IoT Enabled Computer-Aided Systems for Smart Buildings 3031266846, 9783031266843

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

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Gonçalo Marques  •  Jagriti Saini Maitreyee Dutta Editors

IoT Enabled Computer-Aided Systems for Smart Buildings

Editors Gonçalo Marques Technology and Management School of Oliveira do Hospital Polytechnic Institute of Coimbra Oliveira do Hospital, Coimbra, Portugal Maitreyee Dutta Department of Information Management and Emerging Engineering National Institute of Technical Teachers’ Training and Research Chandigarh, Chandigarh, India

Jagriti Saini Department of Electronics and Communication Engineering National Institute of Technical Teachers’ Training and Research Chandigarh, Chandigarh, India

ISSN 2522-8595     ISSN 2522-8609 (electronic) EAI/Springer Innovations in Communication and Computing ISBN 978-3-031-26684-3    ISBN 978-3-031-26685-0 (eBook) https://doi.org/10.1007/978-3-031-26685-0 © European Alliance for Innovation 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Smart buildings can improve quality of life by a considerable level. It is not just about the comfort of having everything under control with remote controllers and mobile-based actions; instead, smart buildings even provide an enhanced lifestyle to elderly people, disabled patients, and children as well. There are plenty of advanced technologies that can be used to create smart building environments. This book focuses on the integration of IoT and computer-aided systems for the development of smart buildings. The scope of this book includes, but is not restricted to, advanced technologies for monitoring, energy management, protection, safety, assisted living, and intelligent operations. It covers the wide aspects of interconnected smart services with convenient interfacing to the end-users. To be more precise about the content of this book, the seven chapters shed light on the assessment, control, and management of various smart building applications. In the first chapter entitled “Environmental Data Control in Smart Buildings: Big Data Analysis and Existing IoT Technological Systems,” David Galán-Madruga exhibited diverse arguments evidencing the implementation of technological tools relying on IoT systems for managing smart buildings, which helps preserve or improve the comfort and wellbeing of indoor occupants, to provide potential readers with an informative benchmark, encompassing aspects such as the control of thermal, security, lighting, and noise and air quality. In the second chapter entitled “Need of Technological Interventions for Indoor Air Quality and Risk Assessment upon Short-Term Exposure: A Futuristic Approach,” Khan et al. analyzed conditions causing short-term exposure to polluted air along with the need for technological interventions and risk assessment for better outcomes. Beucker et al. in their chapter entitled “Climate-Neutral Districts with Decentralized Energy Production, E-Mobility and Through the Formation of an Energy Community Exchange of Electricity and Heat,” described various modern energy production methods in different types of buildings along with essential requirements and constraints in the e-mobility application domain. Mohapatra et  al. in their chapter entitled “IoT Enabled Zero Water Wastage Smart Garden” provided experimental details of a low-cost sensor-based soil moisture and temperature monitoring system to ensure real-time assessment of plant health. These smart systems can further help in v

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Preface

controlled irrigation management for roadside plants, city park fields, and gardens. These zero water waste gardens can be an impressive addition to smart buildings. Another chapter entitled “IoT-Based Human Activity Recognition for Smart Living” by Saha et al. highlighted the importance of IoT-based human activity recognition system for smart living. The chapter provided valuable insights into general architecture, design principles, essential components, and research challenges associated with developing a proposed IoT-based human behavior supervision system. Willets et  al. in their chapter entitled “Application of Data Mining to Support Facilities Management in Smart Buildings” discussed data mining applications using big data gathered from smart sensor systems that can be further utilized to support resource conservation, energy management, and sustainability. In the last chapter entitled “Application of Artificial Intelligence in Ambient Assisted Living to Support Elderly People in Smart Homes,” Bastaki et al. presented valuable information on ambient intelligence paradigms to improve healthcare services to older residents living in care centers or independent homes. This study emphasizes the contribution of artificial intelligence, context awareness, wearable technologies, and ubiquitous/pervasive computing to design ambient assisted living environments. The chapters in this book provide valuable information on the utilization of advanced technologies for designing smart building applications. This book throws light on challenges, opportunities, and applications of IoT and computer-aided systems to enhance human lifestyle with improved building environments. This book may help upcoming researchers to understand the potential of emerging technologies to create smart building environments along with considerable problems in this research area. The editors want to thank the contributions of several insightful writers, expert reviewers, and the supporting editorial team of European Alliance for Innovation (EAI) and Springer to complete this book. We congratulate all the writers for their valuable efforts in creating, submitting, and updating articles as per the reviewer comments. Finally, we would like to extend our sincere gratitude to Eliška Vlčková for his support in the entire book publishing process. Oliveira do Hospital, Coimbra, Portugal Chandigarh, Chandigarh, India 

Gonçalo Marques Jagriti Saini Maitreyee Dutta

List of Reviewers

Editors want to extend special thanks to all the reviewers who participated in the double-blind review process for this book: • David Galán-Madruga • Department of Atmospheric Pollution (National Reference Laboratory for Air Quality in Spain), National Center for Environmental Health (Health Institute Carlos III), Madrid, Spain • Alfred Lawrence • Department of Chemistry, Isabella Thoburn College, Lucknow-226007, U.P., India • Tahmeena Khan • Department of Chemistry, Integral University, Lucknow-226026, U.P., India • Hitesh Mohapatra • School of Computer Engineering, KIIT Deemed to be University, Odisha, India • Nazgol Hafizi • Department of Architecture, Eastern Mediterranean University, Famagusta, Cyprus • Chandreyee Chowdhary • Department of Computer Science and Engineering, Jadavpur University, India • Priya Roy • Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India • S. Müjdem VURAL • Faculty of Architecture, Department of Architecture, Eastern Mediterranean University, Northern Cyprus

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About the Book

Smart buildings can improve quality of life by a considerable level. Smart buildings provide an enhanced lifestyle to elderly people, disabled patients, and children as well. There are plenty of advanced technologies that can be used to create smart building environments. This book focuses on the integration of IoT and computer-­ aided systems for the development of smart buildings. The scope of this book includes, but is not restricted to, advanced technologies for monitoring, energy management, protection, safety, assisted living, and intelligent operations. It covers the wide aspects of interconnected smart services with convenient interfacing to the end-users. The chapters in this volume provide valuable information on the utilization of advanced technologies for designing smart building applications. Moreover, this book throws light on challenges, opportunities, and applications of IoT and computer-aided systems to enhance human lifestyle with improved building environments. This book may help upcoming researchers to understand the potential of emerging technologies to create smart building environments along with considerable problems in this research area.

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Contents

Environmental Data Control in Smart Buildings: Big Data Analysis and Existing IoT Technological Systems��������������������������������������������������������    1 David Galán-Madruga  Need of Technological Interventions for Indoor Air Quality and Risk Assessment Upon Short-Term Exposure: A Futuristic Approach��������������   19 Tahmeena Khan and Alfred J. Lawrence Climate-Neutral Districts with Decentralized Energy Production, E-Mobility and Through the Formation of an Energy Community Exchange of Electricity and Heat ������������������������������������������������������������������   39 Severin Beucker, Walter Konhäuser, Ingo Schuck, and Olaf Ziemann  IoT-Enabled Zero Water Wastage Smart Garden����������������������������������������   71 Hitesh Mohapatra, Mohan Kumar Dehury, Abhishek Guru, and Amiya Kumar Rath  IoT-Based Human Activity Recognition for Smart Living��������������������������   91 Anindita Saha, Moumita Roy, and Chandreyee Chowdhury Application of Data Mining to Support Facilities Management in Smart Buildings ������������������������������������������������������������������������������������������  121 Matthew Willetts and Anthony S. Atkins Application of Artificial Intelligence in Ambient Assisted Living to Support Elderly People in Smart Homes��������������������������������������������������  145 Benhur Bakhtiari Bastaki, Mohamed Sedky, Russell C. Campion, and Anthony Atkins Index������������������������������������������������������������������������������������������������������������������  165

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About the Editors

Gonçalo  Marques  holds a Ph.D. in Computer Science Engineering and is a Senior member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as Assistant Professor lecturing courses on programming, multimedia, and database systems. Furthermore, he worked as a Software Engineer in the Innovation and Development unit of Groupe PSA automotive industry from 2016 to 2017 and in the IBM group from 2018 to 2019. His current research interests include the Internet of Things, Enhanced Living Environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 80 publications in international journals and conferences, is a frequent reviewer of journals and international conferences, and is also involved in several edited book projects. Jagriti Saini  was born in Himachal Pradesh, district Mandi in 1992. She holds a Diploma in Electronics and Communication Engineering (2010) from GPW Kandaghat and completed her B.Tech. in Electronics and Communication Engineering (2013) from HPU. She received a Master’s degree in Electronics and Communication Engineering from the National Institute of Technical Teacher’s Training and Research (NITTTR), Chandigarh (Panjab University), India (2017). She was awarded a Gold Medal for securing the highest percentile in the entire university during her Master’s degree. Jagriti completed her Ph.D. thesis from NITTTR (Panjab University), Chandigarh. She also recieved an INSPIRE fellowship from the Department of Science and Technology (DST), India, for carrying out her research work. Her current research interests include artificial intelligence, the Internet of Things, environmental monitoring, indoor air quality monitoring and prediction, healthcare systems, e-Health, and autonomous systems. Her Ph.D. thesis entitled “Design and Development of Intelligent Indoor Air Quality Monitoring and Prediction System – Vayuveda” is mainly focused on developing cost-effective realtime monitoring and prediction system for indoor air quality management. She published more than 25 papers in reputed peer-reviewed international journals and conferences. Other than this, she is a frequent reviewer of journals and international conferences and is also working on several edited book projects. xiii

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About the Editors

Maitreyee  Dutta  was born in Guwahati, India. She received a B.E. degree in Electronics and Communication Engineering in 1993 from Guwahati University and was Gold Medalist in the same year. She obtained an M.E. degree in Electronics and Communication Engineering and a Ph.D. degree in the Faculty of Engineering from Panjab University. She is currently a Professor and Head of Information Management and Emerging Engineering and a Joint Professor in the Computer Science and Engineering Department, at the National Institute of Technical Teachers’ Training and Research, Chandigarh, India. She has more than 22 years of teaching experience. Her research interests include the Internet of Things, security of data, IP networks, Internet, authorization, data privacy, Public Key encryption, pattern clustering, cloud computing, and data compression. She has more than 100 research publications in reputed journals and conferences. She completed two sponsored research projects: Establishment of Cyber Security Lab, funded by the Ministry of IT, Government of India, New Delhi, amounting to Rs. 45.65 lac; and Establishment of Advanced Cyber Security Lab sponsored by MeitY, New Delhi, amounting to 62 lacs. One sponsored project Securing Billion of Things-SEBOT funded by All India Council of Technical Education, New Delhi of amount Rs. 14.98 lacs is in progress.

Environmental Data Control in Smart Buildings: Big Data Analysis and Existing IoT Technological Systems David Galán-Madruga

1 Introduction Throughout history, the study and development of the different branches of science have provided primordial advances for improving human beings’ life quality and wellness. For example, progress in medicine has allowed the cure of diseases that in preterit times were deadly. In addition, easy access to food and better nutrition has led to greater longevity of human beings. Although scientific and technological advances are translated into an indisputable enhancement for the human being, they may lead to issues within the Public Health frame. In this sense, a general population rise at the global level has generated a disproportionate waste increase toward the different environmental compartments (air, water and soil matrices) [1] as a consequence of exponential industrial growth and an expansion of transportation networks, among other factors [2]. This scenario is accented in urban environments, given that a high percentage of the population is involved in these areas [3]. Research studies record the urban zones as the most polluted atmospheres [4]. The great cities are complex environments at the urbanist level, diverse and dynamic places that include a wide variety of sectors, such as industry, health, and education. Considering all aspects, the “smart cities” concept arises, referring to those cities whose urbanistic plan incorporates the existing technology to solve urban problems such as energy consumption or environmental pollution. According to European Commission, the parameters for making an ideal smart city are ranked into three groups: (i) production and consumption of urban energy, (ii) urban transport and mobility and (iii) urban information and D. Galán-Madruga (*) Department of Atmospheric Pollution. National Center for Environmental Health, Carlos III Health Institute, Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 G. Marques et al. (eds.), IoT Enabled Computer-Aided Systems for Smart Buildings, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-26685-0_1

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communication technologies [5]. The application of technology resources in smart cities provides advantages the enterprises and the general population that resides in that concrete city. It solves environmental and social inequalities, generating the most sustainable urban environment. The creation of smart cities is developed by respecting the environment, which minimizes the potential climate change impact. Due to the progressive rise in population in urban areas, the development of smart cities results in primordial strategy and a future challenge. It is estimated that 68% of people worldwide will live in urban areas by 2050 [6]. A most focused approach within the smart cities’ conceptualization includes smart buildings, particularly environmental control in indoor spaces. It is relevant to highlight that people spend 90% of their time indoors, converting these locations into determining zones to minimize the impact on human beings from exposure to inside environmental pollutants and control meteorological conditions and noise levels. Another determining agent in the environmental monitoring of smart buildings is the energy factor. Nowadays, 40% of the energy used globally is spent on keeping and adequately working buildings to supply occupants’ wellness at the professional and personnel levels [7]. In this sense, there are specific sensors for monitoring indoor air quality and me-meteorological variables; nevertheless, given the vast data amount generated, analyzing the recorded datasets is a challenge to get helpful information that establishes behavior patterns. Automating processes is a viable alternative for featuring a solution to the posed challenge. Over the last decades, the enormous development of technologies and connectivity, information networks, and computer tools has allowed advancing the intelligent control of environmental data monitored in smart buildings. The control of environmental conditions in smart buildings favors the occupant’s security and wellness. It is an expensive protocol requiring long-term monitoring, specific equipment, and specialized personnel. Within this context, the Internet of Things (IoT) plays a leading key. Its objective is to interconnect physics elements with the Internet [8] and to provide interactions among themselves and with people worldwide. This chapter aims to provide an overview of controlling environmental data within smart buildings using IoT tools, evidencing its need, architecture, problems and limitations and discussing potential future challenges. The application of IoT-­ based systems for controlling indoor environmental quality reaches a highly relevant meaning in the current society framework, given the emergence of a worldwide pandemic. The new coronavirus (SARS-CoV-2) discovered in December 2019 in Wuhan, Hubei province (China) [9], causing the infectious disease named COVID-19 and contributing to a crisis at the global level, urged countries to force lockdown measures to prevent the SARS-CoV-2 transmission. Therefore, control of the indoor environmental quality, with particular emphasis on air quality status, became an essential issue in terms of health. In this regard, the enforcement of IoT for monitoring indoor ambient spaces may offer remarkable advantages in order to secure human beings’ health. Table 1 shows several research studies focused on the link between IoT and COVID-19 and similar viruses. The present chapter is structured as follows: the conceptualization of smart building and IoT is addressed in Sects. 2 and 3, respectively. Section 4 describes the

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Table 1 Investigation studies encompassing the link between IoT and COVID-19 and similar viruses References Akbarzadeh et al. [10] Ghaleb et al. [11] Meraj et al. [12] Siddiqui et al. [13] Chamola et al. [14] Fatima et al. [15] Kumar et al. [16] Mohammed et al. [17] Mohammed et al. [18] Singh et al. [19] Yang et al. [20] Sareen et al. [21]

The base of the study Design a smart hospital by using detectors to manage the number of visitors and zones they occupy Investigation of the IoT response to the COVID-19 Investigation of IoT solutions to detect and predict infectious diseases, such as the flu, Zika, or COVID-19 Development of an IoT architecture to uphold the social distance in the pandemic cases Assessment of COVID-19 impact on the worldwide economy using IoT Proposal of an IoT fuzzy framework to predict and monitor COVID-19 Suggestion of an IoT-based architecture to reduce the COVID-19 spreading Development of a smart appliance with thermal and face recognition to identify COVID-19 infected individuals Development of a drone-based technology to accelerate the location of COVID-19 infected individuals and places IoT applications to discover symptoms of COVID-19 Integration of the IoT with the geographic positioning system included in smartphones for tracking infected case Investigation of IoT solutions that address the COVID-19-like viruses

basic aspects related to big data analysis and governance are reported, Sect. 5 includes the IoT systems used in the smart building to manage the indoor environmental factors are exhibited, and in Sect. 6, the applications of IoT systems, learned lessons, and future challenges are discussed.

2 Definition of Smart Building First, it is necessary to highlight a controversy between the “intelligent” and “smart” building definitions. An interesting study in 1988 provided a simple conceptualization of an intelligent building, defining it as a building that thoroughly monitors its inside environment [22]. Other authors enlarged the intelligent building concept, including the inside occupants. So, [23] reported that an intelligent building develops an environment allowing occupants maximum efficiency and proficiency in managing the building resources at the lowest cost using appropriate technology. In the case of a smart building, broadly, it has not been set as a consensual definition [24]. Nevertheless, the scientific community understands its broader conceptualization regarding intelligent building. The smart building‘s concept surges when integrating advanced technology into buildings to control all processes involving it for human beings’ convenience.

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In order to offer the broadest overview to potential readers, the smart building conceptualization will be divided into two definitions: 1. Definition 1: Those buildings allow remote controlling of everyday actions at home, such as switching on heating, fireplace, lighting, electronic apparatus, and opening curtains, among others. These performances can be driven over distance using a smartphone or computer, and executing these remote actions requires previous programming [25]. 2. Definition 2: Those buildings learn to predict future home sceneries to conduct specific actions. To achieve that, the smart building programs analyze the behavior of inside occupants (developed activity, inhabitant number, among others) and indoor environment (as examples: meteorological comfort, light or noise). In this case, the target home is accommodated to the indoor occupant’s life cycle, learning from them to make their own decisions, and favoring the comfort of inside inhabitants or occupants [26]. In any case, the smart building is a concept covering several emerging technologies, which involves efficient sensors that allow automatizing the inside building activities, using predictive data to improve the life quality of the population at indoor locations (smart building). The elements comprising smart buildings infrastructure include three individual elements [27]: (i) Devices receive signs or messages from indoor sensors, and then they send this information to other sensors or central system, (ii) sensors dispatching information collected inside the building, and (iii) communication network connecting all active elements with a central system. For example, the equipment included in point (i) might be a switch, and a lighting control sensor, as equipment the point (ii). Finally, a Wi-Fi network would fit within the point (iii). In this sense, Fig. 1 displays several inside smart building components.

Fig. 1  Indoor smart building components

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The development of smart buildings may be translated into notable benefits listed below: 1 . Improving occupant’s convenience, 2. Implementing domotic, 3. Guaranteeing occupant’s health, 4. Favoring energy saving. 5. Easing occupant’s time-saving. Smart buildings are projected to interrelate with the surrounding environment, pondering, acquiring knowledge and making decisions to solve the issue in indoor spaces affecting human beings [28].

3 Definition of Internet of Things IoT is an enabled object network consisting of smart sensors or actuators [29] that interact with one another and with the environment collecting information and sharing learning to make human being life easier [30]. IoT features a broad spectrum of tools connected by the Internet using IP addresses belonging to each sensor. The International Telecommunication Union defines the IoT as a worldwide infrastructure within the information field, providing advanced services when interconnecting IoT elements [31]. The employment of IoT allows rising the efficiency of resource utilization and minimizing human efforts. Smart buildings are projected to interrelate with the surrounding environment, pondering, acquiring knowledge, and making decisions to solve the issue in indoor spaces affecting human beings. Environmental data collection is the responsibility of the IoT, whereas the recorded dataset treatment points to Artificial Intelligence (AI). Despite the potential applications of IoT in diverse fields or sectors, the related IoT technology ranks its architecture in four groups: data collecting and transporting, storing, processing and availability [32]. In this case, Fig. 2 shows IoT-based systems architecture associated with smart buildings.

COLLECTING

TRANSPORTING

DATA MINING

AVAILABILITY Application Programming Interface

ACTUATORS Graphical user interface

SMART BUILDINGS

Fig. 2  IoT-based systems architecture in smart buildings

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At the taxonomic level, the IoT involves the following categories: Energy, communication (networks), functional attributes (data treatment algorithm), local user interface, cloud storage data and hardware and software resources [33, 34]. The energy results are an essential characteristic of most sensors or actuators. There are several energy sources, such as the environment (solar panels), recharged sources (batteries), and electric sources, among others [35]. Regarding communication, some actuators present several communication interfaces. Within this property, security plays a primordial key. The functional attributes associated with IoT technology allow registering of several types of smart buildings’ environmental data, such as temperature, relative humidity and light level, among other variables. The local user interface lets the user to configure the sensor settings. The interface may be active (it involves a direct user interaction) or passive (the user interacts with the sensors automatically). Another primordial aspect of IoT technology depends on hardware and software resources. Regarding hardware, the quantity of random access memory and central processing unit will influence the sensor performance and software updates.

4 Big Data Analysis and Governance Despite different definitions for the big data concept, it broadly refers to large and complex datasets collected by companies and public organizations, among others, that require nontraditional data processing applications to process them correctly and reveal partners and trends [36]. Datasets may come from distinct sources such as medical records, social networks, banking and polices data networks and police data information. Doug Laney firstly used the big data term in 2001. His definition involved fundamental characteristic threes within the big data conceptualization, named “3Vs.” First, “Volume”: This feature is related to data storage. There is no data storage problem because of computing’s massive advances. Second, “Velocity”: Is linked to data acquisition. It is currently collected in real-time at high-speed rates. Finally, “Variety”: Relates to the collected data type [37]. Subsequently, two characteristics were added: “Value” refers to the cost, and “Veracity” points to the original dataset cleaning [38]. Figure 3 represents the general characteristics of big data analysis. Based on the previously mentioned, it can be sensed that the employment of big data grants a notable advantage to the companies using it over those that do not. This relevant aspect confers a competitive benefit to those businesses that utilize it effectively in managing, making prompter and better-informed business decisions. According to Sect. 3, IoT technology collects vast data from sensors or actuators. In contrast, big data would ease the registered data storage and processing efficiently and speedily, sustaining linked technologies. Big data analysis aims to expand efficient methodologies that allow predicting future events and laying down links between the original information dataset and the reached outcomes for revealing scientific knowledge. The leading advantages of big data analysis against conventional analysis techniques using small data groups are: (i) scanning heterogeneity

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Fig. 3  Basic features of big data analysis

among subgroups and (ii) drawing common factors among elements included in each subgroup [39]. Big data has already been implemented practically in diverse fields, such as agriculture [40], personalized medicine [41], consumer habits [42], marketing [43], airline route profitableness optimization [44], and cybersecurity [45], to name a few examples. In order to reach reliable outcomes when using big data analysis, excellent datasets governance is a fundamental process. It involves aspects such as readiness, integrity, and security of data, protecting data in each big data analytic step. Therefore, the governance process drives dataset management [46].

5 IoT Technological Systems for Smart Buildings A smart building comprises sensors to monitor the specific inside building conditions. The data recorded by these sensors may be controlled by proceeded technology. A highlighted technology supporting this objective leads to IoT technology; several examples offer readers IoT tool applications in smart buildings.

5.1 Thermal Comfort Thermal comfort has become a relevant variable for stimulating human wellness in general and raising the employee productivity level in offices and industrial buildings [47, 48] implemented IoT to estimate personal thermal comfort. They created

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a system that collects telemetric information using an IoT sensors network, user suggestions and meteorological measurements (temperature, humidity and air speed). They introduced all registered information into an algorithm of machine learning. The proposed approach evidenced good outcomes. Another relevant research group from the School of Information Science and Technology, North China University of Technology, Beijing, China, set a novel approach for controlling thermal comfort within a livable environment. Similar to the previous study, they monitored indoor temperature and relative humidity using sensors. In addition, they took into account individuals’ activity and clothing conditions via video cameras integrated into the IoT network of sensors. They concluded that the individuals’ metabolic rates and clothing insulation greatly affected thermal comfort at the personal level [49]. In order to supply additional information within this section context, a highly highlighted research study performed a bibliography review process between 2017 and 2021 to collect recent literature on methodologies for controlling thermal comfort in buildings. They gathered 166 works [50]. Broadly, the control of thermal comfort in smart buildings may be directly translated into the benefit of the healthcare of the occupants. Another significant consequence of controlling thermal comfort is supporting energy efficiency; aspect is essential because 40% of the energy consumption at the global level takes place inside buildings.

