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Intelligent IoT for the Digital World : Incorporating 5G Communications and Fog/Edge Computing Technologies.

By: Contributor(s): Material type: TextTextPublisher: Newark : John Wiley & Sons, Incorporated, 2021Copyright date: ©2021Edition: 1st edDescription: 1 online resource (339 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119593553
Subject(s): Genre/Form: Additional physical formats: Print version:: Intelligent IoT for the Digital WorldDDC classification:
  • 4.678
LOC classification:
  • TK5105.8857 .Y364 2021
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 IoT Technologies and Applications -- 1.1 Introduction -- 1.2 Traditional IoT Technologies -- 1.2.1 Traditional IoT System Architecture -- 1.2.1.1 Sensing Layer -- 1.2.1.2 Network Layer -- 1.2.1.3 Application Layer -- 1.2.2 IoT Connectivity Technologies and Protocols -- 1.2.2.1 Low‐power and Short‐range Connectivity Technologies -- 1.2.2.2 Low Data Rate and Wide‐area Connectivity Technologies -- 1.2.2.3 Emerging IoT Connectivity Technologies and Protocols -- 1.3 Intelligent IoT Technologies -- 1.3.1 Data Collection Technologies -- 1.3.1.1 mmWave -- 1.3.1.2 Massive MIMO -- 1.3.1.3 Software Defined Networks -- 1.3.1.4 Network Slicing -- 1.3.1.5 Time Sensitive Network -- 1.3.1.6 Multi‐user Access Control -- 1.3.1.7 Muti‐hop Routing Protocol -- 1.3.2 Computing Power Network -- 1.3.2.1 Intelligent IoT Computing Architecture -- 1.3.2.2 Edge and Fog Computing -- 1.3.3 Intelligent Algorithms -- 1.3.3.1 Big Data -- 1.3.3.2 Artificial Intelligence -- 1.4 Typical Applications -- 1.4.1 Environmental Monitoring -- 1.4.2 Public Safety Surveillance -- 1.4.3 Military Communication -- 1.4.4 Intelligent Manufacturing and Interactive Design -- 1.4.5 Autonomous Driving and Vehicular Networks -- 1.5 Requirements and Challenges for Intelligent IoT Services -- 1.5.1 A Generic and Flexible Multi‐tier Intelligence IoT Architecture -- 1.5.2 Lightweight Data Privacy Management in IoT Networks -- 1.5.3 Cross‐domain Resource Management for Intelligent IoT Services -- 1.5.4 Optimization of Service Function Placement, QoS, and Multi‐operator Network Sharing for Intelligent IoT Services -- 1.5.5 Data Time stamping and Clock Synchronization Services for Wide‐area IoT Systems -- 1.6 Conclusion -- References -- Chapter 2 Computing and Service Architecture for Intelligent IoT.
2.1 Introduction -- 2.2 Multi‐tier Computing Networks and Service Architecture -- 2.2.1 Multi‐tier Computing Network Architecture -- 2.2.2 Cost Aware Task Scheduling Framework -- 2.2.2.1 Hybrid Payment Model -- 2.2.2.2 Weighted Cost Function -- 2.2.2.3 Distributed Task Scheduling Algorithm -- 2.2.3 Fog as a Service Technology -- 2.2.3.1 FA2ST Framework -- 2.2.3.2 FA2ST Application Deployment -- 2.2.3.3 FA2ST Application Management -- 2.3 Edge‐enabled Intelligence for Industrial IoT -- 2.3.1 Introduction and Background -- 2.3.1.1 Intelligent Industrial IoT -- 2.3.1.2 Edge Intelligence -- 2.3.1.3 Challenges -- 2.3.2 Boomerang Framework -- 2.3.2.1 Framework Overview -- 2.3.2.2 Prediction Model and Right‐sizing Model -- 2.3.2.3 Boomerang Optimizer -- 2.3.2.4 Boomerang with DRL -- 2.3.3 Performance Evaluation -- 2.3.3.1 Emulation Environment -- 2.3.3.2 Evaluation Results -- 2.4 Fog‐enabled Collaborative SLAM of Robot Swarm -- 2.4.1 Introduction and Background -- 2.4.2 A Fog‐enabled Solution -- 2.4.2.1 System Architecture -- 2.4.2.2 Practical Implementation -- 2.5 Conclusion -- References -- Chapter 3 Cross‐Domain Resource Management Frameworks -- 3.1 Introduction -- 3.2 Joint Computation and Communication Resource Management for Delay‐Sensitive Applications -- 3.2.1 2C Resource Management Framework -- 3.2.1.1 System Model -- 3.2.1.2 Problem Formulation -- 3.2.2 Distributed Resource Management Algorithm -- 3.2.2.1 Paired Offloading of Non‐splittable Tasks -- 3.2.2.2 Parallel Offloading of Splittable Tasks -- 3.2.3 Delay Reduction Performance -- 3.2.3.1 Price of Anarchy -- 3.2.3.2 System Average Delay -- 3.2.3.3 Number of Beneficial TNs -- 3.2.3.4 Convergence -- 3.3 Joint Computing, Communication, and Caching Resource Management for Energy‐efficient Applications -- 3.3.1 Fog‐enabled 3C Resource Management Framework -- 3.3.1.1 System Resources.
