HUMAN-TECHNOLOGY COMMUNICATION internet-of robotic-things and ubiquitous.

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Internet of Robotic Things: A New Architecture and Platform
	1.1 Introduction
		1.1.1 Architecture
			1.1.1.1 Achievability of the Proposed Architecture
			1.1.1.2 Qualities of IoRT Architecture
			1.1.1.3 Reasonable Existing Robots for IoRT Architecture
	1.2 Platforms
		1.2.1 Cloud Robotics Platforms
		1.2.2 IoRT Platform
		1.2.3 Design a Platform
		1.2.4 The Main Components of the Proposed Approach
		1.2.5 IoRT Platform Design
		1.2.6 Interconnection Design
		1.2.7 Research Methodology
		1.2.8 Advancement Process—Systems Thinking
			1.2.8.1 Development Process
		1.2.9 Trial Setup-to Confirm the Functionalities
	1.3 Conclusion
	1.4 Future Work
	References
2 Brain–Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things
	2.1 Introduction
	2.2 Electroencephalography Signal Acquisition Methods
		2.2.1 Invasive Method
		2.2.2 Non-Invasive Method
	2.3 Electroencephalography Signal-Based BCI
		2.3.1 Prefrontal Cortex in Controlling Concentration Strength
		2.3.2 Neurosky Mind-Wave Mobile
			2.3.2.1 Electroencephalography Signal Processing Devices
		2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications
	2.4 IoRT-Based Hardware for BCI
	2.5 Software Setup for IoRT
	2.6 Results and Discussions
	2.7 Conclusion
	References
3 Automated Verification and Validation of IoRT Systems
	3.1 Introduction
		3.1.1 Automating V&V—An Important Key to Success
	3.2 Program Analysis of IoRT Applications
		3.2.1 Need for Program Analysis
		3.2.2 Aspects to Consider in Program Analysis of IoRT Systems
	3.3 Formal Verification of IoRT Systems
		3.3.1 Automated Model Checking
		3.3.2 The Model Checking Process
			3.3.2.1 PRISM
			3.3.2.2 UPPAAL
			3.3.2.3 SPIN Model Checker
		3.3.3 Automated Theorem Prover
			3.3.3.1 ALT-ERGO
		3.3.4 Static Analysis
			3.3.4.1 CODESONAR
	3.4 Validation of IoRT Systems
		3.4.1 IoRT Testing Methods
		3.4.2 Design of IoRT Test
	3.5 Automated Validation
		3.5.1 Use of Service Visualization
		3.5.2 Steps for Automated Validation of IoRT Systems
		3.5.3 Choice of Appropriate Tool for Automated Validation
		3.5.4 IoRT Systems Open Source Automated Validation Tools
		3.5.5 Some Significant Open Source Test Automation Frameworks
		3.5.6 Finally IoRT Security Testing
		3.5.7 Prevalent Approaches for Security Validation
		3.5.8 IoRT Security Tools
	References
4 Light Fidelity (Li-Fi) Technology: The Future Man–Machine–Machine Interaction Medium
	4.1 Introduction
		4.1.1 Need for Li-Fi
	4.2 Literature Survey
		4.2.1 An Overview on Man-to-Machine Interaction System
		4.2.2 Review on Machine to Machine (M2M) Interaction
			4.2.2.1 System Model
	4.3 Light Fidelity Technology
		4.3.1 Modulation Techniques Supporting Li-Fi
			4.3.1.1 Single Carrier Modulation (SCM)
			4.3.1.2 Multi Carrier Modulation
			4.3.1.3 Li-Fi Specific Modulation
		4.3.2 Components of Li-Fi
			4.3.2.1 Light Emitting Diode (LED)
			4.3.2.2 Photodiode
			4.3.2.3 Transmitter Block
			4.3.2.4 Receiver Block
	4.4 Li-Fi Applications in Real Word Scenario
		4.4.1 Indoor Navigation System for Blind People
		4.4.2 Vehicle to Vehicle Communication
		4.4.3 Li-Fi in Hospital
		4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry
		4.4.5 Li-Fi in Workplace
	4.5 Conclusion
	References
5 Healthcare Management-Predictive Analysis (IoRT)
	5.1 Introduction
		5.1.1 Naive Bayes Classifier Prediction for SPAM
		5.1.2 Internet of Robotic Things (IoRT)
	5.2 Related Work
	5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM)
		5.3.1 FTI SPAM Using GA Algorithm
			5.3.1.1 Chromosome Generation
			5.3.1.2 Fitness Function
			5.3.1.3 Crossover
			5.3.1.4 Mutation
			5.3.1.5 Termination
		5.3.2 Patterns Matching Using SCI
		5.3.3 Pattern Classification Based on SCI Value
		5.3.4 Significant Pattern Evaluation