5.2 Visual Comfort First, it is appropriate to highlight that the visual comfort of the occupants of buildings is usually sensed using photometric sensors, which assess the luminaire intensity concerning daylight readiness [51]. The leading drawback of this sensor type resides in its location within the building. If the photometric sensor is close to a daylight focus, as a window, its performance may cause an inside light attenuation, generating occupants’ discomfort. Therefore, this one should be placed near the occupant emplacement. Other aspects, such as light spectrum or blinding, also are factors to consider since they may influence the control of visual comfort by employing photometric sensors [52]. Like the thermal comfort variable, optimal monitoring of visual comfort carries energetic savings. As practical examples, two studies are reported [53]. conducted a study to contrast two different inside-office environmental situations regarding evening lighting levels to identify which one displayed higher energy efficiency using photo sensors. In the same line, [54] revealed an adapted focusing on controlling the public lighting in a smart city, sparing lighting and keeping visual comfort in illuminated zones.

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5.3 Noise Level The application of IoT tools in controlling noise levels aids decision-making concerning the implementation of noise control strategies in smart buildings, given that acoustic pollution generates a psychologically and physiologically unbalance in humans [55]. The authors of [56] developed an IoT-based noise-controlling system consisting of a sound sensor and an IoT network. They created an IoT-based noise-­ controlling system consisting of a sound sensor and an IoT network. This combined system aimed to issue a real-time alert when noise levels exceeded legislative limits.

5.4 Occupant Security Another practical application of IoT technology in smart buildings is associated with security subjects. For example, occupancy sensors may detect whether a concrete room is occupied or not [57], if opening windows or doors are, and if it occurs, among others. Another example of applying IoT based on security systems in smart buildings is cases of illegal intrusion. Movement sensors may lock the main door and activate the security alarm. As a smart action, the system calls a security company to proceed with intruder detention. The final objective in implementing the security IoT tools in smart buildings is to protect the building occupants’ integrity by monitoring real-time information for ulterior decision-making that derive smart actions.

5.5 Indoor Air Quality Atmospheric pollution is the leading environmental risk for human beings [58] since that numerous scientific studies sustain links between air pollutants exposure and damaging human beings [59, 60]. Therefore, poor air quality derives a health issue, generating a severe public administration concern. This worry is aggravated in indoor spaces, given that there are no air quality standards for protecting human beings [61], translated into uncertainty on exposure to air pollutants levels. For this reason, controlling air quality in indoor spaces becomes a primordial action. In this regard, IoT utilization in smart buildings allows monitoring of indoor air quality and managing the smartly monitored scenery, if necessary. Suppose high air pollutant levels are measured within a smart target building; preventive measures should be implemented to protect occupants’ health, such as leaving the indoor space or opening windows to force a decrease in air pollutants concentrations. Sometimes, this last measure is not appropriate since ambient outdoor air pollutants levels are higher than indoor, which harms indoor air quality in an air indoor/outdoor interchange process through opening windows.

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Table 2  Research works employed IoT technology for controlling air pollutants in indoor spaces References Tagliabue et al. [62] John et al. [63] Wall et al. [64] Jo et al. [65] Saini Dutta [66] Dhanalakshmi et al. [67] Jose y Sasipraba [68] Zhao et al. [69] Firdhous et al. [70] Parmar et al. [71] Belyakhina et al. [72]

Monitored compounds CO2a PM2.5b, PM10c, and CO2 Air quality index Aerosol, VOCd, COe, and CO2 Air quality index CO2 CO2 equivalent, ethanol, and TVOCf CO2 and formaldehyde O3g CO, CO2, SO2, and NO2h PM10

Carbon dioxide Particles with an aerodynamic diameter lower than 2.5 μm c Particles with an aerodynamic diameter lower than 10 μm d Volatile organic compounds e Carbon monoxide f Total volatile organic compounds g Ozone h Nitrogen dioxide a

b

IoT based on systems for controlling indoor air quality in smart buildings has been implemented successfully (see Table 2). Broadly, the application of air quality monitoring IoT-based systems in indoor environments has been implanted for a low pollutants number. Essential compounds in health, such as benzene, should be addressed using this technology. Since indoor air quality control is a worldwide issue, the answer set should be addressed globally. The emergence of smart buildings using IoT technology tools might offer a solution at the global level for controlling air pollution at indoor locations. This reflection reaches a relevant magnitude because indoor air pollution may differ for each indoor place. Within the frame of smart buildings, the progress of IoT technology provides solutions when it comes to designing the buildings inside and selecting building materials to ease the occupants’ comfort, wellness and security of buildings. IoT in smart buildings allows for better personalizing each occupant’s comfort within the building. The combined action of sensors used for monitoring a smart building‘s environment (as for thermal, visual, fire, security, or indoor air quality sensors, among others) generates a convenient environment for the inside occupants.

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6 Discussion Environmental control in buildings (indoor spaces) is a primordial action to know the occupants’ exposure to environmental agents. In this line, the use of IoT-based sensors and prediction models to control the environmental status of indoor spaces are practices already implemented. To provide some examples, a blockchain-based personalized IoT system was developed to control indoor temperature. They validated the system using a chamber experiment and a field experiment. In conclusion, thermal comfort was improved, keeping personnel privacy and security [73]. The Shiv Nadar University (India) developed a context-aware IoT-enabled framework to analyze and predict indoor air quality using indoor pollutants and meteorological data [74]. The proposed approach could perform various tasks such as data collection, preprocessing, defining rules, and forecasting the predicting states to determine the status of the indoor environment. Due to the leading health repercussions, controlling pollution in the indoor air matrix is a vital action. Elevated polluting levels may cause damaging effects on the human being, for which its monitoring results in essential for protecting human health, which acquires high importance in indoor than outdoor environments, given that the exposure period is notably significant. Forecast techniques may become a valuable tool to anticipate potential high pollution episodes. In this frame, diverse analysis techniques such as linear regression, support vector regression, and the gradient-boosted decision tree have been used to magnify air pollution monitoring and prognosis. This evidence sustains that IoT-based systems using hybrid AI techniques help improve human beings’ wellness [75, 76]. CO2 and particulate matter concentrations (PM10 and PM2.5 particles) were predicted using an artificial neural networks [77]. The prediction models, precise enough, may be integrated within indoor control systems in buildings. Based on previously cited arguments, it is easy to understand that the control of airflow (inlets and outlets) in indoor spaces is a topic highly significant for controlling the occurrence and distribution of indoor air pollutants. In this regard, an investigation group from Chonnam National University (Republic of Korea) developed a framework to control the airflow pattern using AI to remove indoor airborne pollutants effectively [78]. Considering building factors influencing building occupants’ wellness, the use of machine learning and deep learning as subfields of AI have already been implanted in areas such as architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management [79]. Similarly, machine learning and deep learning methods have been implemented to enhance building energy efficiency, an aspect highly relevant in situations of energetic emergence [80]. These techniques can easily enhance knowledge of responsible energy consumption factors, such as building shape, construction material, and building orientation [81].

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Based on the previous evidence, it is deduced that IoT technology implemented in smart buildings favors building management and occupant’s comfort. Nevertheless, diverse factors relating to IoT use in these environments must yet be addressed, listed following: 1. Collected data storage: given that information recorded in smart buildings supposes a vast amount of data, repository systems that require the lowest resources than those current ones should be developed. 2. Data treatment: high-capacity processing tools should be expanded in their application level. In this sense, machine learning and deep learning are fundament implementations within the IoT frame. 3. Network security: this aspect is primordial to implementing IoT technology in smart buildings to avoid attacks from outside networks. 4. Occupants’ privacy: due to visual sensors within smart buildings, a protocol should secure occupants’ privacy in smart buildings [82]. 5. Sensors cost: affordable costs would favor the implementation of IoT at the global level. Within this section, sensors that monitor indoor environmental quality in smart buildings require a leading challenge. In order to provide reliable measurements, those sensors should be previously validated regarding reference methodologies. For example, indoor air quality sensors should be tested against reference air quality methods laid down in current legislation. In addition, those sensors monitored air pollutants in gaseous and particulate phases, whereas the composition of these particles is not covered. In this sense, a critical challenge leads to developing sensors that analyze the particulate composition. 6. Technical training: the employment of IoT systems demands qualified personnel, given the technical requirement to use those technological tools. The notable IoT technology advance suggests a vast implementation of this type of technology in the future, thereby raising the number of smart buildings globally. This powerful reason sustains the necessity of considerably increasing the number of training university centers to solve the potential future demand for professionals within this science field. The taxonomy of the previously cited factors is represented in Fig. 4 to summarize the previously reported. As learned lessons, the implementation of IoT-based systems to control indoor environmental agents in order to improve human wellness has already implanted Fig. 4  Classification of the current challenges to be addressed within the IoT use frame in smart buildings

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success, covering a wide range of indoor environmental factors able to harm occupants’ comfort in any sense. Given that the human beings’ exposure to an indoor environmental quality poor is a global issue, the application of AI-based existing algorithms may offer a valuable solution worldwide due to the advantages of sustaining versus conventional methods (measurement equipment of indoor environmental factors, such as instrumentation for measuring noise, light amount, air pollution, among other). As future challenges, the use of IoT-based sensors to monitor the indoor environmental quality status needs to be tested to secure the performance adequate of the target sensors. For example, those instruments monitoring indoor air quality should be validated versus conventional reference instrumentation. This last one corresponds to equipment measuring that relied on standard methodologies accepted at the scientific level. This reflection may be applied to any measurement equipment for any indoor environmental agent. On the other hand, using existing AI algorithms within the IoT frame has advantages and limitations. Variables such as data availability, practicability and computational cost are features that should be considered to select an appropriate algorithm. The development of algorithms should finish with their validation. As previously evidenced, the application of IoT and data analysis has been demonstrated to be a valuable tool to manage indoor environmental factors in favoring human health and wellbeing. Nevertheless, its use is limited to specific buildings, while large-scale applications should be addressed. The IoT systems utilization in a global scope would allow getting remarkable information based on a multitude of indoor environment scenarios, which would be translated into enhanced predictions and global conclusions. This fact is relevant because it would let public and private managers of buildings establish common patterns in order to safeguard and favor the indoor spaces’ occupants’ wellness. Its implementation would improve data acquisition and share related to enhancing existing wireless networks and data storage. Therefore, the optimization of IoT and AI tools should be addressed as a future challenge, covering factors such as connectivity, network security or privacy. Finally, indoor environmental factors control using data analysis and IoT-based systems offer a unique opportunity for solving global issues by framing factors related to human beings’ wellness.

7 Conclusions The present chapter offers an overview of using data analysis and IoT systems to manage environmental data in smart buildings in order to provide a benchmark to potential readers. Today, informatics tools linked to IoT have been employed successfully in managing indoor spaces, particularly smart buildings. Sensors have been used to control the temperature inside buildings, visual control has been used to arrange the entrance to intelligent buildings and guarantee an adequate occupation level, securing occupants’ security. Similarly, sensors monitoring indoor air

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quality have also been used, and IoT systems to improve and optimize the design of buildings. As a main learned lesson, it has been demonstrated that the use of IoT systems in controlling indoor environmental quality improves the occupants’ wellness. Nevertheless, as limitations, improvements in the infrastructures (connection, Wi-Fi, networks, among others) should be addressed, and the use of IoT systems should foster worldwide, given that its current application is conducted punctually in specific buildings, which sustains the leading future dare. The global application of IoT to indoor management would help solve severe present-day problems, such as controlling human beings’ exposure to indoor air pollutants and security issues, among others. The management of environmental matters highlighted recently, such as disease control (e.g., COVID-19) and energy consumption (particular emphasis on energetic deficit situations), also can be globally controlled through IoT systems use.

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Need of Technological Interventions for Indoor Air Quality and Risk Assessment Upon Short-Term Exposure: A Futuristic Approach Tahmeena Khan and Alfred J. Lawrence

1 Introduction Growing population and urbanization have led to environmental deterioration and posed a threat to global air quality [1] and a rise in global temperature [2]. Therefore, it is crucial to create a healthy environment especially indoors where extreme air conditioning is used. Efficient energy management systems for sustainable development and living have become requirements, for instance, Home Energy Management Systems (HEMS) [3]. Short-term variation in pollution and exposure is often neglected, although it has long-term consequences on human health. In the Indian context where the air quality of prominent cities including the National capital Delhi is already deteriorating day by day with the worst particulate (PM2.5) pollution [4] due to vehicular exhaust, industrial emission, waste and stubble burning, construction work as major contributors [5], the pollution load only worsens by episodic events such as Diwali, which is the mega festival of Hindus celebrated at the onset of winters. During the festival, customary firecracker burning takes place and facilitated by the meteorological conditions creates a disaster for the environmental health. Burning of fireworks leads to the release of As, S, Mn, sodium oxalate, potassium perchlorate, sodium nitrite, barium nitrate, Al and Fe dust. Other than this, SO2, CO2, CO and suspended particles are also released associated with severe health hazards [6]. Emergency hospital admissions have been associated with the drastic variation in air quality during the Diwali festival and elevated asthma attacks. The risk of mortality from different causes is also associated with short-term exposure to pollutants. Short-term exposure to particulate matter and SO2 also aggravate T. Khan Department of Chemistry, Integral University, Lucknow, Uttar Pradesh, India A. J. Lawrence (*) Department of Chemistry, Isabella Thoburn College, Lucknow, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 G. Marques et al. (eds.), IoT Enabled Computer-Aided Systems for Smart Buildings, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-26685-0_2

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the conditions of asthma and bronchitis [7]. The retention of pollutants favored by atmospheric inversion under cold climatic conditions leads to the formation of fog which stays closer to the ground before getting dispersed in the atmosphere. The formation of fog caused by short-term variation in air quality may worsen lung, heart and neurological diseases. The burning of firecrackers adds to a short-term increase in ambient air pollution and also reaches indoors hampering the IAQ. The smoke contributes to the level of particulate matter, SO2, NO2, CO2, heavy metals and volatile organic compounds. Animal and controlled exposure studies depicted that the PM2.5, NO2 and O3 exposure increases the chances of asthma mortality, although the underlying mechanism is not known. Countries like China and India which have high pollutant exposure may have direct irritant and inflammatory responses [8]. At a lower level, the pollutants can induce airway inflammation which can, later on, aggravate asthma. Airway hyperresponsiveness can also be promoted upon NO2 and O3 exposures [9]. These pollutants also lead to oxidative stress. The technological interventions may be used to alert the exposed public about the variations in the pollution level and also to be aware of the meteorological factors and their effect on the transport of the pollution and its fate. Ambient-Assisted Living (AAL) has emerged as an essential aid in field for the assistance of ambient and personal healthcare monitoring [10]. These devices generally work on wireless technologies like ZigBee, Ethernet, Wi-Fi and Bluetooth. However, several challenges are faced while designing and implementing of effective AAL system including interaction design, usability and accessibility [11]. Privacy and confidentiality are also important factors to deal with while accessing these devices and the Internet of Things (IoT) provides an integrated urban-scale ICT medium for a smart environment [12]. IoT has the potential to simulate real-life scenarios. The concept has to be explored to extract accurate predictions, as the data is provided at the microspatial scale [13]. Air pollutants consist of a mixture of physical, chemical and biological agents posing a risk to humans and the environment. They consist of solid particles and gaseous contaminants [14]. As per the World Health Organization (WHO), 2.4 million fatalities take place due to air pollution annually, and 1.5 million of those are linked to Indoor Air Pollution (IAP) [15, 16]. The indoor pollutants may be much higher than the outdoor levels. Environmental Protection Agency also included IAP as a prominent risk factor [17]. Poor Environment Indoor Air Quality hampers productivity at work and induces lethargy, headache and mental fatigue [18]. The problem of poor IAQ is more severe in developing nations. Even short-term exposure to pollutants is very dangerous to the elderly and children. CO2 serves as an indicator of IAQ [19]. Thermal comfort is also linked to IAQ and associated with CO2 [20]. Several respiratory and cardiovascular diseases are linked to [21] are linked to CO, CO2, NO2, O3, PM and VOCs. Air exchange is also significant in the indoor environment and is associated with the comfort level. Confined spaces can lead to serious health implications including sick building syndrome (SBS) [22]. Air pollution sensors also detect gaseous pollutants and PM [23].

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The indoor air quality index (IAQI) can also be calculated using the cumulative pollutant concentration ratio of toxicity level. More studies and research are needed to be focused on the policy legislation, scrutiny and real-time mechanisms for accurate predictions in the indoor environment. It is therefore necessary to perform real-­ time monitoring and to ensure a healthy environment. There are four pollutants found in the indoor environment and four pollutants governing the thermal comfort level which are linked to human health [24]. An index categorized in four different levels has been used to assign the quality of the indoor air [25]. • Excellent: The EIAQI ranges between five and six, with small or no risk associated with the air quality. • Good: The EIAQI ranges between three and five with acceptable air quality. A few toxins may be present in the air posing respiratory threats to the occupants. • Bad: EIAQI values are between one and three. Harmful for the sensitive groups which are likely to be more affected than others. • Worst: EIAQI is below 1, triggering alarming health effects for everyone who is exposed. Smart environmental monitoring (EM) works on addressing the environmental pollutants challenges effectively. Pollution can be caused both by natural and man-­ made sources and environmental monitoring is necessary for environmental protection [26]. With the advent of technological advancements, artificial intelligence (AI) and machine learning, more precise environmental monitoring and optimal pollution control has become possible. The concept of a smart environment is based on wireless networks operating on methods based on AI [27]. Devices based on IoT are employed in WSNs for effective pollution control. Modern environment monitoring methods comprising IoT and AI like wireless sensors are known as SEM systems [28]. Smart Environmental Monitoring is being explored for sustainable environmental protection and to curb undesirable effects to regulate the health and growth of the society  and weather forecasting [29]. Technological interventions like IoT and wireless networking have enabled pollution assessment more feasible. The Smart Environment Management (SEM) systems make use of sensors, wireless sensor networks (WSN) and IoT devices and these devices communicate through networks. Smart environments are comprised of gadgets and sensors that are interconnected to monitor air pollution and are employed in objects of daily usage and may work efficiently through interaction [30]. Smart devices have useful applications in HEMS and the monitoring of atmospheric variations like temperature and relative humidity [19]. The present chapter is intended to elaborate on the health hazards associated with short-term variation in pollutants’ concentration, which often goes unnoticed but has a delirious impact on human health and how with the help of IoT-based applications, the short-term variation can be predicted through different strategies like fuzzy logic controllers. Similarly, the assessment of the health impact associated with short-term exposure to air pollution is also significant and different exposure assessment models and computational strategies are discussed in the course of the study. The authors have presented a case study around Diwali, a prominent festival

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involving customary firecracker burning and how the short-term variation in air pollutants’ concentration impacts the exposed people. The authors have also presented a tentative methodology to predict and curb the short-term exposure of firecrackers and how their detrimental impact can be averted.

1.1 Role of Technological Intervention in Air Quality Management Of late, technological interventions for the monitoring and management of air quality have been growing, especially with the help of computational strategies. The integration of IoT with technological interventions has emerged as a promising solution for the expansion of state-of-the-art air quality monitoring systems. With a clear understanding of the air quality at different levels, the decision-making process may become easier. The following section gives a deeper insight into the applicability of the IoT-based air quality management and prediction systems particularly related to indoor air quality.

1.2 Internet of Things (IoT) and Applicability for IAQ Management IoT-based devices are connected with sensors and the obtained data may be used for sustainable living and enhanced performance. IoT-based devices may be used effectively for environmental regulation [31]. The artificial intelligence-based technique makes the exchange of data easier. Several IoT systems for IAQ monitoring have been proposed to process the transmission of data and microsensors for the acquisition of data and also enable the transfer of data [32]. Monitoring of O3, CO, NOx, SOX, CO2, VOCs, particulate matter, temperature and humidity has been made possible by IoT-based sensors which used Raspberry Pi-based sensor module [33]. Another WSN for IAQ monitoring has been developed using Arduino, XBee modules and microsensors for storing the monitoring data in real time [34]. The sensor nodes receive data through several sensor nodes through the ZigBee protocol [35]. Another ZigBee WSN system has been proposed to monitor CO2, VOCs, temperature and humidity has been proposed based on the Arduino platform [36]. However, the system does not offer any mobile computing solution. An IAQ monitoring system for AAL based on hybrid IoT/WSN to monitor atmospheric variables has been proposed [37]. It is based on open-source technologies like Arduino and Zigbee. It is of prime importance to monitor IAQ for global health assessment. Henceforth, further exploration is needed to develop cost-effective and open-source monitoring systems for IAQ monitoring [38].

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1.3 Fuzzy Logic Controller A fuzzy inference system is used to analyze indoor pollutants in rule-based operation mode. Data is collected from different sources. An environment indoor air quality index (EIAQI) is developed using the fuzzy theory which is aimed to improve air quality. The fuzzy logic-based measurement is used for different pollutants and also simulates comfort level and AQI for different pollutants to predict health hazards and toxicity [39, 40]. The controller also ascertains the output response as dependent on toxicity levels [41]. Simulation of fuzzy logic using lighting and windows as a control system has been done for thermal comfort and the regulation of humidity [42]. The building’s quality can be improved if the indoor environmental quality (IEQ) can be improved [43]. On this principle, using the EIAQI as the main reference index status level containing indoor and thermal comfort pollutants, the clustering technique divided the results of the fuzzy logic controller of IAQI and TCI values. The EIAQ system classifies indoor air and thermal comfort pollutants with differential health impacts. The conjugation of the EIAQ system with the fuzzy logic controller will be significant for the accurate determination of environmental quality.

1.4 Air Pollution Sensors The Global Burden of Diseases study, 2015 identified air pollution as a major contributor to global morbidity, particularly in developing countries [44]. Personal air pollution exposure is a crucial point to explore to assess negative health impact; however, it is difficult to estimate personal exposure and identify its sources posing significant challenges owing to variability in exposure [45]. Most monitoring stations are fixed and continuously monitor air pollution in urban areas. They consist of highly sophisticated instruments specific for the analysis of an array of contaminants. However, certain drawbacks are associated with them like the requirement of large infrastructure for their installation, excessive cost and handling and maintenance protocols. Spatial variability occurring at macro- and microscale levels, temporal variability, occurring over time and inter-individual variability must be monitored and identified. The high-resolution analytical techniques measuring pollutants have high operating costs and are large; therefore, their utilization in mobile air quality monitoring is not practicable [46]. However, they can be integrated into a mini device called a sensor which has low energy consumption [47]. The principle of detection can be either physical or chemical, but the electrochemical technique is the predominant one as they do not require complex electrical circuits. Most of them are metal oxide semiconductors and the variation in conductance and resistance by chemical adsorption of gases at the surface of the semiconductor is calculated at high temperatures [48, 49]. The presence of pollutants in indoor air poses great health hazards to the occupants [50]. A linear correlation between the enthalpy and

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acceptability of IAQ has been subjected to different studies and the results reveal that the IAQ and temperature are interrelated [51]. The temperature has a stronger effect on the IAQ than humidity [52]. Many low-cost Air Quality Sensors (LCAQS) are becoming available with the superiority of feasible field placement, data accumulation, storage and transfer [53]. For gaseous pollutants, sensors working on infra-red technology are being used. Heterogeneous sensors and gas sensors can work in mobile and immobile environments. The stored information can be processed through the machine learning technique. They both may be implemented for the assessment of a mixture of pollutants [55]. An efficient sensor may be able to detect small changes in the concentrations and also should not interfere with day-to-­ day activities in the indoor environment.

1.5 Air Quality Assessment Through Edge and Cloud Computing Strategies Edge computing enables a cost-effective measurement including personal exposure [56]. An edge-computing system based on IoT, consisting of three layers is developed by [20] which interacts through Zigbee and Wi-Fi. The sensing layer sensed the air quality and transferred it to edge computing or the middle layer. The application layer stores the information and communicates with the user.

2 Health Risk Associated with Pollution Particularly Short-Term Exposure Ambient air pollution is linked to acute events in people having respiratory and cardiovascular troubles upon short-term exposure [57, 58] and the effects on healthy individuals are less prominent. As shown earlier, no significant association was observed between short-term air pollution and non-COPD people [59, 60]. Short-­ term exposure hampers the lung as shown earlier [61]. In a study with 1506 nonsmoking adults between 2011 and 2013, the association between mean particulate levels, NO2 and O3 exposure was assessed showing that the short-term exposure led to a decrease in lung function and pronounced manifestation of inflammatory markers in healthy individuals [62]. Ambient PM1 is another significant contributor to PM2.5. However, health effects associated with PM1 are lesser known as the ground-­ based PM1 measurement is very rare. A recent study relating hospital admission to respiratory outcomes as associated with exposure to air pollution in China for 2 years used conditional logistic regression models. It was found that fine particles inhalation was related to hospitalization pertaining to respiratory problems. PM-hospitalization was also found to be associated with seasonal change, having higher risks in the cold season [63].

