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E-raamat: Human Communication Technology: Internet-of-Robotic-Things and Ubiquitous Computing

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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.

Preface xix
1 Internet of Robotic Things: A New Architecture and Platform 1(26)
V. Vijayalakshmi
S. Vimal
M. Saravanan
1.1 Introduction
2(7)
1.1.1 Architecture
3(6)
1.1.1.1 Achievability of the Proposed Architecture
6(1)
1.1.1.2 Qualities of IoRT Architecture
6(2)
1.1.1.3 Reasonable Existing Robots for IoRT Architecture
8(1)
1.2 Platforms
9(11)
1.2.1 Cloud Robotics Platforms
9(1)
1.2.2 IoRT Platform
10(1)
1.2.3 Design a Platform
11(1)
1.2.4 The Main Components of the Proposed Approach
11(1)
1.2.5 IoRT Platform Design
12(3)
1.2.6 Interconnection Design
15(2)
1.2.7 Research Methodology
17(1)
1.2.8 Advancement Process-Systems Thinking
17(1)
1.2.8.1 Development Process
17(1)
1.2.9 Trial Setup-to Confirm the Functionalities
18(2)
1.3 Conclusion
20(1)
1.4 Future Work
21(1)
References
21(6)
2 Brain-Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things 27(28)
R. Raja Sudharsan
I. Deny
2.1 Introduction
28(2)
2.2 Electroencephalography Signal Acquisition Methods
30(2)
2.2.1 Invasive Method
31(1)
2.2.2 Non-Invasive Method
32(1)
2.3 Electroencephalography Signal-Based BCI
32(8)
2.3.1 Prefrontal Cortex in Controlling Concentration Strength
33(1)
2.3.2 Neurosky Mind-Wave Mobile
34(3)
2.3.2.1 Electroencephalography Signal Processing Devices
34(3)
2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications
37(3)
2.4 IoRT-Based Hardware for BCI
40(1)
2.5 Software Setup for IoRT
40(2)
2.6 Results and Discussions
42(5)
2.7 Conclusion
47(1)
References
48(7)
3 Automated Verification and Validation of IoRT Systems 55(36)
S.V. Gayetri Devi
C. Nalini
3.1 Introduction
56(3)
3.1.1 Automating V&V-An Important Key to Success
58(1)
3.2 Program Analysis of IoRT Applications
59(2)
3.2.1 Need for Program Analysis
59(1)
3.2.2 Aspects to Consider in Program Analysis of IoRT Systems
59(2)
3.3 Formal Verification of IoRT Systems
61(12)
3.3.1 Automated Model Checking
61(1)
3.3.2 The Model Checking Process
62(7)
3.3.2.1 PRISM
65(1)
3.3.2.2 UPPAAL
66(1)
3.3.2.3 SPIN Model Checker
67(2)
3.3.3 Automated Theorem Prover
69(2)
3.3.3.1 ALT-ERGO
70(1)
3.3.4 Static Analysis
71(2)
3.3.4.1 CODESONAR
72(1)
3.4 Validation of IoRT Systems
73(7)
3.4.1 IoRT Testing Methods
79(1)
3.4.2 Design of IoRT Test
80(1)
3.5 Automated Validation
80(8)
3.5.1 Use of Service Visualization
82(1)
3.5.2 Steps for Automated Validation of IoRT Systems
82(2)
3.5.3 Choice of Appropriate Tool for Automated Validation
84(1)
3.5.4 IoRT Systems Open Source Automated Validation Tools
85(1)
3.5.5 Some of Significant Open Source Test Automation Frameworks
86(1)
3.5.6 Finally IoRT Security Testing
86(1)
3.5.7 Prevalent Approaches for Security Validation
87(1)
