Cyber Physical Systems : Architectures, Protocols and Applications

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Title:

Series:

Wireless Networks and Mobile Communications

Physical Description:

xxi, 249 pages : illustrations ; 24 cm

ISBN:

9780367377113

Abstract:

Cyber Physical Systems: Architectures, Protocols and Applications helps you understand the basic principles and key supporting standards of CPS. It analyzes different CPS applications from the bottom up, extracting the common characters that form a vertical structure. It presents mobile sensing platforms and their applications toward interrelated paradigms, highlighting and briefly discussing different types of mobile sensing platforms and the functionalities they offer. It then looks at the naming, addressing, and profile services of CPS and proposes a middleware component to meet the requirements of dynamic applications and sensors/actuators deployment/configurations across different platforms.

Subject Term:

Cooperating objects (Computer systems)

Added Author:

Liu, Chi Harold,

Zhang, Yan,

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... Razak School	33000000006536	TK5105.67 C93 2019	Open Access Book	Book	Searching... Unknown

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Summary

Cyber Physical Systems: Architectures, Protocols and Applicationshelps you understand the basic principles and key supporting standards of CPS. It analyzes different CPS applications from the bottom up, extracting the common characters that form a vertical structure. It presents mobile sensing platforms and their applications toward interrelated paradigms, highlighting and briefly discussing different types of mobile sensing platforms and the functionalities they offer. It then looks at the naming, addressing, and profile services of CPS and proposes a middleware component to meet the requirements of dynamic applications and sensors/actuators deployment/configurations across different platforms.

The middle chapters of the book present a context-aware sensor search, selection, and ranking model which addresses the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. The authors consider various topics in the energy management of CPS and propose a novel energy-efficient framework. They also present the fundamental networking technologies of CPS and focus on machine-to-machine communications for CPS, specifically the open technologies such as IPv6-based solutions that can be integrated into IoT and enable wireless sensor communications.

In the book's final chapters, the authors bring you up to date on mobile cloud computing (MCC) research activities that enhance the capabilities of resource-constrained smart devices in CPS sensory environments. They also present a few representative CPS applications, including connected healthcare, gaming in public transport crowds, and a series of MCC-enabled emerging CPS applications. You will find that these application fields fully demonstrate the great potential of applying CPS in public life.

Author Notes

Chi (Harold) Liuis a Full Professor and Assistant Dean at the School of Software, Beijing Institute of Technology, China. Before that, he was a staff researcher and project manager of wireless and internet of things (IoT) at IBM Research - China. He holds a PhD degree from Imperial College, UK, and a B.Eng. degree from Tsinghua University, China. Before joining IBM Research, he worked as a postdoctoral researcher at the Deutsche Telekom AG in Berlin, Germany, and a visiting researcher at IBM T.J. Watson Research Center, Hawthorne, NY.

Yan Zhang received his PhD degree from the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore, and is employed by Simula Research Laboratory, Lysaker, Norway. He is on the editorial board of many international journals and currently serves as the "Wireless Networks and Mobile Communications" book series editor. He also is serving as co-editor for several books. His research interests include resource, mobility, energy, and security management in wireless networks and mobile computing. He is a member of IEEE and IEEE ComSoc.

Chi Harold Liu and Jianxin Zhao and Yan ZhangChi Harold Liu and Jianxin ZhaoCharith Perera and Prem P. Jayaraman and Srimal Jayawardena and Arkady Zaslavsky and Chi Harold Liu and Peter ChristenChi Harold LiuCharith Perera and Chi Harold Liu and Peter ChristenChi Harold LiuChi Harold Liu and Zhengguo ShengZhengguo Sheng and Hao Wang and Daging GuChi Harold LiuChi Harold LiuChi Harold LiuChi Harold LiuChi Harold Liu and Jianxin Zhao and Yan Zhang