3.3.1.2 Task Model -- 3.3.1.3 Task Execution -- 3.3.1.4 Problem Statement -- 3.3.2 Fog‐enabled 3C Resource Management Algorithm -- 3.3.2.1 F3C Algorithm Overview -- 3.3.2.2 F3C Algorithm for a Single Task -- 3.3.2.3 F3C Algorithm For Multiple Tasks -- 3.3.3 Energy Saving Performance -- 3.3.3.1 Energy Saving Performance with Different Task Numbers -- 3.3.3.2 Energy Saving Performance with Different Device Numbers -- 3.4 Case Study: Energy‐efficient Resource Management in Tactile Internet -- 3.4.1 Fog‐enabled Tactile Internet Architecture -- 3.4.2 Response Time and Power Efficiency Trade‐off -- 3.4.2.1 Response Time Analysis and Minimization -- 3.4.2.2 Trade‐off between Response Time and Power Efficiency -- 3.4.3 Cooperative Fog Computing -- 3.4.3.1 Response Time Analysis for Cooperative Fog Computing with N FNs -- 3.4.3.2 Response Time and Power Efficiency Trade‐off for Cooperative Fog Computing Networks -- 3.4.4 Distributed Optimization for Cooperative Fog Computing -- 3.4.5 A City‐wide Deployment of Fog Computing‐supported Self‐driving Bus System -- 3.4.5.1 Simulation Setup for Traffic Generated by a Self‐driving Bus -- 3.4.5.2 Simulation Setup for a Fog Computing Network -- 3.4.5.3 Numerical Results -- 3.5 Conclusion -- References -- Chapter 4 Dynamic Service Provisioning Frameworks -- 4.1 Online Orchestration of Cross‐edge Service Function Chaining -- 4.1.1 Introduction -- 4.1.2 Related Work -- 4.1.3 System Model for Cross‐edge SFC Deployment -- 4.1.3.1 Overview of the Cross‐edge System -- 4.1.3.2 Optimization Space -- 4.1.3.3 Cost Structure -- 4.1.3.4 The Cost Minimization Problem -- 4.1.4 Online Optimization for Long‐term Cost Minimization -- 4.1.4.1 Problem Decomposition via Relaxation and Regularization -- 4.1.4.2 A Randomized Dependent Rounding Scheme -- 4.1.4.3 Traffic Re‐routing -- 4.1.5 Performance Analysis -- 4.1.5.1 The Basic Idea.
