEE Consumer Electronics Magazine, 12, 83–93. https://doi.org/10.1109/MCE.2021.3140048 13. Garba, A., Chen, Z., Guan, Z., & Srivastava, G. (2021). LightLedger: A novel blockchain-­ based domain certificate authentication and validation scheme. IEEE Transactions on Network Science and Engineering, 8(2), 1698–1710. https://doi.org/10.1109/TNSE.2021.3069128 14. Gayathiri, A., Jayachitra, J., & Matilda, S. (2020). Certificate validation using blockchain. In 2020 7th International Conference on Smart Structures and Systems (ICSSS) (pp.  1–4). IEEE. https://doi.org/10.1109/IC-­SSS49621.2020.9201988 15. Noura, H., Sleem, L., & Couturier, R. (2017). A revision of a new chaos-based image encryption system: Weaknesses and limitations. arXiv:1701.08371v1, 1–7. 16. Wang, Z., Lin, J., Cai, Q., Wang, Q., Zha, D., & Jing, J. (2022). Blockchain-based certificate transparency and revocation transparency. IEEE Transactions on Dependable and Secure Computing, 19(1), 681–697. https://doi.org/10.1109/TDSC.2020.2983022

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17. Bahrami, M., Movahedian, A., & Deldari, A. (2020). A comprehensive blockchain-based solution for academic certificates management using smart contracts. In 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 573–578). IEEE. https:// doi.org/10.1109/ICCKE50421.2020.9303656 18. Afrianto, I., & Heryanto, Y. (2020). Design and implementation of work training certificate verification based on public blockchain platform. In 2020 Fifth International Conference on Informatics and Computing (ICIC) (pp. 1–8). IEEE. https://doi.org/10.1109/ ICIC50835.2020.9288610 19. Budiono, R., & Candra, M. C. Z. (2021). Managing COVID-19 test certificates using blockchain platform. In 2021 International Conference on Data and Software Engineering (ICoDSE) (pp. 1–5). IEEE. https://doi.org/10.1109/ICoDSE53690.2021.9648482 20. Karopoulos, G., Hernandez-Ramos, J.  L., Kouliaridis, V., & Kambourakis, G. (2021). A survey on digital certificates approaches for the COVID-19 pandemic. IEEE Access, 9, 138003–138025. https://doi.org/10.1109/ACCESS.2021.3117781

Blockchain-Based Decentralized Student Verification Platform Nisha Soms, S. Adhithyan, M. S. Lokesh, and K. Madhumitha

1 Introduction Academic certificate authentication is a routine process for the employers to offer employment. Hence, the employer requests the authority of employee to verify the originality of his/her certificate submitted at the time of joining. An Education Verification hunt confirms whether the education, degree, training, or certification claims of an applicant are true and identifies possible disparity, if any, before hiring. This service, also known as an education background check or education check, is used to confirm academic history at high schools, colleges, and universities. A Blockchain is a relatively new technology that prioritizes security. It possesses a distributed, decentralized public record. They display online interactions throughout the entire PC frame. The Blockchain’s immutability contributes to solving the issue of fraudulent records. One million students graduate annually, and it appears that the authorities responsible for issuing documents have been compromised in terms of student data protection. Ineffective anti-forge systems regularly allow falsified documents to go undetected. Besides, there is a large administrative load caused since manual auditing, paper storage, and human verification are all necessary for the verification of physical copies of documents. Many companies unknowingly leave gaps in their verification of the authenticity of the documents [1].

N. Soms (*) Department of Computer Science & Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India S. Adhithyan · M. S. Lokesh · K. Madhumitha Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_14

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To fend off altering or generation by copier machines, the vast majority of the real instructive foundations will have some actual evidence highlights, for instance, microtext lines, UV undetectable ink, watermarking, security 3D image, hostile to filtering ink, and so forth. Most presumably, fake degree declaration merchants might put a phony watermark on their phony degrees to give them a genuine look. The security visualization, Anti-Scanning Ink, and void elements give an extra component of hostile to filtering and avert these from making a variety of replication. If scanned or reproduced, the situation would be very different from the originals. When an attempt is made to clone a degree, the word copy appears in the case of a void feature. The original document does not make reference to this issue [2].

2 Literature Survey Hrithik, Nevil, Rajkumar Gupta, and Amiya Kumar studied the students who completed their edification in each period and continued education or started working [3]. In these circumstances, the certificates must be verified through a careful process of document verification. Due to the necessity of moving documents between organizations for authentication, this causes a substantial gush. The necessity for an automated record verification system that may shorten the time needed to complete the document verification process resulted from this. Such outflows can be decreased with the help of Blockchain technology, which can also speed up document verification from days to mere seconds. With the advent of Decentralized Applications (DApps), Smart Contracts, and Public Blockchain like Ethereum; the authors developed a web application which comprises of a front-end for registering and verifying user requests. The backend has two modules: a Blockchain module and an OCR module. The former module handles data communication and confirmation based on the stored content in the Blockchain. The latter module aids in confirming information from certificates [3]. Lingampalli and Namdeo [4] gave a thorough overview of the prevalence of false diplomas and how they were created by students for their unofficial employment or further education. Numerous organizations and schools rely on third parties to accurately verify the accuracy of educational certificates in order to ensure that they are produced correctly. These third parties then contact the student’s college for verified document authentication. This approach is undoubtedly time and resource-­intensive. Hence, the authors proposed a smart card-based system that applies neural network architecture for the auto-transfer of first four integers derived from a student’s unique identification number as allotted by the universities [4]. Pavitra et al. highlighted how Bitcoin and Cryptocurrencies have urbanized to become the most popular applications of Blockchain. A multi-node private Blockchain network was developed using the Ethereum framework. A private Interplanetary File System (IPFS) was then created for the off-chain storage of records. They evaluated the performance of the proposed Ethereum Blockchain on a range of parameters such as load, network size, and consensus algorithms and

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concluded that their system is reliable and responded faster when compared to other existing frameworks [1]. According to Belurgikar et al. [5], students and consultants must create, communicate, and send testaments that contain sensitive or private information in a way that is similar to how academic records are used for advanced identifications but with an added degree of security. They presented a strategy in this regard that focused on examining individual portfolios using the renowned Blockchain technology. This prompted the development and implementation of a decentralised framework that could be applied to any professional setting where the requirement for one’s own character credits and testaments is present. This would make it crosscountry aware and simple to provide a normalised stage to characterise the panel in a very robust, simple, and secure way [5]. Desai et al. [6] expanded the facts about the education sector that benefitted from the Blockchain technology. It could record the executives, course assessment, assistive learning climate, records assurance, and check the probable regions where institutional associations know how to use Blockchain. VerifyB was created as a Blockchain-based solution that supports interoperability between institutions and organizations for the administration and assurance of student records without the involvement of outside parties. They set up an Ethereum platform and described how (Interplanetary File System) IPFS may be used to store student academic records using Blockchain technology. They also demonstrated how students may use QR codes to share their diplomas with organizations and businesses. Additionally, organizations can use Web3 to sign those documents with access control, and verified employers can check both the signatures and the transcripts on the same platform [6]. Gayathiri et al. [7] explored the importance of certificate validation process in this digital era, where everything is digitalized including all our academic certificates. In their article, they had highlighted how difficult it was to preserve their degree certificates from being forged. For any institute and organization, verification and validation of certificates were monotonous and burdensome. Hence, they proposed a system that could first convert the paper certificates into digital certificates. The chaotic algorithm was used to create a hash code value for the certificate [7]. All the certificates were stored in the Blockchain. The certificates were validated by a mobile application which furnished security and productive digital certificate attestation. Sudha et al. [8] in their article emphasize the fact that data security and privacy are crucial regardless of technology. In educational institutions, student records must be kept for at least 5 years. The amount of storage and time it then takes to retain student information is one of the main issues. This takes up more space, and when data is kept in cloud storage, security also becomes a key consideration. Hence, a Blockchain-based student information management system is proposed in their study. Their designed system has a web interface for the faculty to enter the students’ grades. With the help of smart contracts, Blockchain store these marks.

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The researcher in [9] presents the traditional system followed in their country, China for the student record verification which is a time taking procedure. Instead, he proposes a record verification system which ensures secured communication by applying RSA algorithm. It is implemented as follows. A student’s educational records are encrypted using a public key, known to the student, and an authenticated official from the school acts as the verifier and possesses the private key sent by the student. The verifier encodes all his/her records and those encoded details are stored in a block in Blockchain. Also, there will be an URL where full information of academic record is made available. The requester may compare and verify the contents in both the URL and the extract for trueness. In this way, time spent is minimal and the privacy of students is maintained. The article [10] presents a detailed review to store academic records using Blockchain. The objective of the article is to identify, among those suggested models, the convergent elements that address common problems which could result in a broadly acknowledged de-facto standard. Furthermore, a discussion on the trustworthiness of academic institutions will be required in order to examine if the suggested applications effectively solve the problem given that they will act as oracles for the specific Blockchain applications. In order to encourage quicker adoption and acceptance, the conclusion of this research emphasized the need for a consistent methodology to be implemented on public Blockchain. In order for the system to be sustainable, prediction should also be motivated, and their identities and activities should be recognized and observed. Badr et al. [11], in their paper, suggest a way to authenticate and transfer academic records between academic institutions. According to them, a private permissioned Blockchain like Hyperledger exhibits superior performance, cost-effectiveness, and privacy in comparison to public Blockchain alternatives. Additionally, the system is flexible and may be expanded to accommodate any kind of record in accordance with the unique needs of various institutions. While their system offers a user-friendly and secure solution, it is crucial that numerous institutions embrace it in order to generate the necessary mass momentum. Although it is initially aimed at academic institutions, this may be readily expanded to include firms looking to verify the qualifications of potential employment seekers. In [12], the authors intend to address the drawbacks of the procedures every learned member of low-income regions like Sudan had to undergo for the verification of their academic records. Hence, they proposed a Blockchain-based solution for the creation of a national hybrid Blockchain-based platform specially developed for higher education that unifies all relevant parties, including students, universities, governmental organizations, legislators, and private sector businesses. This investigation looks into the potential commercialization of its contracts as well as the costs and advantages of creating this platform. Additionally, the viability of this platform is assessed in Sudan and Syria, two of the most unstable low-income nations. It is concluded that this platform is practical for all participants in the higher education sector.