3.5.8 IoRT Security Tools
87(1)
References
88(3)
4 Light Fidelity (Li-Fi) Technology: The Future Man-Machine-Machine Interaction Medium 91(22)
J.M. Gnanasekar
T. Veeramakali
4.1 Introduction
92(2)
4.1.1 Need for Li-Fi
94(1)
4.2 Literature Survey
94(4)
4.2.1 An Overview on Man-to-Machine Interaction System
95(1)
4.2.2 Review on Machine to Machine (M2M) Interaction
96(2)
4.2.2.1 System Model
97(1)
4.3 Light Fidelity Technology
98(7)
4.3.1 Modulation Techniques Supporting Li-Fi
99(3)
4.3.1.1 Single Carrier Modulation (SCM)
100(1)
4.3.1.2 Multi Carrier Modulation
100(1)
4.3.1.3 Li-Fi Specific Modulation
101(1)
4.3.2 Components of Li-Fi
102(3)
4.3.2.1 Light Emitting Diode (LED)
102(1)
4.3.2.2 Photodiode
103(1)
4.3.2.3 Transmitter Block
103(1)
4.3.2.4 Receiver Block
104(1)
4.4 Li-Fi Applications in Real Word Scenario
105(6)
4.4.1 Indoor Navigation System for Blind People
105(1)
4.4.2 Vehicle to Vehicle Communication
106(1)
4.4.3 Li-Fi in Hospital
107(2)
4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry
109(1)
4.4.5 Li-Fi in Workplace
110(1)
4.5 Conclusion
111(1)
References
111(2)
5 Healthcare Management-Predictive Analysis (IoRT) 113(24)
L. Mary Gladence
V. Maria Anu
Y. Bevish Jinila
5.1 Introduction
114(2)
5.1.1 Naive Bayes Classifier Prediction for SPAM
115(1)
5.1.2 Internet of Robotic Things (IoRT)
115(1)
5.2 Related Work
116(1)
5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM)
117(7)
5.3.1 FTI SPAM Using GA Algorithm
118(3)
5.3.1.1 Chromosome Generation
119(1)
5.3.1.2 Fitness Function
120(1)
5.3.1.3 Crossover
120(1)
5.3.1.4 Mutation
121(1)
5.3.1.5 Termination
121(1)
5.3.2 Patterns Matching Using SCI
121(1)
5.3.3 Pattern Classification Based on SCI Value
122(1)
5.3.4 Significant Pattern Evaluation
123(1)
5.4 Detection of Congestive Heart Failure Using Automatic Classifier
124(6)
5.4.1 Analyzing the Dataset
125(1)
5.4.2 Data Collection
126(2)
5.4.2.1 Long-Term HRV Measures
127(1)
5.4.2.2 Attribute Selection
128(1)
5.4.3 Automatic Classifier-Belief Network
128(2)
5.5 Experimental Analysis
130(2)
5.6 Conclusion
132(2)
References
134(3)
6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing 137(26)
S. Murugan
R. Manikandan
Ambeshwar Kumar
6.1 Introduction
138(3)
6.2 Literature Survey
141(4)
6.3 Proposed Model
145(10)
6.3.1 Multimodal Data
145(1)
6.3.2 Dimensionality Reduction
146(1)
6.3.3 Principal Component Analysis
147(1)
6.3.4 Reduce the Number of Dimensions
148(1)
6.3.5 CNN
148(1)
6.3.6 CNN Layers
149(2)
6.3.6.1 Convolution Layers
149(1)
6.3.6.2 Padding Layer
150(1)
6.3.6.3 Pooling/Subsampling Layers
150(1)
6.3.6.4 Nonlinear Layers
151(1)
6.3.7 ReLU
151(1)
6.3.7.1 Fully Connected Layers
152(1)
6.3.7.2 Activation Layer
152(1)
6.3.8 LSTM
152(1)
6.3.9 Weighted Combination of Networks
153(2)
6.4 Experimental Results
155(4)
6.4.1 Accuracy
155(1)
6.4.2 Sensibility
156(1)
6.4.3 Specificity
156(1)
6.4.4 A Predictive Positive Value (PPV)
156(1)
