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The book explores long-term implementation techniques and research paths of ambient intelligence and the Internet of Things that meet the design and application requirements of a variety of modern and real-time applications.
Working environments based on the emerging technologies of ambient intelligence (AmI) and the Internet of Things (IoT) are available for current and future use in the diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them to make our lives easier. It is asserted that Am I can successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques and that it will transparently and proactively change the environment to conform to the requirements of the user. Over time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.
The 13 chapters in Ambient Intelligence and Internet of Things: Convergent Technologies provide a comprehensive knowledge of the fundamental structure of innovative cutting-edge AmI and IoT technologies as well as practical applications.
Audience
The book will appeal to researchers, industry engineers, and students in artificial and ambient intelligence, the Internet of Things, intelligent systems, electronics and communication, electronics instrumentations, and computer science.
Md Rashid Mahmood, PhD, is a professor in the Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India. He has published 50 research papers in international/national journals as well as 10 patents.
Rohit Raja, PhD, is an associate professor & Head, IT Department, Guru Ghasidas, Vishwavidyalaya, Bilaspur, (CG), India. He has published 80 research papers in international/national journals as well as 13 patents.
Harpreet Kaur, PhD, is an associate professor in the Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India. Her research interests include vehicle detection and tracking in autonomous vehicles, and image processing.
Sandeep Kumar, PhD, is a professor in the Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India. He has published 85 research papers in international/national journals as well as 9 patents.
Kapil Kumar Nagwanshi, PhD, is an associate professor at SoS E&T, Guru Ghasidas Vishwavidyalaya, Bilaspur, India. He has published more the 25 articles in SCI and Scopus-indexed Journals, and six patents were granted. His area of interest includes AI-ML, computer vision, and IoT.
Preface xv
1 Ambient Intelligence and Internet of Things: An Overview 1Md Rashid Mahmood, Harpreet Kaur, Manpreet Kaur, Rohit Raja and Imran Ahmed Khan
1.1 Introduction 2
1.2 Ambient Intelligent System 5
1.3 Characteristics of AmI Systems 6
1.4 Driving Force for Ambient Computing 9
1.5 Ambient Intelligence Contributing Technologies 9
1.6 Architecture Overview 11
1.7 The Internet of Things 14
1.8 IoT as the New Revolution 14
1.9 IoT Challenges 16
1.10 Role of Artificial Intelligence in the Internet of Things (IoT) 18
1.11 IoT in Various Domains 19
1.12 Healthcare 20
1.13 Home Automation 20
1.14 Smart City 21
1.15 Security 21
1.16 Industry 22
1.17 Education 23
1.18 Agriculture 24
1.19 Tourism 26
1.20 Environment Monitoring 27
1.21 Manufacturing and Retail 28
1.22 Logistics 28
1.23 Conclusion 29
References 29
2 An Overview of Internet of Things Related Protocols, Technologies, Challenges and Application 33Deevesh Chaudhary and Prakash Chandra Sharma
2.1 Introduction 34
2.1.1 History of IoT 35
2.1.2 Definition of IoT 36