List of Figures	p. xiii
List of Tables	p. xix
List of Contributors	p. xxi
1 Background	p. 1
Section I CPS Architecture	p. 5
2 Overall Architecture for CPS	p. 7
3 Mobile Sensing Devices and Platforms for CPS	p. 11
3.1 Introduction	p. 12
3.2 Mobile Sensing in Internet of Things Paradigm	p. 13
3.3 Strategies, Patterns, and Practice of Mobile Sensing	p. 15
3.4 MOSDEN: Mobile Sensor Data Engine	p. 17
3.4.1 Problem Definition	p. 17
3.4.2 MOSDEN: Architectural Design	p. 17
3.4.3 Plugin Architecture	p. 18
3.4.4 General Architecture	p. 19
3.4.5 Interaction with the Cloud and Peers	p. 19
3.4.6 Distributed Processing	p. 21
3.5 Implementation	p. 21
3.5.1 Plugin Development	p. 22
3.6 Performance Evaluation and Lessons Learned	p. 26
3.6.1 Experimental Testbed	p. 27
3.6.2 Stand-Alone Experimentation	p. 27
3.6.3 Collaborative Sensing Experimentation	p. 31
3.7 Open Challenges and Opportunities	p. 36
3.7.1 Automated Configuration	p. 36
3.7.2 Unified Middleware Platform	p. 37
3.7.3 Optimized Data Processing Strategy	p. 38
3.7.4 Multi-Protocol Support	p. 38
3.7.5 Modular Reasoning, Fusing, and Filtering	p. 39
3.8 Summary	p. 40
4 Naming, Addressing, and Profile Services for CPS	p. 41
4.1 Introduction	p. 42
4.1.1 Scope and Assumptions	p. 43
4.1.2 Contributions and Chapter Organization	p. 44
4.2 Related Work	p. 45
4.3 System Flows	p. 46
4.3.1 Device Registration and Configurations	p. 47
4.3.2 Upstream Data Collection	p. 47
4.3.3 Downstream Command Delivery	p. 49
4.3.4 Application Query	p. 49
4.3.5 Integration with Different CPS Platforms	p. 49
4.4 System Designs and Implementations	p. 51
4.4.1 RESTful Interfaces	p. 51
4.4.2 Naming and Addressing Convention	p. 53
4.4.3 Generating the devID	p. 55
4.5 A Case Study	p. 56
4.5.1 Device Deployment, Naming, and Addressing Format	p. 56
4.5.2 A Device Registration Portal	p. 59
4.6 Performance Evaluation	p. 60
4.7 Discussion	p. 64
4.7.1 DDoS Attacks	p. 64
4.7.2 Compatibility with IPv6	p. 65
4.8 Summary	p. 65
5 Device Search and Selection for CPS	p. 67
5.1 Introduction	p. 68
5.2 Internet of Things Architecture and Search Functionality	p. 69
5.2.1 Sensing Device Searching from Functional Perspective	p. 70
5.2.2 Sensing Device Searching from Implementation Perspective	p. 72
5.3 Problem Definition	p. 76
5.4 Context-Aware Approach for Device Search and Selection	p. 77
5.4.1 High-Level Model Overview	p. 77
5.4.2 Capturing User Priorities	p. 80
5.4.3 Data Modelling and Representation	p. 80
5.4.4 Filtering Using Querying Reasoning	p. 82
5.4.5 Ranking Using Quantitative Reasoning	p. 84
5.4.6 Context Framework	p. 85
5.5 Improving Efficiency	p. 85
5.5.1 Comparative-Priority Based Heuristic Filtering (CPHF)	p. 86
5.5.2 Relational-Expression Based Filtering (REF)	p. 87
5.5.3 Distributed Sensor Searching	p. 88
5.6 Implementation and Experimentation	p. 90
5.7 Performance Evaluation	p. 91
5.7.1 Evaluating Alternative Storage Options	p. 94
5.7.2 Evaluating Distributed Sensor Searching	p. 95
5.8 Open Challenges and Future Research Directions	p. 96
5.8.1 Context Discovery, Processing, and Storage	p. 97
5.8.2 Utility Computing Models and Sensing as a Service	p. 97
5.8.3 Automated Smart Device Configuration	p. 98
5.8.4 Optimize Sensing Strategy Development	p. 98
5.9 Summary	p. 99
6 Energy Management for CPS	p. 101
6.1 Introduction	p. 102
6.2 Related Work	p. 103
6.3 System Model	p. 105
6.3.1 Sensors	p. 105
6.3.2 Tasks	p. 106
6.3.3 System Flow	p. 106
6.4 QoI-Aware Sensor-to-Task Relevancy and Critical Covering Sets	p. 107
6.4.1 Information Fusion	p. 108
6.4.2 Critical Covering Set	p. 108
6.5 QoI-Aware Energy Management	p. 109
6.5.1 Duty-Cycling of Sensors	p. 109
6.5.2 Delay Model for Tasks	p. 110
6.5.3 Problem Formulation	p. 111
6.5.3.1 Minimize the Maximum Duty Cycle	p. 111
6.5.3.2 Minimize Weighted Average Duty Cycle	p. 112
6.5.4 A Greedy Algorithm	p. 112
6.6 Performance Evaluation	p. 117
6.6.1 System Model and Simulation Setup	p. 117
6.6.2 Simulation Results	p. 119
6.7 Modeling the Signal Transmission and Processing Latency	p. 123
