Aiot Technologies and Applications for Smart Environments

Cover
Contents
Call for Authors – The IET International Book Series on Applied AIoT
About the editors
Preface
Acknowledgements
1 Introduction to AIoT for smart environments
	Abstract
	1.1 Introduction
	1.2 From IoT to AIoT: smart IoT
		1.2.1 Smart retail
		1.2.2 Smart office buildings
		1.2.3 Autonomous vehicles in fleet management (smart driving)
		1.2.4 Autonomous delivery robots
		1.2.5 Drone traffic monitoring
	1.3 AI implementation and business cases of AIoT
		1.3.1 Business case: ET city brain
		1.3.2 Business case: Tesla’s autopilot
		1.3.3 Business case: classroom monitoring systems
	1.4 AI capable IoT platforms
	1.5 AIoT practical applications
	1.6 AIoT concerns and challenges
		1.6.1 Challenges in AIoT
	1.7 Conclusion
	References
2 Research challenges in smart environments
	Abstract
	2.1 Introduction
	2.2 Literature review
	2.3 Applications
	2.4 Components in smart environment
		2.4.1 Data pre-processing
		2.4.2 Sensors
		2.4.3 Data processing
		2.4.4 Environment control (actuators)
		2.4.5 Sensors for homes
	2.5 Wireless sensor networks
		2.5.1 Home-based sensor platform
		2.5.2 Sensor technologies
		2.5.3 Smart monitoring and controlling hut
		2.5.4 Vehicle system
	2.6 Challenges
	2.7 Conclusion
	References
3 Applications-oriented smart cities based on AIoT emerging technologies
	Abstract
	3.1 Introduction
	3.2 Smart cities overview and AIoT
	3.3 The framework deployment and architecture of Smart City
	3.4 AIoT-powered Smart City transformation
	3.5 Functions and features of Smart Cities
	3.6 Instruments that aid in the creation of a Smart City
	3.7 AIoT and challenges in building Smart City
	3.8 Conclusion and future scope
	References
4 Use of smartphones application to identify pedestrian barriers around existing metro stations in Noida
	Abstract
	4.1 Introduction: background and overview
	4.2 Literature survey
		4.2.1 Current debate on TOD in India
		4.2.2 Walking in Indian cities
		4.2.3 GIS-based walkability evaluation
		4.2.4 Re-assessing land-use and transport planning using station accessibility
	4.3 Study area and data collection
		4.3.1 Online smart questionnaire design
	4.4 Methodology
		4.4.1 Walkable catchment area
	4.5 Results
		4.5.1 Metro station survey analysis
		4.5.2 Analysis of pedestrian catchment areas
	4.6 Discussion and conclusion
	Acknowledgment
	References
5 A hybrid segmentation process for effective disease classification for smart agriculture
	Abstract
	5.1 Introduction
	5.2 Literature survey
	5.3 Proposed work
	5.4 Performance evaluation
	5.5 Conclusion and future workspace
	References
6 AIoT-based water management and IoT-based smart irrigation system: effective in smart agriculture
	Abstract
	6.1 Introduction
	6.2 Smart water management
		6.2.1 Water leakage inside the circulation community
		6.2.2 Water wastage at the consumer locality
	6.3 Smart irrigation
	6.4 Required components
	6.5 Working of G-SM component
	6.6 Working of RELAY
		Relay design
		Relay basics
		Energized relay (ON)
		De-energized relay (OFF)
		Pole-and-throw
		Solicitation of relay
		Relay selection
	6.7 Working of Arduino
		What Arduino is
		Arduino architecture
	6.8 Programming code in Arduino
	6.9 Result discussion
	6.10 Conclusion
	References
7 Adaptive smart farming system using Internet of Things (IoT) and artificial intelligence (AI) modeling
	Abstract
	7.1 Introduction
	7.2 Literature survey
	7.3 AI in agriculture
	7.4 Use of wireless and automation systems in agriculture
	7.5 Discussion
	7.6 Conclusions
	Conflict of interest
	References
8 Time series data air quality prediction using Internet of Things and machine learning techniques
	Abstract
	8.1 Introduction
	8.2 Analyzing the time-series
		8.2.1 Time-series forecasting (projection)
		8.2.2 Estimation of transfer functions
		8.2.3 Analyzing uncommon involvement incidents
		8.2.4 Analyzing multivariate time series
		8.2.5 Discrete control systems
	8.3 ML model (MLM)
		8.3.1 ML applications
		8.3.2 ML types
	8.4 IoT
	8.5 Air quality control (AQC)
		8.5.1 Air quality evaluation
		8.5.2 Flow diagram of AQC
	References
9 Role of AIoT-based intelligent automation in robotics, UAVs, and drones
	Abstract
	9.1 Introduction
		9.1.1 Synergy of IoT and AI
		9.1.2 IoT-aided robotics
		9.1.3 IoT-aided UAVs/drones
	9.2 Literature review
		9.2.1 Robotics