2.1.3 Characteristics of IoT 36
2.2 Messaging Protocols 37
2.2.1 Constrained Application Protocol 38
2.2.2 Message Queue Telemetry Transport 39
2.2.3 Extensible Messaging and Presence Protocol 41
2.2.4 Advance Message Queuing Protocol (AMQP) 41
2.3 Enabling Technologies 41
2.3.1 Wireless Sensor Network 41
2.3.2 Cloud Computing 42
2.3.3 Big Data Analytics 43
2.3.4 Embedded System 43
2.4 IoT Architecture 44
2.5 Applications Area 46
2.6 Challenges and Security Issues 49
2.7 Conclusion 50
References 51
3 Ambient Intelligence Health Services Using IoT 53Pawan Whig, Ketan Gupta, Nasmin Jiwani and Arun Velu
3.1 Introduction 54
3.2 Background of AML 55
3.2.1 What is AML? 55
3.3 AmI Future 58
3.4 Applications of Ambient Intelligence 60
3.4.1 Transforming Hospitals and Enhancing Patient Care With the Help of Ambient Intelligence 60
3.4.2 With Technology, Life After the COVID-19 Pandemic 61
3.5 Covid-19 63
3.5.1 Prevention 64
3.5.2 Symptoms 64
3.6 Coronavirus Worldwide 65
3.7 Proposed Framework for COVID- 19 67
3.8 Hardware and Software 69
3.8.1 Hardware 69
3.8.2 Heartbeat Sensor 70
3.8.3 Principle 70
3.8.4 Working 70
3.8.5 Temperature Sensor 71
3.8.6 Principle 71
3.8.7 Working 71
3.8.8 BP Sensor 72
3.8.9 Principle 72
3.8.10 Working 72
3.9 Mini Breadboard 73
3.10 Node MCU 73
3.11 Advantages 76
3.12 Conclusion 76
References 76
4 Security in Ambient Intelligence and Internet of Things 81Salman Arafath Mohammed and Md Rashid Mahmood
4.1 Introduction 82
4.2 Research Areas 84
4.3 Security Threats and Requirements 84
4.3.1 Ad Hoc Network Security Threats and Requirements 85
4.3.1.1 Availability 86
4.3.1.2 Confidentiality 86
4.3.1.3 Integrity 86
4.3.1.4 Key Management and Authorization 86
4.3.2 Security Threats and Requirements Due to Sensing Capability in the Network 87
4.3.2.1 Availability 87
4.3.2.2 Confidentiality 87
4.3.2.3 Integrity 87
4.3.2.4 Key Distribution and Management 87
4.3.2.5 Resilience to Node Capture 88
4.3.3 Security Threats and Requirements in AmI and IoT Based on Sensor Network 88
4.3.3.1 Availability 88
4.3.3.2 Confidentiality 89
4.3.3.3 Confidentiality of Location 89
4.3.3.4 Integrity 89
4.3.3.5 Nonrepudiation 90
4.3.3.6 Fabrication 90
4.3.3.7 Intrusion Detection 90
4.3.3.8 Confidentiality 91
4.3.3.9 Trust Management 92
4.4 Security Threats in Existing Routing Protocols that are Designed With No Focus on Security in AmI and IoT Based on Sensor Networks 92
4.4.1 Infrastructureless 94
4.4.1.1 Dissemination-Based Routing 94
4.4.1.2 Context-Based Routing 98
4.4.2 Infrastructure-Based 99
4.4.2.1 Network with Fixed Infrastructure 100
4.4.2.2 New Routing Strategy for Wireless Sensor Networks to Ensure Source Location Privacy 100
4.5 Protocols Designed for Security Keeping Focus on Security at Design Time for AmI and IoT Based on Sensor Network 101
4.5.1 Secure Routing Algorithms 101
4.5.1.1 Identity-Based Encryption (I.B.E.) Scheme 101
4.5.1.2 Policy-Based Cryptography and Public Encryption with Keyword Search 102
4.5.1.3 Secure Content-Based Routing 102
4.5.1.4 Secure Content-Based Routing Using Local Key Management Scheme 103
4.5.1.5 Trust Framework Using Mobile Traces 103
4.5.1.6 Policy-Based Authority Evaluation Scheme 103
4.5.1.7 Optimized Millionaire's Problem 104
4.5.1.8 Security in Military Operations 104
4.5.1.9 A Security Framework Application Based on Wireless Sensor Networks 104
4.5.1.10 Trust Evaluation Using Multifactor Method 105
4.5.1.11 Prevention of Spoofing Attacks 105
