The Social Internet of Things (SIoT) has become a hot topic in academic research. It employs the theory of social networks into the different levels of the Internet of Things (IoTs) and has brought new possibilities for the development of IoTs. Essentially, the SIoT is a subset of IoTs. It uses intelligent hardware and humans as the node, a social network as the organization type, the social relationship between things, things and humans, and between humans, formatting research methods and models with social network characteristics to realize the connection, service, and application of the IoTs.
Moreover, SIoT is a form of realization of technology, architecture, and application of the IoTs using social network research methods. It further promotes the integration between real-world and virtual cyberspace, contributes the realization of the IoTs, expands the research scope of the social networking, and provides a new solution for the specific problems of the IoTs. Consequently, there is a tremendous need for researchers to have a comprehensive knowledge of the advances in SIoT.
This special issue is soliciting scientiļ¬c research papers that can present a snapshot of the latest research status of SIoT.
Author(s): Gururaj H. L., Pramod H. B., Gowtham M.
Series: Innovations in Intelligent Internet of Everything (IoE)
Publisher: CRC Press
Year: 2023
Language: English
Pages: 325
City: Boca Raton
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Acknowledgements
Editors
Contributors
1. Internet of Lights: A Way to Energy-Efficient Lighting System
1.1. Introduction
1.2. Overview on IoT
1.3. What Is Smart Lighting?
1.4. The Connection between Smart Lighting and IoT
1.5. Why IoL?
1.5.1. Actual Lighting Specification
1.5.1.1. Building automation controls network (BACnet)
1.5.1.2. KNX
1.5.1.3. The LonTalk data transmission
1.5.1.4. The digital addressable lighting interface (DALI)
1.5.2. Objective of the Internet of Lights Specification
1.5.2.1. Client
1.5.2.2. Open and refillable
1.5.2.3. Composable
1.5.2.4. Compatibility
1.5.2.5. Internal risk
1.5.2.6. Performance
1.5.2.7. Privacy
1.5.2.8. Energy
1.6. Smart Lighting and IoT
1.7. Scope of IoL
1.8. Smart Lighting System Implementation in Real Time
1.9. Implementation Standards
1.10. Lighting Standard Adoption Criteria
1.11. Capabilities of Smart Lighting Systems
1.12. Applications of IoL
1.13. Conclusion
References
2. A Prototype of a Smart Phone-Controlled Lawn Mower Using Android App
2.1. Introduction
2.2. Tools and Technology Overview
2.3. System Architecture
2.3.1. Arduino Uno Board
2.3.1.1. Arduino programming
2.3.2. DC (Direct Current) Motor
2.3.2.1. Motors' specifications
2.3.2.2. Features
2.3.3. Module of Bluetooth HC-05
2.3.3.1. HC-05's technical specifications
2.3.3.2. UART
2.3.3.3. Highlights from the SG-90
2.4. Implementation of the System
2.4.1. Lawn_Main_Activity
2.4.2. Login Activity
2.4.3. Home Activity
2.4.4. Microcontroller Program Snippet
2.5. Conclusion
References
3. Intelligent Optimized Delay Algorithm for Improved Quality of Service in Healthcare Social Internet of Things
3.1. Introduction
3.1.1. IoT in Medical Application
3.2. Challenges in Medical IoT
3.2.1. Delay in Data Transmission
3.2.2. Energy Consumption during Data Transmission
3.2.3. Throughput
3.3. Intelligent Optimized Delay Algorithm (IODA)
3.3.1. Working of IODA
3.3.2. Traffic Control Technique in IODA
3.4. Results
3.5. Conclusion
References
4. Trust Management: Architecture, Components with Emerging Domains in SIoT
4.1. Introduction
4.1.1. Trust Management System (TMS)
4.1.2. SIoT Relationships Parental Object Relationship (POR)
4.1.3. SIoT Architecture - Relationship Management
4.1.3.1. Objects mimic human social behavior
4.1.3.2. The proposed solution
4.1.4. Trust Management Model
4.1.4.1. Deriving trust
4.1.4.1.1. Developing trust metrics
4.1.4.2. Evaluate direct observations and indirect recommendations
4.1.5. SIoT Paradigms
4.1.5.1. Human-device interaction
