{"product_id":"fog-computing-9781119551690","title":"Fog Computing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eSummarizes the current state and upcoming trends within the area of fog computing\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWritten by some of the leading experts in the field, \u003ci\u003eFog Computing: Theory and Practice\u003c\/i\u003efocuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth.\u003c\/p\u003e \u003cp\u003ePresented in two partsFog Computing Systems and Architectures, and Fog Computing Techniques and Applicationthis book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricin\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eList of Contributors xxiii\u003c\/p\u003e \u003cp\u003eAcronyms xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Fog Computing Systems and Architectures \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Mobile Fog Computing \u003c\/b\u003e\u003cb\u003e3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChii Chang, Amnir Hadachi, Jakob Mass, and Satish Narayana Srirama\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Mobile Fog Computing and Related Models 5\u003c\/p\u003e \u003cp\u003e1.3 The Needs of Mobile Fog Computing 6\u003c\/p\u003e \u003cp\u003e1.3.1 Infrastructural Mobile Fog Computing 7\u003c\/p\u003e \u003cp\u003e1.3.2 Land Vehicular Fog 9\u003c\/p\u003e \u003cp\u003e1.3.3 Marine Fog 11\u003c\/p\u003e \u003cp\u003e1.3.4 Unmanned Aerial Vehicular Fog 12\u003c\/p\u003e \u003cp\u003e1.3.5 User Equipment-Based Fog 13\u003c\/p\u003e \u003cp\u003e1.4 Communication Technologies 15\u003c\/p\u003e \u003cp\u003e1.4.1 IEEE 802.11 15\u003c\/p\u003e \u003cp\u003e1.4.2 4G, 5G Standards 16\u003c\/p\u003e \u003cp\u003e1.4.3 WPAN, Short-Range Technologies 17\u003c\/p\u003e \u003cp\u003e1.4.4 LPWAN, Other Medium- and Long-Range Technologies 18\u003c\/p\u003e \u003cp\u003e1.5 Nonfunctional Requirements 18\u003c\/p\u003e \u003cp\u003e1.5.1 Heterogeneity 20\u003c\/p\u003e \u003cp\u003e1.5.2 Context-Awareness 23\u003c\/p\u003e \u003cp\u003e1.5.3 Tenant 25\u003c\/p\u003e \u003cp\u003e1.5.4 Provider 27\u003c\/p\u003e \u003cp\u003e1.5.5 Security 29\u003c\/p\u003e \u003cp\u003e1.6 Open Challenges 31\u003c\/p\u003e \u003cp\u003e1.6.1 Challenges in Land Vehicular Fog Computing 31\u003c\/p\u003e \u003cp\u003e1.6.2 Challenges in Marine Fog Computing 32\u003c\/p\u003e \u003cp\u003e1.6.3 Challenges in Unmanned Aerial Vehicular Fog Computing 32\u003c\/p\u003e \u003cp\u003e1.6.4 Challenges in User Equipment-based Fog Computing 33\u003c\/p\u003e \u003cp\u003e1.6.5 General Challenges 33\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 35\u003c\/p\u003e \u003cp\u003eAcknowledgment 36\u003c\/p\u003e \u003cp\u003eReferences 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Edge and Fog: A Survey, Use Cases, and Future Challenges 43\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCosmin Avasalcai, Ilir Murturi, and Schahram Dustdar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 43\u003c\/p\u003e \u003cp\u003e2.2 Edge Computing 44\u003c\/p\u003e \u003cp\u003e2.2.1 Edge Computing Architecture 46\u003c\/p\u003e \u003cp\u003e2.3 Fog Computing 47\u003c\/p\u003e \u003cp\u003e2.3.1 Fog Computing Architecture 49\u003c\/p\u003e \u003cp\u003e2.4 Fog and Edge Illustrative Use Cases 50\u003c\/p\u003e \u003cp\u003e2.4.1 Edge Computing Use Cases 50\u003c\/p\u003e \u003cp\u003e2.4.2 Fog