4.5.1.12 QoS Routing Protocol 106
4.5.1.13 Network Security Virtualization 106
4.5.2 Comparison of Routing Algorithms and Impact on Security 106
4.5.3 Inducing Intelligence in IoT Networks Using Artificial Intelligence 111
4.5.3.1 Fuzzy Logic- 1 111
4.5.3.2 Fuzzy Logic- 2 112
4.6 Introducing Hybrid Model in Military Application for Enhanced Security 113
4.6.1 Overall System Architecture 114
4.6.2 Best Candidate Selection 114
4.6.3 Simulation Results in Omnet++ 115
4.6 Conclusion 117
References 118
5 Futuristic AI Convergence of Megatrends: IoT and Cloud Computing 125Chanki Pandey, Yogesh Kumar Sahu, Nithiyananthan Kannan, Md Rashid Mahmood, Prabira Kumar Sethy and Santi Kumari Behera
5.1 Introduction 126
5.1.1 Our Contribution 128
5.2 Methodology 129
5.2.1 Statistical Information 130
5.3 Artificial Intelligence of Things 131
5.3.1 Application Areas of IoT Technologies 132
5.3.1.1 Energy Management 132
5.3.1.2 5G/Wireless Systems 134
5.3.1.3 Risk Assessment 136
5.3.1.4 Smart City 138
5.3.1.5 Health Sectors 139
5.4 AI Transforming Cloud Computing 140
5.4.1 Application Areas of Cloud Computing 152
5.4.2 Energy/Resource Management 154
5.4.3 Edge Computing 155
5.4.4 Distributed Edge Computing and Edge-of-Things (EoT) 158
5.4.5 Fog Computing in Cloud Computing 158
5.4.6 Soft Computing and Others 161
5.5 Conclusion 174
References 174
6 Analysis of Internet of Things Acceptance Dimensions in Hospitals 189Subhodeep Mukherjee, Manish Mohan Baral, Venkataiah Chittipaka and Sharad Chandra Srivastava
6.1 Introduction 190
6.2 Literature Review 191
6.2.1 Overview of Internet of Things 191
6.2.2 Internet of Things in Healthcare 191
6.2.3 Research Hypothesis 193
6.2.3.1 Technological Context (TC) 193
6.2.3.2 Organizational Context (OC) 194
6.2.3.3 Environmental Concerns (EC) 195
6.3 Research Methodology 195
6.3.1 Demographics of the Respondents 196
6.4 Data Analysis 196
6.4.1 Reliability and Validity 196
6.4.1.1 Cronbach's Alpha 196
6.4.1.2 Composite Reliability 201
6.4.2 Exploratory Factor Analysis (EFA) 201
6.4.3 Confirmatory Factor Analysis Results 201
6.4.3.1 Divergent or Discriminant Validity 204
6.4.4 Structural Equation Modeling 205
6.5 Discussion 206
6.5.1 Technological Context 206
6.5.2 Organizational Context 207
6.5.3 Environmental Context 208
6.6 Conclusion 209
References 209
7 Role of IoT in Sustainable Healthcare Systems 215Amrita Rai, Ritesh Pratap Singh and Neha Jain
7.1 Introduction 216
7.2 Basic Structure of IoT Implementation in the Healthcare Field 217
7.3 Different Technologies of IoT for the Healthcare Systems 221
7.3.1 On the Basis of the Node Identification 223
7.3.2 On the Basis of the Communication Method 223
7.3.3 Depending on the Location of the Object 224
7.4 Applications and Examples of IoT in the Healthcare Systems 225
7.4.1 IoT-Based Healthcare System to Encounter COVID-19 Pandemic Situations 225
7.4.2 Wearable Devices 226
7.4.3 IoT-Enabled Patient Monitoring Devices From Remote Locations 227
7.4.3.1 Pulse Rate Sensor 227
7.4.3.2 Respiratory Rate Sensors 229
7.4.3.3 Body Temperature Sensors 231
7.4.3.4 Blood Pressure Sensing 232
7.4.3.5 Pulse Oximetry Sensors 233
7.5 Companies Associated With IoT and Healthcare Sector Worldwide 234
7.6 Conclusion and Future Enhancement in the Healthcare System With IoT 237
References 238
8 Fog Computing Paradigm for Internet of Things Applications 243Upendra Verma and Diwakar Bhardwaj
8.1 Introduction 243
8.2 Challenges 247