		9.2.2 UAVs/drones
	9.3 Components of IoRT system
		9.3.1 Components of IoRT system
	9.4 Applications of AIoT in robotics and UAVs/drones
		9.4.1 Application of AIoT robotics
		9.4.2 Application of AIoT in UAVs/drones
	9.5 IoT’s AI challenges
		9.5.1 Challenges faced in using robotics
		9.5.2 Challenges faced in using UAVs/drones
	9.6 Future scope
		9.6.1 Future with robotics
		9.6.2 Future with UAVs/drones
	9.7 Summary and conclusion
	References
10 AIoT-based waste management systems
	Abstract
	10.1 Introduction
	10.2 Related work
	10.3 AIoT
	10.4 Various types and techniques for waste disposal
	10.5 IoT-based waste management system
	10.6 Main features of AIoT-based framework for waste management
	10.7 Data and proposed methodology
		10.7.1 Design model
		10.7.2 Waste collection model
		10.7.3 Working of intelligent bin process
		10.7.4 Intelligent bin control by using AI
	10.8 Conclusion
	10.9 Future scope
	References
11 AIoT technologies and applications for smart environments
	Abstract
	11.1 Introduction
		11.1.1 Motivation
	11.2 IoT in smart manufacturing system
		11.2.1 Challenges for smart manufacturing
		11.2.2 Vertical sector particular necessities
		11.2.3 Challenges in the area of IoT and Big Data analytics
		11.2.4 Challenges in the area of IoT and blockchain computing
	11.3 Security issues and challenges
	11.4 A general outlook on blockchain
		11.4.1 The concept of blockchain technology
		11.4.2 The applications of blockchain technology utilized in the current period
		11.4.3 Advantages and disadvantages of blockchain technology
	11.5 The future of blockchain technology
	11.6 Proposed model for smart manufacturing in the context of Industry 4.0
	11.7 Result and discussion
		11.7.1 Low-level security issues
		11.7.2 Intermediate-level security issues
		11.7.3 High-level security issues
	11.8 Conclusion
	References
12 AIoT-based e-commerce
	Abstract
	12.1 Introduction
		12.1.1 Working of AIoT
		12.1.2 Advantages and challenges of AIoT
	12.2 Applications of IoT in e-commerce
		12.2.1 Inventory management
		12.2.2 Supply chain management
		12.2.3 Maintenance and warranty
		12.2.4 Smart homes
		12.2.5 Personalization
		12.2.6 Customer experience
	12.3 System design
	12.4 Online store
	12.5 Selection approach for offline store
	12.6 Hardware and software requirements
	12.7 Conclusion
	12.8 Future scope
	References
13 AIoT-based smart education and online teaching
	Abstract
	13.1 Introduction
	13.2 Literature review
	13.3 Research methodology
		13.3.1 Course design
		13.3.2 Research model hypothesis
		13.3.3 Research subject
		13.3.4 Research tools
		13.3.5 Definition of measurement methods
	13.4 Experimental results
	13.5 Conclusions and suggestions
	References
14 Autonomous UAV with obstacle management using AIoT: a case study on healthcare application
	Abstract
	14.1 Introduction
		14.1.1 IoT architecture
		14.1.2 Challenges in IoT
		14.1.3 AIoT
		14.1.4 UAV
		14.1.5 Advantages of IoT
		14.1.6 Road map of the chapter
	14.2 Related work
		14.2.1 Motivation and contribution
	14.3 Applications of AIoT
	14.4 AIoT in healthcare
		14.4.1 Case study on COVID-19 pandemic
	14.5 UAV and AIoT for healthcare
		14.5.1 Artificial intelligent IoT in healthcare
		14.5.2 UAV embedded with AIoT in healthcare
	14.6 Proposed algorithm: autonomous drone with obstacle management
		14.6.1 Different methods of UAV can be used in AIoT healthcare
		14.6.2 Algorithm
	14.7 Comparison study and analysis of different UAV methods
	14.8 Conclusion and future scope
	References
15 Effective learning-based attack detection methods for the Internet of Things
	Abstract
	15.1 Introduction: background and driving forces
	15.2 IoT applications
	15.3 Anomaly detection
		15.3.1 Static environment
		15.3.2 Dynamic environment
		15.3.3 An IDS
		15.3.4 Requirement of IDS
	15.4 ML classifiers
		15.4.1 Supervised learning (SL)
		15.4.2 Unsupervised learning (UL)
		15.4.3 Semi-supervised learning
		15.4.4 ML ensemble
		15.4.5 DL
		15.4.6 Auto-encoders
	15.5 Synthesis and conclusions
		15.5.1 Main findings
		15.5.2 Challenges
	15.6 Future directions
	References
16 Future perspectives of AI-driven Internet of Things
	16.1 Summary
	16.2 Potential challenges in AIoT technology
	16.3 Conclusion of the book
	16.4 Future perspectives and research directions
Index
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