Computing Use Cases 54\u003c\/p\u003e \u003cp\u003e2.5 Future Challenges 57\u003c\/p\u003e \u003cp\u003e2.5.1 Resource Management 57\u003c\/p\u003e \u003cp\u003e2.5.2 Security and Privacy 58\u003c\/p\u003e \u003cp\u003e2.5.3 Network Management 61\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 61\u003c\/p\u003e \u003cp\u003eAcknowledgment 62\u003c\/p\u003e \u003cp\u003eReferences 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities \u003c\/b\u003e\u003cb\u003e67\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMi Zhang, Faen Zhang, Nicholas D. Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, and Hui Xu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 67\u003c\/p\u003e \u003cp\u003e3.2 Challenges and Opportunities 68\u003c\/p\u003e \u003cp\u003e3.2.1 Memory and Computational Expensiveness of DNN Models 68\u003c\/p\u003e \u003cp\u003e3.2.2 Data Discrepancy in Real-world Settings 70\u003c\/p\u003e \u003cp\u003e3.2.3 Constrained Battery Life of Edge Devices 71\u003c\/p\u003e \u003cp\u003e3.2.4 Heterogeneity in Sensor Data 72\u003c\/p\u003e \u003cp\u003e3.2.5 Heterogeneity in Computing Units 73\u003c\/p\u003e \u003cp\u003e3.2.6 Multitenancy of Deep Learning Tasks 73\u003c\/p\u003e \u003cp\u003e3.2.7 Offloading to Nearby Edges 75\u003c\/p\u003e \u003cp\u003e3.2.8 On-device Training 76\u003c\/p\u003e \u003cp\u003e3.3 Concluding Remarks 76\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Caching, Security, and Mobility in Content-centric Networking \u003c\/b\u003e\u003cb\u003e79\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOsman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Assad Abbas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 79\u003c\/p\u003e \u003cp\u003e4.2 Caching and Fog Computing 81\u003c\/p\u003e \u003cp\u003e4.3 Mobility Management in CCN 82\u003c\/p\u003e \u003cp\u003e4.3.1 Classification of CCN Contents and their Mobility 83\u003c\/p\u003e \u003cp\u003e4.3.2 User Mobility 83\u003c\/p\u003e \u003cp\u003e4.3.3 Server-side Mobility 84\u003c\/p\u003e \u003cp\u003e4.3.4 Direct Exchange for Location Update 84\u003c\/p\u003e \u003cp\u003e4.3.5 Query to the Rendezvous for Location Update 84\u003c\/p\u003e \u003cp\u003e4.3.6 Mobility with Indirection Point 84\u003c\/p\u003e \u003cp\u003e4.3.7 Interest Forwarding 85\u003c\/p\u003e \u003cp\u003e4.3.8 Proxy-based Mobility Management 85\u003c\/p\u003e \u003cp\u003e4.3.9 Tunnel-based Redirection (TBR) 86\u003c\/p\u003e \u003cp\u003e4.4 Security in Content-centric Networks 88\u003c\/p\u003e \u003cp\u003e4.4.1 Risks Due to Caching 90\u003c\/p\u003e \u003cp\u003e4.4.2 DOS Attack Risk 90\u003c\/p\u003e \u003cp\u003e4.4.3 Security Model 91\u003c\/p\u003e \u003cp\u003e4.5 Caching 91\u003c\/p\u003e \u003cp\u003e4.5.1 Cache Allocation Approaches 91\u003c\/p\u003e \u003cp\u003e4.5.2 Data Allocation Approaches 93\u003c\/p\u003e \u003cp\u003e4.6 Conclusions 101\u003c\/p\u003e \u003cp\u003eReferences 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Security and Privacy Issues in Fog Computing \u003c\/b\u003e\u003cb\u003e105\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAhmad Ali, Mansoor Ahmed, Muhammad Imran, and Hasan Ali Khattak\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 105\u003c\/p\u003e \u003cp\u003e5.2 Trust in IoT 107\u003c\/p\u003e \u003cp\u003e5.3 Authentication 109\u003c\/p\u003e \u003cp\u003e5.3.1 Related Work 109\u003c\/p\u003e \u003cp\u003e5.4 Authorization 113\u003c\/p\u003e \u003cp\u003e5.4.1 Related Work 114\u003c\/p\u003e \u003cp\u003e5.5 Privacy 117\u003c\/p\u003e \u003cp\u003e5.5.1 Requirements of Privacy in IoT 118\u003c\/p\u003e \u003cp\u003e5.6 Web Semantics and Trust Management for Fog Computing 120\u003c\/p\u003e \u003cp\u003e5.6.1 Trust Through Web Semantics 120\u003c\/p\u003e \u003cp\u003e5.7 Discussion 123\u003c\/p\u003e \u003cp\u003e5.7.1 Authentication 124\u003c\/p\u003e \u003cp\u003e5.7.2 Authorization 125\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 130\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 How Fog Computing Can Suppor Latency\/Reliability-sensitive IoT Applications: An Overview and a Taxonomy of State-of-the-art Solutions \u003c\/b\u003e\u003cb\u003e139\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePaolo Bellavista, Javier Berrocal, Antonio Corradi, Sajal K. Das, Luca Foschini, Isam Mashhour Al Jawarneh, and Alessandro Zanni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 139\u003c\/p\u003e \u003cp\u003e6.2 Fog Computing for IoT: Definition and Requirements 142\u003c\/p\u003e \u003cp\u003e6.2.1 Definitions 142\u003c\/p\u003e \u003cp\u003e6.2.2 Motivations 144\u003c\/p\u003e \u003cp\u003e6.2.3 Fog Computing Requirements When Applied to Challenging IoTs Application Domains 148\u003c\/p\u003e \u003cp\u003e6.2.4 IoT Case Studies 152\u003c\/p\u003e \u003cp\u003e6.3 Fog Computing: Architectural Model 154\u003c\/p\u003e \u003cp\u003e6.3.1 Communication 154\u003c\/p\u003e \u003cp\u003e6.3.2 Security and Privacy 156\u003c\/p\u003e \u003cp\u003e6.3.3 Internet of Things 156\u003c\/p\u003e \u003cp\u003e6.3.4 Data Quality 156\u003c\/p\u003e \u003cp\u003e6.3.5 Cloudification 157\u003c\/p\u003e \u003cp\u003e6.3.6 Analytics and Decision-Making 157\u003c\/p\u003e \u003cp\u003e6.4 Fog Computing for IoT: A Taxonomy 158\u003c\/p\u003e \u003cp\u003e6.4.1 Communication 159\u003c\/p\u003e \u003cp\u003e6.4.2 Security and Privacy Layer 165\u003c\/p\u003e \u003cp\u003e6.4.3 Internet of Things 170\u003c\/p\u003e \u003cp\u003e6.4.4 Data Quality 173\u003c\/p\u003e \u003cp\u003e6.4.5 Cloudification 179\u003c\/p\u003e \u003cp\u003e6.4.6 Analytics and Decision-Making Layer 183\u003c\/p\u003e \u003cp\u003e6.5 Comparisons of Surveyed Solutions 189\u003c\/p\u003e \u003cp\u003e6.5.1 Communication 189\u003c\/p\u003e \u003cp\u003e6.5.2 Security and Privacy 191\u003c\/p\u003e \u003cp\u003e6.5.3 Internet of Things 193\u003c\/p\u003e \u003cp\u003e6.5.4 Data Quality 194\u003c\/p\u003e \u003cp\u003e6.5.5 Cloudification 195\u003c\/p\u003e \u003cp\u003e6.5.6 Analytics and Decision-Making Layer 197\u003c\/p\u003e \u003cp\u003e6.6 Challenges and Recommended Research Directions 198\u003c\/p\u003e \u003cp\u003e6.7 Concluding Remarks 201\u003c\/p\u003e \u003cp\u003eReferences 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Harnessing the Computing Continuum for Programming Our World \u003c\/b\u003e\u003cb\u003e215\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, and Micah Beck\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction and Overview 215\u003c\/p\u003e \u003cp\u003e7.2 Research Philosophy 217\u003c\/p\u003e \u003cp\u003e7.3 A Goal-oriented Approach to Programming the Computing Continuum 219\u003c\/p\u003e \u003cp\u003e7.3.1 A Motivating Continuum Example 219\u003c\/p\u003e \u003cp\u003e7.3.2 Goal-oriented Annotations for Intensional Specification 