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Studies have also suggested that pollutants in ambient air may carry microorganisms and pathogens which are invasive to humans and affect immunity [64]. A study has reported a positive correlation between the pollutants concentrations with the confirmed COVID-19 cases [65]. A study with 4.454 people in China who died from asthma showed that short-term exposure to pollutants may enhance the mortality rate associated with asthma [66]. A delayed impact of pollutants has also been observed in different age groups [67]. A novel air pollution index based on short-­ term exposure to pollutants was established to predict the increase in daily mortality risk [68].

2.1 Risk Assessment Associated with Short-Term Exposure Some of the important short and long-term effects of air pollution exposure are summarized below: Short-term exposure 1 . Visits to hospitals owing to heart and pulmonary ailments 2. Mortality 3. Absenteeism from work 4. Other acute symptoms Cardiovascular effects of short and long-term exposure to PM2.5 have been extensively reviewed covering a wider geographic area including Asia. A 10 μg/m3 hike in the PM2.5 exposure leads to 6% (all-cause) and 11% (cardiovascular) mortality. All-cause mortality has also been associated with exposure to elemental carbon and NO2, both primarily originating from combustion sources. In a meta-analysis involving 33 time-series and crossover studies in China for the assessment of mortality effects upon short-term exposure to SO2, NO2, O3, CO and Particulate matter (PM10, PM2.5), it was concluded that the mortality risk increased for all pollutants under consideration [69]. Significant aggravation of COPD and lung function has been linked with short-term exposure. The short-term effect of physical activity and its association with air pollution and the effect of the interaction on cardiovascular and respiratory effects were assessed in a group of healthy individuals in terms of heart rate variability (HRV). Sensors were employed to measure the exposure of black carbon as a marker of air pollution while doing physical activity. HRV and lung function changed drastically response to PA in response to PA (METhours) and logarithmic BC (% change) [70].

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3 The Diwali Mayhem and Need for Technological Interventions to Address the Short-Term Exposure: A Case Study The short-term variation in air pollution during Diwali has gained considerable interest due to adverse health effects [23]. North Indian cities like Lucknow, Delhi and Patna have been severely hit in the last few years after Diwali with the AQI crossing the 400 mark as reported by CPCB. The air quality variation during the festival is particularly valuable to be determined due to the ongoing COVID-19 pandemic because India is one of the worst-hit countries in terms of COVID-19-­ related mortality. Post-Diwali there remains a higher probability of the particles remaining suspended in the air assisting the transmission of the virus and those already affected by the respiratory diseases are more at risk [71]. It is essential to alert the exposed public beforehand as many of us feel safe inside our homes, not knowing that we are exposed to hazardous pollutants during the short-term rise in ambient air pollutant levels which may infiltrate our very homes. The role of technological interventions must be explored to avoid exposure and to safeguard the inhabitants against hazardous pollutant levels. In a case study, we monitored the variation in pollutants’ levels during Diwali 2020 and 2021  in Lucknow city. A questionnaire survey was also done with over 51 doctors post-Diwali to see the effect of the rise in pollution on people’s health. Table 1 summarizes the average concentrations of some major pollutants during Diwali period for two consecutive years (2020 and 2021) obtained from the Central Pollution Control Board website.

3.1 Health Effects Experienced After Firework Burning A questionnaire survey conducted with 51 doctors of the city post-Diwali, 2021 revealed that around a 50–75% of increase in the number of patients post-Diwali. The majority of the patients (57%) reported respiratory issues, followed by allergic reactions reported by 42.9% of patients. Exacerbation of asthma (24.5%) and sinusitis (18.4%) were other reported issues. The symptoms were found prevalent in people who were above 13 years of age (83.7%) followed by children between 5 and 13 years (22.4%) and infants (12.2%). Shortness of breath, cough and restlessness were commonly reported, followed by congestion and runny nose and Itching in the eyes and skin with a percentage of 36.7 and 34.7%, respectively. The results showed that although people remained inside most of the time during the festival time because of the holidays, still it may be suggested that they were exposed to high concentrations leading to health issues. Figure  1 depicts the common troubles reported post-Diwali.

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Table 1  Twenty-four-hour average concentration of pollutants during Diwali 2021 Date PM2.5 (μg/m3) PM10 (μg/m3) Talkatora monitoring station Pre-Diwali 12/11/2020 235.04 359.65 02/11/2021 143.45 421.26 13/11/2020 209.25 320.12 03/11/2021 218.75 504.78 Diwali 14/11/2020 257.62 359.5 04/11/2021 228.22 315.65 Post-Diwali 15/11/2020 401.56 513.67 05/11/2021 16/11/2020 06/11/2021 244.89 410.03 90.1 147.51 211.05 358.22 Central school monitoring station Pre-Diwali 12/11/2020 109.17 304.27 02/11/2021 13/11/2020 03/11/2021 69.95 154.81 94.27 258.52 77.15 160.54 Diwali 14/11/2020 151.46 265.4 04/11/2021 103.61 216.26 Post-Diwali 15/11/2020 211.54 05/11/2021 16/11/2020 06/11/2021 190.34 54.89 84.72 Lalbagh monitoring station Pre-Diwali 12/11/2020 147.51 02/11/2021 13/11/2020 03/11/2021 113.13 174.4 116.42

NO2 (μg/m3) SO2 (μg/m3) CO (mg/m3) O3 (μg/m3)

88.95 94.46 81.99 100.33

10.7 14.3 10.7 14.7

1.13 1.00 0.96 10.6

20.65 47.44 22.89 47.17

59.94 72.27

10.7 17.1

1.07 2.43

31.56 51.15

69.85

8.93

59.50 32.42 46.93

45.6 9.83 16.1

2.13 1.69 1.35

53.46 26.09 80.91

67.94

6.91

0.99

28.21

109.66 62.87 106.60

3.71 7.02 3.44

0.99 0.61 1.12

44.32 27.05 50.71

66.44

9.42

1.46

36.91

100.32

9.40

1.20

45.22

337.54

87.7

9.54

1.42

48.79

200.01 112.64 182.72

91.24 50.53 57.14

10.2 7.17 6.75

1.60 1.27 1.70

42.83 35.24 66.22

299.18

50.83

7.08

1.81

66.71

229.12 358.39 236.87

61.96 42.37 65.17

6.20 10.38 5.71

2.99 3.28 3.19

20.57 40.39 31.73

25.49

(continued)

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Table 1 (continued) Date PM2.5 (μg/m3) Diwali 14/11/2020 194.67 04/11/2021 221.47 Post-Diwali 15/11/2020 385.3 05/11/2021 16/11/2020 06/11/2021 213.00 86.71 72.52

PM10 (μg/m3) NO2 (μg/m3) SO2 (μg/m3) CO (mg/m3) O3 (μg/m3) 316.07

42.7

10.38

3.28

40.39

217.91

56.7

15.64

2.82

38.71

446.15

56.2

22.18

1.17

51.46

306.85 169.37 295.82

51.98 23.1 45.54

12.50 35.32 12.46

2.50 0.5 2.32

37.30 52.8 50.05

Fig. 1  Common troubles reported post-Diwali

3.2 Discussion 3.2.1 Suggested Health Risk Assessment Tools for Indoor Air Quality Management: Exploration of Technological Intervention The developed countries have been implementing different strategies like alterations in industrial processes, emissions and technological interventions to improve air quality. Hundreds of studies worldwide have linked daily changes in air quality with mortality [72]. A simulation strategy was devised to assess the risk over space and time associated with NO2 and ground-level O3 in 24 cities of Canada between 1884 and 2000 [73]. Two multiyear estimators using the previous year’s data were used to approximate the present year’s risk. The estimators were deduced from successive time-series analyses. Some other

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significant computational simulation strategies used for short-term exposure assessment are discussed below: 3.2.2 Human Exposure Model (HEM) The HEM does risk assessment associated with air pollutants and how their transportation is regulated along with associated health effects including cancer and noncancer risk. 3.2.3 Integrated Fuzzy-Stochastic, Proximity and Interpolation Modeling The integrated fuzzy-stochastic modeling (IFSM) works on Monte Carlo simulation for the ambient environment and the development of fuzzy air quality management and health risk assessment. An integrated risk information system (IRIS) evaluates health risks [74, 75]. The interpolation technique is based on deterministic and stochastic approaches [76]. The interpolation technique has a distinct advantage as it is based on real-time monitoring. 3.2.4 Smartphone-Aided Information Smartphones can be used to alert the exposed person and used for the assessment of personal air pollution. Aided with in-built sensors smartphones may be used to send out warnings and alerts if the air quality degrades. Mobile health technologies may aid in exposure estimates. Asthma Mobile Health Study was done to examine the triggering of asthma depending on local exposure to air pollution [42]. Figure  2

Fig. 2  A proposed methodology for dealing with short-term air pollution health effects

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represents a suggestive integrated model which may be used to assess the short-term variation in air quality and its estimated impact on indoor concentrations to alert the exposed people. 3.2.5 Forecasting The dynamic changes in the pollutants’ concentration may be predicted by high-­ speed simulations [77]. Community Multi-scale Air Quality (CMAQ) model, the Nested Air Quality Prediction Modeling System (NAQPMS) and the WRFChem model are some of the dynamic models [78]. Statistical predictions have also been receiving significant attention. The multiple linear regression (MLR) method is the most general method [79], the autoregressive moving average (ARMA) method, the support vector regression (SVR) method [80, 81] and the hybrid method [82]. Nevertheless, these models do not integrate meteorological data which has a considerable impact on the air quality [83]. Deep learning can be effective for feature representation. Models based on deep learning neural networking like the Recurrent Neural Network (RNN) [84], Elman Neural Network [85], Time Delay Neural Network (TDNN) [86] and Geographical Deep Belief Network (Geoi-DBN) [87] have been applied for air pollution predictions. A new short-term memory neural network extended (LSTME) model based on the long short-term memory (LSTM) model integrates the meteorological data as the auxiliary data to extract the spatiotemporal correlations of air quality [88]. The model worked on input data from neighboring monitoring stations. Another model based on spatiotemporal deep neural network (ST-DNN) integrates long short-term memory (LSTM) to derive temporal features [89]. However, the model does not consider the aerosol data having PM2.5, which is found to be more relevant as compared to the meteorological data [90]. A recent study has proposed a novel spatiotemporal convolutional long short-­ term memory neural network for air quality prediction. The CNN and LSTM-NN were used to extract spatiotemporal features and for long-term prediction [91].

4 Challenges and Future Opportunities Data security and privacy are two main challenges associated with IoT implementation as data sharing on a public platform are not encouraged. A connectivity issue between different IoT devices is another major challenge to overcome. Security risk also increases exponentially as the number of connecting devices increases (Fig. 3). The servers must have a large storage capacity to store massive data. This can be apportioned on local servers or public/private cloud; however, invariably, it will be associated with an increase in storage cost. However, the most imperative opportunity of IoT-based technologies is for the development of a smart indoor environment to trace human activity and improve living conditions. Thermal comfort and energy efficiency can also be significantly

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Fig. 3  Suggested dummy model to curb Diwali hazard

improved when monitored and regulated by IoT-based devices as they can predict the energy consumption per device and the period of consumption and how the indoor and outdoor activities affect the IAQ and their impact on the health of the

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occupants. The most significant application of interconnected IoT-based devices is in large buildings to optimize and equalize the distribution of total resources. Using the data from IoT devices to the Cloud, future patterns of IAQ can be predicted. A critical area to explore the use of IoT-based devices is in futuristic prediction before indoor air pollution even reaches the threshold limit and how preventive measures can be adopted. Another crucial opportunity to explore is the prediction of noise pollution by the use of combined models/algorithms. The machine learning and artificial intelligence approaches in conjugation with IoT devices can help in decision-­making for policymakers and draw preventive strategies [92].

5 Conclusion In developing countries like India, IAQ monitoring and control have become very significant in the wake of the COVID-19 spread. Short-term variation in ambient air quality may also deteriorate the IAQ.  The present study has explained the short-­ term variation in pollutants’ levels through a case study during the Diwali period in India and also discussed the gruesome health hazards of the exposure and how the impact can be averted by timely information through a technological intervention like sensors and smartphones. This chapter has highlighted the importance of air quality and the effect of short-term variation on the health of the occupants. Most of the research is focused on monitoring and quantification of air pollution; however, the role of technological interventions based on IoT in health assessment is less explored, particularly in the indoor environment and that very factor is dwelled upon in this chapter. The authors have discussed several efficient computational strategies, tools and models to assess the short-term changes in pollutants’ concentration and their health impact. They have also presented a case study around Diwali festival showing how gruesome the effects of short-term exposure can be on the exposed population.

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Climate-Neutral Districts with Decentralized Energy Production, E-Mobility and Through the Formation of an Energy Community Exchange of Electricity and Heat Severin Beucker, Walter Konhäuser, Ingo Schuck, and Olaf Ziemann

Abbreviations ADSL Asymmetric Digital Subscriber Line AI Artificial Intelligence CHP Combined Heat and Power DSO Distribution System Operator EMS Energy Management System EV Electric Vehicle FTTB Fiber To The Building FTTC Fiber To The Curb FTTH Fiber To The Home GW Gateway H2 Hydrogen HVAC Heating, Ventilation, and Air Conditioning KNX-RF Open standard for Radio Frequency-links in buildings LAN Local Area Network LoRaWAN Long-range Radio Technology Wide Area Network LTE Long-Term Evolution MID Measuring Instruments Directive MTC Machine-Type Communication OCPP Open Charge Point Protocol S. Beucker Research, Borderstep Institute for Innovation and Sustainability, Berlin, Germany e-mail: [email protected] W. Konhäuser (*) · I. Schuck · O. Ziemann Oktett64 GmbH, Berlin, Germany e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 G. Marques et al. (eds.), IoT Enabled Computer-Aided Systems for Smart Buildings, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-26685-0_3

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PtH Power to Heat PV Photovoltaic SMGW Smart Meter Gateway SW Software UV subdistribution board VDSL Very High-Speed Digital Subscriber Line WiFi Wireless Fidelity

1 Introduction Today’s electricity generation, largely based on fossil fuels and nuclear power, is incompatible with sustainable development [1]. Worldwide, electricity generation accounts for around 26% of greenhouse gas emissions. Without massive expansion of renewable energy, the 1.5° target to limit global warming will not be achievable [2]. High social and economic costs, such as rising sea levels and persistent droughts, would be the result of uncontrolled climate change in many sensitive regions of the world. Not only disasters such as 2011 in Fukushima and 1986 in Chernobyl but also handling of nuclear fuels and their final disposal show that nuclear power also poses great risks and consequential costs for today’s generation and irreversible damages for all future ones. In addition, there are high environmental impacts in the extraction of uranium and fossil fuels. The switch to renewable energy-based electricity generation is therefore urgently needed. The expansion of renewable energies pays off not only because of avoided environmental risks and consequential costs. The costs of renewable power generation have fallen sharply in recent years, and they are already partially competitive [3]. In the future, the price of fossil fuels and uranium is expected to continue to rise and be subject to severe fluctuations, while the costs of renewable energy will fall. As a result, renewable energy generation will be more cost-effective in the foreseeable future. The expansion of renewable energy sources is also based on industrial policies. The world markets for renewable energy technologies are booming and nations are competing against each other for the leading position in these markets. Countries that promote renewable energy at an early stage are in a favorable position [4]. Figure 1 shows the annual CO2 emissions from fossil fuels, by world region between 1980 and 2020. Figure 2 shows the distribution of CO2 emission of private households (living) 36%, traffic 26.6%, and nutrition 12.3%. Seventy-five percent of the private households CO2 footprint can be influenced directly by individuals. The biggest areas are industry and energy. Industry is working hard to reduce their CO2 footprint, often in common concepts with Industry 4.0 deployment. The energy footprint can be reduced most by using renewable energy in production stages. Reducing CO2 emission in buildings and making this also happen for local energy production must play an important role to make our environment greener. Both require a low cost and efficient communication network and energy management integrating systems inside and outside the building [5].

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Fig. 1 CO2 emissions from 1980 to 2020 [6]

Fig. 2 CO2 emission of private households in Germany

Emissions from the building sector vary between states, due to differences in the buildings itself (type of buildings and settlement structures), like heating systems (natural gas, electric, and district heat), air-conditioning, space heating or cooling and water heating. These are the most critical drivers of energy consumption and emissions in buildings in Europe (over 80% of total energy consumption, most of it deriving from natural gas) [7]. In addition, the biggest challenges lie in the quality of the building stock. The majority of older and existing buildings from the postwar era need extensive retrofitting to reach contemporary standards for energy consumption and emission reduction. Decarbonization strategies in the building sector should bear this in mind and focus in a first step on the efficient use and reduction

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of heating energy [8]. In a second step and with an increasing share of renewable energies in power generation, the focus will inevitably shift toward electric energy, due to the need of storage and sector coupling (buildings, electric vehicles, and batteries). The challenge for the building sector is to find the right share between integrated renewable electricity generation and using CO2 -free electricity purchase which requires an advanced system planning, but especially an advanced system operation management. Many investments in the past were mainly characterized by economic requirements. Energy projects with a strong ecological orientation were usually less economically successful. Therefore, short-term profitability was often given preference. Decisions for ecological projects have been and still are hampered by political over-­ regulation. This dilemma is shown graphically in Fig. 3. The goal for future energy projects must therefore be equal treatment of economy and ecology. The remaining of this chapter is structured as follows: Sect. 2 deals with local energy productions with low emissions, as they should be installed in buildings for CO2 reduction. In the following Sect. 3, the capabilities of digital services are shown with the necessary communication technologies. It will be shown how important reliable and cost-effective in-house networks are for digitization. Section 4 discusses the opportunities of energy communities for electricity and heat networks, which should play an important role for future energy supplies. Section 5 describes a concept for the installation of hierarchical energy and charging management systems for Electric Vehicles in underground car parks and Sect. 6 deals with the requirements profile to be investigated and the algorithm implementation. Section 7 briefly summarizes the results and makes recommendations for further action.

Fig. 3  Energy strategy dilemmas

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2 Energy Production in Buildings with Reduced CO2 Emissions With increasing decentralized electricity production especially in the building sector, questions on the kind and quantity of self-generated electricity arise. The combined generation of electricity and heat as well as the use of electricity for heating offers new options. The energy transition in the building sector is shifting from the supply of buildings from single and grid-based fossil sources in the past (natural gas and electric) toward multi and hybrid supply and renewable sources. Although gas-based heating systems (either with natural gas or with green hydrogen used in combined heat and power plants or fuel cells) will still play a significant role in the next years, it is inevitable that major shares of the supply will come from electricity in the future. This is since the current energy transition has an emphasis on renewable energy production from wind power, photovoltaic and hydropower. In addition, the shift in the mobility sector from combustion engines toward electric vehicles is creating a growing demand for renewable electricity supply. At the same time, home batteries and electric vehicles constitute a highly decentralized recipient and storage capacity for electricity from renewable sources. As renewable sources are fluctuating in the course of the day and seasons, a major challenge for a steady supply lies in the combination and coupling of the various sources as well as the conversion (e.g., electricity into heat) and the decentralized storage of energy (electric and thermal) [9]. Depending on buildings and districts and their specific demand, various objectives and functions for an optimized supply are possible. Objectives can range from an optimization of either the heat or the power supply of a building or district but can also encompass a combined heat and power supply with the option of sector coupling, meaning the various forms of energy are transformed into each other or stored on demand. Finally, new energy supply concepts for buildings or districts can comprise such different objectives as grid independence, reduced supply costs or the goal to use and trade energy on the local or regional level in energy communities. For the development of such new energy supply concepts, the following aspects of the development of new technologies and system concepts should be considered: • Green Deal Contributions to reduce CO2 pollution for Living and Mobility and for Industries, • Enhanced Cyber Security, • Circular Economy, • Economic contributions to create new business and new jobs, • Social and human impact, and • Technology Sovereignty.

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2.1 Electrical Energy Production in Buildings with Less CO2 Emission Figure 4 shows subjects and options to be considered for new energy supply concepts in the building sector: 1. Power Supply: • Electricity purchased from the public grid • Electricity sourced from PV systems without storage and from the public grid • Electricity sourced from PV systems with storage to increase self-sufficiency and from the public grid • Charging stations for electric vehicles 2. Heat Supply: • Heating plant optimized • Heating plant, buildings and residential units optimized as a complete solution with optimal heat supply • District heating supply 3. Combined Heat and Power Supply: • Heating plant optimized and supplemented by CHP for electricity and heat supply • Decentralized heat and power supply by several CHPs in the district • Combination of sector coupling and electricity from PV systems • Charging stations for electric vehicles supplied from self-generated power

Fig. 4  Energy supply with power and heat

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Fig. 5  Solutions for decentralized energy–generation–distribution and management

For each of the chosen criteria, advanced technologies are available. The use of technologies is depending on economy, political regulation, acceptance by owners or tenants, and local opportunities. For CO2-free generation of electricity in the building sector, various solutions are developed and in operation. As shown in Fig. 5, the following technologies for CO2-­ free generation of not only electricity but also thermal energy can be considered: • • • •

Combined heat and power (CHP) production with green fuels Fuel cells for heat and power production Wind generators especially constructed for using on buildings PV-systems for electrical production as well as solar driven thermal production

The use of electricity in the building sector with regards to heating is mainly applied with heat pumps for thermal production or electrical boilers for thermal production (Power to Heat PtH). The following options could be taken into consideration for Electrical Energy Supply: • • • • •

Local generation (CHP, PV, and H2-fuel cells) Local generation and purchase from outside Sector coupling Feed in into public networks or using storages or combination Additional Services –– Charging systems for Electric Vehicles –– Storage systems (Batteries and, H2-power to liquid) self-operated or operated by service provider (seasonal dependent) –– Establishing Energy Communities for Electricity (economy and settlement concepts)

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• Electricity Distribution within the quarter –– Via own network –– Via network of DSO • Recommendation for stepwise approaches with clear roadmap • Economy  – new business cases to be developed (Business plan versus anyway costs) If there are different technologies simultaneously installed and in operation across buildings, an efficient spatially distributed Energy Management System is needed. Figure 6 illustrates such a system using different domain-specific algorithm packages for monitoring, control, conduct, communication, management or data exchange and others. Additionally, in Fig. 6, other opportunities to create new value chains are shown considering a surplus of electricity generation in the building sector. One business case could derive from optimized electricity supply for Electric Vehicles. Furthermore, the need for suitably sized storage, both electrical and thermal, must be considered.

Fig. 6  Example for a complex electric-power interoperability in an area with different building complexes

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2.2 Thermal Energy Production in Buildings with Less CO2 Emission The decision between decentralized or centralized generation in the real property is a question of size and structure of users in the considered area. Depending on the local situation geothermal (deep drillings, flat drillings or surface based) sources could also be considered. Heat Supply • Local generation (CHP, solar heat, heat pumps, and H2 heat) • Local generation and purchase from outside (heat plant or district heating or combined) • Sector Coupling • Storage (local or decentralized in each building) • Heat supply within the quarter –– Only within each building –– Within area district heat (cold or hot) • Additional Services –– Establishing of Energy Communities for Heat (Economy-settlement concepts) • Recommendation for stepwise approaches with clear roadmap • Economy  – new business cases to be developed (Business plan versus anyway costs) For heating, many business cases have been developed and are in operation. Often a supplier builds a heating plant in the area and sells heat services operation to an area district heating network. The owners and/or the tenants in the buildings then only hold a contract with the supplier to receive heat energy. The supplier also takes care for the optimization of operation, fuel purchase and settlement with all parties. Even the optimization of operation could decrease the energy consumption significantly. Hydraulic Balancing and Low-Temperature Heating Networks are examples. For sustainable energy savings, hydraulic balancing should be implemented not just at the installation phase, but automatically and permanently during operation. The power dissipation of the network should be calculated and measured carefully since it’s regularly underestimated. Not rarely the amount of wasted energy exceeds the amount needed for room and water heating. This is easily caused by systems that circulate water at high temperatures (> 70 °C) 24 h/day, 365 days a year due to centralized drinking water heating. Decentralized storage combined with smart communication concepts can reduce temperatures significantly. For example, pipes can be put in idle state when water temperature in all storage buffers is still sufficient. Only in case one buffer reaches the low level all other storage buffers on the same string (pipe) will be charged full at the same time. Such so-called Low-Temperature Heating Networks require intense communication among the

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Fig. 7  District buildings with networks for electricity exchange via the grid of the Distribution System Operator (DSO) and with district storage and heat supply via central heating plant, local heating network and decentralized heat storage systems, which can also be supplied via PtH from the PV systems too.

spatially distributed components. The concept is illustrated in Fig.  7 and a good application scope for a modern distributed Energy Management System. Modern Energy Management Systems should be able to control heat supply and power supply at the same time, as sector coupling systems require this. Another essential point is the division into hierarchical levels. Figure 8 shows such a concept that is used in industry. The energy systems (HVAC, batteries, and meters) are connected to the Energy Management Systems in Building Area 1 and 2 via a smart grid and IoT network and carry out their respective tasks via the installed apps. On the district level, the results will be merged for higher level control and analysis. For this purpose, dashboard solutions are used, which can be displayed on various end devices such as mobile phones or laptops/PCs (see Fig. 9). The District Manager also makes use of Internet Web services that allow retrieval of third-party information such as weather forecast and bank holiday calendar for algorithmic decisions.