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3 Existing System Employers evaluate candidates’ education background to screen out seekers who have produced and additionally overstated their capabilities. Such aspirants pretend to safeguard the organization by buying degrees from illegal foundations. Hence, thorough verifications are done monotonously through direct contact with the school authorities or using the assistance from a third party who is deeply involved in investigating personal trusted background information. Job seekers provide information to background screening agencies and pay the stipulated amount accrued to such service. Some pre-verification services provide a secured online service where job appliers upload their education credentials and employers can have access to view such verified information at any point of time. The existing system faces the following problems: • Lack of centralized system which is free from breach and tamper • Total dependency on certificate-verifying third parties • Necessity of manual verification which is usually done through emails, phone calls, or web forms • More time-consuming since it would take weeks or months for collecting responses • Design challenge to create a decentralized trust system where automated verification and tamper-proof detection occur in real time

4 Proposed System The projected system presents a framework to store and validate the learner credentials by means of Blockchain technology. By switching to Blockchain, colleges can now ensure that their students’ certificates are secured and verified. It is imperative for any organization to hire genuine candidates with valid education and authentic degrees. Student verification system closely analyses the original copy of certificates which are provided by a student. The proposed model can identify the registered student so as to ascertain details like year of passing, cumulative grade point average (CGPA), and other sensitive data mentioned in his/her academic degree certificates and mark sheets. Each and every certificate secured by the student during the tenure of his study are scanned and stored in the Blockchain. As and when educational verification query arrives, authenticated college personnel who has proper access rights may verify the candidate’s details with the originals that are stored up in Blockchain. This results in quicker verification response and saving of expenses. The proposed system has the following advantages: • Automate and ensure compliance for the verification process. • Reduce stress on staff for compliance issues. • Verification review and approval, comment codes, and professional judgments.

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Simplifies and manages workflows. It is faster and efficient for certificate verifications. It is paperless and transparent. Saves courier time, use of paper, defined search parameters, etc. Extremely secured with the best technologies and authentications. No money procured during the process.

5 Methodology 5.1 System Architecture This project is designed and developed to address the requirement of institutes and organizations nowadays for end-to-end digitization. The adoption of Blockchain technology brings all the stakeholders together on a single cloud-based web platform and enhances connection for real-world digital activities. It comprises of a student module that compiles all the student information and a HOD module that allows for block storage and verification of all the student information. The admin module helps to create functionalities for the verifier, the college personnel, and the student. Figure 1 depicts the overall system architecture of our proposed work. The Student Verification System Web App is the designed application where the institution head verifies details regarding student verification as requested or enquired by any universities and/or companies. The college admin ensures the data collection of staff and student belonging to the institution and manages the login credentials of its stakeholders. The college admin periodically uploads and integrates student information onto the Blockchain platform and is also responsible for end-user management. The Blockchain now comprises each student’s information as in his semester-wise mark records and certificates. As and when the mail for educational verification for a student is received, the HOD uses his user-friendly interface to enter the Register number of that student. On verification, the app generates a report. The report is then digitally signed and mailed to the recipients. In this way, without much delay in time, the purpose of student verification is completed. Besides, the authenticity of the mark records and certificates are not tampered as those are directly dealt within the reach of officials of institutions.

5.2 Blockchain All the student’s certificates will be verified and stored into the blocks. This is carried out by using the secure hashing algorithm which will provide hash value for all the certificates. The main data, the hash of the antecedent block, the hash of the

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Fig. 1  System architecture

present block, the timestamp, and other information are the five components that make up each block in the Blockchain. All blocks are connected to each other to form the Blockchain. We are using pychain [13] which is implemented in python and it depends on asyncio which helps us to write asynchronous operations and web sockets to have persistent connection between the client and server.

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5.3 Digitalization and Creation The college admin clicks “add new student” tab and a new account will be registered and created for the student. The student can login with their credentials and upload all their academic certificates. All the information will be passed on to the verifier as a notification. The secure hashing algorithm, as illustrated in Fig. 2, is applied to deliver the one-way hashing function. It provides a digital signature for every certificate which will be unique. The digital certificate’s hash value is produced using SHA256 [14]. It functions in a way that it accepts input of various sizes and produces an output of a fixed size. The algorithm chooses a certain number of digits in the hash to be zeros where nonce comes in our Blockchain. We set the number of digits while running this value and can be set to whatever we want. If it meets the condition (number of digits) then we can increase the nonce by it (Fig. 2).

5.4 Validation The college admin creates a verifier account which will have access to all the students’ details. A Faculty member of the institute may act as the verifier. The verifier can login with their credentials into the application. The verifier receives notifications from students. Then the validation process starts to function and determine

Fig. 2  The implementation of SHA256 algorithm

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Fig. 3  Each block detail of the students

whether all the certificates provided by the student are issued originally by the University or not (Fig. 3). Figure 3 showcases a sample block from the blockchain developed for student verification system. If the certificates are original then they provide a provisional certificate and all details get stored in the Blockchain. If the certificates are not original then the student details will not be stored in the Blockchain. The verifier can also search for any particular student’s detail by typing in the student’s unique details such as university number or date of birth or year of passing. On accepting the input, the designed system checks whether the particular student belongs to the university or not based on the details stored in the Blockchain.

6 Discussion The results obtained from our proposed system are addressing the issues of time and space which many researchers are trying to develop solutions for. The authenticities of educational certificates and mark records are verified, at the time of upload onto Blockchain. The time taken for drafting a report for student verification is totally the time taken to look up that respective candidate’s information in Blockchain by entering his unique detail like registration number. Besides, security is guaranteed as to prevent tampering certificates for somebody who doesn’t own one. Thanks to Blockchain and its distributed ledger which keeps track of who accesses the block. The space is saved such that the student details which are physically available may be stored digitally for easy handling and retrieval. Our proposed system is capable of maintaining an integrity-based certificate handling and verification system on a whole.

7 Conclusion The future system will store and verify student information using a Blockchain approach. Most companies or educational institutions send requests to the verifier (likely a faculty member or the head of the institution of the candidate’s place of

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study) for verifying and validating the student credentials using the student’s registration number or name. In general, educational institutions or universities uphold the certificates of degree holders. Every certificate that is maintained on a Blockchain has a distinct ID that serves as secured information for each transaction that is made to validate the certificates. In the future, the issue of phony certificates can be resolved, and there won’t be a time-consuming process for verifying them.

References 1. Pavitra, H., Rashmi, U. B., Narayan, D. G., Nagaratna, K., & Shivaraj, K. (2020). EduBlock: Securing educational documents using blockchain technology. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE. 2. Jayapratha, S., Gowsalya, A., Rasmi, J., & Begum, R. (2022). Trust centric privacy preserving blockchain based digital certificate locker. International Journal of Advanced Research in Computer and Communication Engineering, 11(5), 856–861. 3. Gaikwad, H., D’Souza, N., Gupta, R., & Tripathy, A.  K. (2021). A blockchain-based verification system for academic certificates. In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE. 4. Lingampalli, J. R., & Namdeo, V. (2021). Unique smart card verification system for validating university degree certificates. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE. 5. Belurgikar, D. A., Kshirsagar, J. K., Dhananjaya, K. K., & Vineeth, N. (2019). Identity solutions for verification using blockchain technology. In 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE). IEEE. 6. Desai, A., Shah, P., & Ambawade, D. D. (2021). VerifyB – Students’ record management and verification system. In 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE. 7. Gayathiri, A., Jayachitra, J., & Matilda, S. (2020). Certificate validation using blockchain. In 2020 7th International Conference on Smart Structures and Systems (ICSSS). IEEE. 8. Sudha, V., Kalaiselvi, R., & Sathya, D. (2021). Blockchain based student information management system. In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE. 9. Huang, S. (2020). Academic records verification platform based on blockchain technology. In 2020 International Conference on Computer Science and Management Technology (ICCSMT). IEEE. https://doi.org/10.1109/ICCSMT51754.2020.00048 10. Caldarelli, G., & Ellul, J. (2021). Trusted academic transcripts on the blockchain: A systematic literature review. Applied Sciences, 11, 1842. https://doi.org/10.3390/app11041842 11. Badr, A., Rafferty, L., Mahmoud, Q. H., Elgazzar, K., & Hung, P. C. K. (2019). A permissioned blockchain-based system for verification of academic records. In 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE. 12. Alnafrah, I., & Mouselli, S. (2021). Revitalizing blockchain technology potentials for smooth academic records management and verification in low-income countries. International Journal of Educational Development, 85, 102460. https://doi.org/10.1016/j.ijedudev.2021.102460 13. https://github.com/kemoszn/PyChain 14. https://coinmarketcap.com/alexandria/glossary/sha-­256

Application of Internet of Things Systems for Aerosol Monitoring of Quarries in Morocco Ghizlane Fattah, Jamal Mabrouki, Fouzia Ghrissi, Mourade Azrour, and Mohamed Elouardi

1 Introduction Humans began to dig the ground with rudimentary tools, made of wood, horn, or bone for soft soil, and flint for rocks. To shape the soft rocks, he used hard rock tools. But to shape the hard rocks, he had to wait for the advent of metals, powerful abrasives such as diamond, then that of explosives [41]. Rock extraction industries, generally located outside of cities, contribute significantly to particle air pollution. These plants do, in fact, produce a lot of particulate matter (PM) [24]. A particulate matter is a set of fine particles, solid and liquid, of a given chemical or a mixture of substances, which are suspended in a gazen environment. Issued by human or natural sources, aerosols are also a global and local component of air quality and allergy symptoms [20]. Ground-level aerosols are significant because they have a significant direct and indirect impact on the climate system. The latter includes both the direct and indirect effects of scattering and absorbing radiant energy, as well as modifying the

G. Fattah · F. Ghrissi Water Treatment and Reuse Structure, Civil Hydraulic and Environmental Engineering Laboratory, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco J. Mabrouki · M. Elouardi Laboratory of Spectroscopy, Molecular Modelling, Materials, Nanomaterial, Water and Environment, CERNE2D, Mohammed V, University in Rabat, Faculty of Science, Rabat, Morocco M. Azrour (*) Moulay Ismail University, Faculty of sciences and Techniques, Department of Computer Science, IDMS Team, Errachidia, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_15