221\u003c\/p\u003e \u003cp\u003e7.3.3 A Mapping and Run-time System for the Computing Continuum 222\u003c\/p\u003e \u003cp\u003e7.3.4 Building Blocks and Enabling Technologies 224\u003c\/p\u003e \u003cp\u003e7.4 Summary 228\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fog Computing for Energy Harvesting-enabled Internet of Things \u003c\/b\u003e\u003cb\u003e231\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eS. A. Tegos, P. D. Diamantoulakis, D. S. Michalopoulos, and G. K. Karagiannidis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 231\u003c\/p\u003e \u003cp\u003e8.2 System Model 232\u003c\/p\u003e \u003cp\u003e8.2.1 Computation Model 233\u003c\/p\u003e \u003cp\u003e8.2.2 Energy Harvesting Model 235\u003c\/p\u003e \u003cp\u003e8.3 Tradeoffs in EH Fog Systems 238\u003c\/p\u003e \u003cp\u003e8.3.1 Energy Consumption vs. Latency 238\u003c\/p\u003e \u003cp\u003e8.3.2 Execution Delay vs. Task Dropping Cost 239\u003c\/p\u003e \u003cp\u003e8.4 Future Research Challenges 240\u003c\/p\u003e \u003cp\u003eAcknowledgment 241\u003c\/p\u003e \u003cp\u003eReferences 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control \u003c\/b\u003e\u003cb\u003e245\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDelaram Amiri, Arman Anzanpour, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Nikil Dutt, and Marco Levorato\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 245\u003c\/p\u003e \u003cp\u003e9.2 Background 247\u003c\/p\u003e \u003cp\u003e9.3 Related Topics 249\u003c\/p\u003e \u003cp\u003e9.4 Design Challenges 250\u003c\/p\u003e \u003cp\u003e9.5 IoT System Architecture 251\u003c\/p\u003e \u003cp\u003e9.5.1 Fog Computing and its Benefits 252\u003c\/p\u003e \u003cp\u003e9.6 Fog-assisted Runtime Energy Management in Wearable Sensors 253\u003c\/p\u003e \u003cp\u003e9.6.1 Computational Self-Awareness 255\u003c\/p\u003e \u003cp\u003e9.6.2 Energy Optimization Algorithms 255\u003c\/p\u003e \u003cp\u003e9.6.3 Myopic Strategy 258\u003c\/p\u003e \u003cp\u003e9.6.4 MDP Strategy 259\u003c\/p\u003e \u003cp\u003e9.7 Conclusions 263\u003c\/p\u003e \u003cp\u003eAcknowledgment 264\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Latency Minimization Through Optimal Data Placement in Fog Networks \u003c\/b\u003e\u003cb\u003e269\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNing Wang and Jie Wu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 269\u003c\/p\u003e \u003cp\u003e10.2 RelatedWork 272\u003c\/p\u003e \u003cp\u003e10.2.1 Long-Term and Short-Term Placement 272\u003c\/p\u003e \u003cp\u003e10.2.2 Data Replication 272\u003c\/p\u003e \u003cp\u003e10.3 Problem Statement 273\u003c\/p\u003e \u003cp\u003e10.3.1 Network Model 273\u003c\/p\u003e \u003cp\u003e10.3.2 Multiple Data Placement with Budget Problem 274\u003c\/p\u003e \u003cp\u003e10.3.3 Challenges 274\u003c\/p\u003e \u003cp\u003e10.4 Delay Minimization Without Replication 275\u003c\/p\u003e \u003cp\u003e10.4.1 Problem Formulation 275\u003c\/p\u003e \u003cp\u003e10.4.2 Min-Cost Flow Formulation 276\u003c\/p\u003e \u003cp\u003e10.4.3 Complexity Reduction 277\u003c\/p\u003e \u003cp\u003e10.5 Delay Minimization with Replication 279\u003c\/p\u003e \u003cp\u003e10.5.1 Hardness Proof 279\u003c\/p\u003e \u003cp\u003e10.5.2 Single Request in Line Topology 279\u003c\/p\u003e \u003cp\u003e10.5.3 Greedy Solution in Multiple Requests 280\u003c\/p\u003e \u003cp\u003e10.5.4 Rounding Approach in