3 Digital Control of Real Estate with Efficient Processes and Communication Concepts The rapidly growing and decentralized expansion of fluctuating renewable energy production increases the requirements for higher-level control systems to ensure efficiency and security of supply. The real estate industry is becoming increasingly critical as user and customer of such systems. On the one hand, due to the considerable amounts of local energy consumption and on the other hand due to local generation and storage of energy. Balancing energy consumption between energy

Climate-Neutral Districts with Decentralized Energy Production, E-Mobility… District Manager (central EMS) Automation

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Fig. 8  Hierarchical energy management concept with building area level and district manager level

Fig. 9  Example of a dashboard showing measured IoT sensors

self-generation and energy purchase can only be achieved by a powerful, reliable, and cost-effective Energy Management System (EMS). Primary goal is the implementation of strategies allowing previously unachievable reduction of thermal energy for heating and hot water and electrical energy for the power supply of the buildings or integration of CO2 -free sources. In addition, a variety of other tasks and functions in a modern property can be solved cost-effectively with the help of digital components.

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3.1 Digital Solutions in Real Estate Digital solutions for energy management in the real estate industry range from classic purchase of energy sources to technical systems and optimized processes. Furthermore, intelligent networking is crucial as a basis for the integrated operation of real estate and downstream processes. The use of digital components can create significant added value for residents and management (see Fig. 10). When planning a digital energy management solution, the following services should be considered: • Remote maintenance of local power generation systems (CHP and PV), local energy storage and energy distribution (control cabinets, pumps, mixers, and valves), • Energy generation and consumption measurement via smart meter devices (electricity, heating, and water) • Heating and home control with new operating concepts • Lighting control, awning/blind control • Tenant electricity concepts • Indoor climate control with measurement and influence of air quality to prevent mold • Integrated billing system: delivery of data for billing of rent, incidental rent expenses and other services • Smoke, fire and water detectors • Access control and security • Charging systems for electric vehicles • Media control (FTTH: TV, Internet, Telephone): Delivery of data for billing, if reporting interface is provided by the operator.

Fig. 10  Important digital solutions for real estate industry

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Buildings in urban districts and cities today produce a lot of CO2 for living (see Fig. 2) and will have to be given greater consideration in future emission targets. It is imperative to use new technologies and to provide appropriate communication networks. Especially the installation of data networks in existing buildings is often a big hurdle. Therefore, internal and external mobile data networks can be a cost-­ effective solution. For CO2 reduction concepts in buildings, digitization plays a crucial role. New digital technologies, renewable local energy production systems, and communication networks must be deployed in the real estate sector. In buildings, the following communication technologies should be available: • Different mobile communication standards for data transmission • IoT networks (Fig. 11) • Fixed networks (Fig. 12) The goal is to find out different architectures for a common Communication Platform (IoT, mobile and fixed networks best merge) to manage applications for CO2 reduction within the building. The future objective should be obtaining all the communication services for the digitization in buildings (e.g., energy management, monitoring, control) from one operator for a reasonable price. Local data generation in the Smart Home/buildings/urban districts/cities from sensors, actuators and meters should use suitable radio technologies as, e.g., Bluetooth LE, ZigBee, EnOcean, Z-Wave, and KNX-RF. Figure 13 shows an example of a holistic energy-efficient solution for a district or residential building with broadband supply. The platform controls the energy supply in the buildings according to a demand-optimized strategy, so that as little CO2 as possible is generated and little electrical energy must be purchased from the public grid. This results in lower costs for electricity, hot water, and heating. Data transmission to external clouds, where the processing applications could be deployed, should be managed by central transmission via gateways and mobile

Fig. 11  Mobile and IoT communication standards used in real estate [10]

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Fig. 12  Fixed network standards used in real estate [3]

Fig. 13  Example of a holistic energy-efficient solution for a district or residential building with broadband supply [5]

networks. Local data generation can be managed by SW platforms [5] with fast links to different IoT radio technologies. Data could be transported to the external cloud via fixed and mobile networks or LoRaWAN (Fig. 14). In the external cloud, the services for digitization of buildings should be deployed based on Enterprise-IT technology with SW solutions in a container architecture (Figs. 15 and 20).

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Fig. 14  Communication structure for digitization of real estate [5, 14]

Fig. 15  5G Architecture with integrated application layer and GW to the buildings

4 Energy Communities with Decentralized Energy Exchange of Electricity and Heat Supply Via Heating Network with Hydraulic Balancing of the Buildings Energy Communities (energy sharing) make use of the concepts described above, organize collective and citizen-oriented energy measures that foster the energy transition and at the same time put citizens first. They help to increase public acceptance

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of renewable energy projects and allow private investment into the energy transition. Thus, energy communities are built with the purpose to significantly support cross-sector decarbonization. At the same time, they have the potential to deliver direct benefits to citizens by promoting energy efficiency and reducing their costs. Energy communities for electricity and heat offer a means of restructuring their energy systems by using the energy generated in the own premises (energy sharing within buildings or districts) and enabling citizens to actively participate in the energy transition and thereby achieve greater benefits. Energy communities can take any form of legal entity, e.g., an association, cooperative, partnership, nonprofit organization, or small- and medium-sized enterprise. They can enable citizens, together with other market participants, to join forces. It is also possible to invest together into energy systems (local infrastructure as well as local market schemes). This, in turn, contributes to a more decarbonized and flexible energy system, as energy communities can act as a unit and operate on an equal footing with other market participants in all appropriate energy markets [11].

4.1 Concepts for the Formation of Energy Communities (Energy Sharing) Basically, electrical energy can be distributed within an energy community, used among each other, and charged via own (local) power grids, as they can be installed relatively easily in new development areas during the construction phase or via the Network of the Distribution System Operator (DSO), where the various buildings belonging to the Energy Community are connected. Most applications will have to use the network of DSOs. Therefore, it is crucial to involve the DSO in such a concept at a very early stage and to develop business advantages for him, especially for the security of the operation of the power grids. The same concepts apply for heat supply. 4.1.1 Energy Community with Own Power Grid and Own Local Heating Network for Heat Supply This requires a system technology that • supplies electricity and heat to the buildings, • generates electricity and heat in buildings, • records and stores electricity deliveries from the distribution grid to the Energy Community and from the Energy Community back to the distribution grid securely and makes them available to the DSO and billing service providers. The quantities of energy exchanged or available for trading in energy communities shall be accounted for. To this end, aggregators (electricity traders or other

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energy service providers) are involved at an early stage. An existing heating plant supplies the buildings with heat via a local heating network. Generated electricity (usually CHP/fuel cells or PV) can flow as tenant electricity into the buildings (households of the energy community) or be sold externally. The buildings can draw heat for heating and hot water, but also generate it on site, e.g., through solar thermal energy or with excess electricity for heat generation (heat pump, PtH). Heat pumps are in favor for reducing CO2 emissions, since the excess electricity generated results in the up to fourfold amount of heat (Fig. 16).The infrastructure of the system technology used can not only serve to record reference and consumption data but should also carry out energy management in the buildings as a synergy. Through the resulting communication between the buildings of the energy community, the heat distribution can actively be regulated, and, for example, hydraulic balancing can be enforced. Even in energy communities with small local heating networks, more than 20% of energy can often be saved per year (by means of low-­ temperature local heating). 4.1.2 Energy Community with Electricity Exchange Via the Grid of the Distribution System Operator (DSO) The following architectures of energy communities can be considered: 1. Energy Communities that use the DSO’s network to exchange energy between the buildings of the community. The DSO’s grid is used with electricity flows to feed in, draw or distribute energy. The DSO, as well as the parties involved, have access to the stored data in a trustworthy environment (see Fig. 16 “Blockchain-­ Light” and Figs. 17 and 18), in which all relevant measurement data is stored that is/has been distributed over the DSO’s network. The electricity exchange is

Fig. 16  Interoperability within an energy community controlled by an energy management system with blockchain light communication

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Platform system technology recorded in the cloud heat supply to the buildings, heat consumption in the buildings, heat supply from the heating plant and the heat pump to the district. Communicates with the others building complexes around detect heat consumption, to control and bill. Detects solar thermal energy and PtH in the resoective building complexes, controls hydraulic balancing in the buildings

System cloud with all heat consumption data

Building complex with PV, solar thermal and PtH possibility

Transfer station of the Hydraulic balancing Heat accumulator

Building complex with charging station

Local heating network with hydraulic Reconciliation*

Building complex without PV and without PtH with charging station

Central Heating

Building complex with PV, solar thermal and PtH possibility

Central heat pump *Cold local heating network

Fig. 17  District building with heating plant and heat pump for heating water and hot water distribution via the local heating network and with solar thermal energy, PtH, and heat storage in some building complexes and hydraulic balancing in all buildings

Building complex with PV

Provides electricity and draws electricity Living Quarter Battery Building complex with CHP

Provides electricity and draws electricity Building complex without power generation

Enterprize IT Platform Technology captured in the system cloud electricity supply to the buildings, electricity consumption in the buildings, electricity supply form the DSO to the district. Power transmission from the buildings via the DSO network. Communication with the others building complexes around to detect current exchange to control and bill. Allows DSO access to the cloud to calculate the usage fee.

DSO network Draws electricity

Building complex with CHP and PV

Provides electricity, and draws electricity

Aggregator (Electricity trader) Electricity supplier)

VNB

System cloud with all power supply and power consumption data

Distribution system operator/Aggregator has access to the system cloud to all current data transmitted via the DSO network. The power is controlled in such a way that the contractually defined balancing group responsibility is adhered to. For the use of the DSO network, paid a usage fee. Aggregator operates district storage. Aggregator leased parts of the neighborhood storage to the buildings with monthly scalability (customer loyalty). Aggregator uses district storage for other services.

Fig. 18  Energy community with electricity networking for electricity exchange via the grid of the distribution system operator (DSO) with swarm storage and with district storage

c­ ontrolled in such a way that the contractually defined balancing group responsibility is adhered to. 2. Like 1, but with the addition that several buildings of the energy community have installed electricity storage systems that are combined to a swarm storage system. The storage capacity may be made available, e.g., to the DSO in an agreed share to remedy network bottlenecks. 3. Like 1, but with the addition that a third party installs a large electricity storage in the district of the energy community. It operates the district storage and rents

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out shares of the district storage to the buildings with a scalability that can be changed periodically (e.g., monthly depending on the season). The third party uses the district storage for further services. The third party could be an aggregator, e.g., electricity supplier or electricity trader or electricity service provider. 4. Combination of 2 and 3.

4.2 Blockchain-Light Technology for Recording and Billing Blockchain-Light technology is used for the continuous, simultaneous, and fine-­ grained (seconds to a few minutes) as well as forgery-proof recording of the distributed measured variables of the participants of the Energy Community. For example, for the billing of grid charges, the amount of energy at all connection points to the grid operator (electricity meter readings for purchase and delivery) in each time interval would be the distributed measure. Blockchain-Light technology enables the recording and fixing of all measured variables within an immutable transaction (without the need of energy intensive “proof-of work” transactions of “classical” blockchains), each updated in time. The immutability of a transaction is ensured by a cryptographic hash function, whereby two consecutive transactions are also based on the hash value of the previous and thus all previous transactions. Manipulation in the past would immediately invalidate all subsequent hash values and thus immediately attract attention. All or just a selection of relevant transactions is provided to all participants as an identical copy and thus enable verifiability at any time.

5 E-Mobility Concepts and Algorithms at Work The following section describes a concept for the installation of hierarchical energy and charging management systems for Electric Vehicles in underground car parks (see Fig. 19). The concept illustrates efficient handling of complex algorithms for integrating many different systems that are widely distributed across buildings in a representative way. The same mechanisms can be applied to heating/ventilation/ air-conditioning, power generation, storage, Energy Communities and so on. The aim of the concept in this concrete use-case is the gradual expansion of parking spaces with charging systems without expanding or replacing the existing electrical infrastructure and overloading the power grid. The core of the concept is a dynamic load management based on Enterprise IT Technology system technology with load detection in the different subdistributions, symmetrical load distribution to the 3 phases of the power grid and maximum control of the charging systems. Another aspect is the inclusion of local renewable electricity generation in charging management.

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Fig. 19  Control of charging stations on the private distribution grid in buildings or districts to avoid overloading the existing power grids

5.1 Starting Point of Concept Development for Charging Electric Vehicles in Nonpublic Underground Car Parks Primary objective is to develop a concept for the installation of charging systems in underground car parks without overloading the installed infrastructure. In most projected buildings, the underground car parks already have three-phase (400 V) supply lines with different load capacities as well as preinstalled Schuko sockets (230 V, 16 A) at the parking spaces. Since a car on a Schuko socket can typically only be charged with up to 2.3 kW, wall boxes with up to 11 kW should also be considered (22 kW connections may be excluded due to the lack of government aids). For reasons of cost efficiency and the rapidly developing technical standards in e-mobility, it seems advisable to first exploit existing infrastructure as much as possible and then, depending on emerging needs, expanding it step by step. For this purpose, a concept is to be developed that can initially cover the demand with existing resources through the tactical expansion of parking spaces and prevent the overload of the system before reaching the structural limits, due to too many simultaneous charging processes, using active controls. The integration of Electric Vehicle charging into the further building infrastructure with CHP, heating, PV, and electricity storage should be considered as an overall concept. Some other parts of the necessary infrastructure (like LAN or WiFi) may already be available in the property under consideration and could be reused. For the new Electric Vehicles charging infrastructure, therefore, two main aspects must be considered: 1. Connection to the mains 2. Connection to the IT network

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(1) is limited to the wiring hierarchy. If there is only one central connection point of the property, no additional measures are necessary regarding covering of self-consumption via CHP/PV/storage. However, the question of the most cost-effective cable design arises. Here, two variants are obvious:





(a) Reuse of the single-phase lines already available at some parking spaces, whereby these are connected to a wall box instead of a Schuko socket. Thus, (instead of 2.3 kW limit) up to 3.7 kW of power can be charged into the Electric Vehicles, which would mean only a charging time of a few hours with a daily mileage of 100 km and would therefore already be suitable for everyday use. Good wall boxes can be connected both single-phase and three-phase, i.e., simply continue to be used when the cable is upgraded later. (b) Laying of new, three-phase lines to the wall boxes at the parking space with connection to designated subdistributions in the underground car parks. Accordingly, another three-phase electricity meter assigned to the parking space must be installed there or the meter must be provided (MID calibrated) in the wall box. (2) An IT network for building equipment and appliances needs to be created or reused from other rooms and systems. This is the key to the integration of CHP/PV/storage systems with Electric Vehicles charging. If wall boxes are purchased, they should be connected to this network accordingly and support a suitable charging protocol. Currently OCPP from version 1.6 is recommended. Good wall boxes for the private sector (3.7–22 kW) support this protocol and often also come with a WLAN module. The corresponding WiFi network may be spawned from the IT-network mentioned above, so that no additional cables to the parking spaces would be necessary. For management reasons, it is advisable to limit the choice of wall boxes to one or a few models.

With the help of a charging protocol, controlled by a higher-level Energy Management System, this network can then be used to communicate between the Electric Vehicles (for charge level, charging power, desired time for a minimum charge), the building’s power connection (current reference power and forecast), the PV system (current power generation and forecast) and the electricity storage (free capacity). The management system thus regulates the controllable variables (e.g., target charging power of each car connected to a charging point or target charging/ discharging power of the power storage). At the same time, monitoring is recommended to visualize the load developing over time which forms the basis for future control of charging processes in the next step. Besides the basic requirements the system technology used needs to fulfill the requirements of control, regulation, and communication safely and cost-effectively. Figure 5 shows the system architecture, and the integrated Enterprise IT software

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architecture is outlined in Fig. 20 which is based on software container technology. It is split into • Hardware-related containers with interfaces to low level devices, bus systems (used in real estate) and IoT adapters (for Web services from the Internet) • Middleware containers (holding logical and virtual views of the “real world” and data persistence) • Application-level containers (including Application Logic, User Interfaces, Data Flow Processing, or Third-Party Interfaces) The Application Logic represents the core of the Monitoring and charging management. An internal cloud solution may also be integrated here in case external cloud access is not desired. Further advanced functions like dashboards, use of AI applications or a Blockchain-Light connector for recording and billing the charging energy consumption are thinkable.

5.2 Considered Points of Concept Development The following boundary conditions and variants should be considered at least: • Use of the existing infrastructure if possible and practicable (in terms of number of vehicles and charging time) • Use of the existing cabling by equipping with Schuko socket or wall box with Type 2 connector

Fig. 20  Architecture of an energy control platform based on Enterprise IT [10]

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• As evenly as possible distribution of the single-phase sockets or wall boxes to the existing phases of the three-phase current network and control considering other loads (such as lighting) • Avoidance of skewed loads and overloading of today’s plant when charging vehicles at the same time • Use of a management system through electronic control and corresponding actuators to enable as many simultaneous charging processes as possible • Possibility of gradual expansion of the capacity of the facility, starting with the underground car parks, through the connection to the main supply room to the building’s connection Especially for controlling the charging stations, there must be a secure digital, bidirectional communication interface. It must be controllable via common, standardized communication protocols, so that it can exchange data with other components within the power system (e.g., via the standardized OCPP [12] and an Internet connection for update capability). For cost-efficient use of electricity, it should be possible to limit the power of the charging stations or to postpone charging according to appropriate conditions. In addition, the charging station must be able to receive software updates [13] for implementing future technical developments  – such as a secure connection to a smart meter gateway, the integration of energy management systems and new functions. Figure 21 shows a holistic approach. The electricity for the building is generated locally via PV systems on the roof and by a CHP. A battery system stores the part

Fig. 21  Holistic building solution: Energy management and smart digitization for tenant supply in apartment buildings with electricity, heating/hot water, charging systems for electric vehicles and broadband supply

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of the generated electricity, which cannot be consumed immediately. If the locally generated electrical energy is not sufficient, additional electricity can be obtained via the distribution grid. The energy management of the building regulates the electrical current flows in such a way that the locally generated electricity always has the highest priority in consumption inside the buildings. This also applies to charging the Electric Vehicles. In this concept, each charging station is connected by its own supply cable, which is also used to measure the electricity consumption for billing and avoiding overload supporting symmetrical phase control. The consumption recorded in the meters can be stored and billed in a cloud solution. Optionally, the user can identify himself via a web interface or smartphone app for the charging process and retrieve information about the energy consumption.

5.3 Impact of Bottlenecks and Solution by Distributed Algorithms Without charging management there will be an overload in the underground car parks quite quickly and, due to the likely 1-phase connections, also inadmissible skewed load. In Germany, a permissible skewed load of 20 A between the phases applies in the “TAB” (Technical Connection Conditions) of the distribution system operator. If the property is only connected to the distribution grid via a central access point, it is not necessary to balance the imbalance of each subdistribution, but only the resulting sum at the building’s connection point to the grid. However, to make the best possible use of cable capacities, it may make sense to balance each underground car park segment about a uniform phase load with the help of the management system. Overloads can thus occur in the first level at the connections of each individual underground car park subdistribution, in the second level at the connections of the subdistributions of the buildings of the quarter, as well as in the third level at the building’s connection of the entire property. Figure 21 shows the topology of the power distribution systems. The perpetuation of the previous, unregulated operation would have the following effect with increasing and simultaneously occurring charging processes: 1. Low utilization (already from three vehicles per subdistributor): Consequence: Triggering of the fuse of one or more phases within the assigned subdistributions. Impact: All vehicles connected to these phases can no longer be charged until the fuses are replaced. Switching on again will most likely lead to the retriggering of the fuses after a short time. 2. Medium utilization (already from six vehicles per UV): Consequence 1: as above

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Consequence 2: Triggering the fuse of one or more phases within the UV of a house Impact: As above, however, all consumers of the building (lighting and appliances) are without electricity until the fuses are replaced. Switching on again will most likely lead to the retriggering of the fuses after a short time. 3. High utilization Consequences: as above, but also with impairment of the high-current fuses of the house connection Impact: As above, however, all consumers of the entire property are affected. Due to the high inrush currents, a new switch-on can only be carried out by specially trained specialists and without additional local shutdowns fuses will be retriggering after a short time with a high probability. In conclusion, not only a dynamic charging management system is required, but merely a suitable Hierarchical Energy Management System that is able avoiding the failures outlined in the bottlenecks above. It also must continue to exploit the different connection capacities available, so that initially existing cables and subdistributions can continue to be operated, if possible, without noticeable loss of comfort for the users. The Hierarchical Energy Management System should permanently record the utilization data for all technically relevant plant components and store them persistently. This helps in analyzing the behavior of the plant and to identify bottlenecks in good time for planning extensions to cables and equipment in the next step. The utilization data is mainly recorded by the consumption meters. Due to the digital interface, not only the electric current can be recorded, but also the meter reading be transmitted online to the property management or the parking space owner. The Hierarchical Energy Management System requires knowledge of the entire structure and relationships of the physical system (the so-called plant topology) and implements strategies that offer an optimal balance between component load, charging speed and fairness of simultaneously charging vehicles. If buildings produce their own electricity (see Fig. 21), then a high level of flexibility needs to be supported by the Management System for adding flexible strategies implementing optimal and price-efficient use of the self-generated electricity. Especially when operating electricity storage systems, intelligent integration into the charging strategy is essential, as it can virtually expand storage capacities and achieve significant savings both in the proportion of external energy required and in the dimensioning of the storage system. Finally, the energy management system is responsible for communication with the Schuko sockets/wall boxes. In the case of sockets, this is limited to the allocation to the lowest possible loaded phase, in the case of wall boxes, the charging power is regulated via a so-called charging protocol (OCPP) and communicated with the vehicle.

