Оглавление
Группа авторов. Human Communication Technology
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
Human Communication Technology. Internet of Robotic Things and Ubiquitous Computing
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
13.6.1.1.1 DHT-11 Temperature and Humidity Sensor
13.6.1.1.2 Arduino Soil and Moisture Level Sensor
13.6.1.1.3 Raspberry Pi 4 Model B
13.6.1.1.4 Analog to Digital Converters (ADS1115)
13.6.1.2 Connections
13.6.1.3 Program
13.6.1.3.1 ADS1115 Software Installation
13.6.1.3.2 Program Code
13.6.2 Water Quality Monitoring System
13.6.2.1 Dissolved Oxygen Monitoring System
13.6.2.1.1 Connections
13.6.2.1.2 Program Code [13]
13.6.2.2 pH Monitoring System
13.6.2.2.1 Connections
13.6.2.2.2 Program Code [13]
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
ALGORITHM 1: Middleware framework algorithm
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
ALGORITHM 1: Data capturing algorithm
16.6 Conclusion
References
Index
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