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physical characteristics of clouds in an indirect way, and hence their insulating characteristics and lifespan [32, 33]. The main sectors emitting fine particles into the atmosphere are still residential/ tertiary (45%) and manufacturing industry/energy processing by industry (26%). Wood combustion for heating purposes brings the most harmful emissions. It is also noted that 18% of PM2.5 emissions are from road traffic (a slight increase compared to PM10) [12]. The degree of toxicity of suspended dust depends on its physicochemical composition, its size, and its capacity to absorb other pollutants present in the ambient air [18, 26, 29]. The consequences of chronic exposure to high concentrations of fine particles are respiratory complications, an increase in the frequency and intensity of asthma attacks in susceptible individuals, an increase in allergies or pre-existing diseases, and a reduction in life expectancy from a few months to more than a year, depending on the duration and intensity of exposure. Suspended dusts have the ability to absorb and scatter light and thus cause a limitation of visibility [6, 42]. Undesirable effects can also be observed on ecosystems as fine particles can be deposited on the soil or can be absorbed by plants resulting in a decrease in plant growth. By depositing on real estate, particles also contribute to the degradation of buildings and monuments in urban areas [36, 40]. Information and Communication Technologies (ICT), through wireless communications and the Internet of Things (IoT) [1, 2, 13, 17, 19, 21, 37], are transforming traditional cities into smart cities, in other words into connected cities. The term IoT refers to all objects connected to a network, through wireless or wired communication, whether through Wi-Fi, Bluetooth, or LPWAN (Low Power Wide Area Network) or cellular broadband networks (3G, 4G, 5G). According to Cisco, more than 50 billion objects will be connected by 2020 [8, 10]. The size of particle and shape are considered a crucial term; in the study of particulate matter's (PM) effect on human health and the environment; small particles provide a larger threat to one’s health than large particles [22]. As industrialization progresses and the vehicle fleet grows, the challenge of air pollution from gaseous sprays, and airborne materials has a the moment extended with Morocco’s growing economic progress in the latter years, the country is anticipated to be increasingly confronted by this problem [31]. The goal of this study is to carry out a satellite surveillance system focused on the Moroccan western Rif and to construct a high-­ resolution particle-capturing system and other conventional systems based on hybrid satellite models.

2 Literature Review All airborne liquid and solid particles in the air including dust, pollen, soot, smoke, and droplets are harmful. Simply put, fine particles are dust. In the case of air pollution, this dust is often the result of combustion that is not total. They generate what we call unburnt particles. When we see smoke coming out of the chimney, from an exhaust pipe or when we spit out cigarette smoke, it is because there are a lot of

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particles, of more or less small sizes [9, 15, 16]. The particles are of anthropogenic (human) and natural origin. Particles of natural origin come mainly from volcanic eruptions and natural wind erosion or from the advance of deserts (sometimes of anthropogenic origin), fires, and vegetation fires. Human activities, such as heating (especially wood), combustion of fossil fuels in automobiles, thermal power plants, and many industrial processes, all produce considerable amounts of CO2. They have been increasing for two centuries. The health effects of fine particles include the fact that they penetrate the lungs at a deep level. They can promote inflammation and make those with heart and lung issues sicker. Currently, suspended particulate matter (PM) and ozone are a serious health risk in many cities in developed and developing countries. A quantitative relationship can be established between the level of pollution and certain health criteria (increased mortality or morbidity) [11]. This provides valuable insights into the health gains that can be expected from reducing air pollution. The sampling and measurement of PM10 was done using the reference technique outlined in EN 12341 (1999) [35]: “Air quality for determining the PM10 fraction of suspended particulate matter using the reference technique and an in situ test process to show measuring method equivalence.” The sampling and measurement of PM2.5 was done using the reference technique outlined in EN 14907 (2005) [9]: “The suspended particulate matter mass fraction PM2.5 is determined using a gravimetric measuring technique as a reference.”

3 System Proposed We will design and deploy a new artificial intelligence-based system for weather surveillance and climatic conditions. For particle and air parameter monitoring, the suggested system will employ Internet of Things (IoT) connections with sensors for weather. The project consists to be developed a programmable board for the program for survival and as a center of command and control [3–5, 25, 38]. This programable board is capable to communicate the collected data through network. Information about quality of air and PM weather information will be determined using the connected sensors. The measured data will be transmitted to a map for immediate treatment. Following an uncomplicated processing, the computer card will send the values that have been processed to the database computer. The website allows remote access to the saved data. When an unfavorable value is found, an e-mail notice is delivered to the end user. Figure 1 depicts the basic steps of the suggested system. There are a few tried-­ and-­true methods for measuring solid particles at high resolution, for instance, the oscillating microbalance with tapered element. The gravitational principle outlines a quantitative analysis method based on a solid’s mass. These long-established instruments for high measurement accuracy are all huge, fixed, and pricey, and thus are not often deployed.

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Fig. 1  Architecture of designed system

IoT-based smart city solutions also enable the monitoring of environmental parameters. For example, to monitor air quality, sensors collect data on the amount of microscopic particles PM2.5 and PM10, sulfur dioxide, Ozone, and nitrogen, and transmit the data to a platform that examines and displays the sensor data, in order for users to visualize quality of air and be alerted whenever air pollution is critical [10, 27, 28]. Smart city solutions based on the Internet of Things also help optimize waste pickup schedules by tracking waste levels. Each smart waste container collects information about the amount of waste in a container, When the waste management system gets close to a given threshold, it receives a recording from the sensor, interprets it, and notifies a transporter’s mobile app, telling him to dump the full container instead of the half-empty ones [10]. The system is based on artificial intelligent technology and can detect aerosols, we suggest an intelligent detecting system for pollution in the atmosphere (PM) or particulate, which will give an affordable solution for the spatiotemporal monitoring. The system we put forward for finer-grained measurements is the Hybrid Ambient Synchronized in real-time Particulates (SHARP) method which gives a real-time determination of particle mass using a beta counter for attenuation in combination (14C) and a short-term response diffusion counter. The filtering digital is used in the system to continually calibrate the photometer based on the combined data on beta attenuation. It uses Moisture Reduction with Intelligence (IMR) in conjunction with regular filter for sample changes to reduce interference with moisture and volatile component loss. Based on the usage of readily available commercial sensors (COTS) [26, 34].

4 Materials and Methods 4.1 Classic System and Sites Study The studied area is in the south of Morocco, more specifically, in a section of Morocco’s Rif mountain range in the northwest (shown in Fig. 2). The geosyncline organic of the Rif chain is distinguished by repeated and orogenic chain occurrences, as well as magmatic late phases and teletectonic occurrences [14]. The first zones comprise the inner Rif domain or the so-called North Rif chain [7, 15, 16].

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Fig. 2  Area of study and site coordinates

The study area is surrounded by the Mediterranean Sea to both east and north, while the plains that separate it extend from the Middle Atlas to the south and the Atlantic to the west. The topography is very strong (high mountains) and the climate is mild with heavy rains in the winter. Tangier, Tetouan, and Chefchaouen are the main cities of the Western Rif [14, 38]. In this paper, we work on the area of the following coordinates (Fig. 2): −5.6679, 35.0852, −5.2943, 35.9531. The PM satellite studies were also based on a new spatiotemporal model designed to predict PM 1.0 as well as PM 2.5. They used the MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and the Aqua Satellites. By employing this NASA Earth Observing System GEOS model and the Modern Era retrospective analysis for use in investigation and graduate schools (MERRA-2) is a NASA satellite-era reanalysis utilizing the GEOS version 5 with its model Atmospheric Development System (ADAS), version 5.12.4. The MERRA effort concentrates on archival weather or climate analyses for a wide range of time scales of weather, presenting the NASA EOS recordings in a climate context [23, 39].

4.2 Suspended Particle Sensor Honeywell’s HPM Series PM monitor (Sensor 1) with universal analog input/output (UART) permits the end customer to examine the product more deeply and at a lower cost efficiently track or monitor particulate matter pollutants in the atmosphere. These HPM sensor series are engineered for PM2.5 and use a light laser

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Fig. 3  Sensors for capturing airborne particles

scattering method of particle capture to detect [3–5, 34]. Sensor GP2Y1026AU0F (Sensor 2) is a high-level detector with a built-in computer and a signal-based digital output for temperature adjustment (UART). The GP2Y1030AU0F transducer has a built-in personal digital computer and a single digital waveform (UART) output [30] (Fig. 3).

5 Results and Discussion Aerosol operational density is a major satellite output for aerosol particle models and is the level of protection provided by aerosols by the passage of visible energy by absorbing or diffusing it. It is the degree to some extent to come which light transmission is hampered by aerosols by light emission Scattering or absorption of airborne light. To test our system and evaluate the response, 2 sensor units were tested for 5 days in a station of our research laboratory. The intercomparison of the sensors shows for PM2.5 (sensor 1) an R2 between 0.98 and 0.99, and for PM2.5 (sensor 2) an R2 between 0.99 and 1. As a result, the PM2.5 signal has a greater range. To determine the sensor’s calculate the reproducibility, the name was computed. Better response for PM2.5 (sensor 1) (9–24%) than for PM10 (sensor 2) (10–37%). Using the correlation results as a guide, the reproducibility analysis shows the best result for the PM2.5 signal presented in Fig. 4. Furthermore, a comparison of the conventional method and the IoT sensor concentration (1 day average) shows an R2 between 0.91 and 0.95 for PM2.5, indicating a good trend for both methods. HR values >95% affect the sensor results (Table 1). All of these studies allow for the experimental use of low-cost sensors that are simple to build and have an excellent temporal resolution, but preliminary work is

Application of Internet of Things Systems for Aerosol Monitoring of Quarries in Morocco 229 Fig. 4 Laboratory experiments of PM2.5 measurement by our own system during 5 days

Table 1  Quartile deviation values of PM2.5 and PM10 (μg m−3)

Days 1 2 3 4 5

PM2.5 47 59 93 232 125

PM10 22 35 62 210 103

required to assure the quality of the data collected by the sensors. As proof, in northern Morocco in 2018, a project of chemical characterization of particulate matter was carried out with the help of specialists in the area. The used monitoring strategy is in line with the advancement of worldwide suspension trends. Investigational monitoring using sensors. The use of low-cost sensors for monitoring and screening has gone well; after being subjected to high levels of pollution, they haven’t brought up any measurement problems, and it has also been possible to know concentration levels in a number of metropolitan areas without obtaining official permission. It was possible to determine concentration levels at urban points where there are no official surveillance stations. Also possible to identify diurnal trends for each site.