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5.4 Algorithms Are the Key for Integrating Many Widely Distributed Different Systems The implementation of the solution drafted above requires a powerful ecosystem for running the distributed algorithms: Enterprise IT. 5.4.1 Enterprise IT: From Business Processes to Building Technology Enterprise IT proves its strengths when it comes to merging data and controlling spatially distributed components, e.g., components distributed across a property: Using so-called software containers, which are standard in the business processes of larger companies for a long time already, plant planners and developers can organize their work as if all sensors and actuators were converging at a central location. Even the programming is done like in a centralized system, although in fact dozens of spatially distributed systems work together in (often intentionally) separated LAN and WLAN networks (e.g., for security). The technology also comes with inherent redundancy and stability features: If individual components or even an entire network segment fail, each node contains enough logic for autonomous emergency operation as an island. For the considered use case, this means that affected users of an island usually only notice a reduction in the charging power. For everyone else, operations continue as normal. The architecture of the distributed software easily follows the physical structure of the power grid. As a result, this avoids additional error patterns, increases operational reliability, and often makes complex algorithms devised by the plant planner possible in the first place. Here is an example: 5.4.2 Algorithms at Their Best The hierarchical load balance strategy of the EMS is based on a graph structure analogous to the cable topology, starting at the base level in all buildings’ connection room of the property down to the smallest connection level in the subdistributions of the underground car parks. It develops from the structure shown in the Fig. 22 above. For each phase of electrical current, each level communicates separately the load capacity currently available to the next levels of its individual branches in the direction to the charging points. The available capacity is calculated from the cable diameter and fuse protection values of the cables supplying it minus the load already drawn from it. This communication continues across all levels and ends at the lowest connection level in the subdistribution of the underground car parks. Finally, in the event of an overload situation, charging points are lowered in charging power or switched off at this lowest connection level. This is done according to the fairest possible procedure. For this purpose, the load management system

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Fig. 22  Topology of the property and the power distribution systems

must keep statistics about which vehicle is charged at which charge level or, in the case of Schuko sockets, how long the charging process has already lasted or what percentage of the maximum charging power is still being obtained (as an indicator of the progress of the charging process). Vehicles with a low charge level are preferred over those with a high charge level or already longer charging time. The statistics are used to calculate a sequence of reduction or shutdown events for the charging points. The network resulting from the tree structure or the islands in case the network should have disintegrated into such due to disturbances, continuously determine a “favorite list” of charging points at each level. This is calculated across all underground car parks and segments of the level and results in the charging points that are to become “victims” of a power reduction or shutdown. In the event of an overload, starting with the first place of the favorites list, charging power is reduced in the first run with adjustable wall boxes. If this is not sufficient, charging points are finally switched off completely in the second run. Of course, a “favorite” is only reduced or switched off if it occupies the overloaded electrical current phase at all. The favorites list is processed until the required power reduction is achieved. As soon as more power is available again because vehicles are increasingly charged or the power consumption of the building’s decreases, the previously

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switched off charging points are switched on in reverse order of the favorites list and finally the adjustable wall boxes are increased in performance again. If the charging process of a new vehicle begins during a power bottleneck, the favorites list is updated accordingly. Following to the logic described, this can lead to vehicles that have already been charged relatively full having their charging power reduced or switched off to allow the newcomer to charge (possibly with increased power).The iteration of the described cycle takes place in fractions of a second and thus far below the inertial time of automatic circuit breakers or fuses. At the same time, a similar algorithm uses a similar approach to avoid imbalances, also taking consumers in the buildings into account and thus ensuring overall compliance with the technical connection conditions (“TAB”) of the power grid operator. 5.4.3 Strategy Example: Dynamic Phase Allocation A wall box alone never causes more than the prescribed skew load of 20 amps according to approval requirements. However, manufacturers have hardly implemented solutions that guarantee that a given number of wall boxes divide the phase load evenly. However, the uneven load is caused by vehicles that can only charge single-phase. This means that the appropriate phase allocation is also the responsibility of the hierarchical management system. This even has the advantage that the skew load balancing can be carried out not only within an underground car park subdistribution, but on all levels of the tree structure and considering all loads up to the buildings’ central connection point. Accordingly, the load management system also keeps statistics on how differently the phases are loaded on each level. This also starts at the buildings’ central connection, continues over all levels, and ends again at the smallest connection level, analogous to the load balance. Instead of a shutdown (as in the case of overload), only a phase reassignment takes place in phases that are out of balance. This is relatively easy to do for the Schuko sockets and single-phase connected wall boxes. Special, network-capable, and space-saving relay groups have been developed for this purpose. With 3-phase connected wall boxes, a skewed load balancing is much more complex. Although this only occurs when charging single-phase vehicles charging with alternating current, there is currently no standardized procedure for solving this problem. If such a wall box becomes a “victim” of an intervention by the management system due to the described list of favorites, currently only its complete shutdown remains – until balance can be established in another way and the wall box can be switched on again. If the customer also wants a three-phase dynamic phase assignment in the future or if the wall box manufacturers agree on a treatment of the problem within the wall boxes, for example by enhancing the OCPP protocol, Enterprise IT is again key for offering fast support with the help of the modular software. This demonstrates how physical component changes that were previously necessary for functional enhancements are now increasingly being mapped to software in building technology – on demand simply via online update.

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6 General Procedure for Requirement Profile and Algorithm Implementation Among the above-mentioned constraints and requirements in the application domain of e-mobility, it is recommended proceeding in the following steps for many other domains of building automation. The steps can be carried out at longer intervals and different speeds in different parts of buildings. They depend on the evolving demand and the measurement data collected. 1. Establishment of a continuous monitoring system for recording time-series of relevant data and locations (e.g., electrical loads for each subdistribution, temperatures, and pressures). 2. Identification of issues, strategic planning of improvements (e.g., expansion of the equipped parking spaces). 3. Implementation of an active, hierarchical management for the relevant components (e.g., electrical subdistributions, valves, pumps, and mixers) and integration with other systems • e.g., integration of CHP, PV system, electricity storage with • e.g., interconnection of spatially distributed components (car parks located in other buildings). 4. Balancing of cost and benefit over time. Demand is usually changing slowly, and technology is still evolving. Therefore, it often makes sense utilizing existing capacities and shifting cost-intensive infrastructure investments if possible. Placement of electricity storage as a buffer in the underground car parks including active management is an example allowing to shift the investment into new cabling for a substantial time. The existing cable capacity can thus be used more evenly (avoiding peak loads) and the electricity stored directly on site in the underground car parks can be used directly for charging vehicles. Depending on the size of the power storage, the parallel charging capacity can be multiplied. Especially if equipping a property with PV systems, a high synergy is created that way, since electricity storage systems also significantly increase the share of self-consumption. 5. Plan for long-term investments (e.g., expansion of cable capacity). A Hierarchical Energy Management System ensures consideration of many simultaneous processes, parameters, sensors, and actuators of a site. As a precondition, the following requirements must be met: 1. Customer goals need to be defined: the goal definition should not only include technical conditions, but also usability aspects like intended audience for User Interfaces or fairness conditions (see “victims” of a power reduction or shutdown in the E-Mobility example).

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2. Physical and IT structure: an IT network needs to be created for making distributed real-time communication happen. The structure should be aligned to the physical structure of the building’s systems for avoiding additional error patterns and increasing operational reliability. 3. Interface communication: interface standards for protocols, data direction (read-­ only/read-write) or timely reaction need to be agreed between the relevant components. 4. Data Storage, Fault Tracking and Visualization: all data should be persisted for later analysis and fixing of implementation issues that were causing a faulty behavior. The data can also be used for visualization of trends happening in cycles or over longer time periods as well as a deeper analysis of correlations. All the requirements mentioned above have been applied to the concept for the installation of charging systems in underground car parks. They can also be applied to other application domains in existing buildings as well as new properties and may be gradually expanded according to new requirements over time. The Enterprise IT used in the concept proves its strengths in merging the resulting data and controlling components distributed over the property. In the high-performance software containers (Fig. 20), which are today’s standard in business processes of larger companies for quite some time, plant planners and developers can think and work in a way as if all sensors and actuators were converging at a central location. As a result, use of existing systems can be better extended up to the boundaries, enhancements planned more thoroughly providing a larger security in investment.

7 Conclusion This chapter describes how the residential building sector can contribute to a CO2-­ reduced production and use of energy. This sector has a big potential for decentralized applications including e-mobility. Another driver for savings in the residential building sector is the cooperation and the exchange of energy between participating parties. However, it is obvious that an efficient energy exchange for both heat and electricity needs an advanced energy management system to supervise and steer the energy flows. Among the above-mentioned examples and new processes for energy production in buildings with less CO2 emission as well as the establishment of Energy Communities with decentralized energy exchange of electricity and heat supply show the necessity for digitalization in the public building sector. Investments for heating network and their hydraulic balancing between the connected buildings develop opportunities for new business cases. Especially the development of new energy management systems with new technologies for the public building sector is described. Furthermore, there are new applications to be established not only in the public building sector but also in collaboration with the energy sector which means firstly the network operators.

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Additional to the optimization with an Energy Community there are also potentials for network conducive measures to support the network operator and to avoid congestions in the network. The chapter explains in detail the integration of e-mobility charging like connecting to both the electricity grid and an IT-network which ensures the communication between the different components as well to partners outside the building complex. Nevertheless, this study shows the need for further developments like: –– Avoiding congestions in-house by simultaneously charging of electrical vehicles including delivering network conducive behavior of the components. –– Output-input-optimization of generation and consumption –– Legal and regulatory rules must be developed for a simply and speeding deployment of new energy productions and energy exchanges between consumers. –– A market development for energy provided from and purchased for public buildings or Energy Communities. –– Development of interoperability of public buildings for the optimization energy production and consumption, e.g., including heat pumps and e-mobility charging/discharging. –– Development of a personal-based energy optimization in buildings (e.g., offices) [14].

References 1. Kastner, T., Konhäuser, W., Ingo Schuck; Energieeffizienz dank effizienter Prozesse; Die Wohnungswirtschaft DW 05/2021 2. IPCC. (2018). Summary for policymakers. In Global warming of 1.5°C.  An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P.  Zhai, H.-O.  Pörtner, D.  Roberts, J.  Skea, P.R.  Shukla, A.  Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I.  Gomis, E.  Lonnoy, T.  Maycock, M.  Tignor, and T.  Waterfield (eds.)]. Cambridge University Press, pp. 3–24, https://doi.org/10.1017/9781009157940.001. 3. IRENA. (2021). Renewable power generation costs in 2020. International Renewable Energy Agency, Abu Dhabi. 4. Burger, A., Lünenbürger, B., Osiek, D.  Nachhaltige Stromversorgung der Zukunft, Bundesumweltamt Pressestelle Wörlitzer Platz 1 06844 Dessau-Roßlau. 5. Konhäuser, W. (2021). Digitalization in buildings and smart cities on the way to 6G. Springer: Wireless Personal Communications. https://doi.org/10.1007/s11277-­021-­09069-­9. 6. Ritchie, H., Roser, M., Rosado, P. (2020). CO2 and greenhouse gas emissions. Published online at OurWorldInData.org. Retrieved from https://ourworldindata.org/co2-­and-­other-­greenhouse-­ gas-­emissions [Online Resource]. 7. European Commission. (2022). Energy use in buildings, factsheet. [Online]. Available EU-Gebäude-Factsheets | Energie (europa.eu) 8. IEA, EC. (2022). Playing my part, how to save money, reduce reliance on Russian energy, support Ukraine and help the planet. International Energy Agency, https://iea.blob.core.windows. net/assets/cbc97c70-­8bcf-­4376-­a8a9-­4cd875195f6a/Playingmypart.pdf

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9. Fares, R. (2015). Renewable energy intermittency explained: Challenges, solutions, and opportunities, plugged IN, Scientific American, https://blogs.scientificamerican.com/plugged-­in/ renewable-­energy-­intermittency-­explained-­challenges-­solutions-­and-­opportunities/ 10. Konhäuser, W. (2022). From 5G technology to 6G green deals. River Publisher. 11. Ableitner, L. (2022). Energiegemeinschaften; Exnaton AG; Open District Hub. 12. Open Charge Alliance: Global consortium of public and private electric vehicle infrastructure leaders (n.d.) 13. ADAC (2020). Studie Lastmanagement beim Laden von Elektro-Autos; Erstellt: 09/2020 Aktualisiert. 14. Konhäuser, W. (2022). Passenger based energy optimization and support for airport carbon accreditation. WPMC 2022; Conference contribution.

IoT-Enabled Zero Water Wastage Smart Garden Hitesh Mohapatra, Mohan Kumar Dehury, Abhishek Guru, and Amiya Kumar Rath

1 Introduction Irrigation began as a means of improving productivity in a natural way by increasing the production capacity of the land that is available and thus increasing the total output of agriculture, particularly in the arid as well as semiarid portions of the world [1]. For the production of crops, the creation of assets and the expansion of developing frontiers irrigation seemed to be essential. The success of Asia’s green revolution was largely due to the recent rapid expansion of irrigated areas and the accessibility and availability of new technology, such as high-yielding varieties (HYV), fertilizers, and tube well and water extraction techniques, in the late 1960s and 1970s. Improved access to irrigation infrastructure paved the ground for the “modernization” of the agricultural industry by facilitating crop intensification and input usage. Irrigated agriculture is an important component of global food production that has significantly contributed to global food security and rural poverty alleviation. In addition to ensuring food security, irrigated agriculture makes a considerable contribution to rural employment and livelihoods, which is crucial given the recent drop in real-world market food prices. In poor countries, irrigation is the most difficult H. Mohapatra (*) School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India M. K. Dehury Amity Institute of Information and Technology, Amity University Jharkhand, Ranchi, India A. Guru Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India A. K. Rath Veer Surendra Sai University of Technology, Burla\Sambalpur, Odisha, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 G. Marques et al. (eds.), IoT Enabled Computer-Aided Systems for Smart Buildings, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-26685-0_4

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aspect of agriculture. The main reason for this is that there is not enough rain, which means that more land isn’t watered. Another critical concern is the waste of water due to the indiscriminate usage of water resources. According to several UN reports, by 2025, over 20% of the global population will be directly impacted by water scarcity. This will also have an indirect impact on the remaining people, economies, and ecosystems on the globe. Internet of Things-based smarter ways of water systems utilizing big data, and AI technologies can be helpful in preventing these forecasts and neutralizing the damage caused by inefficient water resource use. Management of smart water techniques necessitates the combination of systems and a set of measures like monitoring, controlling, and regulating the usage of water resources, as well as the upkeep of the accompanying machinery like pumps and pipes. The hardware and software components that link people to water systems include sensors, actuators, data processing and visualization tools, meters, and, online and mobile controls. Other critical causes of water waste during gardening include pipe leakage, overwatering, and improper water channelization, as shown in Fig. 1. Water losses are primarily caused by leaks, which are highly dependent on pressure and water consumption [2] and contribute to the contamination of water. The traditional method

Fig. 1  Water loss during traditional gardening

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for detecting leaks in water distribution systems is the use of acoustic sensors [3]. Other developed leakage detection techniques include ground-penetrating radar [4], infrared thermography [5], and electromagnetic sensors. These leakage detection methods only cover a small area throughout their surveys. Recent developments in science and technology are helping researchers create smart water treatment systems that can detect and locate burst events automatically. The drip system is the only way to provide water to the plant zone, which saves a lot of water. At regular intervals of power supply, an autonomous irrigation system can deliver water to plants as needed. There is no need to turn the valves on or off in this case. This self-­ contained irrigation system will water plants at precise times based on soil conditions, enhancing crop growth by collecting water and nutrients as needed. The purpose of this work is to create a network of low-cost soil moisture and temperature sensors for monitoring purposes which will be helpful in getting data in real-time and watering plants accordingly. Plants and trees in smart cities must be irrigated on a regular basis to maintain lush foliage, such as irrigation of city park fields and roadside plants. Earlier methods included water channeling or manual watering, both of which resulted in plant death if proper care was not taken. In addition, mechanically running water pumps for filling tanks and controlling sprinklers resulted in a waste of water and electricity due to unnecessary activities. This IoT-­ based module, similar to an agriculture or drip system, provides for optimal plant development while conserving the area’s natural beauty. With its efficiency, the ground-level deployment achieves the goal. A detection module for a soil hygrometer, which is buried in the soil and delivers a continuous measurement, can easily be used to detect the soil moisture content. Sprinklers can be operated manually or mechanically depending on the plant’s needs in order to achieve the desired outcomes and maintain the greatest possible conditions for the growth of plants, and indicators to check the level of water are used to keep the right level of water in water tanks for irrigation purposes. The Lab-­ View module is depicted in Fig. 2 for a variety of situations, including controlling and getting the status of water sprinklers, water pumps, fountain pumps, fountain lights, and the moisture level of soil during the operation of sprinklers (rated in the range of 1–100%), and level of water in the heavy capacity water tank. The proposed model focuses on watering through an automated system. Less intervention from humans is required as per the growth of the irrigation and agricultural system. In this work, internet of things (IoT), an integrated model, is suggested that works automatically based on the moisture amount of the soil. That prevents over or under-­ watering problems. This leads to the conservation of water from getting wasted. Low cost and accurate decision-making are the two unique qualities of the proposed model. The proposed work is for transforming the conventional watering model into a smart watering model. The proposed model has been demonstrated in the garden. Every smart building is occupied by a garden. To make the building smart in all aspects, we can’t ignore the garden which is a primary component of the smart building. The proposed work helps in that aspect to make the building IoT-enabled smart building where all the work is happening with our human intervention.

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Software Control

Water Level Indicator

Network Analyzer Arduino Set

Smart Water Sprinkler

DC Power Supplies

Smart Garden Light

Weather proof Tx End

Soli Moisture Detector

DUT

Fig. 2  Layout of smart garden

In this paper, we have discussed the related work done in Sect. 2. The proposed method is explained in Sect. 3. The existing challenges in IoT-enabled water irrigation systems are discussed in Sect. 4. The results obtained are analyzed in Sect. 5. Section 6 presents a few points on future directions for IoT-based smart water irrigation systems and finally, Sect. 5 concludes the paper.

2 Related Work This section discusses the previous research that has been done on the proposed system. Water management has become more crucial for the long-term viability of irrigated agriculture due to rising pressure on existing water allocations [6]. The irrigation system should be intelligent, self-contained, and efficient in order to increase agricultural water delivery, and reduce manual operations. IoT Planting for the Elderly, which is managed by an Android application and aids in addressing mental health and memory concerns in the elderly, in light of research on elderly people, gardening, and IoT technology is proposed by [7]. This application can close the digital divide between the elderly and technology by using the activity of planting trees as an intermediary and avoiding accidents from the activity of planting trees. We have categorized the literature study based on common applications of smart water irrigation systems.

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2.1 Smart Terrace Garden The problem, according to the study done by [8], is that systems are either too expensive or incompatible with the app, or both. Reduced water loss is a solution to this problem that will save time, and money, and benefit the environment. The monitoring node, central node, and cloud are the three key components of the proposed system [9]. The monitoring nodes, which are fitted with sensors to monitor both soil and the environment, are positioned across the field at various points. Using the ZigBee network, these nodes connect and relay data to the central node. Based on a review of the current system, it is clear that it has a number of flaws. The proposed system can be used to address these restrictions [10]. The majority of the related work has concentrated on different projects like a resilient smart garden, conservation of water, support systems for gardening, designing education kits for gardening, and also DIY (do it yourself) projects that are based on microcontrollers. This categorization is done according to a thorough analysis of the work.

2.2 Small-Scale Water Conservation Projects As a hardware tool, a few microcontroller-based boards are used with extra sensors for designing water conservation projects that are small-scale. The authors of [11] provide instructions for water-saving systems that may be produced at home by a hobbyist utilizing microcontroller equipment. The authors of [12] presented a guide for conserving water which can be done in 10 to 20 hours utilizing a water pump, a water sensor, and an Arduino UNO board. The authors of [13] describe the building and the process of installing a microcontroller-based water-saving device that uses a flow sensor for measuring the amount of water consumed on a tap. The technology helps decrease excessive water wastage by turning on red LEDs after one liter of flow. The project will take 5 to 10 hours to complete. The project presented by [14] was carried in a vineyard and targeted lesser consumption of water for agriculture. A microcontroller, inexpensive moisture sensors, and a solar module are all used in this project. The authors of [15] created a basic controller for irrigation based on a clock with the goal of allowing people with little or no IT experience to develop up a simple and low-cost approach too closely monitor and regulate the amount of irrigated water. A real-time clock, a microcontroller, and a moisture sensor are used in this project. All these projects aim to employ microcontroller boards to put water-­ saving techniques into practice. These innovative concepts will be used in the proposed Resilient Smart Crop project whose aim is to irrigate small-scale vegetable farms with the saved water. In [16], the authors proposed a wireless sensor network-­ based system for optimally watering agricultural crops. The primary objective of this project is to develop and implement a system that can regulate crops in the field by using sensor nodes and a web application running on a smartphone to manage the related data. Hardware, web applications, and mobile applications are used in

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this case. The field is monitored using soil moisture sensors. Crop information and field data are both manipulated using a web-based tool. Additionally, data are analyzed in order to forecast future temperatures, humidity levels, and soil moisture levels that will be ideal for managing crop growth. The crop watering process is managed using a mobile application. It supports both automatic and manual control.

2.3 Other Resilient Smart Farming and Gardening Systems Many projects are there other than Resilient Smart Garden (RSG) whose attempts to combine gardening with technology that uses sensors and microcontrollers for making gardening easier and more accessible, especially to people with poor plant knowledge. These initiatives capture data ranging from temperature to soil moisture. These captured data may be utilized to improve garden care by ensuring that plants are growing in the best possible conditions. This also makes domestic food production easier, opens up possibilities for gardens by communities, and can also be used in agriculture. The University of Central Florida installed Connected Garden [17] employed sensors that are controlled by a microcontroller to capture data in an outdoor garden. To convey data to servers, the project used a number of sensors. The primary goal of this project was to gather information about the natural environment as well as interactions with the garden. Temperature, light, and moisture were the three types of natural data. The tools while being used in the garden were tracked using interaction data. In comparison to the Resilient Smart Garden, the Connected Garden project focused on only data collection and testing of the integrated server. This project, without being linked to any system of irrigation, just collected data through sensors [18]. The Arduino connects a range of digital and analog sensors, including sensors like moisture sensor, temperature sensor, and light sensor. The system then collects the data from the sensors in order to determine whether the associated irrigation system is on or off. One of the goals of this project, like the Resilient Smart Garden, was to aid in food production by maximizing the supply of water that is provided to plants in the case of infrequent availability of resources [19]. A long-term project that monitors the health of the garden and a fish tank is presented by the Automated Aquaponics Design report. The term “Aquaponics” is based on the notion that an ecosystem is created by a garden and fish in which the plants get nutrients from fish wastes [20]. The interplay between plants, fish and bacteria is simulated in this system. Despite the fact that the system is used to monitor two independent parts, the project comprises of the components which are analogous to the Resilient Smart Garden system. A microcontroller was utilized to monitor a variety of sensors as well as a garden setting that had been modified for inside use. The controller was not used to regulate the irrigation system, which pushed water into the garden from the fish tank. Just the level of water in the garden bed, temperature, and level of LED lights were supervised by the monitoring system. While aquaponics is a fantastic endeavor, the majority of small-scale installations in Southern California can only

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be used inside because of evaporation in the dry heat, which soon dries out the fish tank. MIT’s Open AG project, often known as Open Agriculture, aims to simplify gardening and establish a more controlled environment for garden bed maintenance [21]. The most advanced of the systems revealed is Open AG, which necessitates substantially more hardware. Their product is described as a personal food computer. A Raspberry Pi is used to keep the garden bed in a controlled environment. Instead of natural sunlight, UV lamps are used, and sensors are used to keep track of the garden and adjust the environment in which the plants grow. In a hydroponics system, irrigation is also done. Rather than establishing and maintaining a garden in the open, Open AG concentrates on doing it in a controlled setting. In [22], the authors concentrated on the best possible use of the internet of multimedia sensors in optimizing the process of irrigation in smart farming. Using image processing mechanisms and machine learning methods on IoT sensors, best decision for irrigation was made. This work aimed on the leaves getting yellow in color and used multimedia sensors to detect thirstiness in plants and sprinkles in soil in the case of smart farming. The readings obtained from sensors were used to train datasets to act as an indicator of plant’s thirstiness, and use methods of machine learning and deep learning to take decision for the best option. The experimental results indicate that using a deep learning mechanism may be a better approach in the environment of the internet of multimedia things.

2.4 Garden Monitoring Systems In the market, some gadgets are available that are categorized as commercial-off-­ the-shelf (COTS) gadgets that can be helpful for gardeners to cultivate plants. During the search, the Smart Garden System and Green IQ Smart Garden Hub were uncovered [7]. Gardeners may use the smart Garden Sensor to for monitoring the environmental conditions [23]. The sensor used has the ability to track many environmental conditions like humidity and temperature. It then analyses the data to build a database having information about different plants and learn about the science of soil, as well as provide advice to gardeners. The Edyn Garden system consists of installing mobile apps that can run on both Android and iOS and can deliver data about the garden to gardeners in real time via the Wi-Fi network, such as the status of the garden and recommendations on how to enhance it. These systems are also helpful to gardeners by allowing them to: 1. Access a database containing information on more than 5000 plants, that also includes the optimal climate conditions for each plant. 2. Get feedback and suggestions depending on the plant’s growth stage. 3. Receive an alert message whenever a plant requires extra care. The Edyn water valve, which can sense local weather conditions and adjust the irrigation system automatically based on the number of plants in the garden, is

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another impressive feature [24]. This can be used in conjunction with the Edyn Garden sensor and considering the local conditions watering can be done in a smart way. The Edyn water valve also offers a manual way of watering that can work without using the Edyn Garden Sensor and allows users to set up a specific time and date for watering and thereby control water usage as well as. The Green IQ Smart Garden hub is a device that leverages Internet Cloud and mobile technologies to help gardeners cultivate plants from anywhere and at any time by intelligently managing an irrigation system and lighting schedule [25]. All Green IQ models have been water sense approved by the International Code Council Evaluation Service (ICC-ES), which can help is saving up to 50% cost of current water usage. There can be 6, 8, or 16 irrigation zones depending on the size of the restricted area. By the use of PC or mobile apps, users have the controlling and scheduling ability of the garden’s watering and lighting system. They can do so from anywhere and at any time. The system connects to Wi-Fi networks to receive weather data from the closest weather station, and after that, it calculates how much water the garden needs. The Green IQ Smart Garden Hub is linked to irrigation valves of a garden and lighting circuit by Wi-Fi, a mobile device, or an Ethernet connection to control irrigation and lighting schedules. Using the Green IQ Mobile App, users may create scheduling regimens for each irrigation zone and channel the lighting. The Green IQ Cloud keeps track of system settings and user programs, communicates with mobile devices, and makes updates. Both instruments make gardening and irrigation easier, but they are not meant to be used as teaching tools. To automate farming and offer precision farming capabilities, the authors in [26] suggested the use of actuators enabled by a system of smart sensors. People with little experience with technology may find it easier to understand and maintain a smart board system with the help of this system. This board aids in keeping track of farm conditions and giving instructions to farm equipment. This forum may be able to notify farmers about government announcements pertaining to agriculture. A smart sensor network system is one of several technologies on which this system is built.