	5.4 Detection of Congestive Heart Failure Using Automatic Classifier
		5.4.1 Analyzing the Dataset
		5.4.2 Data Collection
			5.4.2.1 Long-Term HRV Measures
			5.4.2.2 Attribute Selection
		5.4.3 Automatic Classifier—Belief Network
	5.5 Experimental Analysis
	5.6 Conclusion
	References
6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing
	6.1 Introduction
	6.2 Literature Survey
	6.3 Proposed Model
		6.3.1 Multimodal Data
		6.3.2 Dimensionality Reduction
		6.3.3 Principal Component Analysis
		6.3.4 Reduce the Number of Dimensions
		6.3.5 CNN
		6.3.6 CNN Layers
			6.3.6.1 Convolution Layers
			6.3.6.2 Padding Layer
			6.3.6.3 Pooling/Subsampling Layers
			6.3.6.4 Nonlinear Layers
		6.3.7 ReLU
			6.3.7.1 Fully Connected Layers
			6.3.7.2 Activation Layer
		6.3.8 LSTM
		6.3.9 Weighted Combination of Networks
	6.4 Experimental Results
		6.4.1 Accuracy
		6.4.2 Sensibility
		6.4.3 Specificity
		6.4.4 A Predictive Positive Value (PPV)
		6.4.5 Negative Predictive Value (NPV)
	6.5 Conclusion
	6.6 Future Scope
	References
7 AI, Planning and Control Algorithms for IoRT Systems
	7.1 Introduction
	7.2 General Architecture of IoRT
		7.2.1 Hardware Layer
		7.2.2 Network Layer
		7.2.3 Internet Layer
		7.2.4 Infrastructure Layer
		7.2.5 Application Layer
	7.3 Artificial Intelligence in IoRT Systems
		7.3.1 Technologies of Robotic Things
		7.3.2 Artificial Intelligence in IoRT
	7.4 Control Algorithms and Procedures for IoRT Systems
		7.4.1 Adaptation of IoRT Technologies
		7.4.2 Multi-Robotic Technologies
	7.5 Application of IoRT in Different Fields
	References
8 Enhancements in Communication Protocols That Powered IoRT
	8.1 Introduction
	8.2 IoRT Communication Architecture
		8.2.1 Robots and Things
		8.2.2 Wireless Link Layer
		8.2.3 Networking Layer
		8.2.4 Communication Layer
		8.2.5 Application Layer
	8.3 Bridging Robotics and IoT
	8.4 Robot as a Node in IoT
		8.4.1 Enhancements in Low Power WPANs
			8.4.1.1 Enhancements in IEEE 802.15.4
			8.4.1.2 Enhancements in Bluetooth
			8.4.1.3 Network Layer Protocols
		8.4.2 Enhancements in Low Power WLANs
			8.4.2.1 Enhancements in IEEE 802.11
		8.4.3 Enhancements in Low Power WWANs
			8.4.3.1 LoRaWAN
			8.4.3.2 5G
	8.5 Robots as Edge Device in IoT
		8.5.1 Constrained RESTful Environments (CoRE)
		8.5.2 The Constrained Application Protocol (CoAP)
			8.5.2.1 Latest in CoAP
		8.5.3 The MQTT-SN Protocol
		8.5.4 The Data Distribution Service (DDS)
		8.5.5 Data Formats
	8.6 Challenges and Research Solutions
	8.7 Open Platforms for IoRT Applications
	8.8 Industrial Drive for Interoperability
		8.8.1 The Zigbee Alliance
		8.8.2 The Thread Group
		8.8.3 The WiFi Alliance
		8.8.4 The LoRa Alliance
	8.9 Conclusion
	References
9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks
	9.1 Introduction
	9.2 Existing Methodology
	9.3 Proposed Methodology
	9.4 Hardware & Software Requirements
		9.4.1 Hardware Requirements
			9.4.1.1 Gas Sensors Employed in Hazardous Detection
			9.4.1.2 NI Wireless Sensor Node 3202
			9.4.1.3 NI WSN Gateway (NI 9795)
			9.4.1.4 COMPACT RIO (NI-9082)
	9.5 Experimental Setup
		9.5.1 Data Set Preparation
		9.5.2 Artificial Neural Network Model Creation
	9.6 Results and Discussion
	9.7 Conclusion and Future Work
	References
10 Hierarchical Elitism GSO Algorithm For Pattern Recognition
	10.1 Introduction
	10.2 Related Works
	10.3 Methodology
		10.3.1 Additive Kuan Speckle Noise Filtering Model
		10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition
	10.4 Experimental Setup
	10.5 Discussion
		10.5.1 Scenario 1: Computational Time
		10.5.2 Scenario 2: Computational Complexity
		10.5.3 Scenario 3: Pattern Recognition Accuracy
	10.6 Conclusion
	References
11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things)
	11.1 Machine Learning—An Introduction
		11.1.1 Classification of Machine Learning
	11.2 Internet of Things
	11.3 ML in IoT
		11.3.1 Overview
	11.4 Literature Review