8.3 Fog Computing: The Emerging Era of Computing Paradigm 248
8.3.1 Definition of Fog Computing 248
8.3.2 Fog Computing Characteristic 249
8.3.3 Comparison Between Cloud and Fog Computing Paradigm 250
8.3.4 When to Use Fog Computing 250
8.3.5 Fog Computing Architecture for Internet of Things 251
8.3.6 Fog Assistance to Address the New IoT Challenges 252
8.3.7 Devices Play a Role of Fog Computing Node 253
8.4 Related Work 254
8.5 Fog Computing Challenges 254
8.6 Fog Supported IoT Applications 262
8.7 Summary and Conclusion 265
References 265
9 Application of Internet of Things in Marketing Management 273Arshi Naim, Anandhavalli Muniasamy and Hamed Alqahtani
9.1 Introduction 273
9.2 Literature Review 275
9.2.1 Customer Relationship Management 276
9.2.2 Product Life Cycle (PLC) 277
9.2.3 Business Process Management (BPM) 278
9.2.4 Ambient Intelligence (AmI) 279
9.2.5 IoT and CRM Integration 280
9.2.6 IoT and BPM Integration 280
9.2.7 IoT and Product Life Cycle 282
9.2.8 IoT in MMgnt 282
9.2.9 Impacts of AmI on Marketing Paradigms 283
9.3 Research Methodology 284
9.4 Discussion 284
9.4.1 Research Proposition 1 288
9.4.2 Research Proposition 2 290
9.4.3 Research Proposition 3 291
9.4.4 Research Proposition 4 294
9.4.5 Research Proposition 5 294
9.5 Results 295
9.4 Conclusions 296
References 297
10 Healthcare Internet of Things: A New Revolution 301Manpreet Kaur, M. Sugadev, Harpreet Kaur, Md Rashid Mahmood and Vikas Maheshwari
10.1 Introduction 302
10.2 Healthcare IoT Architecture (IoT) 303
10.3 Healthcare IoT Technologies 304
10.3.1 Technology for Identification 305
10.3.2 Location Technology 306
10.3.2.1 Mobile-Based IoT 306
10.3.2.2 Wearable Devices 308
10.3.2.3 Ambient-Assisted Living (AAL) 314
10.3.3 Communicative Systems 315
10.3.3.1 Radiofrequency Identification 316
10.3.3.2 Bluetooth 316
10.3.3.3 Zigbee 317
10.3.3.4 Near Field Communication 317
10.3.3.5 Wireless Fidelity (Wi-Fi) 318
10.3.3.6 Satellite Communication 318
10.4 Community-Based Healthcare Services 319
10.5 Cognitive Computation 321
10.6 Adverse Drug Reaction 323
10.7 Blockchain 325
10.8 Child Health Information 327
10.9 Growth in Healthcare IoT 328
10.10 Benefits of IoT in Healthcare 328
10.11 Conclusion 329
References 330
11 Detection-Based Visual Object Tracking Based on Enhanced YOLO-Lite and LSTM 339Aayushi Gautam and Sukhwinder Singh
11.1 Introduction 340
11.2 Related Work 341
11.3 Proposed Approach 343
11.3.1 Enhanced YOLO-Lite 344
11.3.2 Long Short-Term Memory 346
11.3.3 Working of Proposed Framework 347
11.4 Evaluation Metrics 349
11.5 Experimental Results and Discussion 350
11.5.1 Implementation Details 350
11.5.2 Performance on OTB-2015 350
11.5.3 Performance on VOT-2016 353
11.5.4 Performance on UAV-123 354
11.6 Conclusion 356
References 356
12 Introduction to AmI and IoT 361Dolly Thankachan
12.1 Introduction 362
12.1.1 AmI and IoT Characteristics and Definition of Overlaps 362
12.1.1.1 Perceptions of "AmI" and the "IoT" 363
12.1.2 Prospects and Perils of AmI and the IoT 364
12.1.2.1 Assistances and Claim Areas 364
12.1.2.2 Intimidations and Contests Relating to AmI and the IoT 365
12.2 AmI and the IoT and Environmental and Societal Sustainability: Dangers, Challenges, and Underpinnings 366
12.3 Role of AmI and the IoT as New I.C.T.s to Conservational and Social Sustainability 367
12.3.1 AmI and the IoT for Environmental Sustainability: Issues, Discernment, and Favoritisms in Tactical Innovation Pursuits 368