4.1.5.2 Competitive Ratio of ORFA -- 4.1.5.3 Rounding Gap of RDIP -- 4.1.5.4 The Overall Competitive Ratio -- 4.1.6 Performance Evaluation -- 4.1.6.1 Experimental Setup -- 4.1.6.2 Evaluation Results -- 4.1.7 Future Directions -- 4.2 Dynamic Network Slicing for High‐quality Services -- 4.2.1 Service and User Requirements -- 4.2.2 Related Work -- 4.2.3 System Model and Problem Formulation -- 4.2.3.1 Fog Computing -- 4.2.3.2 Existing Network Slicing Architectures -- 4.2.3.3 Regional SDN‐based Orchestrator -- 4.2.3.4 Problem Formulation -- 4.2.4 Implementation and Numerical Results -- 4.2.4.1 Dynamic Network Slicing in 5G Networks -- 4.2.4.2 Numerical Results -- 4.3 Collaboration of Multiple Network Operators -- 4.3.1 Service and User Requirements -- 4.3.2 System Model and Problem Formulation -- 4.3.2.1 IoT Solutions in 3GPP Release 13 -- 4.3.2.2 Challenges for Massive IoT Deployment -- 4.3.2.3 Inter‐operator Network Sharing Architecture -- 4.3.2.4 Design Issues -- 4.3.3 Performance Analysis -- 4.4 Conclusion -- References -- Chapter 5 Lightweight Privacy‐Preserving Learning Schemes* -- 5.1 Introduction -- 5.2 System Model and Problem Formulation -- 5.3 Solutions and Results -- 5.3.1 A Lightweight Privacy‐preserving Collaborative Learning Scheme -- 5.3.1.1 Random Gaussian Projection (GRP) -- 5.3.1.2 Gaussian Random Projection Approach -- 5.3.1.3 Illustrating Examples -- 5.3.1.4 Evaluation Methodology and Datasets -- 5.3.1.5 Evaluation Results with the MNIST Dataset -- 5.3.1.6 Evaluation Results with a Spambase Dataset -- 5.3.1.7 Summary -- 5.3.1.8 Implementation and Benchmark -- 5.3.2 A Differentially Private Collaborative Learning Scheme -- 5.3.2.1 Approach Overview -- 5.3.2.2 Achieving ϵ‐Differential Privacy -- 5.3.2.3 Performance Evaluation -- 5.3.3 A Lightweight and Unobtrusive Data Obfuscation Scheme for Remote Inference -- 5.3.3.1 Approach Overview.
5.3.3.2 Case Study 1: Free Spoken Digit (FSD) Recognition -- 5.3.3.3 Case Study 2: Handwritten Digit (MNIST) Recognition -- 5.3.3.4 Case Study 3: American Sign Language (ASL) Recognition -- 5.3.3.5 Implementation and Benchmark -- 5.4 Conclusion -- References -- Chapter 6 Clock Synchronization for Wide‐area Applications1 -- 6.1 Introduction -- 6.2 System Model and Problem Formulation -- 6.2.1 Natural Timestamping for Wireless IoT Devices -- 6.2.2 Clock Synchronization for Wearable IoT Devices -- 6.3 Natural Timestamps in Powerline Electromagnetic Radiation -- 6.3.1 Electrical Network Frequency Fluctuations and Powerline Electromagnetic Radiation -- 6.3.2 Electromagnetic Radiation‐based Natural Timestamping -- 6.3.2.1 Hardware -- 6.3.2.2 ENF Extraction -- 6.3.2.3 Natural Timestamp and Decoding -- 6.3.3 Implementation and Benchmark -- 6.3.3.1 Timestamping Parameter Settings -- 6.3.3.2 Z1 Implementation and Benchmark -- 6.3.4 Evaluation in Office and Residential Environments -- 6.3.4.1 Deployment and Settings -- 6.3.4.2 Evaluation Results -- 6.3.5 Evaluation in a Factory Environment -- 6.3.6 Applications -- 6.3.6.1 Time Recovery -- 6.3.6.2 Run‐time Clock Verification -- 6.3.6.3 Secure Clock Synchronization -- 6.4 Wearables Clock Synchronization Using Skin Electric Potentials -- 6.4.1 Motivation -- 6.4.2 Measurement Study -- 6.4.2.1 Measurement Setup -- 6.4.2.2 iSEP Sensing under Various Settings -- 6.4.3 TouchSync System Design -- 6.4.3.1 TouchSync Workflow -- 6.4.3.2 iSEP Signal Processing -- 6.4.3.3 NTP Assisted with Dirac Combs -- 6.4.4 TouchSync with Internal Periodic Signal -- 6.4.4.1 Extended Design -- 6.4.4.2 Numeric Experiments -- 6.4.4.3 Impact of Clock Skews -- 6.4.5 Implementation -- 6.4.6 Evaluation -- 6.4.6.1 Signal Strength and Wearing Position -- 6.4.6.2 Impact of High‐Power Appliances on TouchSync.