	11.5 Different Machine Learning Algorithm
		11.5.1 Bayesian Measurements
		11.5.2 K-Nearest Neighbors (k-NN)
		11.5.3 Neural Network
		11.5.4 Decision Tree (DT)
		11.5.5 Principal Component Analysis (PCA) t
		11.5.6 K-Mean Calculations
		11.5.7 Strength Teaching
	11.6 Internet of Things in Different Frameworks
		11.6.1 Computing Framework
			11.6.1.1 Fog Calculation
			11.6.1.2 Estimation Edge
			11.6.1.3 Distributed Computing
			11.6.1.4 Circulated Figuring
	11.7 Smart Cities
		11.7.1 Use Case
			11.7.1.1 Insightful Vitality
			11.7.1.2 Brilliant Portability
			11.7.1.3 Urban Arranging
		11.7.2 Attributes of the Smart City
	11.8 Smart Transportation
		11.8.1 Machine Learning and IoT in Smart Transportation
		11.8.2 Markov Model
		11.8.3 Decision Structures
	11.9 Application of Research
		11.9.1 In Energy
		11.9.2 In Routing
		11.9.3 In Living
		11.9.4 Application in Industry
	11.10 Machine Learning for IoT Security
		11.10.1 Used Machine Learning Algorithms
		11.10.2 Intrusion Detection
		11.10.3 Authentication
	11.11 Conclusion
	References
12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids
	12.1 Introduction
	12.2 Existence of Acoustic Feedback
		12.2.1 Causes of Acoustic Feedback
		12.2.2 Amplification of Feedback Process
	12.3 Analysis of Acoustic Feedback
		12.3.1 Frequency Analysis Using Impulse Response
		12.3.2 Feedback Analysis Using Phase Difference
	12.4 Filtering of Signals
		12.4.1 Digital Filters
		12.4.2 Adaptive Filters
			12.4.2.1 Order of Adaptive Filters
			12.4.2.2 Filter Coefficients in Adaptive Filters
		12.4.3 Adaptive Feedback Cancellation
			12.4.3.1 Non-Continuous Adaptation
			12.4.3.2 Continuous Adaptation
		12.4.4 Estimation of Acoustic Feedback
		12.4.5 Analysis of Acoustic Feedback Signal
			12.4.5.1 Forward Path of the Signal
			12.4.5.2 Feedback Path of the Signal
			12.4.5.3 Bias Identification
	12.5 Adaptive Algorithms
		12.5.1 Step-Size Algorithms
			12.5.1.1 Fixed Step-Size
			12.5.1.2 Variable Step-Size
	12.6 Simulation
		12.6.1 Training of Adaptive Filter for Removal of Acoustic Feedback
		12.6.2 Testing of Adaptive Filter
			12.6.2.1 Subjective and Objective Evaluation Using KEMAR
			12.6.2.2 Experimental Setup Using Manikin Channel
	12.7 Performance Evaluation
	12.8 Conclusions
	References
13 Internet of Things Platform for Smart Farming
	13.1 Introduction
	13.2 History
	13.3 Electronic Terminologies
		13.3.1 Input and Output Devices
		13.3.2 GPIO
		13.3.3 ADC
		13.3.4 Communication Protocols
			13.3.4.1 UART
			13.3.4.2 I2C
			13.3.4.3 SPI
	13.4 IoT Cloud Architecture
		13.4.1 Communication From User to Cloud Platform
		13.4.2 Communication From Cloud Platform To IoT Device
	13.5 Components of IoT
		13.5.1 Real-Time Analytics
			13.5.1.1 Understanding Driving Styles
			13.5.1.2 Creating Driver Segmentation
			13.5.1.3 Identifying Risky Neighbors
			13.5.1.4 Creating Risk Profiles
			13.5.1.5 Comparing Microsegments
		13.5.2 Machine Learning
			13.5.2.1 Understanding the Farm
			13.5.2.2 Creating Farm Segmentation
			13.5.2.3 Identifying Risky Factors
			13.5.2.4 Creating Risk Profiles
			13.5.2.5 Comparing Microsegments
		13.5.3 Sensors
			13.5.3.1 Temperature Sensor
			13.5.3.2 Water Quality Sensor
			13.5.3.3 Humidity Sensor
			13.5.3.4 Light Dependent Resistor
		13.5.4 Embedded Systems
	13.6 IoT-Based Crop Management System
		13.6.1 Temperature and Humidity Management System
			13.6.1.1 Project Circuit
			13.6.1.2 Connections
			13.6.1.3 Program
		13.6.2 Water Quality Monitoring System
			13.6.2.1 Dissolved Oxygen Monitoring System
			13.6.2.2 pH Monitoring System
		13.6.3 Light Intensity Monitoring System
			13.6.3.1 Project Circuit
			13.6.3.2 Connections
			13.6.3.3 Program Code
	13.7 Future Prospects
	13.8 Conclusion
	References
14 Scrutinizing the Level of Awareness on Green Computing Practices in Combating Covid-19 at Institute of Health Science-Gaborone