2.5 Other Microcontroller Projects The use of single-board microcontrollers that are quite easily programmable has made electronic DIY projects more accessible. The authors of [27] provide directions for making an outdoor autonomous garden watering device that measures soil moisture levels using an Arduino-UNO. The project is housed in a box with a 12-volt battery and a screen based on the liquid crystal to show the current moisture level. In [28], the authors have created a tutorial for a system that is advanced and automatic for watering the garden and that will keep track of different environmental conditions like moisture and temperature, and store these values in a database. A 12  V battery powers the controller, which

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communicates with a server via an Ethernet Shield. The authors of [29] demonstrated the use of a hardware clock in building a data logger based on temperature. A digital card that is secure is used to save data instead of sending it to a server. Using DIY moisture sensors, the authors of [30] demonstrate how to construct a self-watering garden. Two nails are linked to a wire and a microcontroller is used to detect the amount of soil moisture. This is done by passing a small current through the soil with one nail and measuring the resistance with the other. The earth’s resistance decreases as there is more water present and vice versa. The serial port is used to transmit temperature, humidity, and moisture data, but no database is used to store this information. In order to show the data and power the microcontroller, the serial port is required to be connected to a computer. In all of these designs, an automatically watering garden is implemented in a similar way. The Resilient Smart Garden has some similarities to the DIY Resilient Smart Garden in terms of conserving water while providing a healthy environment for plants, but it goes further. The authors in [31] concentrated on the efficient use of water resources through simplified irrigation on a variety of agricultural farms. A cloud and Internet of Things-based framework is described in this paper for implementing a smart system for irrigation. A framework is defined that is used to develop an automated system for smart irrigation, and a mechanism is defined to effectively use excess water generated by showers to raise groundwater levels. In real time, this developed system can be helpful to farmers for monitoring their farms. In [32], the authors have proposed the use of trust model which is based on the concept of Blockchain. The proposed model has used smart contract prototype and an Ethereum network for addressing few of the security related issues in smart agriculture enabled by the use of IoT devices. In [33], authors have presented a Bee Smart kit which is an instructional compact garden package that may be utilized at home or in the classroom. The instructional package is designed to teach kids about gardening systems who are studying in grades 3 to 6 in a straightforward manner. The objective is to educate kids about food, plants, pollinators, and gardens by building pollinator habitats. As the kit is simple and intended for educational purposes, it has limited features with respect to gardening. Apart from this, there is an aeroponic system for educational purposes that is the Tower Gardens school kit [34] that is used to grow products quickly and efficiently using water, liquid nutrients, and a soil-less growing medium. The kit comes prebuilt and requires little effort for setting up. It utilizes a grow light that is portable and that illuminates the vegetation from within without requiring any hardware setup from the user. As part of the DIY process, the seeds are planted easily in the kit and allowed to grow. As the kit was developed for indoor gardens, users must maintain pH and water level since sunlight is not required. The rate of the kit is $45.25 for a month which is costly for an education system. According to [35], to achieve sustainability in ubiquitous computing, the research should be more focused on the requirements of a single customer.

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2.6 Other Educational Garden Kits The authors of [36] focused on irrigation systems that have automated monitoring facility for small as well as large estates of plantation to be able to remove the old system that works manually. Physical quantities like moisture of soil, content of humidity in environment and temperature can be measured with low-cost equipment. It can also monitor physical factors like the presence of major pollutants in the air like PM2.5, PM10, and CO. The dataset from previous surveys were taken in to account for comparing with crop yield and for finding out other factors that are useful in prediction of irrigation requirement.

3 Proposed Work We propose a system based on rational and sustainable water resource management, because the rising population, environmental concerns, and pressure on the food and agricultural sectors make water even more valuable. We considered a scenario in which a network of temperature sensors is deployed in a garden in our proposed system. These sensors measure the moisture content and temperature of the environment on a regular basis and send the information to the controller. The controller activates smart water sprinklers to sprinkle water in the garden when the moisture content or temperature exceeds a predefined threshold value. Figure 2 depicts the proposed system block diagram. We collected data on the moisture content of the soil using a Soil Moisture detector. An Arduino Kit is used to receive sensor signals and transfer the data to a GSM module for transmission to the Smart City base station. To transmit data from the Arduino Kit, a GSM Kit is used. To detect the level of water in the tank, a Water Level indicator is used. The fountain light and sprinklers are controlled by a Relay Single Pole. The next subsection goes over the specifics of the component used. A temperature and humidity sensor is used to track the temperature and humidity of soil. The soil moisture sensor is connected to an Arduino Uno board for analog input, which helps in tracking the temperature content present in soil. In the proposed system, ambient temperature and humidity are measured and displayed by using a combined temperature and humidity sensor with Arduino Uno. IoT helps in connecting each and every network with a common controller using which the smart watering system is controlled. This system displays the values of several sensors on the smartphone screen or computer screen. When a high signal value is detected, such as during an overflow or when the moisture level exceeds the threshold, the relay card performs the necessary action, which is to turn the pump off. In other words, the relay card might be considered the principal operating unit for large motors [37]. Figure 3 shows the flowchart of the process. A simulation-based investigation was conducted with a single relay to drive up a single motor as shown below. In order to put this circuit into practice, a large number of relay cards must be connected to operate each motor for the necessary

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Fig. 3  Soil moisture measurement flowchart

purpose. While the received signals from the base station are decoded at the master card illustrated, which performs the necessary calculations and decoding of received data before sending the signal to the relay card for individual motor action.

3.1 Hardware Requirements Following is the list of components required for designing the module. Figure  4 illustrates the circuit diagram among all the hardware components. It illustrates the moisture amount in the soil. Also, it demonstrates the set value from the user end to measure the actual moisture content against the set point.

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Fig. 4  Circuit diagram of proposed model

• Soil Moisture Detector  – It is of type PIC/Atmel and is used to collect data related to the content of moisture in the soil. The obtained results are in the form of conductivity and resistance in-between two electrodes; higher resistance (i.e., less conductivity) is due to less content of moisture and lesser resistance (i.e., high conductivity) is due to more content of moisture. • Arduino Kit  – It is of Arduino UNO type of build and is used to receive signals from sensors like moisture sensors or water level indicators. It transmits the received signal data to the Smart City base station using the GSM module. • GSM Kit: (Rx and Tx) – It is of SIM 900 type of build and is basically used for data transmission and receiving. It receives data from the Arduino module and transmits data to the base station of the smart city to securely operate the sprinklers. • Water Level indicator – This indicator is an SD512 Resistive (noncorrosive) type and is modeled to read the water level in the tank. • Relay Single Pole – This relay node is a JQC3F 5 Pin SPDT type of build and is used to control and do the switching of fountain lights and sprinklers.

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4 Challenges in IoT-Enabled Water Irrigation With the Internet of Things (IoT), researchers face a number of challenges. This paper discusses a few significant issues.

4.1 Standard Protocols A variety of sensors, controllers, and actuators must be integrated to make IoT-­ based applications adaptable and interoperable, resulting in multiple challenges in standardizing communication protocols. Further, a variety of devices and gadgets must be integrated, necessitating the development of global standards. Major problems with the Internet of Things include the number of components required, interoperability, communication protocols, and power sources. Communication protocols used by various devices include MQTT, ZigBee, and TCP/IP.  Despite being the most widely used protocol, TCP/IP causes a slew of complications. As a result, numerous studies were done in this direction to comply with the complexity issue.

4.2 Security in IoT-Based Systems When it comes to IoT, data security is critical, but because the system is evolving and there are no developed standards, security has become a serious problem with unclear solutions. The security of routing protocols is also a difficult and ongoing subject, in addition to the security of communication protocols. Security must be ensured in order for applications to be used successfully.

4.3 Connectivity Equipping agricultural fields with internet access in developing and underdeveloped countries may be more difficult than anticipated. Many IoT applications appear to be connectable, but available bandwidth must be increased. In order to simplify connectivity, internet service providers must broaden their geographic reach.

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4.4 Reliability of the Devices Involved With IoT, heterogeneous devices are converted into a single application, and the selection of long-lasting devices is critical to the reliability of implementations. When a single component fails, the entire system fails.

5 Result and Discussion After the module is installed in the city, greater control of sprinklers, tracking of water levels, and simultaneous operation of the water pump can be successfully achieved, and therefore, resources such as water and electricity may be managed and conserved to a large extent. The conventional model of watering consumes a substantial amount of energy which is not in favor of any consumers explicitly farmers. Data from both sources is collected and analyzed manually. In the given graphs, the water consumption and electricity consumption have been validated against the traditional models. Figure 5 illustrates the water consumption during the watering process in both smart and conventional ways. Figure  6 illustrates the electricity consumption during the watering process in both smart and conventional ways. Less electricity consumption will lead to fewer tariffs. Table 1 illustrates a critical analysis by considering a few explicit properties like timing, reliability, etc. The proposed model has been validated against a few existing techniques. The comparison has been done by considering a few explicit factors like timing, reliability, scalability, fault tolerance, processor, connection type, monitoring mode, and cost.

Fig. 5  Water consumption comparison

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Fig. 6  Electricity consumption comparison Table 1  Critical analysis between existing models and proposed model (Supporting (S) and Nonsupporting (NS)) Models [17]

Fault Timing Reliability Scalability Tolerance Processor S S NS NS Banana Pi

[14] [38]

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[39]

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[40]

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[41]

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[42]

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Connection Monitoring Type mode Cost Wi-Fi Android High App ESP8266 Website Website High Ethernet Wi-Fi Android Medium Shield App Node MCU Wi-Fi Android Medium App ATmega328 Wi-Fi Android High App ESP8266 Wi-Fi Android Medium App ESP8266 Wi-Fi Android High Node MCU App Arduino Wi-Fi Website Low Uno

6 Conclusion Using IoT, an Arduino-based integrated system (smart watering system) is proposed in this work. This system is primarily comprised of an Arduino Uno board, a soil moisture sensor, and a temperature-humidity sensor. Soil moisture sensors detect soil moisture levels, while temperature-humidity sensors monitor soil temperature

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and humidity. The primary goal of this system is to optimize the use of water in garden irrigation. This system is extremely beneficial to people because it automatically monitors the irrigation process and is simple to implement in irrigation fields at a low cost. In comparison to other traditional approaches, the approach focused on this work can be considered a suitable approach in garden irrigation because it saves time, money, and manpower. • Energy Efficiency – Future studies should focus on reducing implementation costs while enhancing the energy efficiency of sensing devices and sensor nodes. Research investigations are needed to create high-performance and reliable energy-efficient approaches for IoT-based applications. • Improved Sensor and Communication Coverage – Large-scale applications necessitate extensive coverage. As a result, it is crucial to conduct additional research for developing low-price, less power-consuming, and long-range smart sensors and wireless communication technologies are critical. • Optimal Sensor Placement  – Water quality and leak detection will both increase with rigorous monitoring of the water supply network pipes. To achieve this, more water quality and pressure sensors are required to be installed at each node or pipe in the network. But due to tight financial restrictions, it is not possible to achieve. To address this issue, sensors are required to be strategically placed along the pipes. Thus, placing these sensors in an optimized way can be a research area with the objective to achieve good coverage and minimize the required sensors. Highly effective and quick algorithms should be looked into to address issues with choosing the best sensor site along the pipe. This is done to provide the precise measurements needed for the examination of water quality and leaks. Poor decision-making might result from inaccurate measurements. • Advance Data Analysis Tools – Depending on the type of application, massive volumes of data are produced by IoT systems from connected smart devices and sensors. This requires the conduction of further research in smart analytical solutions for the generated application. These analytical solutions need to be efficient computationally as well as power efficient. To improve overall performance, most IoT-based smart water irrigation applications require smart analytical solutions. • Data Security – Traditional approaches to data security, such as encryption, are insufficient as they are no longer able to significantly improve data security because of rising processing power, which has resulted in the development of numerous decoding algorithms. As a result, future IoT-based systems will necessitate an advanced and dynamic security mechanism for IoT-based smart water irrigation applications. Furthermore, the proposed work can be extended by integrating machine learning and deep learning concepts. Automatic water monitoring behaviors can be used based on the soil and crop quality. Water is a nonrenewable source on earth. The wise use of this resource will be beneficial for future generations and farmers.

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IoT-Based Human Activity Recognition for Smart Living Anindita Saha, Moumita Roy, and Chandreyee Chowdhury

1 Introduction Buildings are designed to supply comfortable shelter to their occupants [1]. In earlier days, structures made up of wood, stones, animals’ kins, and so on were used to serve the primary purpose only. However, buildings evolved over time from their basic design to modern towers subject to various trends such as the durability of the construction, modernization of the interior environment, entertainment, cost-­ efficiency, and so on. Nowadays, buildings are getting more sophisticated with the integration of technologies with the structure. This initiates the notion of smart living [2]. In this regard, the Internet of Things (IoT) serves as a key innovation driver that influences every aspect of human lives. IoT enables device-to-device interactions without the intervention of human being. Hence, it promotes ubiquitous computing intending to enhance the quality of living [3]. Nowadays, people could get facilities of having highly advanced automatic systems in their surroundings. Smart home appliances, smart wearable health monitoring devices, smart lighting, smoke/gas detection, and smart gadgets have not only enriched the way of living but also provide virtual assistance to elderly people who stay alone. However, furnishing accurate and opportune information on people’s activities and behaviors is one of the pivotal tasks in pervasive computing. Hence, Human Activity Recognition (HAR) becomes an active field of research over the decades [4]. A. Saha Techno Main Saltlake, Kolkata, India M. Roy Institute of Engineering and Management, Kolkata, India C. Chowdhury (*) Jadavpur University, Kolkata, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 G. Marques et al. (eds.), IoT Enabled Computer-Aided Systems for Smart Buildings, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-26685-0_5

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Activity recognition embraces people’s movement across different locations as well as several activity states like walking, standing, sitting, laying down, climbing stairs up and down, or driving, etc. to analyze human behavioral patterns [5]. HAR can be defined as the process of sensing, recognizing classifying, and recording the daily actions of a human being so that the intensity of those activities in daily life could be monitored and predicted. HAR and monitoring system aim to concurrently identify and evaluate the actions performed by a person on a daily basis through a series of observations in real conditions of the surrounding environment [6, 7]. A wide range of applications [2] at the individual level including Ambient Assisted Living (AAL), well-being management, medical diagnosis, elderly care, rehabilitation, robotics, entertainment, and surveillance can be benefited from the context-­ aware feedback provided by HAR and monitoring system. Besides, the community-level application of HAR can cover analysis of the aggregate behavioral patterns through the assessment of traditional and emerging risk factors for human populations for different demography [5]. For instance, nowadays, with the increase in global population and aging issues, there arise corresponding concerns about the impact of the living environment (particularly the quality of the air experienced inside and outside of buildings) on human health [8]. Here, one of the key factors that pollute indoor air quality is the human behavior inside a building including routine activities like cooking and opening or closing windows or doors. The excessive increase in indoor pollution levels, i.e., the levels of nitrogen dioxide and carbon monoxide resulting from daily activities might cause severe health hazards. Hence, there arises a strong need to monitor these activities and understand the correlation with the pollutant emission so that the behavioral changes of the home occupant with indoor air quality can be anticipated. Here, advanced sensor technology and modernized IoT devices play a pivotal role to collect various activity data and develop HAR systems. Another relevant instance could be constant health supervision of patients with chronic health diseases such as diabetes, and cardiac issues. In fact, monitoring is essential for patients with dementia to identify abnormal behavior and activities [4]. In this regard, daily activity recognition systems could send an alert on any emergency condition in order to provide feedback to the concerned caregiver or the doctor [9]. The significant application domains of HAR have been summarized in Fig. 1. In the past decade, vision-based techniques were mostly used to track human daily activities where camera systems were utilized to provide the necessary context [10]. Image sequences [11, 12] and window tracking process [13, 14] were the key techniques used to identify human activities. Later, more sophisticated approaches like vector quantization [15], Hidden Markov Model (HMM) [16], and 3D segment tracking [17] are included in this field. However, with the advancements in wireless technologies, a paradigm shift toward sensor-based HAR could be observed. Nowadays, small, inexpensive, and low-powered IoT devices (such as accelerometers, gyroscopes, and magnetometers) are widely available which can provide sensitive and responsive services subject to users’ current ambience and context and thus can be effectively utilized in several HAR applications. Even, the sensor nodes that measure body vitals can form a network in, on, or around the human body known as

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Gesture and posture analysis Behaviour analysis Healthcare

Applications of Human Activity Recognition

Sports Ambient Assisted Living

Smart surveillance Entertainment

Robotics

Fig. 1  Representative application domains of HAR

Wireless Body Area Network (WBAN) for reliable and efficient end-to-end communication [18, 19]. Furthermore, in IoT-based HAR, the privacy of the person is secured since the camera is not involved. However, the incorporation of Machine Learning (ML) techniques [20] in both vision-based and sensor-based HAR adds another dimension to this domain of research by improving the overall performance and classification accuracy. Being active fields of research, a number of works could be found individually in both IoT and HAR domains. Although, the interrelation between these two trades is rarely investigated. In addition, there are several inherent research challenges [4, 7] in IoT-based HAR that create motivation for further development of new techniques to enhance accuracy under more realistic conditions. For instance, the selection of the attributes for estimation, the development of an unobtrusive, and inexpensive data acquisition system to collect data under realistic conditions, and the extraction of principal features from the collected data are some of the primary challenges to be taken care of. Smart building scenario requires substantial attention to the issues like location-based activity recognition, mapping the activities of multiple residents, persons performing multiple activities at the same time, diversity of the user, and so on. Hence, this chapter contributes an extensive survey of the state-of-the-art works on IoT-based HAR utilizing several ML techniques. This could enable the researcher to acquire a useful perspective of the problem domain and find further avenues.

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Moreover, the case study demonstrated in this chapter could contribute to the baseline experimentation and outcome analysis in this field of research. Consequently, this chapter first discusses the background of HAR in Sect. 2. Section 3 illustrates the role of IoT in HAR including the research challenges and various performance metrics. State-of-the-art works are analyzed thoroughly in the following Sect. 4. The next section (i.e., Sect. 5) presents a case study in brief. Finally, Sect. 6 concludes.

2 Background of Human Activity Recognition Because of its substantial and large-scale contribution to various applications such as remote healthcare, surveillance, and fitness monitoring, HAR has turned out to be one of the most in-demand research areas in literature in the recent past. HAR refers to sensing, identifying, classifying, evaluating, and monitoring actions carried out by individuals on a daily basis, in real time, to provide context-aware feedback for several medical applications that include elder care at home, postsurgery trauma rehabilitation, detection of falls in elders or differently abled individuals [21]. Furthermore, HAR has been effective for patients with chronic health conditions such as heart disease, diabetes, or obesity who need monitoring on regular basis to detect abnormalities. Traditionally, HAR was highly dependent on vision-based techniques using image sequences [12] or window tracking processes [14], which had their own limitations such as feature point selection or body motion modeling. With the advancement in technology, several methodologies like vector quantization for converting time-sequential images to image vectors or applying statistical methods like Hidden Markov Model (HMM) [16] evolved, which was found effective in recognizing human activities efficiently. Further research covered the way for 3D tracking approaches such as the proximity space method [22] or through 3D-shaped models of collision and occlusion [23]. In the recent past, with the advancement of pervasive and ubiquitous computing, a continuous improvement in existing technologies has been exhibited worldwide. Wireless technologies with sensors play an essential role in activity recognition. For instance, nowadays, wearable sensors like accelerometers are popular ways to recognize and monitor daily activities [4] as they are small, inexpensive and low-powered devices. In the 1990s, a few works were found in this field of research that explored several aspects. The authors in [24] described the development of a triaxial accelerometer and portable data units for activity recognition. Furthermore, the suitable placement of accelerometers for posture detection [25] or generating context awareness by analyzing accelerometer data [26] was also found to support literature in this domain. HAR approaches are strongly dependent on data and analysis of the same for prediction. Hence, in the last decade, ML has become a popular choice of research due to exponential progress in computing and communication techniques to be

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implemented in various applications across the globe. Being a subcategory of Artificial Intelligence (AI), ML encompasses several algorithms that learn from benchmark datasets prepared through past experiments and make predictions for future applications. Through ML, machines can learn the patterns in a structured dataset with thousands of rows, to build a model without being explicitly programmed. A substantial amount of human intervention is required in performing essential preprocessing of a raw dataset through feature engineering, using heuristic techniques, and hence have profound applications in fraud detection, pattern recognition, recommender system as well as in HAR. [4] Here, physical activities are recognized by exploring the standard ML algorithms such as K-nearest neighbor (KNN), Decision Tree, Random Forest (RF), and Naive Bayes (NB). Besides, probabilistic models like HMM are found in the literature to extract features from collected data and multimodal sensor data analysis. Vision-based activity recognition is well supported by traditional ML algorithms such as Support Vector Machine (SVM) [27] from sequences. Research has also shown that human activities may be recognized either using a single camera [28] or even multiple cameras [29]. When it comes to sensors, ML techniques have exhibited significant improvement in the overall performance of the predictive analysis. In early works like [30], detection of abnormal activity was possible from wearable sensors through a two-­ phase ML approach with SVM and Kernel Nonlinear Regression (KNLR), whereas in [31] the authors have proposed a method to combine inertial sensors and an optical motion capture system, to perform activity recognition using Neural Networks and Bayes theorem respectively. The use of a hierarchical weighting decision scheme as proposed in [32] claims to significantly improve robustness and scalability by taking advantage of the majority voting model. Literature works with multiple classifiers like KNN, SVM, RF, and Gaussian Mixture Models were proposed to perform predictive analysis from multiple wearable sensors at different body parts, as depicted in [33]. A few ensemble classifiers are also used in this area [34] to improve the accuracy of a single classifier used otherwise, retaining the generalization capability of the same. With the passing of time, Smartphones, equipped with sensing and communication facilities with embedded accelerometers and gyroscopes, provided a better way to monitor human activities, especially in the last decade. In such mobile devices, data can be collected, processed, through appropriate applications installed, and communicated to remote servers for analysis and classification. This eliminates the need of wearing a separate wearable sensor at the hand of the chest or on belts [35]. Most standard ML algorithms work effectively on Smartphone collected data as exhibited in [36, 37], and even Neural Networks [38]. Activities to be recognized may be either static (sitting or standing) or dynamic (walking or running). However, the variation of hardware configuration or the position of the smartphone might remain a concern as discussed in [39]. However, this can be handled smartly with appropriate feature selection and the choice of a suitable ensemble classifier that primarily predicts on the basis of majority voting. To understand the concept further, the basic framework of HAR for the application of a standard ML model may be summarized in Fig. 2. Data gets collected

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Fig. 2  Basic framework of HAR

Dataset

Data Retrieved

Data Preprocessing

Feature Extraction

Feature Selection

Modeling

ML Algorithm

Model Evaluation

Development

through sensors (wearable or smartphones) and cleaned to remove noise present in it, while collection. For example, data cleaning can be done with a bandpass filter that removes both noise and low-frequency gravity components present in accelerometer data along with linear acceleration. Then the data can be segmented into windows (overlapping or non-overlapping) for temporal pattern analysis. The next step is to perform feature extraction on these segments to represent the raw data into a feature vector. Accordingly, it becomes a training sample for the ML model to be trained over in the subsequent step. Feature selection might be performed as well at this stage so that prediction accuracy may be improved. The trained model is then tested with data points and could be deployed for real-time recognition of activities. Along with ML, Deep Learning or DL has emerged in recent times as a step ahead of the former, which is based on the operational methods of the human brain, that can handle larger datasets to solve complex AI problems requiring

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more computational time. The term “deep” in deep learning (DL) refers to the depth of these numerous hidden layers in ANNs, and the quantity of hidden layers in a Neural Network rises to permit the composition and recombination of basic input data into complex features [5]. DL can handle unstructured datasets unlike ML and can deal with a dataset with millions of instances in it and this huge volume of data enables it to get trained appropriately as it incorporates an increased number of hidden layers in between. One of DL’s main benefits is the automatic extraction of high-­level features, which can enhance the effectiveness of HAR.  With an enhanced self-­learning capacity, DL can support parallel and distributed algorithms with advanced analytics and scalability for predictive purposes. Some of the popular applications of DL can be seen in Image Processing, Natural Language Processing, Object Recognition, Computer Vision, and many others. In recent times, Deep Learning (DL) has also gained substantial importance for Smartphone-based activity recognition as it can auto-extract useful features from the data for improved accuracy and better prediction with respective models. The authors of [40], have proposed a Deep Convolutional Neural Network that exploits the time-series signal with alternative convolution and pooling layers and auto extracts basic as well as complex features from the input data. Further, literary works like [41] utilize vector magnitude accelerometer data to reduce rotational interference in raw data in a one-dimensional (1D) Convolutional Neural Network (CNN)-based model. Long Short-Term Memory (LSTM), another popular DL model can detect human activities by learning features from raw accelerometer data without generating hand-crafted features [42]. Authors have also considered Bi-directional LSTM [43] and stacked LSTM [44] to predict activity from Smartphone data. There are few literary works on the combination of CNN-LSTM [45] where the former learns complex activity patterns and the latter effectively capture temporal information of the time series data generated from accelerometers. It is observed that sensor data collected from smartphones produce high-dimensional feature vectors out of which few contribute to the actual identification process. This leads to a curse of dimensionality. Hence, works like [46] use the standard Sequential Forward Floating Selection (SFFS) method to select optimum features from the feature vector to be fed into a Support Vector Machine (SVM) for the classification of activities. Besides, few works make use of a dimensionality reduction technique to address this problem. For instance, in [47], the authors proposed Fast Feature Dimensionality Reduction Technique (FFDRT) that reduces the feature vector of standard datasets with less time consumption and fed it as input to the RF classifier for accurate predictions. Recently, metaheuristic approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have given a new dimension to the research of HAR by improving feature selection, feature extraction, and hyper-parameter tuning of data collected from smartphones or wearable sensors [5]. This has drawn the attention of researchers to a considerable extent.