Multiple Requests 282\u003c\/p\u003e \u003cp\u003e10.6 Performance Evaluation 285\u003c\/p\u003e \u003cp\u003e10.6.1 Trace Information 285\u003c\/p\u003e \u003cp\u003e10.6.2 Experimental Setting 285\u003c\/p\u003e \u003cp\u003e10.6.3 Algorithm Comparison 286\u003c\/p\u003e \u003cp\u003e10.6.4 Experimental Results 287\u003c\/p\u003e \u003cp\u003e10.7 Conclusion 289\u003c\/p\u003e \u003cp\u003eAcknowledgement 289\u003c\/p\u003e \u003cp\u003eReferences 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim++ \u003c\/b\u003e\u003cb\u003e293\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTariq Qayyum, Asad Waqar Malik, Muazzam A. Khan, and Samee U. Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 293\u003c\/p\u003e \u003cp\u003e11.2 Modeling and Simulation 294\u003c\/p\u003e \u003cp\u003e11.3 FogNetSim++: Architecture 296\u003c\/p\u003e \u003cp\u003e11.4 FogNetSim++: Installation and Environment Setup 298\u003c\/p\u003e \u003cp\u003e11.4.1 OMNeT++ Installation 298\u003c\/p\u003e \u003cp\u003e11.4.2 FogNetSim++ Installation 300\u003c\/p\u003e \u003cp\u003e11.4.3 Sample Fog Simulation 300\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 305\u003c\/p\u003e \u003cp\u003eReferences 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Fog Computing Techniques and Applications \u003c\/b\u003e\u003cb\u003e309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Distributed Machine Learning for IoT Applications in the Fog \u003c\/b\u003e\u003cb\u003e311\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAluizio F. Rocha Neto, Flavia C. Delicato, Thais V. Batista, and Paulo F. Pires\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 311\u003c\/p\u003e \u003cp\u003e12.2 Challenges in Data Processing for IoT 314\u003c\/p\u003e \u003cp\u003e12.2.1 Big Data in IoT 315\u003c\/p\u003e \u003cp\u003e12.2.2 Big Data Stream 318\u003c\/p\u003e \u003cp\u003e12.2.3 Data Stream Processing 319\u003c\/p\u003e \u003cp\u003e12.3 Computational Intelligence and Fog Computing 322\u003c\/p\u003e \u003cp\u003e12.3.1 Machine Learning 322\u003c\/p\u003e \u003cp\u003e12.3.2 Deep Learning 326\u003c\/p\u003e \u003cp\u003e12.4 Challenges for Running Machine Learning on Fog Devices 328\u003c\/p\u003e \u003cp\u003e12.4.1 Solutions Available on the Market to Deploy ML on Fog Devices 331\u003c\/p\u003e \u003cp\u003e12.5 Approaches to Distribute Intelligence on Fog Devices 334\u003c\/p\u003e \u003cp\u003e12.6 Final Remarks 340\u003c\/p\u003e \u003cp\u003eAcknowledgments 341\u003c\/p\u003e \u003cp\u003eReferences 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Fog Computing-Based Communication Systems for Modern Smart Grids \u003c\/b\u003e\u003cb\u003e347\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMiodrag Forcan and Mirjana Maksimović\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 347\u003c\/p\u003e \u003cp\u003e13.2 An Overview of Communication Technologies in Smart Grid 349\u003c\/p\u003e \u003cp\u003e13.3 Distribution Management System (DMS) Based on Fog\/Cloud Computing 356\u003c\/p\u003e \u003cp\u003e13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak 359\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 366\u003c\/p\u003e \u003cp\u003eReferences 367\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems \u003c\/b\u003e\u003cb\u003e371\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChu-ge Wu and Ling Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 