6 Conclusion The most dangerous particles for human health are the very fine particles (PM2.5 to PM0.1). They reach the narrowest bronchial branches and can penetrate into the pulmonary alveoli. The smallest particles can even cross the cell membrane and cause cardiovascular problems. The finest detail that can be identified on an image is called spatial resolution. It is also commonly defined as the size of the pixel. In an

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image, the objects that can be discerned will depend on the spatial resolution of the sensor used. Generally, as the spatial resolution increases, the surface area visible to the sensor decreases: a very high-resolution image will cover a smaller area than a medium-resolution image. In remote sensing, the quantity used to describe pixels is reflectance. The reflectance will translate the behavior of a surface when it receives sunlight. Their temporal and temporal resolution can occasionally be missing Due to the unique time span of overviews. Offering an Internet of things-based sensor system that is simple to operate and gather data is of interest for issuing air quality alerts and to monitor air quality from a distance. This work focuses on the development of the detection of particles in air, PM10, and PM2.5 which are the finer particles of middle diameter. In this first scope that is the evaluation of remote sensing pictures, the locations of the monitoring sites are situated in northern Morocco and are one of the most intense major radiation sources. The study of these pictures obscures the fact that the air in this area is very polluted. The fundamental criteria for determining the construction of an air pollution network for an urban area is to monitor the alert status for a long length of time for the depth of the survey. Disclosure Statement  The authors pronounce that there isn’t any irreconcilable situation in regards to the publication of this manuscript.

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Blockchain Networks for Cybersecurity Using Machine-Learning Algorithms H. M. Moyeenudin, G. Bindu, and R. Anandan

1 Introduction Cybersecurity has become a challenge in cryptocurrency because of the many new types of malware programs, such as those behind recent ransomware attacks. The errors from BHUNT, a malware program that attacks blockchain arrangements, can be exorbitant, particularly in unauthorized organizations, because anybody can use these arrangements and because attackers’ activities are unknown and generate unpredictable errors [1]. The attacks on blockchains are trajectories and target the weaknesses of particular domains. The investigation in this chapter focuses on how to overcome cyberattacks with machine-learning (ML) algorithms to ensure high security and safety. However, given the unchanging nature of blockchains, security reviews and point-by-point testing are vital before deployment. Cyberattacks come in various types: eclipse attacks, Sybil attacks, selfish mining attacks, time jack attacks, Finney attacks, race

H. M. Moyeenudin (*) School of Hotel & Catering Management, Vels Institute of Science, Technology, and Advanced Studies, (VISTAS) Deemed to be University, Pallavaram, Chennai, Tamil Nadu, India e-mail: [email protected] G. Bindu Department of Computer Science & Engineering, KL College of Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, Andhra Pradesh, India R. Anandan Department of Computer Science Engineering, Vels Institute of Science, Technology and Advanced Studies, (VISTAS), Pallavaram, Chennai, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_16

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attacks, parity multisig wallet attacks, and decentralized autonomous organization (DAO) attacks [2]. The eclipse malware attack complicates a user’s assessment of whether their data are genuine, whereas Sybil attacks target an overall organization. Implementing an algorithm that can achieve maximum accuracy in security and safety is necessary to overcome Sybil attacks. Multilayer perceptrons (MLPs) belong to a group of neural networks otherwise known as feedforward artificial neural networks (ANNs), each of which has an input layer, a hidden layer, and an output layer that help identify threats, especially to blockchains. The attacker could ruin the organization with an enormous number of hubs featuring pseudonymous characters and thereby attempt to infect its systems with malware [3]. The domains of malware programs look like they are disconnected from each other, but they are operated by a solitary individual. Their main target is not a single user but rather numerous organizations: to make the victim’s data available so as to further authorize the attacker to make more payments in additional attacks. The common virtualized advanced cash conceptualized in 2008 includes a dispersed exchange framework. Cryptocurrency exchanges utilize distributed network hubs without an intermediary, and the exchanges can be confirmed by the hub. Although cryptocurrency networks have displayed high productivity in monetary exchange systems, their payment systems have recently proven powerless against a few ransomware attacks. Therefore, many strategies are dealing with ransomware attacks leveled against payment methods during cryptocurrency exchanges. Furthermore, organizations aim to forestall such unsafe cyberattacks. Cryptocurrency exchanges need predictive frameworks that examine the cryptocurrency method of payment through a particular system in order to gather information for designs that can perceive ransomware attacks in heterogeneous cryptocurrency networks [4].

2 Related Work Deep learning is a branch of artificial intelligence (AI) using algorithms, in this case for the prediction of cryptocurrency pricing. In addition, some of these algorithms even protect against malware attacks. Consequently, the application of algorithms in network security has become common, with examples in cryptocurrency payments, specifically artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this approach, AI puts together predictive models to produce a new cryptocurrency exchange data set that prioritizes exactness as a key presentation marker and other key assessment measurements, such as the disarray grid, positive prescient worth, and genuine positive rate, and it then compares the data set against anticipated errors [5]. In this way, the model’s exactness results outperform many cutting-edge models created to handle cryptocurrency payments and protect against various ransomware exchanges. The advancement of digitalization has brought numerous clients to the cryptographic money marketplace. Numerous digital currencies exist nowadays, and cryptocurrency has turned into the most famous and the most significant form of

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computerized money [6]. Cryptocurrency is a decentralized virtual framework that utilizes a distributed system with advanced cash, which is put into virtual wallets and not claimed or directed by a formal authority. The clients of cryptocurrency can easily demand payment without client confirmation, and the currency payment addresses are created secretly; for this reason, cryptocurrency payment systems have been utilized all over. The currency exchanges can be made between clients through network hubs without an intermediary, and the exchanges can be confirmed by the hubs where cryptocurrency and blockchain innovations were constructed. This has brought a few advantages to the organization of cryptocurrency, such as better-developed security, decentralization, and the laying out of distributed systems [7]. The ongoing development of cryptocurrency clients requires more investigation into the dangers of cybercrime. In addition, cryptocurrencies lack defenses against cyberattacks, and the Internet Protocol (IP) locations of cryptocurrency clients can be effortlessly leaked. Therefore, the anonymity of cryptocurrency clients improves their security; unfortunately, at the same time, it opens the door for Sybil attacks. Hence, some cryptocurrency clients depend on outside devices, such as Virtual Private Network (VPNs), to produce unknown IP locations. The point of this arrangement is not to upgrade security. Any data security system depends on the Confidentiality, Integrity, and Availability (CIA) set of privacy, honesty, and accessibility to keep up with, oversee, and manage security concerns [8]. Along these lines, ransomware attacks can compromise classification, bogusinformation infusion assaults can undermine trustworthiness, and Distributed Denial of Service (DDoS) assaults endanger accessibility. Ransomware is malware programming that can lock clients’ information or screens; hence, affected clients are impeded from accessing their own information. Ransomware codes client information in such a way that the client is not able to unscramble it. To unscramble the information, the client must pay a ransom [9].

3 Cybersecurity in Cryptocurrency Many types of cryptographic forms of money have been used in recent years thanks to the growth of cryptocurrency, and new cryptocurrencies have recently been introduced. Digital currency has many benefits in buying and selling, permitting institutions and organizations to handle the currencies of individuals and to make transactions without banks; instead, the public and different mediators run its administration. These computerized resources are decentralized, autonomous, and independent of national banks, eliminating charges. Countless misuses are possible with cryptocurrency because it is run by the public and because the controllers of these cryptocurrencies have not sorted out proper business standards and legitimate designs for overseeing and administering digital currencies [10]. This is where cybercriminals can benefit. The decentralization of digital money marks a great opportunity for attackers. Cybercriminals can hack into cryptographic money-exchanging stages and take reserves. Digital currency is now the most favored type of trade in instances of ransomware assaults. The blockchain

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of cryptocurrency uses proof of work (POW) and proof of stake (POS). This alone presents a magnificent opportunity for organizations and people to make fortunes, and that is why experts and businesses grow only with protection. But in reality, some malware programs can remove types of protection, so additional protection is required when using online currencies [11]. Where there is an opportunity to bring in cash, there are also people who want to steal it. As digital currencies continue to develop, they will continue to be the targets of cybercrimes. Now is the time to contemplate the optimal level of safety for cryptocurrency networks. Figure 1 illustrates how to train a data set with LSTM and an MLP to achieve accuracy in predicting payments; this model provides an opportunity for intrusion detection during online transactions. Digital money is an electronic type of an advanced resource or cash that works as a vehicle of trade. It is a type of electronic installment that utilizes cryptography to confer extra security while making exchanges. Ransomware occurrences for the most part have a repeating theme. Cybercriminals can conceal their actual personalities while requesting payoffs in computerized monetary standards [12]. While making a trade, they can change digital currencies into customary structures while never being found. They can assault any business and request ransom in advanced monetary standards, because this type of cyberattack is untraceable thanks to the characteristics of malware, which leave behind none of the culprits’ details. With cryptographic forms of money scattering throughout the world of technology, cybersecurity has turned into a genuine concern.