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3 IoT in Human Activity Recognition The IoT paradigm has enabled the availability of different low-cost sensing devices that can collect activity data for HAR-based applications. These devices can protect the privacy of the residents as the camera is not utilized, secondly, these devices do not require any present Smart Home infrastructure. That’s why such devices are becoming a more convenient option for context-aware services. The architecture of such IoT-based HAR system is typically based on a server-client setup. The client is the IoT device that implements the sensing layer as designated in Fig.  3. Such devices are connected to a server through the Internet connection as indicated by the Network layer in the figure. The server implements the data preprocessing and application layer where actual data analysis takes place. The application layer in the figure lists the different services that can be provided by such an architecture. In the following subsections, there search challenges and performance metric are discussed that builds the required perspectives for reviewing the state-of-the-art works on IoT-based HAR systems for smart living applications.

Fig. 3  Layered architecture of IoT-based HAR system

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3.1 Research Challenges The main research challenges that are commonly faced by researchers are listed as follows. • Handling grossly labeled data: Normally, a user does not perform an activity in isolation. Rather, a sequence of basic activities is performed by a user as part of a composite activity. For instance, a sequence of stand and walk can be performed while walking in the queue. Consequently, precise labeling of each individual activity could be difficult. In fact, the duration of standing and walking while walking in the queue may differ from user to user and/or from time to time. Thus, the learning techniques to be applied should be able to handle such data annotations. • Utilizing data from multiple sensors: HAR data could be collected from various sensors, such as an accelerometer, gyroscope, and magnetometer. Moreover, a user is not expected to carry one dedicated device always. Thus, data is collected from different sensing devices each having a different calibration. This device diversity could be challenging in interpreting data for the fusion of features. A trade-off between the number of sensors deployed and the efficiency of the system to collect data for activity recognition is critical. • Diversity of users: The individual gait of users varies from one another as the activity patterns depend on the age of the users and/or their physical conditions. Depending on the habit, the way they carry the sensing device may also vary. For instance, a smartphone could be treated as a potential sensing IoT device. A smartphone can be carried at hand, or kept in a trouser pocket or shirt pocket. Now, if the smartphone is kept closer to the center of gravity, such as the trouser pocket, better accelerometer data is going to be collected. On the contrary, when it is carried at hand, noise builds up. Thus, the usage behavior of the users also affects the quality of the data collected. Thus, the features should be extracted from the dataset in such a way that it mitigates this variance of usage behavior.

3.2 Performance Metric The effectiveness of the HAR system depends on various factors [4] such as the activity set, the quality of the training data, the feature extraction technique, and the learning algorithm. For instance, activities like sitting, walking, and standing still are relatively easier to distinguish than to differentiate between more complex ones such as watching TV and eating. The recognition performance is quantified in terms of some standard metrics [4]. Accuracy, recall, precision, F-measure, Kappa statistic, Receiver Operating Characteristic (ROC) curves, etc. are prevailing among them. In HAR, the results obtained through applying the classification algorithm are organized as confusion matrix 𝐶𝑚×𝑚 for a classification problem having 𝑚 classes.

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Predicted negative

Predicted positive

Actual True positivee False negativee positive (ttp) (ffn) Actual False positivee True Tru negativee negative (ffp) (tn)

Fig. 4  Structure of the confusion matrix for binary classification problem

Here, each element 𝐶𝑖𝑗 is described as the number of instances from class 𝑖 which is classified as class 𝑗. For instance, the structure of the confusion matrix for binary classification problems is depicted in Fig. 4. Here, the confusion matrix can have the following values: • • • •

True Positives (𝑡𝑝) – The number of positive instances classified as positive. True Negatives (𝑡𝑛) – The number of negative instances classified as negative. False Positives (𝑓𝑝) – The number of negative instances classified as positive. False Negatives (𝑓𝑛) – The number of positive instances classified as negatives.

Few standard metrics are defined as follows: 1. Accuracy – It is defined as the number of correctly predicted data points subject to all the data points. For instance, in the case of a binary classification problem as mentioned earlier, accuracy can be defined as follows [4, 48]: Accuracy 

t p  tn t p  t n  f p  fn

(1)

2. Precision – It can be described as the quality of the positive prediction made by the model. In other words, the positive predictive value refers to the ratio of correctly classified positive instances to the total number of instances classified as positive [4, 48]. Hence, Precision 

tp t p  fn

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3. Recall – It is evaluated as the ratio between the numbers of positive instances correctly classified as positive to the total number of positive samples [4, 48]. Hence, Recall 

tp t p  fn

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4. F-measure – It is quantified as the harmonic mean of precision and recall where each is given equal weight [4]. Thus,



F  measure 

2  Precision  Recall Precision  Recall (4)

5. Kappa statistic – This metric compares an observed accuracy with an expected accuracy obtained through the agreement with a random classifier. Apart from performance evaluation of a single classifier, the kappa statistic is also useful to assess classifiers among themselves [48]. 6. ROC curve – It is a graph that shows the performance of a classification model in terms of two parameters, i.e., true positive rate (𝑡𝑝𝑟), and false-positive rate (𝑓𝑝𝑟) [48]. True positive rate is defined similarly as recall. The false positive rate can be defined as the ratio between the number of negative instances classified as positive to the total number of negative samples. Accordingly, f pr 

fp f p  tn

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7. Negative predictive value – It gives the probability that the predicted negative instance is a true negative [4]. Negative predictive value 

tn t n  fn

(6)

8. Specificity – It is defined as the ratio of correctly predicted negative instances with respect to the total number of negative samples [33]. Hence, Specifity 

tn tn  f p

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4 State-of-the-Art Works A significant amount of literary work has been conducted on IoT-based HAR in the last decade including the emergence of Embedded Intelligence (EI). Some of the relevant state-of-the-art works are presented in Table 1. Guo et al. in [49] focused on

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Table 1  Some of the relevant state-of-the-art works on IoT-based HAR Types of devices Existing Learning work methodology used [51] ANN Wear able devices

Types of sensor used Accelerometer &Heart Rate Sensor

[52]

N/A

Smart watch

Accelerometer

[53]

SVM and DT

Smart-­ phone and Smart watch

Accelerometer & Proximity Sensor

[54]

Rule Tree classifier C4.5, Bayesian classifier

Wearable (Bio Harness 3)

[55]

Autoencoder Wi-Fi enable IoT Long term devices Recurrent CNN

Heart rate, Respiration Rate, Skin Temperature, Posture Accelerometer N/A

Data sets used Accuracy Remarks Collected 99.96% Recognizes Data set body postures as well as general human activities like standing, walking etc. Identifies intake Collected 100% Data set (putting of medications through several pill relevant inside activities. mouth) Collected Greater Recognizes Data set than 91% standing, walking, sitting, lying in a noncontrolled environment. The effect of transition from static to dynamic activities was observed. Proposes a novel method for data preprocessing based on DCT. Collected 95.83% Recognizes Data set standard activities in a pre-established order. PCA is used for feature selection. Collected 97.6% Recognizes Data set activities like walking, sitting, watching TV, etc. in a device free environment (no wearables/ no Smartphone). (continued)

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Table 1 (continued) Existing Learning work methodology [57] Dictionary learning combined with rule-based reasoning

[58]

[2]

[59]

1D CNN, 2D CNN, Multiheaded CNN, SVMKNN, & RF CNN

LR, DT, SVM

Types of devices Types of sensor used used IoT devices N/A deployed within home

Smart-­ phone and Smart watch

Wi-Fi enabled wearable sensor

Wearable device, Sensor-Tag IoT device

Data sets used Accuracy Remarks Collected 70% Identifies both Data set low level as well as high level and low level activities. The former is inferred from signal fluctuations, and the latter is inferred from the former along with object usage and location information. Accelerometer& WISDM1 96.4% Recognizes Gyroscope activities without any requiring manual feature engineering. Collected 97% Identifies Accelerometer Data set standard &Gyroscope& activities with a Magnetometer relatively small dataset which enables to implement the system calibrated on different class of problem. Accelerometer Collected 92% Activity Data set Recognition is performed with tradeoff between constrained computational capabilities of IoT devices and relatively power hungry communication system. (continued)

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104 Table 1 (continued) Existing Learning work methodology [60] LSTM CNN

Types of devices used Smartphone and smart watch

Types of sensor used Accelerometer & Gyroscope

Accelerometer, Sensors like GPS, magnetic field, sound level, and light capturing context information

[61]

LSTM

Wearable sensors& Smart phone

[62]

LSTM

IoT devices N/A deployed within home

[63]

Calibrated RF

N/A

Environmental wireless sensor device, Actuators

Data sets used Accuracy Remarks UCIHAR Greater Recognition of [66], than 90% activity is done WISDMa with fusion of temporal and spatial sensor data Collected 95% Applies Data set semisupervised learning for Activity recognition with DL models for classification and DQN technique to solve the labeling problems. Collected 92.29% Activity Data set recognition is done without the need of any wearable devices or a smartphone. Ensures privacy for subjects. 80% Activity UCAmI recognition Data Set decision is [67] taken from analyzing data with complete knowledge of uncertainty in IoT system and integrating the same with ML outputs. (continued)

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Table 1 (continued) Types of devices Existing Learning work methodology used [64] One Class IoT device SVM

Types of sensor used N/A

[65]

Bi-LSTM

Wearable sensor

Accelerometer, Gyroscope, Magnetometer

[69]

KNN, RF, NB, DT

Smart phone

Accelerometer & Gyroscope

Data sets used Accuracy Remarks Collected 89.52% The system is Data set robust, works well under the conditions of uncertainty, and specifically work when there are missing value in the acquired data, which is common in real time activity recognition. Recognizes UCIHAR, Greater USCHAD than 90% activities using deep learning [68] and deploying CGO algorithm for optimizing hyper-­ parameter for improved recognition. UCI HAR 98.03% Compares (SVM) different ML models to choose the best in benchmark datasets. SVM outperforms others as it is able to find a minimum hyperplane for classifying input data into different categories.

https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+B iometrics+Dataset+

a

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revealing the behavior of individuals based on spatial context and social patterns with urban dynamics by mining the digital remnants left by individuals during their interactions with “smart” things. Smart homes can be considered to be the most significant applications of IoT as the activities of the users generate a tremendous amount of data from the interaction with the devices. Wu et al. in [50] processed these data and modeled them as useful contexts for activity recognition. Here, spatial features along with temporal features are clubbed to discover more useful recognition patterns through semisupervised learning. IoT has always been worthwhile to improve the quality of living, especially for the elderly population of the world, through regular assessment of their health status and timely treatment in case of abnormalities. In [51], Oniga et al. have designed and implemented wearable devices embedded with sensors that form is cognition system for simple activities like standing, sitting, walking, or running using neural networks, especially for disabled individuals. IoT technologies are effective in monitoring patients in real-time to acquire sensitive data that can be subsequently analyzed for medical diagnosis. Interesting work was proposed by Serdaroglu et al. in [52] where a medication intake adherence framework is designed with IoT for forgetful patients, especially elders with direct recognition of the medicine intake activity using smartphones, instead of indirect indicators. Furthermore, Amroun et  al. introduced accurate activity recognition using IoT even in a noncontrolled environment using smartwatches and smartphones and classifiers like SVM and DT in [53]. The authors also proposed a novel method of data preprocessing based on Discrete Cosine Transform (DCT) and observed the effects of the transition of the user from one activity to the other and also the change of the location of the movable device like a smartphone. IoT-based HAR can be a boon for patients with chronic diseases that require regular monitoring and instant actions in case of health aberrations. Castro et al. in [54] developed a novel system by joining the benefits of HAR and IoT. The proposed system uses specialized hardware that includes sensors to monitor vital signals and implements two standard classifiers such as Naive Bayes and Decision Tree for recognition of activities. The IoT component integrated into the system performs tasks including remote consultation, feedback while and after activity, and the control using a remote monitoring component with remote visualization, remote data access as well as programmable alarms. Next, Zou et al. in [55] proposed deep learning-based HAR schemes that can identify activities automatically from Wi-Fi-­ enabled IoT devices. A novel Open Wrt-based IoT platform is developed to collect CSI data. An innovative deep learning framework was designed with Autoencoder Long-­term Recurrent Convolutional Network, to extract high-level salient features and figure out temporal dependencies among data for recognition of activity. Research reveals that the rapid emergence of IoT-enabled technology is effectively facilitating activity recognition to open and uncontrolled yet connected environments with the help of wearable devices and mobile applications. Qi et al. in [56] explored new research trends and challenges in physical activity recognition and monitoring in an IoT environment. The authors demonstrated how IoT sequentially

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incorporates the sensing layer, network layer, processing layer as well as application layer of communication in a distinct manner. The rising trend of IoT is continuously pushing concepts like ambient intelligence more toward progress by expanding the scope and scale of the healthcare domain at a substantial level. In this regard, Yao et al. in [57] proposed an end-to-end web-based monitoring system catering to personal well-being to monitor activities and abnormalities in IoT-based Smart homes. The authors designed a hybrid recognition framework that unifies data and knowledge driven techniques to observe multilevel activities in real-time in Smart homes. Here, a scalable IoT-based middleware was developed for seamless connectivity, learning of information as well as management of ambient sensors to continuously track the activities of residents to detect abnormalities if any and take timely actions if required. Existing HAR works are mostly dependent on shallow feature learning techniques that make their implementation a challenge in real life. Zhang et al. in [58] catered to this issue by employing multi-head CNNs that extract and select appropriate features for better predictive analysis. This makes the HAR process more IoT compatible as it doesn’t require manual feature engineering and automatically learns deep features for classification purposes. Bianchi et  al. in [2] gave a new direction to the IoT-based HAR system. The authors proposed an innovative system that integrates Wi-Fi enabled Inertial Measurement Unit (IMU) that can send collected data to a remotely located cloud service through the Internet across the home router. Here, the CNN model was utilized to provide information about abnormalities in daily activities. The authors observed that IoT generates a humongous volume of data which is difficult to process for a cloud-centric approach. Consequently, it results in poor network latency and causes fatal outcomes in real-time applications like healthcare. Edge computing is found to address the issue to a certain extent by processing data at the source (IoT devices) only but that often fails to make a trade-off between computational complexities and onboard processing. Furthermore, an approach was proposed by Samie et al. in [59] to address the issue by a hierarchical classification approach. Here, in the first layer, a lightweight classifier works directly on IoT devices and decides whether the computations can be carried away locally or need to be offloaded to the gateway. The second layer classifier does the remaining work, based on the decision made by the former. However, deep learning is undoubtedly a suitable choice for the huge amount of data generated by IoT devices which has been explored by many researchers in the recent past. Abdel et al. in [60] presented a novel supervised model for fine-grained recognition by exploiting the LSTM classifier. It was designed by modeling longterm temporal representation of data generated from the raw collection as well as using an advanced residual network to extract features hidden into a high-­ dimensional dataset. The work combines two major DL models such as LSTM and CNN where the latter is incorporated for multichannel spatial fusion. Whereas Zhou et al. in [61] focused on semisupervised deep learning frameworks in the IoT environment that efficiently and effectively analyze grossly labeled data for training the classifier. The system incorporates an auto-labeling module based on a deep Q-network with a novel distance-based reward rule, along with an LSTM

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classification module, that deals with sequential motion data for detecting finegrained patterns in the collected data. Moreover, Khan et al. in [62] introduced a differential CSI-based HAR model proposed using an LSTM classifier in the IoT environment. Here, the Channel State Information (CSI) is sensed by IoT devices that not only mitigate the background noise of the signal so eliminate the need for traditional wearable sensing the privacy of the person being monitored. The LSTM model extracts useful features automatically and classifies human activities from differential CSI. As IoT deals with a huge volume of data from different types of sensors, the complexity of the system increases in different dimensions such as heterogeneity, timeliness, and scalability. Hussain et al. in [63] proposed a risk-based decision-making framework for IoT in the domain of HAR that mainly focuses on minimizing risk and improving decision-­making particularly in critical scenarios like healthcare. The proposed framework integrates data- and knowledge-driven approaches in decision making under the IoT environment that concentrates on bridging lower-level data-driven activity recognition and higher-level knowledge for explanation and classification as well as handling uncertainties. Besides, Gope et al. in [64] designed an enhanced system that is based on Physical Unclonable Function (PUF) authentication scheme and a fault-tolerant decision-making scheme for IoT-based healthcare as well as activity recognition. It also encompasses the conditions of uncertainty as well as security features for the purpose. Al-Wesabi et al. demonstrated how human actions are captured in a sensor-enabled IoT environment in [65]. The authors proposed an Optical DL technique that uses a Mobile Net-v2 model to extract features and a Bi-LSTM classifier whose hyper-parameters are being fine-tuned by the Chaos Game Optimization algorithm in order to improve recognition performance. The above discussion summarizes substantial literary work on IoT-based HAR; however, several open research areas such as feature selection using metaheuristics approaches like Genetic Algorithm or further research on hyperparameter tuning of standard Deep Learning classifiers may be explored by interested researchers to enhance the contribution in this field. Table 1 provides an outline of the above discussion in a precise manner. It is observed that most of the authors in the above-­ mentioned literary works, haven’t used Standard datasets, apart from the few mentioned in the table, and worked on datasets prepared by collecting data through mobile apps, whose access is not been provided publicly. Hence, the term “Collected Dataset” is used to complete the table.

5 Case Study In order to exhibit the implementation of HAR, we hereby present a case study that shows how standard human activities such as walking, standing, sitting, climbing upward and downward, and laying can be identified and predicted, in real life, with the help of benchmark datasets available in the public domain for research purpose. This dataset is available on a public platform and is already preprocessed, with a

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train test split of 70–30 respectively. A number of researchers have implemented HAR by comparing the performance of several standard ML classifiers and analyzing them for better predictive calculations. Datasets play a pivotal role in such performance analysis as the choice of the same may exhibit varied results in general. Broadly, datasets may be classified into two categories (i) Benchmark datasets and (ii) Datasets prepared by the researchers collected through applications such as G-Sensor or others. In this chapter, we have presented a case study of the implementation of HAR through a standard benchmark dataset called UCIHAR,1 which is commonly used by researchers for this purpose. The main objective of this case study is to provide an insight into the basic evaluation of performance on several learning algorithms (both ML and DL) on a standard dataset as well as the performance analysis of the same for naive readers and upcoming researchers. The accuracies of prediction for different ML and DL models have been computed and compared, along with hyperparameter tuning of ML classifiers like SVM has been conducted with different kernels to develop a holistic idea of predictive analysis.

5.1 Description of the Dataset The dataset chosen for implementing HAR is UCI-HAR [66], which is a 6-activity dataset containing three-dimensional raw (x, y, z) signals obtained from a Smartphone embedded accelerometer and gyroscope strapped at the waist of a user. During the experiment, 30 users or subjects were selected within an age limit of 19 to 48 years, and they were allowed to perform 6 activities such as sitting, standing, laying, walking, walking upstairs, and walking downstairs. Fig. 5 shows the graphical representation of the samples for each activity in the dataset. The Smartphone chosen was Samsung Galaxy S II and using the embedded accelerometer and gyroscope, 3 axial acceleration and 3 axial angular velocities were captured by the team at a constant rate of 50 Hz. The obtained dataset has been randomly partitioned into two sets of “Train” and “Test” where 70% of the subjects were selected for the former and the remaining 30% contributed to the latter. Further, the sensor signals were preprocessed through noise filters and sampled in a specific sliding window of 2.56 s with a 50% overlap which pertains to 128 readings/window. The total acceleration signal obtained from the sensor of the Smartphone had two components of body motion as well as gravitational, which were separated using a low pass filter like the Butterworth filter. From each window, a set of feature vectors were obtained by calculating the variables from the time domain such as mean, median, min, max, etc. as well as the frequency domain. The preprocessed data is fed into standard ML and DL classifiers as shown in the subsequent sections. A brief description of the database is shown in Table 2.

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Number of samples for each activity 2000 1750 1500

count

1250 1000 750 500 250

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YI LA

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D

IN

G

0

activity

Fig. 5  Graphical representation of class distribution

Table 2  Dataset description of UCI-HAR Number of Users Age range Activities performed Device Position Sensors Sampling rate Train test split No. of features

30 19–48 Walking, walking upstairs, walking downstairs, sitting, standing, laying Smartphone Waist Accelerometer, Gyroscope 50 Hz 70:30 561

5.2 Machine Learning Models Applied on UCI-HAR Three standard Machine Learning classifiers have been chosen such as SVM, KNN, and Logistic Regression (LR), and Python is used as a programming language along with Scikit learn toolkits to analyze the performance of the aforementioned

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Comparison of ML Models 1.0 0.8 0.6 0.4 0.2

lo

gi

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classifiers on this featured dataset. We have used Google Colab for conducting the experiments. The initial value of k is taken to be 5 and a linear kernel is chosen for SVM to begin with. A comparison of all three classifiers has been shown graphically in Fig. 6. It is observed that SVM shows a better classification accuracy as it works well with high dimensional data and captures nonlinearity better than its contemporaries. To take the work a step further, hyper-parameter tuning of the best model (SVM here) is also conducted as shown in Fig. 7. The main hyper-parameter of SVM is the kernel that maps the observations into some appropriate feature space. There are multiple standard kernels such as linear, radial basis, and polynomial. It is observed that all three kernels provide an accuracy of greater than 98% with hyper-­parameters tuned with three different kernels in SVM.

5.3 Deep Learning Models Applied on UCI-HAR As discussed, DL models such as Recurrent Neural Network (RNN), LSTM, and CNN can also be applied in UCI-HAR.  The confusion matrix of each has been shown in Figs. 8, 9, and 10, respectively. As the dataset possesses 6 classes to be classified by the ML and DL algorithms, the confusion matrix is a 6 × 6 matrix. As mentioned in the Sect. 3.2, the accuracy in the confusion matrix is obtained diagonally (denoted by dark blue in the middle), where the predicted label matches with the actual label in the dataset. A comparison of all three classifiers has been shown

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Fig. 7  Comparison of accuracy of SVM model with different kernels

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Fig. 9  Confusion matrix of LSTM

graphically in Fig. 11. It was observed that RNN performed the best with an accuracy of 96%, whereas CNN exhibited an accuracy lesser in comparison with the other two, as the dataset is already preprocessed. CNN can extract high dimensional features from the raw dataset and fails to show better results with preprocessed data, which is evident from the graphs shown. So, from the above experiments, we can conclude that given a benchmark dataset like UCIHAR that consists of several basic daily life activities of human beings can be used to implement several standard ML as well as DL classifiers. The main significance of these implemented case studies is to fetch predictions made by the classifiers and analyze them for basic understanding as well as modify the same for further research. We can also compare the models and comprehend the accuracies obtained in each case and conclude the best classifier in the given setup. Further, we can also alter the hyperparameters of the classifiers like SVM such as the kernels, and observe the variations in performance with each of them.