6.4.5 Negative Predictive Value (NPV)
156(3)
6.5 Conclusion
159(1)
6.6 Future Scope
159(1)
References
160(3)
7 AI, Planning and Control Algorithms for IoRT Systems 163(30)
T.R. Thamizhvani
R.J. Hemalatha
R. Chandrasekaran
A. Josephin Arockia Dhivya
7.1 Introduction
164(3)
7.2 General Architecture of IoRT
167(3)
7.2.1 Hardware Layer
168(1)
7.2.2 Network Layer
168(1)
7.2.3 Internet Layer
168(1)
7.2.4 Infrastructure Layer
168(1)
7.2.5 Application Layer
169(1)
7.3 Artificial Intelligence in IoRT Systems
170(10)
7.3.1 Technologies of Robotic Things
170(2)
7.3.2 Artificial Intelligence in IoRT
172(8)
7.4 Control Algorithms and Procedures for IoRT Systems
180(7)
7.4.1 Adaptation of IoRT Technologies
183(3)
7.4.2 Multi-Robotic Technologies
186(1)
7.5 Application of IoRT in Different Fields
187(3)
References
190(3)
8 Enhancements in Communication Protocols That Powered IoRT 193(26)
T. Anusha
M. Pushpalatha
8.1 Introduction
194(1)
8.2 IoRT Communication Architecture
194(4)
8.2.1 Robots and Things
196(1)
8.2.2 Wireless Link Layer
197(1)
8.2.3 Networking Layer
197(1)
8.2.4 Communication Layer
198(1)
8.2.5 Application Layer
198(1)
8.3 Bridging Robotics and IoT
198(2)
8.4 Robot as a Node in IoT
200(6)
8.4.1 Enhancements in Low Power WPANs
200(3)
8.4.1.1 Enhancements in IEEE 802.15.4
200(1)
8.4.1.2 Enhancements in Bluetooth
201(1)
8.4.1.3 Network Layer Protocols
202(1)
8.4.2 Enhancements in Low Power WLANs
203(1)
8.4.2.1 Enhancements in IEEE 802.11
203(1)
8.4.3 Enhancements in Low Power WWANs
204(2)
8.4.3.1 LoRaWAN
205(1)
8.4.3.2 5G
205(1)
8.5 Robots as Edge Device in IoT
206(3)
8.5.1 Constrained RESTful Environments (CoRE)
206(1)
8.5.2 The Constrained Application Protocol (CoAP)
207(1)
8.5.2.1 Latest in CoAP
207(1)
8.5.3 The MQTT-SN Protocol
207(1)
8.5.4 The Data Distribution Service (DDS)
208(1)
8.5.5 Data Formats
209(1)
8.6 Challenges and Research Solutions
209(1)
8.7 Open Platforms for IoRT Applications
210(2)
8.8 Industrial Drive for Interoperability
212(2)
8.8.1 The Zigbee Alliance
212(1)
8.8.2 The Thread Group
213(1)
8.8.3 The WiFi Alliance
213(1)
8.8.4 The LoRa Alliance
214(1)
8.9 Conclusion
214(1)
References
215(4)
9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks 219(26)
R. Anitha
S. Anusooya
V. Jean Shilpa
Mohamed Hishaam
9.1 Introduction
220(1)
9.2 Existing Methodology
220(1)
9.3 Proposed Methodology
221(2)
9.4 Hardware & Software Requirements
223(9)
9.4.1 Hardware Requirements
223(9)
9.4.1.1 Gas Sensors Employed in Hazardous Detection
223(3)
9.4.1.2 NI Wireless Sensor Node 3202
226(2)
9.4.1.3 NI WSN gateway (NI 9795)
228(1)
9.4.1.4 COMPACT RIO (NI-9082)
229(3)
9.5 Experimental Setup
232(8)
9.5.1 Data Set Preparation
233(3)
9.5.2 Artificial Neural Network Model Creation
236(4)
9.6 Results and Discussion
240(3)
9.7 Conclusion and Future Work
243(1)
References
244(1)
10 Hierarchical Elitism GSO Algorithm For Pattern Recognition 245(18)
IlavazhagiBala S.