6.4.6.3 Evaluation in Various Environments.
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Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 IoT Technologies and Applications -- 1.1 Introduction -- 1.2 Traditional IoT Technologies -- 1.2.1 Traditional IoT System Architecture -- 1.2.1.1 Sensing Layer -- 1.2.1.2 Network Layer -- 1.2.1.3 Application Layer -- 1.2.2 IoT Connectivity Technologies and Protocols -- 1.2.2.1 Low‐power and Short‐range Connectivity Technologies -- 1.2.2.2 Low Data Rate and Wide‐area Connectivity Technologies -- 1.2.2.3 Emerging IoT Connectivity Technologies and Protocols -- 1.3 Intelligent IoT Technologies -- 1.3.1 Data Collection Technologies -- 1.3.1.1 mmWave -- 1.3.1.2 Massive MIMO -- 1.3.1.3 Software Defined Networks -- 1.3.1.4 Network Slicing -- 1.3.1.5 Time Sensitive Network -- 1.3.1.6 Multi‐user Access Control -- 1.3.1.7 Muti‐hop Routing Protocol -- 1.3.2 Computing Power Network -- 1.3.2.1 Intelligent IoT Computing Architecture -- 1.3.2.2 Edge and Fog Computing -- 1.3.3 Intelligent Algorithms -- 1.3.3.1 Big Data -- 1.3.3.2 Artificial Intelligence -- 1.4 Typical Applications -- 1.4.1 Environmental Monitoring -- 1.4.2 Public Safety Surveillance -- 1.4.3 Military Communication -- 1.4.4 Intelligent Manufacturing and Interactive Design -- 1.4.5 Autonomous Driving and Vehicular Networks -- 1.5 Requirements and Challenges for Intelligent IoT Services -- 1.5.1 A Generic and Flexible Multi‐tier Intelligence IoT Architecture -- 1.5.2 Lightweight Data Privacy Management in IoT Networks -- 1.5.3 Cross‐domain Resource Management for Intelligent IoT Services -- 1.5.4 Optimization of Service Function Placement, QoS, and Multi‐operator Network Sharing for Intelligent IoT Services -- 1.5.5 Data Time stamping and Clock Synchronization Services for Wide‐area IoT Systems -- 1.6 Conclusion -- References -- Chapter 2 Computing and Service Architecture for Intelligent IoT.

2.1 Introduction -- 2.2 Multi‐tier Computing Networks and Service Architecture -- 2.2.1 Multi‐tier Computing Network Architecture -- 2.2.2 Cost Aware Task Scheduling Framework -- 2.2.2.1 Hybrid Payment Model -- 2.2.2.2 Weighted Cost Function -- 2.2.2.3 Distributed Task Scheduling Algorithm -- 2.2.3 Fog as a Service Technology -- 2.2.3.1 FA2ST Framework -- 2.2.3.2 FA2ST Application Deployment -- 2.2.3.3 FA2ST Application Management -- 2.3 Edge‐enabled Intelligence for Industrial IoT -- 2.3.1 Introduction and Background -- 2.3.1.1 Intelligent Industrial IoT -- 2.3.1.2 Edge Intelligence -- 2.3.1.3 Challenges -- 2.3.2 Boomerang Framework -- 2.3.2.1 Framework Overview -- 2.3.2.2 Prediction Model and Right‐sizing Model -- 2.3.2.3 Boomerang Optimizer -- 2.3.2.4 Boomerang with DRL -- 2.3.3 Performance Evaluation -- 2.3.3.1 Emulation Environment -- 2.3.3.2 Evaluation Results -- 2.4 Fog‐enabled Collaborative SLAM of Robot Swarm -- 2.4.1 Introduction and Background -- 2.4.2 A Fog‐enabled Solution -- 2.4.2.1 System Architecture -- 2.4.2.2 Practical Implementation -- 2.5 Conclusion -- References -- Chapter 3 Cross‐Domain Resource Management Frameworks -- 3.1 Introduction -- 3.2 Joint Computation and Communication Resource Management for Delay‐Sensitive Applications -- 3.2.1 2C Resource Management Framework -- 3.2.1.1 System Model -- 3.2.1.2 Problem Formulation -- 3.2.2 Distributed Resource Management Algorithm -- 3.2.2.1 Paired Offloading of Non‐splittable Tasks -- 3.2.2.2 Parallel Offloading of Splittable Tasks -- 3.2.3 Delay Reduction Performance -- 3.2.3.1 Price of Anarchy -- 3.2.3.2 System Average Delay -- 3.2.3.3 Number of Beneficial TNs -- 3.2.3.4 Convergence -- 3.3 Joint Computing, Communication, and Caching Resource Management for Energy‐efficient Applications -- 3.3.1 Fog‐enabled 3C Resource Management Framework -- 3.3.1.1 System Resources.