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6 Conclusion This chapter presents a thorough study of IoT-based HAR for smart living. To begin with, the background of HAR is discussed and analyzed in detail to provide a basic idea of the concerned topic. Next, the impact of IoT on HAR is pointed out by the evolution of research in this domain. Subsequently, the impact of IoT on HAR is investigated which reflects the interrelation between these two trades. Besides, the discussion on the challenges can motivate the researcher to explore this domain. An extensive survey of the state-of-the-art works is presented that enables a better understanding of the problem domain and existing solutions. This also opens various avenues of future directions for interested researchers to demonstrate their expertise. Finally, a case study gives a meaningful insight into the implementation of the application of HAR using a benchmark dataset and standard ML as well as DL classifiers with graphical visualization.

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Application of Data Mining to Support Facilities Management in Smart Buildings Matthew Willetts

and Anthony S. Atkins

1 Introduction Climate change has had an observable effects on the environment worldwide including the loss of sea ice, accelerated sea level rise, and longer more intense heat waves, with the temperature expected to rise further between 2.5 and 10 degrees Fahrenheit over the next century [1]. Buildings are responsible for around 36% of global energy consumption and 39% of greenhouse gas emissions [2]. It is expected that the largest impact of climate change is that 18% off the worldwide GDP could be wiped off by 2050 if the global temperature rises by 3.2 °C [3]. Over 2000 businesses of all sizes globally have signed up to the United Nation’s Race to Zero campaign to achieve net zero carbon emissions by 2050, a third of which are British companies, including 30 of the United Kingdom’s FTSE 100 companies [4]. Evergreen Action [5] suggests that the impact of climate change pose significant risks to the stability of the financial systems and the economy, stating that a recent study found that unchecked climate change will reduce global economic output by 11% to 14% by 2050, approximately $23 trillion a year. The World Economic Forum’s [6] Global Risk Report identified Climate action failure was identified as the most impactful and second-most likely long-term risk facing the world as the world continued to struggle to mitigate the impacts of the COVID-19 pandemic. Therefore, facilities management has a large role in helping to achieve net zero carbon emissions. Additionally, the COVID-19 pandemic has introduced new challenges for facilities management. Siemens report that their research has found that 54% of employees will opt not to return to the offices in future. Similarly, Harvard Business Review suggests that while more than 90% of employers are planning to adopt a hybrid M. Willetts (*) · A. S. Atkins Staffordshire University, Stoke on Trent, UK e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 G. Marques et al. (eds.), IoT Enabled Computer-Aided Systems for Smart Buildings, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-26685-0_6

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working model for their knowledge workers in 2022, they expect that many high-­ profile companies will demand that their employees return full time to the office [7]. This suggests that Smart Buildings will perform a key role in facilities management as they will need to be able to adapt to the changes in capacity, with differing numbers of staff occupying the building throughout the week, providing opportunities to reduce operating costs and power consumption. An additional challenge will be keeping the buildings safe, offering for social distancing measures where necessary to minimize the transmission of viruses. Businesses are adopting new ways of working, for example, considering hoteling seating models whereby staff can dynamically schedule the use of office workspaces such as desks and cubicles, thereby limiting the number of the of employees in the office [8]. COVID-19 has increased the demand for Smart Buildings solutions as businesses are exploring options for enabling smarter workflow, more efficiently managed facilities, safer and healthier workplaces [9]. McKinsey [10] reported that although more than 20% of the workforce could work remotely three to five days per week from home, more than half of the workforce has little or no opportunity for remote work. Therefore, facilities management will play a vital role in the maintaining buildings, supported by Internet of Things (IoT) technology. However, Marjani et al. [11] suggest that depending on the requirements of IoT applications, different types of analytics may be required. For example, real-time analytics which are typically performed on sensor data because situations can change frequently; therefore, rapid data analytics are required, whereas offline data analytics can be utilized when a quick response is not required. Emergen Research [9] reported that the global Smart Buildings market was USD 66.29 Billion in 2020 and is expected to reach USD 141.71 Billion in 2028. The large volumes of Big Data generated by Smart Buildings provide an opportunity to utilize machine learning techniques such as association rule mining for the purposes of identifying patterns in the data to improve energy efficiency, reduce operating costs and reduce climate change. The chapter commences with a literature review of Smart Buildings, facilities management, Big Data Analytics and Data Mining applications in facilities management. A case study is then presented using a simulated dataset to demonstrate how data mining can be adopted by facilities management to monitor occupancy levels in terms of usage and optimize energy efficiency. This is then followed by Discussion and Conclusions sections.

2 Methods 2.1 Smart Buildings The term Smart Building originates from Intelligent Building, which was initially used by the United Technology Building Systems Corporation of the United States in 1981, with the City Place Building in Hartford Connecticut, USA, becoming the

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first Intelligent Building [12]. There appears to be no agreed definition of Smart Buildings. The term Smart Building has now superseded Intelligent building. Qolomany et al. [13] state that Smart Buildings are: “the integration of a wide range of systems and services into a unified environment that involve energy management systems, temperature monitoring systems, access security systems, fire and life safety, lighting control and reduction, telecommunications services, office automation, computer systems, area locating systems, LANs, management information systems, cabling and records, maintenance systems, and expert systems.” There are different types of Smart Buildings, including Smart homes, Smart hospitals, Smart libraries and Smart shopping centers [13, 14]. The number of Smart Building connections continues to grow on a yearly basis, Vailshery [15] reported that there were 5.08 million IoT connections from Smart Buildings in the EU in 2016, with 154.06 million connections expected in 2025, as shown in Fig. 1 [15]. Ciholas et al. [16] suggest the drivers for the adoption of Smart Buildings is to increase energy efficiency and therefore costs, providing an example of Building Energy Management Systems (BEMS) which utilize inputs from subsystems including occupancy detection, weather data, indoor temperature, humidity and lighting sensors to optimize heating, ventilation and air conditioning. Smart Buildings can be characterized as having the following five basic features [17, 18]:

Number of active connections in millions

1 . Automation: accommodate automatic devices or perform automated functions. 2. Multifunctionality: the ability to undertake multiple functions within the building. 3. Adaptability: the ability to learn, predict and satisfy the needs of the users within the building. 4. Interactivity: to facilitate the interaction between users. 5. Efficiency: to perform functions while provide energy efficiency, save time and reduce costs.

180 160 140 120 100 80 60 40 20 0

2016

2019

2022

2025

Year Fig. 1  Number of Internet of Things (IoT) Smart Buildings active connections in the European Union (EU) in 2016, 2019, 2022, and 2025 using data from Vailshery [15]

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Smart Buildings can be divided into three layers [16]: • The field layer consists of the various sensors located in the building which utilize the field layer specific protocols and can be either wired or wireless. There are generally two types of sensors at this level: sensors which communicate ­utilizing simple electric signals to the controller which translates the signal into information which is readable to humans; and sensors which can communicate to the management layer utilizing Internet Protocol (IP) for example building access controls with access card readers which communicate directly with the management layer. • The automation layer consists of different controllers which are frequently designated as either Programmable Logic Controllers or Direct Digital Controls to receive data from the sensors within the Smart Building. The Controllers aggregate the data from the sensors to share with other controllers or servers on the network or utilize the data to undertake programmed routines such as switching a Heating, Ventilation, and Air Conditioning (HVAC) unit on or off depending upon the temperature reading from a sensor. • The management layer centralizes all the data from the controllers where a local supervisor server supervises the Smart Building, records the activity data which is then utilized to optimize the Smart Building. One of the most common applications of Smart Buildings are Smart homes. Alsulami and Atkins [19, p. 12] state that: “the main benefits of Smart homes are improving comfort, performing medical rehabilitation, supervising mobility and physiological parameters, providing therapy, delivering convenience, improving security, and saving energy.” Technologies within a Smart home include: sensors to detect environmental factors including light, temperature, and motion; monitors; interfaces; appliances (such as heating and hot water systems), and other devices that are networked together, enabling the environment to be controlled on an automated or manual basis, locally or remotely [20]. An example of the application of Smart Building technology is to support ambient assisted living (AAL). AAL sensors in Smart homes include magnetic switches, temperature sensors, photosensors, pressure pad sensors, water flow sensors, infrared motion sensors, force sensors, smoke sensors, and biosensors [21]. An example of how Smart homes are assisting AAL is through the reduction of falls by utilizing machine learning to learn the regular behavior of residents to allow abnormalities such as a fall to be identified, allowing family or carers to intervene. There are a wide range of sensors found in Smart Buildings. Humidity sensors are utilized to monitor the amount of water vapor in the air, which can be equally as important as measuring the temperature, for example excessive moisture can result in condensation which could damage equipment. Humidity sensors enable homes and business to control their heating, ventilation and air conditioning systems [22]. Humidity sensors can be utilized to prevent illness. If the relative humidity is maintained between 40% and 60%, the spread of cold and flu reduced by up to 70% [2]. One area in which Smart Buildings can help to reduce operating costs is HVAC.  Traditional HVAC systems can consume over 40% of a commercial

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buildings energy, with many of them being oversized, with building operators utilize trial and error to manage the HVAC system, prioritizing comfort rather than energy savings [23]. At present, many businesses are utilizing hybrid working with staff spending time away from the office; therefore, optimizing HVAC costs could result in significant savings. Smart HVAC systems can significantly reduce energy consumption and improve the comfort for building occupants, with the Smart Building software utilizing data from HVAC sensors throughout the building, which can be stored for the purpose of algorithms can be applied to optimize the monitoring and control of the HVAC system. This can be utilized for purposes such as limiting HVAC consumption in unoccupied areas of a building, the detection and diagnosis of faults and reduce HVAC usage during times of peak energy demand [23]. Temperature sensors are the most significant and commonly utilized sensors in Smart Buildings [24] for a wide variety of purposes including monitoring duct temperatures, chilled and heated water loops and internal and external air temperatures, in addition to fan or valve control [25]. Hayat et al. [24] outline four types of temperature sensors, each with differing characteristics such as the measurement range, accuracy, response time and cost: 1. Thermocouples are thermometric devices which contain two wires of metal which are joined together at one end and when the temperature changes at the junction, a voltage is created which is used to read the temperature (the Seebeck effect). 2. Resistance temperature detectors (RTDs) which measure temperature the based on the change in resistance of the metal resistor inside them. 3. Thermistors which are similar to RTDs; however, they utilize a ceramic or polymer rather than a metal resistor inside the sensor. 4. Integrated Circuits (IC) that utilize two terminal integrated circuit temperature transducers which provide an output current proportional to the absolute temperature. IC sensors are the least expensive of the four sensors. An application of temperature sensors is monitoring for Legionella, which can be fatal [26]. Legionella bacterium thrives within a specific temperature range and can exist within any water system, commonly found in public buildings where water can stand still [27]. While temperature sensors can be utilized, specific sensors such as the sensor developed by Remote Tech recently launched in the UK market, with a battery life of 10 years specifically designed for monitoring Legionnaire’s disease. Dong et al. [28] propose that virtual sensing could be used an alternative to physical sensing and has been utilized in Smart Building applications, for example HVAC equipment monitoring and fault diagnostics. However, sensor fusion techniques utilizing data collected such as the room air temperature, relative humidity, carbon dioxide (CO2) and indoor illuminance level could be utilized to detect room occupancy [28]. Additionally, apps or web portals can be utilized to record attendance [29, 30]. Apps are also utilized in academic environments to allow students to register from their mobile devices, which can allow the educational institution to verify their attendance from the IP address and location of the devices [31]. This can provide

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further opportunities to triangulate this data with other sensors such as motion sensors and swipe cards to identify the location of frequently used areas or facilities. Contact sensors are utilized to detect whether a door, window or another mechanism is open or closed [22]. The sensors are derived of two components, one which is attached to the door or window and another which is attached to the frame. Contact sensors can be utilized for a wide range of reasons such as identifying unlocked doors or to determine if a door has been opened or closed to detect occupancy. Faenza [8] suggests that Smart credentials and access control systems can enable building managers to maintain security as they can manage access to every area of a building, with visibility of who is going in and out of each room. Additionally, lockers could be utilized for unattended delivery services, as they can provide a secure, contactless solution [8]. Gas and air quality sensors are utilized to detect the presence of gases in the air, allowing toxic, combustible, or other hazardous gases to be detected. There are three common types of air quality sensors: oxygen, carbon monoxide (CO) and CO2 [22]. It is essential for gas and air quality to be monitored, for example, high concentration of carbon dioxide in a building could result in occupants experiencing headaches, dizziness, breathing difficulties, sweating, tiredness, and increased heart rate [32]. Similarly, carbon dioxide sensors can be utilized to reduce the costs of air conditioning by tracking carbon dioxide for Demand Control Ventilation to recirculate the air inside or to introduce fresh air if required [25]. Electrical current monitoring sensors have the ability to monitor the energy consumed at a circuit, zone or machine level, providing facilities management the ability to identify where energy is being wasted and therefore switch of unused devices consuming power [22]. Additionally, through monitoring electrical usage, unusual activity can be detected, for example machinery which is malfunctioning and drawing a higher operating current suggesting that it may have been overloaded, allowing maintenance to be scheduled. The effectiveness of Smart lighting control systems is dependent upon the appropriate control and sensitivity of the environment; therefore, the appropriate type of sensors needs to be installed in optimal locations [33]. For example, pyroelectric infrared sensors have been extensively in both indoor and outdoor application because of their low cost, ease of use, and they are widely available; however, as they utilize infrared, they are more suitable for detecting moving objects and they are more susceptible to making false detections but are more suitable to hallways and entrances [33]. An additional challenge is that the COVID-19 pandemic has changed how people work and how buildings are utilized, with many staff continue to work predominantly from home. Hollenkamp [34] suggests that traditional lighting systems are designed for static use, whereas Smart lighting control technology is more appropriate with the new hybrid work model as fully-integrated lighting control systems will be vital for facilities managers trying to identify energy savings. King and Perry [23] report that one third of commercial building HVAC energy use is caused by heat gained and lost through windows, with the California Energy Commission estimating that 40% of the cooling requirement for a building in the

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state is because of solar heat gained through windows. Similarly, 75% of EU buildings have been reported to be energy inefficient; therefore, Smart windows may provide the EU Smart window project CLIMAWIN could provide a solution [35]. Burke [35, p. 1] states that: “in their triple-glazed window frame there is a small gap (around 5 mm wide) at the bottom level on the outside. This gap allows air to pass in between the window’s glazing layers and be warmed by sunlight. It then enters the room through a small valve at the top of the window facing inside the building.” Similarly, Smart window shading solutions provides an opportunity to optimize HVAC systems. Automated shading solutions utilize a mixture of sensors, timers, and daylight-responsive software which integrate with a building management system to optimize the balance of natural and electrical savings resulting in increased comfort and reduced energy costs [36]. The Fire Industry Association [37] estimates that false alarms cost the United Kingdom over £1 billion per year and that modern properly maintained fire alarm systems rarely suffer equipment malfunctions. Smart fire detection systems such as the Sinteso fire safety range are designed to be operate in situations where instant and accurate fire detection is essential [38]. Sinteso utilizes innovative detections algorithms which transform signals such as temperature and smoke density, combining optical, thermal and electrochemical CO sensors to monitor smoke, heat and CO to accurately identify fires [38]. Verizon [39] outline that Disruptive Technologies (DT) sensors (19 × 19 × 2.5 mm) in size, which is around the same size of a stamp, with a battery life of 15 years, can be attached to pipes, walls, ceilings and other surfaces, without requiring maintenance. A DT client reduced their energy costs by 31% per month and reduced its carbon footprint by 30% within 5 months. DT’s sensors transmit data wirelessly, with connectors relaying data to the Cloud via Ethernet or 4G with the types of sensors available including temperature, touch, proximity, water, humidity, CO2, motion and other industrial sensors [40].

2.2 Facilities Management ISO [41, p.  1] defines facilities management as: “organizational function which integrates people, place and process within the built environment with the purpose of improving the quality of life of people and the productivity of the core business.” Facilities management covers a range of services including building/asset maintenance, financial systems, productivity, resource management, health and safety compliance, space management, sustainability and domestic services [42]. Daissaoui et al. [43, p. 164] state that: “Traditional FM [facilities management] has problems with lower data quality, longer notification times and delays in relevant operation and maintenance.” Some of the benefits of Smart Buildings for facilities management include: the possibility to create an activity-based and need-based workplace; improving the health and comfort of the occupants; the ability to improve the quality of the

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facilities services; the ability to optimize space within the building; reduced running costs such as the water and energy; and more efficient planning and resource utilization [44]. Konanahalli, Marinelli, and Oyedele [42] indicate that although “digital efficiency” is emerging as a critical differentiator which enables facilities management to reduce costs, create value for customers, increase profit margins, enable efficient business operations and find new streams of revenue, only a small number of studies have reported the adoption of Big Data Analytics in facilities management. In the last 6 months of 2021, the recovery for European Office demand increased despite the Omicron variant which appeared to be a significant barrier [45]. Similarly, it is expected that staff hiring is expected to increase and therefore potentially the demand for floorspace, with the results from a Deloitte European Chief Financial Officers (CFOs) survey from Autumn 2021 indicating that 42% of CFOs expect to increase hiring over the next 12 months [45]. This may provide challenges for facilities management as in addition to accommodating flexible working, they may need to consider that the overall workforce may increase. The Royal Institution of Chartered Surveyors [46] reported that: “in Q3 of 2021, demand for commercial property across the office and industrial sector increased, in the main across Europe. The Commercial Property Sentiment Index improved in nineteen of the twenty European nations covered by the monitor. Greece, the Netherlands, the Czech Republic, Austria, Portugal, Ireland and the UK are showing positive signs, but Cyprus slipped this quarter, with respondents pointing to the pandemic still hampering local economic growth.” This suggests that the price of rent will increase, and therefore, facilities managers will need to optimize the space they occupy to decrease the need for further space. Figure  2 displays the rental prices of prime office properties in selected European cities in the second quarter of 2021 using data from Statista [47]. The Royal Institution of Chartered Surveyors undertook a study and produced a report into Big Data Analytics adoption in facilities management [48]. The study found that companies surveyed were in the early stages of Big Data Analytics adoption, despite that Big Data Analytics present major opportunities to generate value for safety and maintenance operations, with the potential to integrate and visualize a wide range of building data including energy usage, air quality, temperature variations, and lighting [48].

2.3 Big Data and Big Data Analytics Big Data is defined as: “an umbrella term used to describe a wide range of technologies that capture, store, transform and analyses complex data sets which can be of a high volume, generated at a high velocity in a variety of formats” [49, p. 3034]. There is no agreed definition of Big Data and a survey of researchers found that although a number of participants described Big Data with the traditional Vs definition, they could not agree on the number of Vs [50]. This is consistent with other

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Fig. 2  Rental prices of prime office properties in selected European cities in 2nd quarter 2021 using data from Statista [47]

research, with the number of Vs ranging from 3 to 51 [21], originally starting as Volume, Velocity and Variety [51]. The volumes of Big Data is typically quantified in terabytes and petabytes, with one terabytes being considered as the minimum threshold [21]. Big Data is generated from a wide range of sources, with Internet of Things (IoT) and social media being two of the main sources of Big Data. Three categories of Big Data were defined by Saggi and Jain [52]: machine-­ generated data originating from sources such as computer networks, sensors, satellites, audio, video and streaming; human-generated data including social media content and identification data; and business-generated data in the form of transactional, corporate, and government agencies’ data. Using these classifications, Smart Building data would therefore be classified as machine-generated Big Data. Konanahalli, Marinelli, and Oyedele [42, p.  3] indicate that data in facilities management can be both structured and unstructured data: “Structured data are normally referred in Building Information Models (BIMs) but a typical facility maintenance project also generates additional volumes of unstructured data, captured in photos, graphics, videos, and scanned documents.” Smart building appliances and devices generate massive volumes of streaming Big Data, containing valuable information that needs to be mined for the purposes of improved decision making and to facilitate actions to be taken [13]. Traditionally, Business Intelligence applications have been utilized to analyze and present data; however, Big Data Analytics solutions are required to extract insights from Big Data not achievable through Business Intelligence solutions. Big Data Analytics is the evolution of Business Intelligence, providing the capability to

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analyze enormous datasets larger than 2  TB, generated a rapid pace, stored in a variety of structured, semistructured, and unstructured formats [21]. Mikalef et al. [53, p. 1] state that a widely used definition of Big Data Analytics is: “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high velocity capture, discovery and/or analysis.” Sivarajah et al. [54] outline five categories of Big Data Analytics: Descriptive analytics, Inquisitive analytics, Predictive analytics, Prescriptive analytics, and Pre-emptive analytics. Types of Big Data Analytics include text analytics, audio analytics, video analytics, social media analytics, predictive analytics and data mining [55, 56]. Some of the reported benefits achieved through the adoption of Big Data Analytics include: increased profitability; reduced unplanned downtime through predictive maintenance; enhanced productivity and growth; improved data access and management; and providing better products and services [49]. Despite the potential benefits of Big Data Analytics, facilities management has been slower than other industries to adopt the technology, despite the potential benefits which can could be utilized such as optimizing HVAC and reducing operating costs [57] which may be due to a number of barriers including data quality, technological barriers, inadequate preparation, governance issues and skillsets required [42]. Similarly, Big Data studies in the construction sector are also scarce [58]. One application of Big Data Analytics which could be applied to facilities management of Smart Buildings is data mining. Data mining has been utilized in other Smart Building studies to monitor the occupancy of buildings utilizing power usage to learn high usage locations [43]. Big Data Analytics also includes data mining, a category of which is Association Rule Mining which has been extended to Big Data [59].

3 Results 3.1 Data Mining in Facilities Management Case Study Qolomany et al. [13] suggest that it is difficult to develop predictive models utilizing traditional approaches as they do not provide accurate insights when analysis large volumes of data such as the Big Data harvested from Smart Buildings, summarizing that there are four potential categories for the application of machine learning in Smart Buildings: detection, recognition, prediction and optimization. The intention is to focus on the detection of anomalies in the data which would not be detected utilizing traditional data analysis tools due to the large volumes of Big Data generated by the Smart Building. Alanne and Sierla [60] suggest that the evolution of Artificial Intelligence (AI) and machine learning has provided the opportunity to learn, outlining the opportunities for optimizing energy utilization through reinforcement learning techniques and AI to automate building control and energy

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management. Other applications of machine learning include forecasting the indoor temperature [61], predicting repair times [62] and utilizing association rule mining to identify HVAC problems [63]. However, given that there has been a shift to hybrid working patterns, it appears that there may be a greater opportunity to optimize Smart Buildings by monitoring the activity within a Smart Building to identify patterns in the occupant’s behavior which can be used for a variety of purposes, including optimizing energy management based on the number of occupants at specified times throughout the day. Data mining the Big Data generated by the IoT devices found within a Smart Building could provide facilities management with insight to optimize their buildings, resulting in reduced operating costs and reduced carbon emissions. The scenario proposed in this example is of an office building of a business operating in the public sector. The building has 5 floors, each divided into 8 zones which were based on workflow patterns and process operations, resulting in 40 zones throughout the building. To produce the examples shown in this scenario, a dataset was been constructed based on examples [64, 65]. The dataset was analyzed using Weka [66] for the purposes of illustrating association rules for the scenario; however, other data mining applications could be utilized in the real world such as RapidMiner and SPSS. Association Rule Mining is widely used to identify associations between items or item sets, which has extended to Big Data [59]. In the scenario on facilities management, Apriori and Predictive Apriori are two possible types of Association Rule Mining algorithms which could be used. Apriori is a seminal algorithm for mining frequent item sets for Boolean Association and utilizes an iterative approach for finding rules, known as a level-wise search [67]. García et al. [68] state that in the association rule X ⇒ Y, in a transaction where X occurs, the probability of Y also occurring is high. X is known as the antecedent and Y is known as the consequent [68]. Association rules are measured by support and confidence as the criteria to identify the most important relationships, with support being the number of transactions which contain both X and Y, and confidence being the number of transactions which contain both X and Y divided by the number of transactions containing X. Witten et al. [69] discuss four metrics for ranking association rules: Confidence, Lift, Leverage and Conviction. Oweis et al. [70] state that Lift can be used to identify interesting patterns in the data. Lift >1 signifies a positive correlation and Lift