Latha Parthiban
10.1 Introduction
246(1)
10.2 Related Works
247(1)
10.3 Methodology
248(7)
10.3.1 Additive Kuan Speckle Noise Filtering Model
249(2)
10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition
251(4)
10.4 Experimental Setup
255(1)
10.5 Discussion
255(5)
10.5.1 Scenario 1: Computational Time
256(1)
10.5.2 Scenario 2: Computational Complexity
257(1)
10.5.3 Scenario 3: Pattern Recognition Accuracy
258(2)
10.6 Conclusion
260(1)
References
260(3)
11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things) 263(38)
Anurag Sinha
Pooja Jha
11.1 Machine Learning-An Introduction
264(3)
11.1.1 Classification of Machine Learning
265(2)
11.2 Internet of Things
267(1)
11.3 ML in IoT
268(2)
11.3.1 Overview
268(2)
11.4 Literature Review
270(1)
11.5 Different Machine Learning Algorithm
271(2)
11.5.1 Bayesian Measurements
271(1)
11.5.2 K-Nearest Neighbors (k-NN)
272(1)
11.5.3 Neural Network
272(1)
11.5.4 Decision Tree (DT)
272(1)
11.5.5 Principal Component Analysis (PCA) t
273(1)
11.5.6 K-Mean Calculations
273(1)
11.5.7 Strength Teaching
273(1)
11.6 Internet of Things in Different Frameworks
273(3)
11.6.1 Computing Framework
274(2)
11.6.1.1 Fog Calculation
274(1)
11.6.1.2 Estimation Edge
275(1)
11.6.1.3 Distributed Computing
275(1)
11.6.1.4 Circulated Figuring
276(1)
11.7 Smart Cities
276(3)
11.7.1 Use Case
277(1)
11.7.1.1 Insightful Vitality
277(1)
11.7.1.2 Brilliant Portability
277(1)
11.7.1.3 Urban Arranging
278(1)
11.7.2 Attributes of the Smart City
278(1)
11.8 Smart Transportation
279(6)
11.8.1 Machine Learning and IoT in Smart Transportation
280(3)
11.8.2 Markov Model
283(1)
11.8.3 Decision Structures
284(1)
11.9 Application of Research
285(5)
11.9.1 In Energy
285(1)
11.9.2 In Routing
285(1)
11.9.3 In Living
286(1)
11.9.4 Application in Industry
287(3)
11.10 Machine Learning for IoT Security
290(4)
11.10.1 Used Machine Learning Algorithms
291(2)
11.10.2 Intrusion Detection
293(1)
11.10.3 Authentication
294(1)
11.11 Conclusion
294(1)
References
295(6)
12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids 301(36)
G. Jayanthi
Latha Parthiban
12.1 Introduction
302(1)
12.2 Existence of Acoustic Feedback
303(1)
12.2.1 Causes of Acoustic Feedback
303(1)
12.2.2 Amplification of Feedback Process
304(1)
12.3 Analysis of Acoustic Feedback
304(6)
12.3.1 Frequency Analysis Using Impulse Response
305(1)
12.3.2 Feedback Analysis Using Phase Difference
306(4)
12.4 Filtering of Signals
310(10)
12.4.1 Digital Filters
310(1)
12.4.2 Adaptive Filters
311(1)
12.4.2.1 Order of Adaptive Filters
311(1)
12.4.2.2 Filter Coefficients in Adaptive Filters
311(1)
12.4.3 Adaptive Feedback Cancellation
312(3)
12.4.3.1 Non-Continuous Adaptation
312(2)
12.4.3.2 Continuous Adaptation
314(1)
12.4.4 Estimation of Acoustic Feedback
315(2)
12.4.5 Analysis of Acoustic Feedback Signal
317(3)
12.4.5.1 Forward Path of the Signal
317(1)
12.4.5.2 Feedback Path of the Signal
317(2)
12.4.5.3 Bias Identification
319(1)
12.5 Adaptive Algorithms
320(5)
12.5.1 Step-Size Algorithms
321(4)
12.5.1.1 Fixed Step-Size
322(1)
12.5.1.2 Variable Step-Size
323(2)
12.6 Simulation
325(3)
12.6.1 Training of Adaptive Filter for Removal of Acoustic Feedback
325(1)
12.6.2 Testing of Adaptive Filter
326(13)
12.6.2.1 Subjective and Objective Evaluation Using KEMAR
326(1)
12.6.2.2 Experimental Setup Using Manikin Channel
327(1)
12.7 Performance Evaluation
328(5)
12.8 Conclusions
333(1)
References
334(3)
13 Internet of Things Platform for Smart Farming 337(34)
R. Anandan
Deepak B.S.