Fig. 1  Data processing for intrusion detection using an MLP with long short-term memory (LSTM)

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4 Multilayer Perceptrons A multilayer perceptron (MLP) is a kind of feedforward neural network that is connected with at least one layer of neurons. The data from the input layer are transmitted by using multiple layers of neurons with many hidden layers or one hidden layer, depending on the categories of perception, and its predictions are based on the resulting layer, namely the output layer, which is also called the apparent layer [13]. It may be hard for a beginner in the field of deep learning to utilize an MLP according to their needs. The flexibility of transactions permits the user to make use of various sorts of data. The total pixels of an image can be reduced to a single datum in order for an MLP to process this information [14]. The terminologies of an archive can similarly be reduced to a datum and inputted into an MLP. Indeed, even the insights into a time-series expectation issue can be reduced to a long column of information and inputted into an MLP. A volume of data with an assumed structure other than a plain data set, such as a picture, record, or time-series MLP, could be a better option to train and test the MLP. The outcomes can be utilized to establish a point of comparison to determine which models are better qualified than others. Cryptocurrencies stand out enough to be noticed because they offer opportunities for costly types of settlements. Figure 2 illustrates hidden layers, such as L2, L3, and L4, through the implementation of an MLP, which is a supervised learning technique. Here, the output will give an authenticated value, which acts as proof during payments. This method of payments requires no nation or place. The cybersecurity follows a simple inventory

Fig. 2  Feedforward multilayer perceptron neural network

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network through recordkeeping that validates merchants’ accounts. Artificial intelligence (AL) has astounding learning capacities that can be applied to the blockchain to make the chain smarter than previously used technologies could [15]. This mix can improve the security of the conveyed record of the blockchain. Likewise, the calculation force of ML can decrease the calculation time taken, and ML can improve information sharing. Lastly, we can fabricate many better AI models by utilizing the decentralized information engineering component of blockchain innovations [16].

5 Long Short-Term Memory These types of algorithms work with the prediction and classification of the data in specific networks, namely artificial recurrent neural networks (RNNs), which are also good at identifying anomalies in network traffic by using intrusion detection systems (IDSs) to learn about request reliance in grouping prediction issues [17]. This is a behavior expected in complex issues in areas such as machine interpretation and discourse acknowledgment. LSTM is a perplexing area of deep learning because it works with neural networks. It is difficult to understand the possibilities of what LSTMs are and terms such as multidirectional approach, even in an arrangement that groups the data and connects them with networks. LSTM utilizes previous data along with time-series data to learn and foster techniques and to apply them to new, significant issues. Few people are better at plainly and definitively articulating both the guarantees of LSTM and how they work than the specialists who created them [18]. Figure 3 features a unique sort or predominant rendition of an RNN in that it works as if it were made by humans; it is used in artificial intelligence to identify which accounts are using long short-term memory, in order to validate them by encoding and decoding their data to obtain results from the extraction layer. LSTM has disapproval suggestions and aims to stay away from long-haul conditions. It cannot handle just informative items or entire groupings of information. For example, LSTM is appropriate for tasks such as unsegment information with an associated acknowledgment design. This discourse acknowledgment and irregularity recognition are in network traffic and IDSs. A typical LSTM unit contains four fundamental parts: an input modulation gate, an input gate, a forget gate, and an output gate. LSTM networks characterize, handle, and make predictions according to time-series data because time-series data frequently suffer lags of varying lengths. LSTM networks have been designed to solve the vanishing angle issue that can be experienced while preparing conventional RNNs. Relative obtuseness toward the length is more common in LSTM networks than in RNNs. AI models can utilize the information stored in the blockchain network for predictive or investigative purposes [19]. Storing the information in the organization of blockchain minimizes the blunders of the ML models because the information in the organization will not

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Fig. 3  Long short-term memory as single layer and double layer

have missing qualities, copies, or clamor in it. Avoiding these hindrances is essential for the AI model to achieve high precision. Figure 3 shows the variation among designs for AI in crypto-based applications. Something else is going unnoticed when attackers try to utilize computerized resources in their illegal practices. Cryptographic forms of money offer methods for monetary transactions [20]. Having the right network safety setup can safeguard users from cyberattacks by using hybrid algorithms such as MLPs with LSTM. Digital currency may mark the beginning of another age of security in that it is already being targeted in cybercrimes. Such crimes come in many forms, from ransomware to email scams. Indeed, cybercriminals often launder cash and plot against organizations that use digital money. Because digital forms of money are completely decentralized, they lack a formal authority to screen their exchanges. Moreover, cryptographic forms of money lack guidelines. This makes digital currencies shelters for lawbreakers. Any business that utilizes cryptographic forms of money is a target, except if it raises its network protection measures [21]. Cybercriminals can trade virtual monetary standards while never being found. The organizations that make trades by utilizing digital currencies expose themselves to too many dangers. Trade clients and digital money dealers hazard making terrible exchanges that can bring about critical losses. The following is probably the best strategy for security online protection: An approach that can gain proficiency in predicting market responses to new information with a pattern or design that can be analyzed for accuracy through neural networks or algorithms could protect against malware programs.

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6 Cyberattacks Many ransomware programs have been found in recent years from payments made with Cerber, which is ransomware accessed by cybercriminals to attack the security of a program: It assaults clients to acquire their information and spreads their captured information among attackers [22]. It runs quietly while encoding documents, and it may attempt to forestall antivirus software and Windows security software from running, to keep clients from re-establishing the framework. Whenever it effectively encodes documents on the machine, it shows a payoff note. Cryptolocker is another malware program, one that has impacted around half a billion personal computers (PCs). It commonly contaminates PCs via email, recordsharing destinations, and unprotected downloads [23]. It scrambles records on the local machine yet can likewise filter planned network drives and encode documents. It has authorization to write novel variations of Crypolocker that can evade antivirus programming and firewalls. Petya, another a ransomware program, compromises the security of AI and can corrupt entire hard disks by targeting master file tables (MFTs) to acquire secured information, which makes the entire group difficult to reach, although the real records are not encoded [24]. The cryptocurrency market has become a center of attraction thanks to its growth in recent years. It has limitations, but this market has still proven to be the best option for investing money [25].

7 Conclusion An MLP with LSTM, as a hybrid algorithm for intrusion detection, processes trained data to predict responses. This approach proves that using an MLP with LSTM is a legitimate method to provide online security to financial transactions, especially cryptocurrency transactions. In the near future, cybersecurity will be the greatest challenge for cryptocurrencies. Here, LSTM is combined with an MLP to resolve issues in predicting data because an MLP assists in identifying the hidden layer; such identification is an essential part of transactions. Several recent ransomware viruses have been able to attack this network. To prevent such cyberattacks and to secure cryptocurrency payments, using a random pattern assisted by an MLP and LSTM will be a better option. The implementation of DL methods with a hybrid approach has yielded higher accuracy in cybersecurity. Thanks to its accuracies, this design is able to provide security against ransomware attacks. Furthermore, this system can provide secure transactions to many neglected regions in the cross-disciplinary field of cybersecurity.

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References 1. Liang, G., et al. (2018). Distributed blockchain-based data protection framework for modern power systems against cyber-attacks. IEEE Transactions on Smart Grid, 10(3), 3162–3173. 2. Aggarwal, S., & Kumar, N. (2021). Attacks on blockchain. In The blockchain technology for secure and smart applications across industry verticals (Advances in Computers) (Vol. 121, pp. 399–410). Elsevier. 3. Caporale, G. M., et al. (2021). Cyber-attacks, spillovers and contagion in the cryptocurrency markets. Journal of International Financial Markets, Institutions and Money, 74, 101298. 4. Zhang-Kennedy, L., et al. (2018). The aftermath of a crypto-ransomware attack at a large academic institution. In Proceedings of the 27th USENIX security symposium (USENIX Security 18). USENIX Association. 5. Mihoub, A., et  al. (2022). Denial of service attack detection and mitigation for internet of things using looking-back-enabled machine learning techniques. Computers & Electrical Engineering, 98, 107716. 6. Amsyar, I., et  al. (2020). The challenge of cryptocurrency in the era of the digital revolution: A review of systematic literature. Aptisi Transactions on Technopreneurship (ATT), 2(2), 153–159. 7. Mikhaylov, A. (2020). Cryptocurrency market analysis from the open innovation perspective. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 197. 8. Titov, V., et  al. (2021). Cryptocurrency open innovation payment system: Comparative analysis of existing cryptocurrencies. Journal of Open Innovation: Technology, Market, and Complexity, 7, 102. 9. Zimba, A., Wang, Z., & Chen, H. (2018). Multi-stage crypto ransomware attacks: A new emerging cyber threat to critical infrastructure and industrial control systems. ICT Express, 4(1), 14–18. 10. Demirkan, S., Demirkan, I., & McKee, A. (2020). Blockchain technology in the future of business cyber security and accounting. Journal of Management Analytics, 7(2), 189–208. 11. Muheidat, F., & Tawalbeh, L.’a. (2021). Artificial intelligence and blockchain for cybersecurity applications. In Artificial intelligence and blockchain for future cybersecurity applications (pp. 3–29). Springer. 12. Almashhadani, A. O., et al. (2019). A multi-classifier network-based crypto ransomware detection system: A case study of Locky ransomware. IEEE Access, 7, 47053–47067. 13. Kumar, D., & Rath, S. K. (2020). Predicting the trends of price for Ethereum using deep learning techniques. In Artificial intelligence and evolutionary computations in engineering systems (pp. 103–114). Springer. 14. Jay, P., et  al. (2020). Stochastic neural networks for cryptocurrency price prediction. IEEE Access, 8, 82804–82818. 15. Derbentsev, V., et  al. (2020). Forecasting of cryptocurrency prices using machine learning. In Advanced studies of financial technologies and cryptocurrency markets (pp.  211–231). Springer. 16. Khedr, A. M., et al. (2021). Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey. Intelligent Systems in Accounting, Finance and Management, 28(1), 3–34. 17. Liu, Z., & Yin, X. (2021). LSTM-CGAN: Towards generating low-rate DDoS adversarial samples for blockchain-based wireless network detection models. IEEE Access, 9, 22616–22625. 18. Jadidi, Z., et  al. (2020). Securing manufacturing using blockchain. In 2020 IEEE 19th international conference on trust, security and privacy in computing and communications (TrustCom). IEEE. 19. Kim, S.-K. (2022). Automotive vulnerability analysis for deep learning blockchain consensus algorithm. Electronics, 11(1), 119.

