Human Communication Technology: Internet-of-Robotic-Things and Ubiquitous Computing
- Length: 496 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2021-11-16
- ISBN-10: 1119750598
- ISBN-13: 9781119750598
- Sales Rank: #0 (See Top 100 Books)
HUMAN COMMUNICATION TECHNOLOGY
A unique book explaining how perception, location, communication, cognition, computation, networking, propulsion, integration of federated Internet of Robotic Things (IoRT) and digital platforms are important components of new-generation IoRT applications through continuous, real-time interaction with the world.
The 16 chapters in this book discuss new architectures, networking paradigms, trustworthy structures, and platforms for the integration of applications across various business and industrial domains that are needed for the emergence of intelligent things (static or mobile) in collaborative autonomous fleets. These new apps speed up the progress of paradigms of autonomous system design and the proliferation of the Internet of Robotic Things (IoRT). Collaborative robotic things can communicate with other things in the IoRT, learn independently, interact securely with the world, people, and other things, and acquire characteristics that make them self-maintaining, self-aware, self-healing, and fail-safe operational. Due to the ubiquitous nature of collaborative robotic things, the IoRT, which binds together the sensors and the objects of robotic things, is gaining popularity. Therefore, the information contained in this book will provide readers with a better understanding of this interdisciplinary field.
Audience
Researchers in various fields including computer science, IoT, artificial intelligence, machine learning, and big data analytics.
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 Also of Interest Check out these published and forthcoming related titles from Scrivener Publishing EULA
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