3.3.1.2 Task Model -- 3.3.1.3 Task Execution -- 3.3.1.4 Problem Statement -- 3.3.2 Fog‐enabled 3C Resource Management Algorithm -- 3.3.2.1 F3C Algorithm Overview -- 3.3.2.2 F3C Algorithm for a Single Task -- 3.3.2.3 F3C Algorithm For Multiple Tasks -- 3.3.3 Energy Saving Performance -- 3.3.3.1 Energy Saving Performance with Different Task Numbers -- 3.3.3.2 Energy Saving Performance with Different Device Numbers -- 3.4 Case Study: Energy‐efficient Resource Management in Tactile Internet -- 3.4.1 Fog‐enabled Tactile Internet Architecture -- 3.4.2 Response Time and Power Efficiency Trade‐off -- 3.4.2.1 Response Time Analysis and Minimization -- 3.4.2.2 Trade‐off between Response Time and Power Efficiency -- 3.4.3 Cooperative Fog Computing -- 3.4.3.1 Response Time Analysis for Cooperative Fog Computing with N FNs -- 3.4.3.2 Response Time and Power Efficiency Trade‐off for Cooperative Fog Computing Networks -- 3.4.4 Distributed Optimization for Cooperative Fog Computing -- 3.4.5 A City‐wide Deployment of Fog Computing‐supported Self‐driving Bus System -- 3.4.5.1 Simulation Setup for Traffic Generated by a Self‐driving Bus -- 3.4.5.2 Simulation Setup for a Fog Computing Network -- 3.4.5.3 Numerical Results -- 3.5 Conclusion -- References -- Chapter 4 Dynamic Service Provisioning Frameworks -- 4.1 Online Orchestration of Cross‐edge Service Function Chaining -- 4.1.1 Introduction -- 4.1.2 Related Work -- 4.1.3 System Model for Cross‐edge SFC Deployment -- 4.1.3.1 Overview of the Cross‐edge System -- 4.1.3.2 Optimization Space -- 4.1.3.3 Cost Structure -- 4.1.3.4 The Cost Minimization Problem -- 4.1.4 Online Optimization for Long‐term Cost Minimization -- 4.1.4.1 Problem Decomposition via Relaxation and Regularization -- 4.1.4.2 A Randomized Dependent Rounding Scheme -- 4.1.4.3 Traffic Re‐routing -- 4.1.5 Performance Analysis -- 4.1.5.1 The Basic Idea.