12.4 The Environmental Influences of AmI and the IoT Technology 369
12.4.1 Fundamental Properties 370
12.4.2 Boom Properties 370
12.4.3 Oblique Outcomes 371
12.4.4 Straight Outcome 372
12.5 Conclusion 374
References 379
13 Design of Optimum Construction Site Management Architecture: A Quality Perspective Using Machine Learning Approach 383Kundan Meshram
13.1 Introduction 384
13.2 Literature Review 386
13.3 Proposed Construction Management Model Based on Machine Learning 390
13.4 Comparative Analysis 393
13.5 Conclusion 395
References 396
Index 399
Md Rashid Mahmood1*, Harpreet Kaur1, Manpreet Kaur1, Rohit Raja2 and Imran Ahmed Khan3
1Department of ECE, Guru Nanak Institutions Technical Campus, Hyderabad, India
2Department of IT, Guru Ghasidas Vishwavidyala, Bilaspur, India
3Department of ECE, Jamia Millia Islamia, New Delhi, India
Ambient intelligence (AmI) is the ability of technology to make judgments and act on our behalf. AmI is a cutting-edge technology that has the potential to fundamentally alter the way we interact with machines and electronics in our environment. It does not ask the user questions but rather understands the context in which the user is operating. Ambient intelligence (AmI) uses sensors and devices in our homes and offices to gather information about the environment. The AmI system then makes inferences based on proximity, intent, and behavioral patterns. It reacts to the user via a smart device's elegantly built natural interface. The Internet of Things (IoT) is a network of web-connected smart gadgets that collect data from their surroundings and use it to make decisions about their own lives. Ambient intelligence refers to what occurs when various devices connect, and more specifically, what they learn from one another. Ambient computing is a new kind of relationship between computers and employees. It gathers information for us when we ask for it, or even before we ask. Ambient intelligence aims to improve the way people and their environment interact with one another. Ambient intelligence (AI) is a subset of artificial intelligence (AI). Artificial intelligence mimics human cognitive processes such as perceiving, interpreting, and learning, among others. AmI is interlinked with the Internet of Things (IoT).
Keywords: Ambient intelligence, Internet of Things, artificial intelligence, human computer interaction
Ambient intelligence, often known as AmI, is the ability of technology to make judgments and act on our behalf while taking our preferences into consideration depending on the data accessible to it from all of the linked sensors and devices surrounding the user. AmI is a highly intelligent, widespread, and intuitive system. It does not ask the user questions but rather understands the context in which the user is operating. It does not make its physical presence known but instead performs actions that are suited to the user's needs. AmI is a cutting-edge technology that has the potential to fundamentally alter the way we interact with the machines and electronics in our environment. Ambient intelligence (AmI) is a term that is frequently used in conjunction with artificial intelligence (AI), the Internet of things (IoT), big data, machine learning (ML), networks, human-computer interaction (HCI), and pervasive, ubiquitous computing. On the other hand, artificial intelligence owes its success to the amazing growth of information and communication technology (ICTs) [1].