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20. Dibaei, M., et al. (2021). Investigating the prospect of leveraging blockchain and machine learning to secure vehicular networks: A survey. IEEE Transactions on Intelligent Transportation Systems, 23, 683–700. 21. Yazdinejad, A., et al. (2020). Cryptocurrency malware hunting: A deep recurrent neural network approach. Applied Soft Computing, 96, 106630. 22. Pletinckx, S., Trap, C., & Doerr, C. (2018). Malware coordination using the blockchain: An analysis of the Cerber ransomware. In 2018 IEEE conference on communications and network security (CNS). IEEE. 23. Akcora, C. G., et al. (2020). BitcoinHeist: Topological data analysis for ransomware prediction on the bitcoin blockchain. In Proceedings of the twenty-ninth international joint conference on artificial intelligence (pp. 4439–4445). ACM. 24. Aidan, J.  S., et  al. (2017). Comprehensive survey on Petya ransomware attack. In 2017 International conference on next generation computing and information systems (ICNGCIS). IEEE. 25. Sun, W., Dedahanov, A. T., Shin, H. Y., & Kim, K. S. (2020). Switching intention to cryptocurrency market: Factorspredisposing some individuals to risky investment. PloS one, 15(6), e0234155.

Blockchain of Cryptocurrency Using a Proof-of-Work-Based Consensus Algorithm with an SHA-256 Hash Algorithm to Make Secure Payments G. Bindu, H. M. Moyeenudin, and R. Anandan

1 Introduction Blockchain is a kind of information or a data set that is stored in blocks and connected with each block on the basis of timestamps and data types, as these data are distinct from an ordinary data set insofar as the data are stored in blocks. The moment a new datum or a new data set comes in, it is stored in a new, automatically created block. As soon as the data are recorded in block form, they are anchored to a previous block. This way of recording and storing data improves the speed at which it can be accessed [1]. Numerous sorts of files are not included in a blockchain, but the best known algorithm at this point has been a consensus algorithm that is based on proof of work (PoW), which records cash exchanges by using hash data. In the current cryptocurrency trend, data are exploited in a decentralized system in order that no single person or organization can regulate the data. Rather, all account holders have total authority over their own funds; the unreadable hash data are made via cryptographic hashing, and an secure hash algorithm (SHA) - 256 could be used for securing passwords [2]. Blockchains are decentralized in a G. Bindu (*) Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, Andhra Pradesh, India e-mail: [email protected] H. M. Moyeenudin School of Hotel & Catering Management, Vels Institute of Science, Technology, and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India R. Anandan Department of Computer Science Engineering, Vels Institute of Science, Technology, and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_17

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permanent state, which means that the blocks that are loaded with data are unalterable. Cryptocurrency is a decentralized computerized form of cash that has gained popularity since its origin in 2009. Today, Bitcoin is the most common computerized cash on the planet, representing the greatest part of the complete advanced money market according to market capitalization. Cryptocurrency implies that exchanges are forever recorded and visible to anybody who is in that network; thus, cryptocurrency is grounded in the standards of decentralization, whose advantage is circumventing intermediaries to keep a solid and decentralized online record of exchanges [3]. One critical contrast between it and a run-of-the-mill information base is that the data recorded in blockchains are well organized. The data could be received in various forms and sources that are registered in blockchain. Blocks have specific stockpiling limits, and when they have been filled, they are shut and connected to the most recently filled block so that the information from the previous block can be accessed. This framing of a chain of blocks containing information is known as the blockchain [4]. A data set usually constructs its data into tables, while the data in the blockchain are registered or recorded in structures of blocks that are strung together. Hierarchical and behavioral principles ought to be upheld by public controllers and government authorities to improve the security of and people’s trust in cryptographic forms of money. At present, numerous ventures are aiming to execute blockchains so that they can help societies in an assortment of ways other than recording exchanges.

2 The Blockchain of Cryptocurrency The innovation of blockchain is its security, which is due to its decentralization— providing trust and assurance in more ways than one. First, the blocks are stored in chronological order and are carried step by step; second, these blocks are also compared against each other through a linear method; and third, these created blocks are continuously used for reference and subsequently provide the data for future blocks [5]. This is why blocks are connected at the end of a blockchain: to increase the security of the data. After a block has been added at the end, it is remarkably hard to return, adjust, or to make corrections to the data in the added block unless a greater part of an organization has agreed do as so [6]. There are blockchains in which each block holds its individual timestamp and hash code, with a timestamp and hash code on each block before it. A typical hash code contains a numerical capacity that transforms computerized data into a series of records. When the data are altered in any way, the hash code will change too. The world of exchange quickly improved by developing online payment networks [7]. Alongside expanding globalization, the requirements for speed, comfort, and security in the monetary exchanges of cryptocurrencies are expanding [8]. Lastly, cryptocurrency banking clients need dependable and simple online payment methods.

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3 PoW Consensus Algorithm The term PoW became commonplace thanks to cryptocurrency’s blockchain, which has been the main method of carrying out the PoW structure arrangement. The PoW consensus algorithm is the first mechanism of a blockchain [9]. PoW has been promoted as the most dependable method for establishing a blockchain. It helped effect decentralization while barring mediators and guaranteeing the legitimacy of exchanges. Despite what is generally expected, the increase in blockchain size could incur issues related to cryptocurrency. Figure 1 denotes the PoW verification from block n, to n1, to n2. Thus, the viability of a blockchain is in guaranteeing decentralized, secure, and straightforward exchanges between participant nodes. This is one of its key successes with n1, n2, and n3 because these nodes receive the input and process it for authentication in the subsequent stages. Many stakeholders have considered which components help

Fig. 1  PoW consensus blockchain

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confirm exchanges before they are added to a blockchain system [10]. Thus, the PoW consensus algorithm has lately garnered significant consideration, focusing on discovering desirable tools for transactions. The following discussion explains both proof of stake (PoS) and proof of work (PoW), the two normally executed agreement calculations. In 2018, Nguyen compiled a survey of the similarities and differences between the PoW and PoS calculations [11]. Blockchain exchanges disallow intermediaries, and all the subtleties of the exchange are refreshed in order to update the computerized dispersed record. The hubs in the blockchain network also need to participate in the approval process. Figure 2 shows a sample transaction in which clients solve puzzles to confirm the validity of their transactions, to avoid malware and cyberattacks. The approval process follows the standards laid out in the agreement calculation. The PoW and SHA-256 algorithms are the two most famous agreement calculations among blockchains, and debate persists on which is the better of the two. The last response in a confirmation of stake versus a verification of work examination is critical to ensuring the viability of arrangements [12]. Both of them are needed to control the most common way of checking exchanges between clients and adding them to the public record. Fully understanding the two agreement components and their similarities and differences could assist in choosing the best option. The following basic component in understanding PoW shows the mining work of PoW consensus, which solves a cryptographic riddle to secure approval for a specific exchange. This riddle solving could be considered a race, in which cryptocurrency miners contend with one another to be the first to solve the riddle. When a client enters a secret key, the hash esteem is determined and then compared against the traditional table data. This setup assumes that the data are tied up with saved hashes [13].

Fig. 2  PoW consensus arrangement

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4 The SHA-256 Algorithm The main role of the SHA-256 hash algorithm is to deliver online protection. A conventional algorithm such as PoW or PoS could be added to an SHA-256 algorithm to form a hybrid that is applied to the entirety of the secure payment method. SHA-256 algorithms offer better validation and better authentication than the conventional ones because they contain SSL, TLS, SSH, PGP, and IPsec. In Unix and Linux, SHA-256 is used to decode the hashing in cryptocurrencies. The most widely recognized method of verifying the information in a particular record is not easy to carry out. For instance, SHA-256 is used to check the trustworthiness of protected data and documents [14]. The timestamp has a hash code of a safe record that has been kept accessible to clients who need access to the document. In addition, these data contain genuine renditions without any further information pertaining to the discovered record. In Fig. 3, an SHA-256 algorithm shows its hash capacity and its part in contemporary cybersecurity. An SHA-256 algorithm is one of the many kinds of SHA-2 algorithms; SHA-256 is a protected cryptographic hash code that extends 256 pieces in length. In cryptographic hashing, the hashed information is adjusted such that it becomes totally ambiguous. It would be difficult to convert the 256-cycle hash referenced above back to its unique 512-digit structure. Hashes are likewise used to confirm advanced marks. In encryption, data are changed into protected configurations that are scrambled unless the beneficiary has a code. In its scrambled structure, the information might be of a limitless scope, and these scrambled data are decoded with SHA-256. On the contrary, when it comes to SHA-256, data of discretionary nature is confined to a predetermined range [15]. Figure 4 illustrates a 512-digit series of information in an SHA-256 algorithm. This figure also shows plotted messages: Blocks 1, 2, and 3 are processed into bits of string through SHA-256 hashing. A blockchain contains changeless records or records of exchanges that can’t be adjusted or erased. Encryption is a basic piece of

Fig. 3  An iteration of an SHA-256 hash function

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512 bits

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Message Block 1

IV

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Fig. 4  The SHA-256 algorithm for cryptocurrency

present-day online security, so encryption algorithms are used to scramble data through AES 256 and PGP because they provide security when the data arrive for authentication. An SHA-256 algorithm is used when data are scrambled and difficult to unscramble; the hashing comes in the form of a secret confirmation key, which is an especially significant application in cryptographic hashing. Storing client’s passwords in normal values could pose risks for misuse; in order to avoid that, a programmer must figure out how accessing a record would expose a primary layer of insecure PINs. That is why storing passwords in the form of hash keys is considered safer and more protected [16].

5 Secure Payments Payment systems that incorporate online plans for esteem trades between people and monetary establishments are encountering challenges. The advancement in trading exchanges currently lacks dividers, which restricts the groups that advance the payment systems [17]. Figure 5 denotes the transactions using PoW consensus with SHA-256 to ensure that a payment method is secure. The use of online payments has enabled the spread of cryptocurrencies to worldwide business, which needs a quick, secure, and classified payment system [18]. This way, the users of cryptocurrency pay their money to buy products and services; many companies have already incorporated the facility to accept cryptocurrencies, so combining PoW and SHA-256 for online money transactions is often a viable option. Secret digital money can also be used to install frameworks in many countries. Companies that are presently accepting cryptocurrency include Caribou Coffee, Baskin Robbins, and the Pavilions Hotels and Resorts. Importantly, Bitcoin uses blockchains to directly record payments, but blockchains can also be used to permanently record quite a few other items. As mentioned above, these items include monetary exchanges, votes in a political race, item inventories, and identification documents (IDs), among others.

