Human communication technology : internet-of-robotic-things and ubiquitous computing / edited by R. Anandan [and four others].

Format:

Book

Contributor:

Anandan, R., editor.

Series:

Artificial Intelligence and Soft Computing for Industrial Transformation

Language:

English

Subjects (All):

Internet of things.

Artificial intelligence.

Computational intelligence.

Telecommunication.

Physical Description:

1 online resource (512 pages)

Edition:

1st edition.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Incorporated, [2022]

Summary:

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.

Contents:

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

3 Automated Verification and Validation of IoRT Systems

3.1 Introduction

3.1.1 Automating V&amp

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

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

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

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

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

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

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 &amp

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

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

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.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781119752158

1119752159

9781119752165

1119752167

9781119752141

1119752140

OCLC:

1281968190