Intelligence is defined as the capacity to acquire knowledge and use it in novel settings. "Artificial" is anything created by humans, whereas "ambience" is what surrounds us. Additionally, we prefer to think of ambient intelligence (AmI) is an artificial construct since the mechanisms underlying natural AmI are the focus of biology and sociology. Numerous artificial intelligence technologies developed by computers are based on the concept of replicating brain functioning and human intellect.
Everyday life is made up of a combination of hardware, software, user experience, and machine/human-machine interaction and learning. In other words, it is the act of employing a computer, a device having far-field communication capabilities, or an internet-enabled gadget without necessarily being aware of doing so. For example, we no longer need to use a desktop computer in order to operate a computer. They are unseen to us, function in sync with us, and provide an overall seamless experience.
AmI uses a variety of IoT sensors and devices in our homes and workplaces to gather information about the environment and user context. The acquired data is then processed by the AmI system. The processing and analysis of collected data are used to identify user proximity, state, intent and behavioral patterns in the AmI system. Thereafter, it makes inferences based on what it has learned so far, what it has seen before, and any patterns it notices. Once it has determined the appropriate course of action, it reacts to the user via the smart device's elegantly built natural interface.
There are countless ways in which ambient intelligence may improve our lives. Regardless of where we are in the office, living room, shopping mall, or driving, we should always be mindful of our surroundings. Technology will serve as a constant companion. Our health monitoring devices can measure our blood pressure, so it tells us not to eat those high-cholesterol food items. It can be inconvenient to divert the route because it knows that there was an accident on our regular way to work. As soon as we get home from work on a hot summer evening, it turns on the air conditioner to keep us cool.
Consider the following scenario: Peter returns home after a hectic day at work, and AmI systems assist him in relaxing.
Ambient intelligence (AmI) is interlinked with the Internet of Things (IoT). IoT refers to smart lighting, smart transportation, smart homes, smart villages, smart grids, etc., among other things, and the way these items communicate. Ambient intelligence refers to what occurs when various devices connect, and more specifically, what they learn from one another.
The Internet of Things (IoT) is a network of web-connected smart gadgets that collect data from their surroundings and use it to make decisions about their own lives. Interactions between Internet of Things devices and a gateway or other cutting-edge devices transmit sensitive data that may be analyzed remotely or on-site. These gadgets communicate with one another and respond to each other's data. While people can communicate with robots, machines are capable of doing the majority of jobs without the need for human intervention.
It is expected that the Internet of Things will have an impact on society, the economy, and technology as it grows. Sensors and other ordinary items, as well as consumer devices, are becoming increasingly capable of storing and processing data. Despite this, there are a number of significant challenges that could hinder the Internet of Things from realizing its potential. The general public is well aware of the risks associated with Internet-connected gadgets, hacking, surveillance, and privacy violations. There is a new set of policy, legal, and development challenges that have evolved in recent years. The increasing use of Internet of Things (IoT) devices has the potential to transform our lives.
With the Internet of Things (IoT) devices such as Internet-enabled appliances, home automation components, and energy management gadgets, we are getting closer to having a smart house. In addition to other Internet-of-Things-enabled medical equipment, wearable fitness and health monitoring devices are transforming the way healthcare is delivered. The disabled and the elderly will benefit the most from this technology, as it will increase their freedom and quality of life while simultaneously cutting their expenditures [2].
To exchange data and manage message traffic, an Internet of Things device connects directly to a cloud service, such as an application service provider, through a secure connection. When an Internet of Things device connects to a cloud service through an application-layer gateway (ALG), the device-to-gateway model is utilized. On local gateway devices, features like data translation and protocol encoding are accessible.
Using this paradigm, smart devices can communicate with one another without using the Internet Protocol (IP). A gateway is required for IPv4 devices and services to function effectively. This strategy is most frequently used to incorporate new smart gadgets into current parental control systems. In order to conduct an analysis, data from various sources can be integrated with smart object data from the cloud service. A business can even benefit from ambient computing. It can help it work more efficiently, remove unnecessary steps in processes, and collect, analyze, and actively learn from...
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