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BLOCKS

Previous Hash

Previous Hash

Previous Hash

Transactions

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00000000000000 00000000010101 10111000011

00000000000000 00000000010101 10111000011

00000000000000 00000000010101 10111000011

Fig. 5  PoW consensus with SHA-256

6 Distinctions Between Existing Currencies Cryptocurrency is distinguished from existing currencies by the fact that it is not issued by a government, so public authorities cannot obstruct or control it. From the outset, this currency was not supposed to be on a conversion scale with any country’s currency. Because of a quick turn of events, cryptographic money quickly grew in popularity. This electronic cash uses chip-based and server-based technologies that still require an intermediary to solve the problem of similarities between electronic terminals so that client data can be traded in the exchange process. That is why the cryptocurrency technique was used to innovate payment systems [19]. One of the most renowned kinds of digital currency in the world is Bitcoin, and it has been in use since 2009 within a shared network of peer-to-peer transactions. From that point onward, other cryptocurrencies have also come into practice, such as Ethereum (ETH), Binance Coin (BNB), Solana (SOL), Tether (USDT), and many others. Here, the Tether value is maintained as equal to that of the US dollar for those who want to safely invest their money. In one strategy, cryptographic money could be dispersed directly to data miners [20]. The Pavilions Hotels and Resorts are anticipating accepting cryptocurrency soon, at which point travelers will

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be able to pay for a room with cryptographic money. By holding hands with the main worldwide cryptocurrency payment entryway, Coindirect, the Pavilions Hotels and Resorts has become the world’s first hotel chain to accept digital currency for reserving rooms; the hotel chain delivered its reports in a public statement.

7 Trust in Cryptocurrency Many studies have indicated that the payment system of cash is expecting a fruitful response, which should build trust. When a representation of substantial value is upheld by trustworthy cash, it brings promising returns. It is believed that the value of this currency will be approved by governments and groups who trust that it is based on noteworthy costs that are not considered as false [21]. Rare metals were the initial currencies, which were trustworthy because of their inherent value. More recently, the landscape of currencies has changed because people need online access to their money. Most nations use a currency that has the approval of its corresponding government. Government-issued types of money are not attached to anything of actual worth, such as precious metals, but rather are supported by administrative assurance [22]. In financial transactions, exchanges between national currencies incur conversion costs, which become expensive in international trade. Furthermore, the prominence of online transactions has enabled users to make transactions with limited facilities. Because governments are involved in international money transactions, many countries are putting restrictions on sending money to other countries, even when it is processed through properly licensed exchanges. However, because the money is issued from a government, that government must provide trust for all participants when the currency is being exchanged. These online funds transactions are carried out by banks and other private vendors though algorithms that make precise payments [23]. Trust in cryptocurrency could be earned by further securing transactions, such as by gaining the approval of and reaching agreements with the central banks of countries, and meeting the demands of societies. In addition, with government-issued currencies, governments and banks lend assurances to investments and provide safety to account holders; also, if a security breach occurs and the bank is found to be accountable, the government will issue money to compensate the affected account holders. In contrast, for cryptocurrencies such as Bitcoin, which are completely distributed, the value of the currency is transferred directly from one account holder to another in a way that no one else will benefit. There are no monetary benefits for a government, and no government can impose a tax on cryptocurrency users [24]. The cryptocurrency blockchain system is considered the most secure framework on earth because it openly registers each transaction that is made at any point with the cryptocurrency—thanks to its blockchain, which is protected through its novel approach to security [25].

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8 Conclusion The trust problem in cryptographic money lies with the keepers of digital currencies and their customarily approved interactions with cryptocurrency-related organizations; these organizations act as interfaces with government currencies during generally unregulated trades with payment agencies. Many government-issued currencies encounter problems with fake money; another disadvantage is that online bank accounts can be hacked by anonymous users, fostering doubt in the banking network and generating instability in secure payments. In contrast, the blockchain of cryptocurrency confers a high level of security to payments by using the PoW consensus algorithm along with an SHA-256 hash algorithm to increase the level of security when buying products or trading digital money. In specific, when someone visits a hotel in another country, they can easily settle their bills rather than convert their currency into the destination country’s currency. This arrangement ought to increase the security of trades. The Pavilions Hotels and Resorts, for example, has become the first international hotel chain to accept cryptographic money for reserving rooms, perhaps starting a transformation in the travel industry, at least for secure Internet-based payments.

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An Efficient Security-Enabled Routing Protocol for Data Transmission in VANET Using Blockchain Ripple Protocol Consensus Algorithm Manjunath Ramanna Lamani, P. Julian Benadit, Krishnakumar Vaithinathan, and Latha Parthiban

1 Introduction According to recent estimates, 25 billion “things” will be connected to the Internet by 2020, making up a significant portion of automobiles [1]. Internet of Things (IoT) is gaining popularity in a new era of connectivity, and the construction of a conventional Vehicular Ad-hoc NETworks (VANET) for Internet worldview cars (IoV: Internet of Vehicles) had also started. A vehicle is regarded as a vital hub in the alleged VANET, and its primary function is to relay messages between vehicles. According to the IoV worldview, each vehicle is viewed as an intelligent system with such a field multi-sensor core of exchanges and a sophisticated intra-vehicle framework, which includes processing units, advances, web, and an immediate or intersection IP network to other vehicles [2]. IoV is a multi-client, multi-vehicle, multi-thing, multi-network open and integrated system framework with strong plausibility, controllability, operationalization, and validity [1]. Over the previous decade, wireless sensor networks (WSNs) have seen the progressively concentrated appropriation of cutting-­edge machine learning techniques [3]. Machine learning, as a noteworthy branch of Artificial Intelligence (AI), creates intellectual frameworks to work in convoluted conditions and has discovered M. R. Lamani · P. J. Benadit Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, Karnataka, India K. Vaithinathan Department of Computer Engineering, Karaikal Polytechnic College, Karaikal, Puducherry, India L. Parthiban (*) Department of Computer Science, Pondicherry University, Pondicherry, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. Goundar, R. Anandan (eds.), Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-35751-0_18

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numerous practical applications in personal computer vision and mechanical autonomy [4, 5]. It creates proficient techniques to examine a considerable measure of information by discovering designs and underlying structures, which can be gainful for supporting future radio terminals [6]. Also, machine learning uses an information-driven approach, making it more powerful to deal with heterogeneous information. Accordingly, machine learning gives a flexible arrangement of apparatus to mine different data sources created in vehicular systems. Internet of Vehicles (IoV) [7, 8] devices bring better driving experiences to customers, bringing new security issues simultaneously. The Spoofing Tampering Repudiation Information disclosure Denial of service Elevation of privilege (STRIDE) Threat Model [7] categorizes assaults and threats in information security into six basic categories: Identity spoofing, data tampering, denial, information disclosure, service denial, and privilege escalation. Especially, due to its dynamic topology, bandwidth limitation, transmit power limitation, abundant resources, mobile limitation, non-interference, and other characteristics, the vehicle’s internet can be attacked from different angles in different ways such as interference and eavesdropping. IoV’s stability, robustness, real-timeliness, security, and privacy are compromised, and hence the ability to provide adequate service is lost, and serious accidents can occur. IoT-based VANET [8] is termed IoV (Internet of Vehicles) and the main intention of IoV is to increase visitors’ efficiency, keep away from accidents, make sure of street safety, etc. It follows a dynamic topological structure with a non-uniform node and mobility limitation. These characteristics make IoV-based systems vulnerable to a variety of threats. Intrusion and packet security are such issues in IoV. We propose using the machine learning technique artificial neural networks (ANN) and Genetic algorithm (GA) to overcome these issues. ANN is used to detect an intrusion, and GA is used for providing security to the data packet by helping to generate a Hash key. The blockchain Ripple protocol consensus provides for distributed consensus systems to enable decentralized, low-cost transactions.

2 Literature Review There are numerous open research fields on the Internet of Things [9]. Because so many IoT devices aren’t built with security in mind, security attacks and solutions are also some of the primary research challenges. There are many open research zones on the Internet of Things, from recognizable proof and correspondence advancements to institutionalization [9]. Since vast numbers of the gadgets which constitute IoT are not planned in light of security, security assaults and arrangements are likewise a portion of the fundamental research challenges. Intrusion, data tampering, denial of service, eavesdropping, and other threats can all affect IoVs. For secure data transmission, there are several routing protocols available. At the same time, the following routing mechanism may perform standard routing functions while also repelling various forms of threats. With the user’s

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interaction signature and one-way hash function, the Secure Ad-Hoc On-Demand Routing (SAODV) [10] ensures the security of routing various disciplines with the count of hops in the networks. The Ariadne [9] protocol uses the authentication of the TESLA method, which confirms the routing information’s authenticity and integrity using a one-way hash message authentication code. It abstains from threats initiated by steering dark openings and hubs using one-way hash and keeps obnoxious hubs formed by fake data or incorporated into the routing data. The spatial reuse protocol (SRP) [11] principle requires creating a secure connection between a sourcing hub and a target hub and the sharing of keys between the two hubs. To avoid direct replay assault, the SRP convention measures media access control (MAC) with the relative mystery of two hubs, verifies the end hubs’ permanence, and presents additional steering with the demand recurrence number. Routing security and privacy systems are required for IoVs to ensure that the regular hub’s information is not leaked during the routing method. Using the possibilities of “The Millionaire’s Problem” [12] to hide the value of each utility can be a viable technique for analyzing two products without revealing their positive attributes. Three computations to assure the area security of moveable hubs in DTNs are SLPD [13], ALAR [14], and STAP [15]. In 2008, IDSs were proposed in [16, 17] in response to a study of the timing and amount of information transit. These IDSs came in handy while dealing with DDoS attacks. For each client activity, they constructed a state lattice and used the Chi-square approach to conduct a recurrence investigation. Whenever the activity was seen, it was either a new or an old behavior. If the new activity was seen, the in-state mobility grid was defined. To manage this state transportation network, it was decided to employ LRU memory management. This strategic action grid was used to develop an algorithm that considered data and yielded movement designs. These relationships were discovered by evaluating threats and flood assaults (DoS assault). The high False Alarm Rate (FAR), of these IDSs, is a disadvantage of roughly 47%. In a scrambled domain, IDS presented in [18] works. Engineers kept a safe distance from discrepancy locations and relied on discovery to detect interruptions. This IDS is based on frameworks and administrations’ natural learning processes. Inter-session and intra-session linkages are performed by these IDS displays, which encounter various physical connections. This association esteems are high for well-­ disposed corresponding connects, but they are low when the agreeable link is coupled with hazardous connects. This algorithm has a capacity of 70.14% identification and a precision of 72.05%. Due to the general gathering mark, [19] suggests a distributed essential administration method to organize security in automotive mobile ad-hoc networks (VANETs). In contrast to the critical concentrated administration recognized by existing gathering mark plans, distributed key management is vital to motivate the cars, in the assistance of the system, and for various security tactics. As a result, in the established structure, every roadside unit (RSU) works as the principal collector, which raises the possibility that semi-trust RSUs will be jeopardized. As a result, security conventions for the plan are produced that can separate negotiated RSUs