G. Suseendran
Noor Zaman Jhanjhi
13.1 Introduction
337(1)
13.2 History
338(1)
13.3 Electronic Terminologies
339(2)
13.3.1 Input and Output Devices
339(1)
13.3.2 GPIO
340(1)
13.3.3 ADC
340(1)
13.3.4 Communication Protocols
340(1)
13.3.4.1 UART
340(1)
13.3.4.2 I2C
340(1)
13.3.4.3 SPI
341(1)
13.4 IoT Cloud Architecture
341(2)
13.4.1 Communication From User to Cloud Platform
342(1)
13.4.2 Communication From Cloud Platform To IoT Device
342(1)
13.5 Components of IoT
343(7)
13.5.1 Real-Time Analytics
343(1)
13.5.1.1 Understanding Driving Styles
343(1)
13.5.1.2 Creating Driver Segmentation
344(1)
13.5.1.3 Identifying Risky Neighbors
344(1)
13.5.1.4 Creating Risk Profiles
344(1)
13.5.1.5 Comparing Microsegments
344(1)
13.5.2 Machine Learning
344(2)
13.5.2.1 Understanding the Farm
345(1)
13.5.2.2 Creating Farm Segmentation
345(1)
13.5.2.3 Identifying Risky Factors
346(1)
13.5.2.4 Creating Risk Profiles
346(1)
13.5.2.5 Comparing Microsegments
346(1)
13.5.3 Sensors
346(3)
13.5.3.1 Temperature Sensor
347(1)
13.5.3.2 Water Quality Sensor
347(1)
13.5.3.3 Humidity Sensor
347(1)
13.5.3.4 Light Dependent Resistor
347(2)
13.5.4 Embedded Systems
349(1)
13.6 IoT-Based Crop Management System
350(17)
13.6.1 Temperature and Humidity Management System
350(11)
13.6.1.1 Project Circuit
351(2)
13.6.1.2 Connections
353(3)
13.6.1.3 Program
356(5)
13.6.2 Water Quality Monitoring System
361(3)
13.6.2.1 Dissolved Oxygen Monitoring System
361(2)
13.6.2.2 pH Monitoring System
363(1)
13.6.3 Light Intensity Monitoring System
364(8)
13.6.3.1 Project Circuit
365(1)
13.6.3.2 Connections
365(1)
13.6.3.3 Program Code
366(1)
13.7 Future Prospects
367(1)
13.8 Conclusion
368(1)
References
369(2)
14 Scrutinizing the Level of Awareness on Green Computing Practices in Combating Covid-19 at Institute of Health Science-Gaborone 371(30)
Ishmael Gala
Srinath Doss
14.1 Introduction
372(9)
14.1.1 Institute of Health Science-Gaborone
373(1)
14.1.2 Research Objectives
374(1)
14.1.3 Green Computing
374(1)
14.1.4 Covid-19
375(1)
14.1.5 The Necessity of Green Computing in Combating Covid-19
376(3)
14.1.6 Green Computing Awareness
379(1)
14.1.7 Knowledge
380(1)
14.1.8 Attitude
381(1)
14.1.9 Behavior
381(1)
14.2 Research Methodology
381(2)
14.2.1 Target Population
382(1)
14.2.2 Sample Frame
382(1)
14.2.3 Questionnaire as a Data Collection Instrument
383(1)
14.2.4 Validity and Reliability
383(1)
14.3 Analysis of Data and Presentation
383(10)
14.3.1 Demographics: Gender and Age
384(2)
14.3.2 How Effective is Green Computing Policies in Combating Covid-19 at Institute of Health Science-Gaborone?
386(2)
14.3.3 What are Green Computing Practices Among Users at Gaborone Institute of Health Science?
388(1)
14.3.4 What is the Role of Green Computing Training in Combating Covid-19 at Institute of Health Science-Gaborone?
388(2)
14.3.5 What is the Likelihood of Threats Associated With a Lack of Awareness on Green Computing Practices While Combating Covid-19?
390(1)
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?