4.1.5.2. Collaborative-awareness
4.1.5.3. Privacy and data protection
4.1.5.4. Integration of social network
4.1.5.5. Ambient intelligence
4.1.5.6. Smart objects and social networks combination
4.1.5.7. XaaS - Everything-as-a-service
4.1.5.8. Device heterogeneity
4.1.5.9. Interoperability
4.1.5.10. Mobility
4.1.5.11. Service discovery
4.1.5.12. Context management
4.1.5.13. Application development
4.1.6. Types of Attacks and Its Roles with Management in Trust SIoT
4.1.7. Emerging IoT Domains, Existing State-of-Art and Unresolved Issues in Research
4.1.7.1. Research questions
4.1.7.2. Emerging technologies
4.2. Conclusion
References
5. Security Threats in SIoT
5.1. Introduction
5.2. SIoT Applications
5.3. SIoT Architecture
5.3.1. Server in SIoT
5.3.2. Gateway and Objects
5.4. Architecture of IoT and Its Threats
5.4.1. Perception Layer
5.4.2. Transport Layer
5.4.3. Application Layer
5.5. SIoT Security
5.5.1. Challenges
5.6. Privacy and Confidentiality in SIoT
5.7. Trust Management in SIoT
5.7.1. Trust Attacks for Security and Privacy
5.8. Conclusion
References
6. Challenges and Solutions of Using Social Internet of Things (SIoT) in Healthcare and Medical Domains
6.1. IoT in Healthcare
6.1.1. SIoT in Healthcare
6.2. Related Works on SIoT in Healthcare
6.3. Suggested Communications and Models for Social IoT
6.3.1. Device-to-Device Connectivity
6.3.2. Device to the Internet (Cloud)
6.3.3. Device to Gateway (Hub)
6.3.4. Cloud-Based Data-Sharing Model
6.4. Hierarchical Network Design for SIoT in Healthcare
6.4.1. Types of Layers
6.4.2. SIoT Relationship Models for Hierarchical Design
6.5. Proposed SIoT Model for Healthcare and Medical Domains
6.5.1. Five-Stage SIoT Solution Architecture for Healthcare
6.5.2. Proposed SIoT Model for Healthcare Domain
6.5.3. Implementation Using CISCO Packet Tracer
6.5.4. IoT Devices Configuration
6.5.5. Monitoring and Controlling the SIoT Model
6.6. Summary
References
7. Social Aspects of D2D Communications in IoT for 5G and beyond Cellular Networks
7.1. Introduction
7.1.1. Social IoT: Social Networks on IoT
7.1.2. Applications of D2D Communication in SIoT
7.1.2.1. Healthcare services
7.1.2.2. Proximity services
7.1.2.3. Online gaming
7.1.2.4. Disaster management
7.1.2.5. Machine-to-machine communications (M2M)
7.1.2.6. Ubiquitous computing
7.1.3. Challenges of D2D Communications in SIoT
7.1.3.1. Device discovery
7.1.3.2. Synchronization
7.1.3.3. Mode selection
7.1.3.4. Resource allocation
7.1.3.5. Interference management
7.1.3.6. Pricing and incentives
7.1.3.7. Mobility management
7.1.3.8. Security
7.2. System Description of Socially Aided D2D Communication
7.2.1. Peer Discovery
7.2.2. Mode Selection
7.2.3. Resource Management
7.2.4. Relay-Assisted D2D Communications (Multi-hop)
7.3. A Social D2D Architecture for People Centric IOT (PIoT)
7.3.1. Motivation
7.3.2. People-Centric IoT Framework
7.3.2.1. P2P physical resources layer
7.3.2.2. Distributed services D2D interaction layer
7.3.2.3. Social D2D enhanced graph layer
7.3.3. Improvement of D2D Communication Using Social Ties
7.3.4. Future SIoT Architecture Including Social D2D Communication
7.4. Caching-Based Socially-Aware D2D Communication
7.4.1. Three Layers of D2D Content Delivery Networks
7.4.2. D2D Caching Using Hypergraph
7.4.2.1. Hypergraph formation
7.4.2.2. Caching capacity
7.5. Open Research Challenges
7.5.1. Heterogeneity and Data Management
7.5.2. Security, Privacy, and Fault Tolerance
7.5.3. Self Operation, Semantic and Context Management
7.5.4. Application Development and New Business Model
7.5.5. Efficient Discovery and Search Engines
7.5.6. Efficient Energy Management
References
8. Real-Time Face Mask Detection and Alert System Using IoT and Machine Learning
8.1. Introduction
8.2. Related Work
8.2.1. Aim
8.2.2. Open CV
8.2.3. Tensor Flow
8.2.4. Keras
8.3. Implementation
8.3.1. Methodology
8.3.2. Algorithms Used
8.3.2.1. Face mask detection
8.3.2.2. Pre-handling