	14.1 Introduction
		14.1.1 Institute of Health Science-Gaborone
		14.1.2 Research Objectives
		14.1.3 Green Computing
		14.1.4 Covid-19
		14.1.5 The Necessity of Green Computing in Combating Covid-19
		14.1.6 Green Computing Awareness
		14.1.7 Knowledge
		14.1.8 Attitude
		14.1.9 Behavior
	14.2 Research Methodology
		14.2.1 Target Population
		14.2.2 Sample Frame
		14.2.3 Questionnaire as a Data Collection Instrument
		14.2.4 Validity and Reliability
	14.3 Analysis of Data and Presentation
		14.3.1 Demographics: Gender and Age
		14.3.2 How Effective is Green Computing Policies in Combating Covid-19 at Institute of Health Science-Gaborone?
		14.3.3 What are Green Computing Practices Among Users at Gaborone Institute of Health Science?
		14.3.4 What is the Role of Green Computing Training in Combating Covid-19 at Institute of Health Science-Gaborone?
		14.3.5 What is the Likelihood of Threats Associated With a Lack of Awareness on Green Computing Practices While Combating Covid-19?
		14.3.6 What is the Level of User Conduct, Awareness and Attitude With Regard to Awareness on Green Computing Practices at Institute of Health Science-Gaborone?
	14.4 Recommendations
		14.4.1 Green Computing Policy
		14.4.2 Risk Assessment
		14.4.3 Green Computing Awareness Training
		14.4.4 Compliance
	14.5 Conclusion
	References
15 Detailed Analysis of Medical IoT Using Wireless Body Sensor Network and Application of IoT in Healthcare
	15.1 Introduction
	15.2 History of IoT
	15.3 Internet of Objects
		15.3.1 Definitions
		15.3.2 Internet of Things (IoT): Data Flow
		15.3.3 Structure of IoT—Enabling Technologies
	15.4 Applications of IoT
	15.5 IoT in Healthcare of Human Beings
		15.5.1 Remote Healthcare—Telemedicine
		15.5.2 Telemedicine System—Overview
	15.6 Telemedicine Through a Speech-Based Query System
		15.6.1 Outpatient Monitoring
		15.6.2 Telemedicine Umbrella Service
		15.6.3 Advantages of the Telemedicine Service
		15.6.4 Some Examples of IoT in the Health Sector
	15.7 Conclusion
	15.8 Sensors
		15.8.1 Classification of Sensors
		15.8.2 Commonly Used Sensors in BSNs
			15.8.2.1 Accelerometer
			15.8.2.2 ECG Sensors
			15.8.2.3 Pressure Sensors
			15.8.2.4 Respiration Sensors
	15.9 Design of Sensor Nodes
		15.9.1 Energy Control
		15.9.2 Fault Diagnosis
		15.9.3 Reduction of Sensor Nodes
	15.10 Applications of BSNs
	15.11 Conclusions
	15.12 Introduction
		15.12.1 From WBANs to BBNs
		15.12.2 Overview of WBAN
		15.12.3 Architecture
		15.12.4 Standards
		15.12.5 Applications
	15.13 Body-to-Body Network Concept
	15.14 Conclusions
	References
16 DCMM: A Data Capture and Risk Management for Wireless Sensing Using IoT Platform
	16.1 Introduction
	16.2 Background
		16.2.1 Internet of Things
		16.2.2 Middleware Data Acquisition
		16.2.3 Context Acquisition
	16.3 Architecture
		16.3.1 Proposed Architecture
			16.3.1.1 Protocol Adaption
			16.3.1.2 Device Management
			16.3.1.3 Data Handler
	16.4 Implementation
		16.4.1 Requirement and Functionality
			16.4.1.1 Requirement
			16.4.1.2 Functionalities
		16.4.2 Adopted Technologies
			16.4.2.1 Middleware Software
			16.4.2.2 Usability Dependency
			16.4.2.3 Sensor Node Software
			16.4.2.4 Hardware Technology
			16.4.2.5 Sensors
		16.4.3 Details of IoT Hub
			16.4.3.1 Data Poster
			16.4.3.2 Data Management
			16.4.3.3 Data Listener
			16.4.3.4 Models
	16.5 Results and Discussions
	16.6 Conclusion
	References
Index
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