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from their conspiring cancerous vehicles. Additionally, the massive computing burden associated with group signature processing is addressed. Leveraging realistic cooperative message authentication methods, the verification burden is reduced, and each vehicle would only need to validate a minimal amount of communications, according to the proposal. Furthermore, highlights of imminent threats are covered in this paper. The work covers the confidentiality of e-information exchanged through a network. For encryption and decryption, GA and lack of reliable sequences are mixed. A pseudorandom sequence is constructed using a Feed Forward Shift Register (FFSR). Furthermore, the crossover operator employs a pseudorandom-generated sequence for encryption. The clock pulse is utilized to generate a binary sequence in pseudorandom. A pseudo-random generator is utilized to swap the bits, and the decryption technique is similar. Because of its specific properties [20], including mobility, advanced topology, and wireless connected vehicle technology, VANETs have gained substantial importance in research domains during the last decade. VANETs are becoming recognized by industry and academics for their larger-scale implementation. The traditional vehicular network’s security flaws are eventually uncovered [21]. The research on intelligent transportation systems (ITS) identifies and categorizes VANET assaults and threats by period.

3 Research Methodology Each IoV-based device follows its routing protocols to ensure reliability and security. Cheng et al. [8] investigated different IoV-based device-routing conventions. These conventions each have their security features, but they also have a few drawbacks. The machine learning method can be used with each directing convention discussed in [8] to overcome security difficulties. Each path has its method for transmitting data from the source to the destination. In IoV-routing conventions, human-made consciousness procedures like ANN, genetic calculation, SVM, etc., can be used to get a proficient and secure directing approach to convey information. The upgraded network may make connected vehicles increasingly susceptible to virtual and physical threats as they become more ubiquitous and deliver considerable benefits to the public. On the contrary, data security in autos is crucial, as erroneous sensor estimates can lead to accidents and casualties. Because of the complexity of brain systems, [22] an interruption recognition framework for vehicular systems is presented. As a pre-processing step, unlabelled deep belief systems are employed to reinstate the parameters. Long short-term memory (LSTM) is also used to detect threats on connected cars [23]. By working out how to predict the next word starting from each car, the LSTM-based locator can accurately identify the integrated abnormalities.

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3.1 Genetic Algorithm GA can be used in conjunction with information encryption [24]. Every routing protocol employs cryptography to maintain information integrity throughout data transmission between IoV devices. The cryptosystem has properties that make it ideal for use in cryptography. It is a scientific computation that involves data of self-­ assertive capacity to a fixed-size bit string (a hash) that is intended to be a decreased capacity (i.e., an ability that can then be inverted). A Genetic Algorithm (GA) is typically used to find optimization and search for problem solutions. The basic idea is that for a group of people to adjust to a situation, it must serve as a specific framework. This indicates that an individual’s survival and reproduction are aided by the elimination of undesirable features and the reinforcement of beneficial behavior. A GA begins with a set of individuals chosen at random. The genetic algorithm begins a loop once the beginning population has been established. A new population was created in the last iteration by applying a specific number of stochastic agents to the original occupants. A generation is a term for this type of iteration. Crossover and mutation are the two reproduction operators used by the genetic algorithm. Parents are coupled together to deploy a crossover operator. There are various sorts of crossover operator operations. However, the available ones are determined by the persons’ representation. The mutation is used for binary string individual people, one-point, two-point, plus normal crossover functions. The mutation operator’s goal is to imitate the impact of transcription errors, which might occur with a very low frequency when a person is modified. A bit inversion is a common binary string mutation operator that turns “0” into “1” and vice versa.

3.2 Artificial Neural Networks Convenience and safety network managers widely use intrusion detection systems (IDS) because they are deemed crucial in maintaining network security. One potential drawback is those specific systems are usually built on identification systems, making them highly reliant on an updated database and, as a result, ineffective against new threats (unknown attacks). Abuse or pattern identification and anomaly detection are two different detection methods utilized in IDS systems. Abuse or Signature Detection: A section analyses its activity for events that are identical to well before intrusion behaviors or other intrusion activities. As a result, a technological limitation is that even the system can only detect known attacks and attacks that have already been recorded within a signature database. This means that the process must be updated regularly in order to deal with threats that are always evolving and changing. Anomaly Detection: Outlier detection presupposes those attacks are distinct from the system’s usual ones. In this strategy, the system produces a set of

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parameters that specify the expected behavior of users and hosts. The drawback is the higher incidence of false alerts caused by users or the system’s unpredictable behavior. ANN is a methodology used to solve the problems and improve the existing IDS solutions. It can be used to ensure that intrusions are detected and stopped while maintaining network availability. In the proposed system, the ANN is validated for known hazards. The training is carried out using datasets of various forms of intrusion for those other routing protocols. All existing routing protocols can benefit from a standard ANN intrusion detection system. In an IoV, users have a pre-defined communication pattern with one another. Every user’s or device’s usage pattern is gathered, or a predefined word is also used to train the ANN. As an outcome, if an attacker acquires control of the IoV network, the network will be vulnerable, the ANN will identify the intrusion and notify the network administrator, who will take appropriate action. ANN can even be used to defeat denial-of-service attacks. Every IoV device has a set number of sensor nodes, and each node will have its own set of characteristics based on the common routing protocols embedded in that device. Every node receives a communication strategy after receiving a request. The ANN is programmed to look for any anomalous node behavior. If abnormal behavior is discovered in a node, that node will be destroyed or, based on the network administrator’s specified measures, will be initiated.

4 Blockchain Technology Blockchain technology is one of the emerging areas in VANET security and it is suitable for a decentralized environment with distributed consensus features. In VANET vehicle communicates with each other and in the complex environment, it’s not highly secure since the vehicles do not trust each other because of unauthenticated data [25]. The idea of blockchain technology is to secure the data; so it cannot be tampered with by any third-party user or hackers. Blockchain technology integrates the peer-to-peer communication technology, asymmetric encryption, incentive, ledger, and smart contract technology. Figure 1 shows the architecture used in the Blockchain technology application layer, contract layer, incentive layer, consensus layer, network layer, and Data layer. The more important aspects of our work in this paper are focused on the authentication algorithms Ripple Consensus algorithm in which how the data is authenticated using this consensus protocol. In the next section, the Ripple consensus algorithm illustrates the consensus process.

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Fig. 1  Architecture of blockchain technology

4.1 The Ripple Protocol Consensus Algorithm Ripple protocol consensus algorithm (RPCA) is a Ripple consensus algorithm [26] as shown in Fig. 2. All parts are “a Ripple server, an open and last closed ledger, a proposer, and a Unique Node List (UNL)” [27]. The consensus protocol is used by the Ripple server. Every few seconds, all nodes use the RPCA protocol to preserve the network’s accuracy and agreement. The far more recent ledger is deemed closed by becoming the least recently closed ledger after an agreement is reached. The lastclosed ledger preserved by every network element will be identical if the consensus procedure succeeds without generating a network fork. Ripple manages a static design that selects five validators (who only trust each other). As a result, it may result in centralization. Distributed consensus systems enable decentralized, low-cost transactions. Correctness, agreement, and utility are the three types of technical issues. Correctness means the system must distinguish between legitimate and illegitimate transactions, and trusted institutions have traditionally guaranteed transactions using cryptographic signatures. Still, in broadly distributed systems, there are no trusted parties. The agreement is a single global truth that must be maintained (i.e., there can be only one set of globally recognized transactions). To prevent the Double-Spend

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Validating nodes 1

Validating nodes 2

Validating nodes 3

Validating nodes 4

YES Transaction Proposal

Transaction Proposal from validating nodes

Training Candidates Get more than 50% votes based on ANN &GA classifier

NO Client process

Training Candidates get more than 80% votes based on ANN & GA classifier

YES

NO

Transaction Set Verified Record the data in the Ledger

Fig. 2  Algorithm flow for Ripple consensus protocol

Problem, this is required. The utility is the most ethereal and ill-defined of the three groups. It has to do with how users experience the network, and latency, computational power necessary for consistency, and user technical proficiency are only a few examples. Each server/node keeps a unique node list (UNL), which would be the sole list of nodes that it consults while determining consensus. Although any individual members of the UNL are not trusted, the UNL forms a trusted subgroup of the network If there are no forks or system problems, every several seconds, all nodes implement the RPCA, resulting in a consensus. The RPCA follows the round process: • Each node creates a candidate set by broadcasting all valid transactions it has received which aren’t actually in the ledger. • Every node in its UNL combines the candidate sets of all of the other nodes in its UNL and then votes on the transaction’s authenticity. • Transactions with a significant percentage of “negative” votes are either rejected or added to the next set of candidates. • A transaction is applied to the ledger if it at minimum 80% of the total of a node’s UNL agrees on it. • The ledger has now been closed and is now the “last-closed” ledger. For z ≤ (m − 1)/5 Byzantine failures, strong accuracy is assured. However, if a 20%