8.3.2.2.1. MobileNetV2
8.3.2.2.2. Dataset development
8.3.2.3. Face recognition
8.3.2.4. Post-processing
8.3.2.4.1. Camera placement at public places
8.3.2.4.2. Dataset collection
8.3.2.4.3. Training and creating a face mask detector model
8.3.2.5. Creating database for users
8.3.2.5.1. Face mask detection and recognition of face
8.3.2.5.2. Sending email to those who do not wear face masks
8.3.2.5.3. Sending SMS to phone number
8.4. Experiment Results and Analysis
8.4.1. Mask Detection Result
8.5. Conclusion
8.6. Future Enhancements
References
9. Social Content Distribution Architecture of SIoT: Applications, Challenges, Security, and Privacy Paradigm
9.1. Introduction
9.2. The Social Internet of Things (SIoT)
9.3. SIoT Smart Framework
9.3.1. Smart Things to Social Things
9.3.2. SIoT Paradigm
9.4. Security and Privacy Challenges in SIoT Applications
9.5. Management of Trust in the Social Internet of Things (SIoT)
9.5.1. Trust Properties
9.5.2. Trust-Related Attacks
9.5.3. Requirements and Limits for Trust Management in SIoT
9.6. Trust Evaluation Mechanism in the Social-Internet-of-Things (SIoT)
9.7. Conclusion
References
10. Efficient and Secured IoT-Based Agriculture Wireless Sensor Network Using Swarm Optimization
10.1. Introduction
10.1.1. Most Important IoT Structure Blocks and Layers
10.1.1.1. Profits and Trials of IIoT
10.2. Literature Survey
10.3. Secured and Efficient Swarm Based Method
10.4. Results and Discussions
10.5. Conclusion
References
11. Review on Autonomous Vehicle and Virtual Controlled Delivery Truck System Using IoT
11.1. Introduction
11.2. Related Work
11.2.1. History on Autonomous Vehicle
11.2.2. Benefits and Disadvantages
11.2.2.1. Prosperity and mishaps
11.2.2.2. Clog
11.2.2.3. Taxi and automobile ownership
11.2.2.4. Streets' capacity
11.2.2.5. Clog comparing
11.2.2.6. Land use
11.2.2.7. Farming countries
11.2.2.8. Climate (Energy and Delivery)
11.3. Description of the Use Instances
11.3.1. Interstate Pilot
11.3.2. Full Automation Using Driver for Extended Availability
11.3.3. Vehicle on Demand
11.3.4. Virtual Controlling Method
11.4. Structure Overview
11.4.1. Raspberry Pi
11.4.2. Camera
11.4.3. L293D Motor Driver
11.4.4. Ultrasonic Sensor
11.4.5. Servo Motors
11.5. Implementation
11.6. Result
11.7. Conclusion
References
12. IoT-Enabled Smart Parking to Reduce Vehicle Flooding
12.1. Introduction
12.2. Related Works
12.3. Methodology
12.4. Results and Discussions
12.5. Conclusion
References
13. IoT Device Discovery Technique Based on Semantic Ontology
13.1. Introduction
13.2. Literature Survey
13.3. Comparative Analysis of the Search Techniques
13.4. Methodology
13.4.1. Extracting Information from the Vendor Specification
13.4.2. Pre-processing of Extracted Information
13.4.3. Keywords Extraction
13.4.4. Mining of Discourse Words
13.4.5. Inference Rules
13.5. Conclusion
References
14. Women Safety and Monitoring System Using Geo-Fence
14.1. Introduction
14.2. Related Works
14.3. System Infrastructure
14.3.1. Geo-Fencing
14.3.1.1. How geo-fencing works
14.3.1.2. Common geo-fencing applications
14.3.2. Sensor Module
14.3.3. Voice Recording Module
14.3.4. Communication Module
14.4. Implementation
14.5. Working
14.6. Conclusion
References
15. Machine-Learning Approach to Predict Air Quality - A Survey
15.1. Introduction
15.1.1. Depending on the Origin
15.1.2. Depending on the State of Matter
15.1.3. Depending on the Sources
15.2. Literature Survey
15.3. Prediction Results of Algorithms
References
16. Air Canvas - Air-Writing Recognition Model for Environmental and Socioeconomic Issues
16.1. Introduction
16.2. Related Work
16.3. Motivation
16.4. Problem Definition
16.5. Methods and Techniques
16.5.1. Fingertip Detection
16.5.2. CRNN
16.5.2.1. Hyper Parameters
16.5.3. Dataset Description
16.5.3.1.
16.6. Proposed Work
16.6.1. Pseudocode for the Proposed Model
16.7. Experiment and Evaluation
16.8. Performance Analysis
16.9. Significance
16.10. Conclusion
References
Index