6.7.1 Model Description and Problem Formulation	p. 124
6.7.2 Satisfactory Region of Delay Tolerance	p. 127
6.7.3 Results	p. 128
6.8 Implementation Guidelines	p. 128
6.9 Summary	p. 130
Section II Enabling Technologies for CPS	p. 131
7 Networking Technologies for CPS	p. 133
7.1 Sensing Networks	p. 134
7.1.1 433MHz Proprietary Solutions	p. 134
7.1.2 ZigBee	p. 134
7.1.3 RFID	p. 135
7.1.4 Bluetooth	p. 135
7.2 Data Connectivity	p. 136
7.2.1 2G/3G SIM Modules	p. 136
8 Machine-to-Machine Communications for CPS	p. 139
8.1 Introduction	p. 140
8.2 Related Works	p. 141
8.3 A RESTful Protocol Stack for WSN	p. 142
8.3.1 6LoWPAN	p. 142
8.3.2 RPL	p. 144
8.3.3 CoAP	p. 145
8.3.4 HTTP-CoAP Protocol Implementation	p. 147
8.3.4.1 Direct Access	p. 147
8.3.4.2 Proxy Access	p. 147
8.4 Prototypiiig Implementation	p. 148
8.4.1 Sensor Node	p. 148
8.4.2 RESTful Gateway	p. 149
8.4.2.1 Libcoap Layer	p. 151
8.4.2.2 CoAP Request/Response Layer	p. 152
8.4.2.3 HTTP-CoAP Mapping Layer	p. 153
8.5 Performance Evaluation	p. 154
8.5.1 System Configuration	p. 154
8.5.2 RTTs and Packet Loss Evaluations of RPL Routing	p. 154
8.5.3 RESTful Method to Retrieve Sensor Resources	p. 155
8.6 Summary	p. 157
9 Mobile Cloud Computing for CPS	p. 159
9.1 Introduction	p. 160
9.2 MCC Definition	p. 162
9.3 Challenges	p. 163
9.3.1 Managing the Task Offloading	p. 163
9.3.2 Encountering Heterogeneity	p. 166
9.3.3 Enhancing Security and Protecting Privacy	p. 169
9.3.4 Economic and Business Model	p. 171
9.4 Future Directions	p. 172
9.4.1 Managing the Task Offloading	p. 172
9.4.1.1 Scalability in the Device Cloud	p. 172
9.4.1.2 Making the Offloading Decision Process Transparent to the Application Developer	p. 173
9.4.1.3 Context Awareness on Trading Off the Optimization between Performance Improvement and Energy Saving	p. 173
9.4.1.4 Tasks Distributing among Sensors	p. 173
9.4.1.5 Offloading Decision Making in a Hybrid Cloud	p. 174
9.4.2 Encountering Heterogeneity	p. 174
9.4.2.1 Efficient Middleware	p. 174
9.4.2.2 Dynamic Adaptive Automated System	p. 174
9.4.2.3 Mobile Big Data	p. 175
9.4.3 Enhancing Security and Privacy	p. 175
9.4.3.1 Finding Protection Solutions That Are More Efficient Is Still a Research Topic	p. 175
9.4.3.2 Context Awareness on Dynamic Security Settings	p. 175
9.4.3.3 Trade Off between the Functional Performance Degradation and Security and Privacy Requirements	p. 176
9.4.4 Economic and Business Models	p. 176
9.5 Summary	p. 177
Section III CPS Applications	p. 179
10 Connected Healthcare for CPS	p. 181
10.1 Introduction	p. 182
10.2 Related Work	p. 183
10.3 System Model	p. 184
10.4 Sensor Proxy Design	p. 185
10.4.1 Data Capture Module	p. 185
10.4.2 Internal Event Pub/Sub Engine	p. 185
10.4.3 Process Service Module	p. 186
10.4.4 Transportation Service Module	p. 187
10.4.5 Device Management Service Module	p. 188
10.5 HTTP Interface	p. 188
10.5.1 Get Naming and Addressing	p. 188
10.5.1.1 Sensor Proxy Naming	p. 188
10.5.1.2 Biomedical Sensors Naming	p. 188
10.5.1.3 Biomedical Sensors Addressing	p. 189
10.5.2 Start Blood Pressure/Glucose Reader	p. 189
10.5.3 Get Social Security Card ID	p. 189
10.5.4 Get Blood Pressure/Glucose Data	p. 190
10.6 Case Studies	p. 190
10.6.1 Stationary HealthKiosk	p. 190
10.6.2 Mobile HealthKiosk	p. 191
10.7 Summary	p. 193
11 Multi-Player Gaming for Public Transport Crowd	p. 195
11.1 Introduction	p. 196
11.2 A CrowdMoG Use Case Scenario	p. 201
11.3 CrowdMoG Design	p. 202
11.3.1 Cloud-Based Game Services	p. 203
11.3.2 Cloud Manager	p. 204
11.3.3 Group Manager	p. 205
11.3.3.1 Peer Manager	p. 205
11.3.3.2 Session Dynamics Manager	p. 206
11.3.4 Network Protocol Manager	p. 206
11.3.5 Game Feature Extractor	p. 207
11.4 Prototype - Phage	p. 207
11.5 Summary	p. 209
12 Mobile Cloud Computing Enabled Emerging CPS Applications	p. 211
12.1 Education	p. 212
12.2 Office Automation	p. 212
12.3 Healthcare	p. 213
12.4 Mission-Critical Applications	p. 214
12.5 Summary	p. 215
13 Conclusion	p. 217
References	p. 219
Index	p. 243

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Table of Contents