371\u003c\/p\u003e \u003cp\u003e14.2 Estimation of Distribution Algorithm 372\u003c\/p\u003e \u003cp\u003e14.3 Related Work 373\u003c\/p\u003e \u003cp\u003e14.4 Problem Statement 374\u003c\/p\u003e \u003cp\u003e14.5 Details of Proposed Algorithm 376\u003c\/p\u003e \u003cp\u003e14.5.1 Encoding and Decoding Method 376\u003c\/p\u003e \u003cp\u003e14.5.2 uEDA Scheme 377\u003c\/p\u003e \u003cp\u003e14.5.3 Local Search Method 378\u003c\/p\u003e \u003cp\u003e14.6 Simulation 378\u003c\/p\u003e \u003cp\u003e14.6.1 Comparison Algorithm 378\u003c\/p\u003e \u003cp\u003e14.6.2 Simulation Environment and Experiment Settings 379\u003c\/p\u003e \u003cp\u003e14.6.3 Compared with the Heuristic Method 381\u003c\/p\u003e \u003cp\u003e14.7 Conclusion 383\u003c\/p\u003e \u003cp\u003eReferences 383\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Reliable and Power-Efficient Machine Learning in Wearable Sensors \u003c\/b\u003e\u003cb\u003e385\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eParastoo Alinia and Hassan Ghasemzadeh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 385\u003c\/p\u003e \u003cp\u003e15.2 Preliminaries and Related Work 386\u003c\/p\u003e \u003cp\u003e15.2.1 Gold Standard MET Computation 386\u003c\/p\u003e \u003cp\u003e15.2.2 Sensor-based MET Estimation 387\u003c\/p\u003e \u003cp\u003e15.2.3 Unreliability Mitigation 388\u003c\/p\u003e \u003cp\u003e15.2.4 Transfer Learning 388\u003c\/p\u003e \u003cp\u003e15.3 System Architecture and Methods 389\u003c\/p\u003e \u003cp\u003e15.3.1 Reliable MET Calculation 390\u003c\/p\u003e \u003cp\u003e15.3.2 The Reconfigurable MET Estimation System 392\u003c\/p\u003e \u003cp\u003e15.4 Data Collection and Experimental Procedures 394\u003c\/p\u003e \u003cp\u003e15.4.1 Exergaming Experiment 394\u003c\/p\u003e \u003cp\u003e15.4.2 Treadmill Experiment 395\u003c\/p\u003e \u003cp\u003e15.5 Results 396\u003c\/p\u003e \u003cp\u003e15.5.1 Reliable MET Calculation 396\u003c\/p\u003e \u003cp\u003e15.5.2 Reconfigurable Design 402\u003c\/p\u003e \u003cp\u003e15.6 Discussion and Future Work 404\u003c\/p\u003e \u003cp\u003e15.7 Summary 405\u003c\/p\u003e \u003cp\u003eReferences 406\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Insights into Software-Defined Networking and Applications in Fog Computing \u003c\/b\u003e\u003cb\u003e411\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOsman Khalid, Imran Ali Khan, and Assad Abbas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 411\u003c\/p\u003e \u003cp\u003e16.2 OpenFlow Protocol 414\u003c\/p\u003e \u003cp\u003e16.2.1 OpenFlow Switch 414\u003c\/p\u003e \u003cp\u003e16.3 SDN-Based Research Works 416\u003c\/p\u003e \u003cp\u003e16.4 SDN in Fog Computing 419\u003c\/p\u003e \u003cp\u003e16.5 SDN in Wireless Mesh Networks 421\u003c\/p\u003e \u003cp\u003e16.5.1 Challenges in Wireless Mesh Networks 421\u003c\/p\u003e \u003cp\u003e16.5.2 SDN Technique in WMNs 421\u003c\/p\u003e \u003cp\u003e16.5.3 Benefits of SDN in WMNs 423\u003c\/p\u003e \u003cp\u003e16.5.4 Fault Tolerance in SDN-based WMNs 424\u003c\/p\u003e \u003cp\u003e16.6 SDN in Wireless Sensor Networks 424\u003c\/p\u003e \u003cp\u003e16.6.1 Challenges in Wireless Sensor Networks 424\u003c\/p\u003e \u003cp\u003e16.6.2 SDN in Wireless Sensor Networks 425\u003c\/p\u003e \u003cp\u003e16.6.3 Sensor Open Flow 426\u003c\/p\u003e \u003cp\u003e16.6.4 Home Networks Using SDWN 426\u003c\/p\u003e \u003cp\u003e16.6.5 Securing Software Defined Wireless Networks (SDWN) 426\u003c\/p\u003e \u003cp\u003e16.7 Conclusion 427\u003c\/p\u003e \u003cp\u003eReferences 427\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Time-Critical Fog Computing for Vehicular Networks \u003c\/b\u003e\u003cb\u003e431\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAhmed Chebaane, Abdelmajid Khelil, and Neeraj Suri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 431\u003c\/p\u003e \u003cp\u003e17.2 Applications and Timeliness Guarantees and Perturbations 434\u003c\/p\u003e \u003cp\u003e17.2.1 Application Scenarios 434\u003c\/p\u003e \u003cp\u003e17.2.2 Application Model 436\u003c\/p\u003e \u003cp\u003e17.2.3 Timeliness Guarantees 436\u003c\/p\u003e \u003cp\u003e17.2.4 Benchmarking Vehicular Applications Concerning Timeliness Guarantees 437\u003c\/p\u003e \u003cp\u003e17.2.5 Building Blocks to Reach Timeliness Guarantees 440\u003c\/p\u003e \u003cp\u003e17.2.6 Timeliness Perturbations 441\u003c\/p\u003e \u003cp\u003e17.3 Coping with Perturbation to Meet Timeliness Guarantees 443\u003c\/p\u003e \u003cp\u003e17.3.1 Coping with Constraints 443\u003c\/p\u003e \u003cp\u003e17.3.2 Coping with Failures 448\u003c\/p\u003e \u003cp\u003e17.3.3 Coping with Threats 448\u003c\/p\u003e \u003cp\u003e17.4 Research Gaps and Future Research Directions 449\u003c\/p\u003e \u003cp\u003e17.4.1 Mobile Fog Computing 449\u003c\/p\u003e \u003cp\u003e17.4.2 Fog Service Level Agreement (SLA) 450\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 451\u003c\/p\u003e \u003cp\u003eReferences 451\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks \u003c\/b\u003e\u003cb\u003e459\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShuja Mughal, Kamran Sattar Awaisi, Assad Abbas, Inayat ur Rehman, Muhammad Usman Shahid Khan, and Mazhar Ali\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 459\u003c\/p\u003e \u003cp\u003e18.2 Proposed Methodology 461\u003c\/p\u003e \u003cp\u003e18.3 Hypothesis Formulation 463\u003c\/p\u003e \u003cp\u003e18.4 Simulation Design 464\u003c\/p\u003e \u003cp\u003e18.4.1 Results and Discussions 464\u003c\/p\u003e \u003cp\u003e18.4.2 Hypothesis Testing 467\u003c\/p\u003e \u003cp\u003e18.5 Conclusions 469\u003c\/p\u003e \u003cp\u003eReferences 470\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness\u003c\/b\u003e \u003cb\u003e473\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDmitrii Chemodanov, Prasad Calyam, and Kannappan Palaniappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 473\u003c\/p\u003e \u003cp\u003e19.1.1 How Can Geospatial Video Analytics Help with Disaster-Incident Situational Awareness? 473\u003c\/p\u003e \u003cp\u003e19.1.2 Fog Computing for Geospatial Video Analytics 474\u003c\/p\u003e \u003cp\u003e19.1.3 Function-Centric Cloud\/Fog Computing Paradigm 475\u003c\/p\u003e \u003cp\u003e19.1.4 Function-Centric Fog\/Cloud Computing Challenges 476\u003c\/p\u003e \u003cp\u003e19.1.5 Chapter Organization 477\u003c\/p\u003e \u003cp\u003e19.2 Computer Vision Application Case Studies and FCC Motivation 478\u003c\/p\u003e \u003cp\u003e19.2.1 Patient Tracking with Face Recognition Case Study 478\u003c\/p\u003e \u003cp\u003e19.2.2 3-D Scene Reconstruction from LIDAR Scans 480\u003c\/p\u003e \u003cp\u003e19.2.3 Tracking Objects of Interest in WAMI 482\u003c\/p\u003e \u003cp\u003e19.3 Geospatial Video Analytics Data Collection Using Edge Routing 