391(2)
14.4 Recommendations
393(1)
14.4.1 Green Computing Policy
393(1)
14.4.2 Risk Assessment
394(1)
14.4.3 Green Computing Awareness Training
394(1)
14.4.4 Compliance
394(1)
14.5 Conclusion
394(1)
References
395(6)
15 Detailed Analysis of Medical IoT Using Wireless Body Sensor Network and Application of IoT in Healthcare 401(34)
Anurag Sinha
Shubham Singh
15.1 Introduction
402(1)
15.2 History of IoT
403(2)
15.3 Internet of Objects
405(2)
15.3.1 Definitions
405(1)
15.3.2 Internet of Things (IoT): Data Flow
406(1)
15.3.3 Structure of IoT-Enabling Technologies
406(1)
15.4 Applications of IoT
407(1)
15.5 IoT in Healthcare of Human Beings
407(2)
15.5.1 Remote Healthcare-Telemedicine
408(1)
15.5.2 Telemedicine System-Overview
408(1)
15.6 Telemedicine Through a Speech-Based Query System
409(3)
15.6.1 Outpatient Monitoring
410(1)
15.6.2 Telemedicine Umbrella Service
410(1)
15.6.3 Advantages of the Telemedicine Service
411(1)
15.6.4 Some Examples of IoT in the Health Sector
411(1)
15.7 Conclusion
412(1)
15.8 Sensors
412(8)
15.8.1 Classification of Sensors
413(2)
15.8.2 Commonly Used Sensors in BSNs
415(5)
15.8.2.1 Accelerometer
417(1)
15.8.2.2 ECG Sensors
418(1)
15.8.2.3 Pressure Sensors
419(1)
15.8.2.4 Respiration Sensors
420(1)
15.9 Design of Sensor Nodes
420(3)
15.9.1 Energy Control
421(1)
15.9.2 Fault Diagnosis
422(1)
15.9.3 Reduction of Sensor Nodes
422(1)
15.10 Applications of BSNs
423(1)
15.11 Conclusions
423(1)
15.12 Introduction
424(4)
15.12.1 From WBANs to BBNs
425(1)
15.12.2 Overview of WBAN
425(1)
15.12.3 Architecture
426(1)
15.12.4 Standards
427(1)
15.12.5 Applications
427(1)
15.13 Body-to-Body Network Concept
428(1)
15.14 Conclusions
429(1)
References
430(5)
16 DCMM: A Data Capture and Risk Management for Wireless Sensing Using IoT Platform 435(28)
Siripuri Kiran
Bandi Krishna
Janga Vijaykumar
Sridhar manda
16.1 Introduction
436(2)
16.2 Background
438(1)
16.2.1 Internet of Things
438(1)
16.2.2 Middleware Data Acquisition
438(1)
16.2.3 Context Acquisition
439(1)
16.3 Architecture
439(7)
16.3.1 Proposed Architecture
439(7)
16.3.1.1 Protocol Adaption
441(2)
16.3.1.2 Device Management
443(2)
16.3.1.3 Data Handler
445(1)
16.4 Implementation
446(8)
16.4.1 Requirement and Functionality
446(2)
16.4.1.1 Requirement
446(1)
16.4.1.2 Functionalities
447(1)
16.4.2 Adopted Technologies
448(4)
16.4.2.1 Middleware Software
448(1)
16.4.2.2 Usability Dependency
449(1)
16.4.2.3 Sensor Node Software
449(1)
16.4.2.4 Hardware Technology
450(1)
16.4.2.5 Sensors
451(1)
16.4.3 Details of IoT Hub
452(2)
16.4.3.1 Data Poster
452(1)
16.4.3.2 Data Management
452(1)
16.4.3.3 Data Listener
453(1)
16.4.3.4 Models
454(1)
16.5 Results and Discussions
454(6)
16.6 Conclusion
460(1)
References
461(2)
Index 463
R. Anandan PhD, completed his PhD in Computer Science and Engineering, is an IBMS/390 Mainframe professional, and is recognized as a Chartered Engineer from the Institution of Engineers in India and received a fellowship from Bose Science Society, India. He is a Professor in the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He has published more than 110 research papers in various international journals, authored 9 books in the computer science and engineering disciplines, and has received 13 awards.

G. Suseendran PhD, received his PhD in Information Technology-Mathematics from Presidency College, University of Madras, Tamil Nadu, India. He passed away during the production of this book.

S. Balamurugan PhD, SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.

Ashish Mishra PhD, is a professor in the Department of Computer Science and Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur [ M.P]. He received his PhD from AISECT University, Bhopal, India. He has published many research papers in reputed journals and conferences, been granted 1 patent, and has authored/edited 4 books in the areas of data mining, image processing, and artificial intelligence.

D. Balaganesh PhD, is a Dean of Faculty Computer Science and Multimedia, Lincoln University College, Malaysia. He has developed software applications Timetable Automation, Online Exam as well as published the book Computer Applications in Business.
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