4.1.5.2 Competitive Ratio of ORFA -- 4.1.5.3 Rounding Gap of RDIP -- 4.1.5.4 The Overall Competitive Ratio -- 4.1.6 Performance Evaluation -- 4.1.6.1 Experimental Setup -- 4.1.6.2 Evaluation Results -- 4.1.7 Future Directions -- 4.2 Dynamic Network Slicing for High‐quality Services -- 4.2.1 Service and User Requirements -- 4.2.2 Related Work -- 4.2.3 System Model and Problem Formulation -- 4.2.3.1 Fog Computing -- 4.2.3.2 Existing Network Slicing Architectures -- 4.2.3.3 Regional SDN‐based Orchestrator -- 4.2.3.4 Problem Formulation -- 4.2.4 Implementation and Numerical Results -- 4.2.4.1 Dynamic Network Slicing in 5G Networks -- 4.2.4.2 Numerical Results -- 4.3 Collaboration of Multiple Network Operators -- 4.3.1 Service and User Requirements -- 4.3.2 System Model and Problem Formulation -- 4.3.2.1 IoT Solutions in 3GPP Release 13 -- 4.3.2.2 Challenges for Massive IoT Deployment -- 4.3.2.3 Inter‐operator Network Sharing Architecture -- 4.3.2.4 Design Issues -- 4.3.3 Performance Analysis -- 4.4 Conclusion -- References -- Chapter 5 Lightweight Privacy‐Preserving Learning Schemes* -- 5.1 Introduction -- 5.2 System Model and Problem Formulation -- 5.3 Solutions and Results -- 5.3.1 A Lightweight Privacy‐preserving Collaborative Learning Scheme -- 5.3.1.1 Random Gaussian Projection (GRP) -- 5.3.1.2 Gaussian Random Projection Approach -- 5.3.1.3 Illustrating Examples -- 5.3.1.4 Evaluation Methodology and Datasets -- 5.3.1.5 Evaluation Results with the MNIST Dataset -- 5.3.1.6 Evaluation Results with a Spambase Dataset -- 5.3.1.7 Summary -- 5.3.1.8 Implementation and Benchmark -- 5.3.2 A Differentially Private Collaborative Learning Scheme -- 5.3.2.1 Approach Overview -- 5.3.2.2 Achieving ϵ‐Differential Privacy -- 5.3.2.3 Performance Evaluation -- 5.3.3 A Lightweight and Unobtrusive Data Obfuscation Scheme for Remote Inference -- 5.3.3.1 Approach Overview.

5.3.3.2 Case Study 1: Free Spoken Digit (FSD) Recognition -- 5.3.3.3 Case Study 2: Handwritten Digit (MNIST) Recognition -- 5.3.3.4 Case Study 3: American Sign Language (ASL) Recognition -- 5.3.3.5 Implementation and Benchmark -- 5.4 Conclusion -- References -- Chapter 6 Clock Synchronization for Wide‐area Applications1 -- 6.1 Introduction -- 6.2 System Model and Problem Formulation -- 6.2.1 Natural Timestamping for Wireless IoT Devices -- 6.2.2 Clock Synchronization for Wearable IoT Devices -- 6.3 Natural Timestamps in Powerline Electromagnetic Radiation -- 6.3.1 Electrical Network Frequency Fluctuations and Powerline Electromagnetic Radiation -- 6.3.2 Electromagnetic Radiation‐based Natural Timestamping -- 6.3.2.1 Hardware -- 6.3.2.2 ENF Extraction -- 6.3.2.3 Natural Timestamp and Decoding -- 6.3.3 Implementation and Benchmark -- 6.3.3.1 Timestamping Parameter Settings -- 6.3.3.2 Z1 Implementation and Benchmark -- 6.3.4 Evaluation in Office and Residential Environments -- 6.3.4.1 Deployment and Settings -- 6.3.4.2 Evaluation Results -- 6.3.5 Evaluation in a Factory Environment -- 6.3.6 Applications -- 6.3.6.1 Time Recovery -- 6.3.6.2 Run‐time Clock Verification -- 6.3.6.3 Secure Clock Synchronization -- 6.4 Wearables Clock Synchronization Using Skin Electric Potentials -- 6.4.1 Motivation -- 6.4.2 Measurement Study -- 6.4.2.1 Measurement Setup -- 6.4.2.2 iSEP Sensing under Various Settings -- 6.4.3 TouchSync System Design -- 6.4.3.1 TouchSync Workflow -- 6.4.3.2 iSEP Signal Processing -- 6.4.3.3 NTP Assisted with Dirac Combs -- 6.4.4 TouchSync with Internal Periodic Signal -- 6.4.4.1 Extended Design -- 6.4.4.2 Numeric Experiments -- 6.4.4.3 Impact of Clock Skews -- 6.4.5 Implementation -- 6.4.6 Evaluation -- 6.4.6.1 Signal Strength and Wearing Position -- 6.4.6.2 Impact of High‐Power Appliances on TouchSync.

6.4.6.3 Evaluation in Various Environments.

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