484\u003c\/p\u003e \u003cp\u003e19.3.1 Network Edge Geographic Routing Challenges 484\u003c\/p\u003e \u003cp\u003e19.3.2 Artificial Intelligence Relevance in Geographic Routing 486\u003c\/p\u003e \u003cp\u003e19.3.3 AI-Augmented Geographic Routing Implementation 487\u003c\/p\u003e \u003cp\u003e19.4 Fog\/Cloud Data Processing for Geospatial Video Analytics Consumption 490\u003c\/p\u003e \u003cp\u003e19.4.1 Geo-Distributed Latency-Sensitive SFC Challenges 491\u003c\/p\u003e \u003cp\u003e19.4.2 Metapath-Based Composite Variable Approach 492\u003c\/p\u003e \u003cp\u003e19.4.3 Metapath-Based SFC Orchestration Implementation 495\u003c\/p\u003e \u003cp\u003e19.5 Concluding Remarks 496\u003c\/p\u003e \u003cp\u003e19.5.1 What Have We Learned? 496\u003c\/p\u003e \u003cp\u003e19.5.2 The Road Ahead and Open Problems 497\u003c\/p\u003e \u003cp\u003eReferences 498\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 An Insight into 5G Networks with Fog Computing \u003c\/b\u003e\u003cb\u003e505\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOsman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Asad Waqar Malik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 505\u003c\/p\u003e \u003cp\u003e20.2 Vision of 5G 507\u003c\/p\u003e \u003cp\u003e20.3 Fog Computing with 5G Networks 508\u003c\/p\u003e \u003cp\u003e20.3.1 Fog Computing 508\u003c\/p\u003e \u003cp\u003e20.3.2 The Need of Fog Computing in 5G Networks 508\u003c\/p\u003e \u003cp\u003e20.4 Architecture of 5G 508\u003c\/p\u003e \u003cp\u003e20.4.1 Cellular Architecture 508\u003c\/p\u003e \u003cp\u003e20.4.2 Energy Efficiency 510\u003c\/p\u003e \u003cp\u003e20.4.3 Two-Tier Architecture 512\u003c\/p\u003e \u003cp\u003e20.4.4 Cognitive Radio 512\u003c\/p\u003e \u003cp\u003e20.4.5 Cloud-Based Architecture 513\u003c\/p\u003e \u003cp\u003e20.5 Technology and Methodology for 5G 514\u003c\/p\u003e \u003cp\u003e20.5.1 HetNet 515\u003c\/p\u003e \u003cp\u003e20.5.2 Beam Division Multiple Access (BDMA) 516\u003c\/p\u003e \u003cp\u003e20.5.3 Mixed Bandwidth Data Path 516\u003c\/p\u003e \u003cp\u003e20.5.4 Wireless Virtualization 516\u003c\/p\u003e \u003cp\u003e20.5.5 Flexible Duplex 518\u003c\/p\u003e \u003cp\u003e20.5.6 Multiple-Input Multiple-Output (MIMO) 518\u003c\/p\u003e \u003cp\u003e20.5.7 M2M 519\u003c\/p\u003e \u003cp\u003e20.5.8 Multibeam-Based Communication System 520\u003c\/p\u003e \u003cp\u003e20.5.9 Software-Defined Networking (SDN) 520\u003c\/p\u003e \u003cp\u003e20.6 Applications 521\u003c\/p\u003e \u003cp\u003e20.6.1 Health Care 521\u003c\/p\u003e \u003cp\u003e20.6.2 Smart Grid 521\u003c\/p\u003e \u003cp\u003e20.6.3 Logistic and Tracking 521\u003c\/p\u003e \u003cp\u003e20.6.4 Personal Usage 521\u003c\/p\u003e \u003cp\u003e20.6.5 Virtualized Home 522\u003c\/p\u003e \u003cp\u003e20.7 Challenges 522\u003c\/p\u003e \u003cp\u003e20.8 Conclusion 524\u003c\/p\u003e \u003cp\u003eReferences 524\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Fog Computing for Bioinformatics Applications \u003c\/b\u003e\u003cb\u003e529\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHafeez Ur Rehman, Asad Khan, and Usman Habib\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 529\u003c\/p\u003e \u003cp\u003e21.2 Cloud Computing 531\u003c\/p\u003e \u003cp\u003e21.2.1 Service Models 532\u003c\/p\u003e \u003cp\u003e21.2.2 Delivery Models 532\u003c\/p\u003e \u003cp\u003e21.3 Cloud Computing Applications in Bioinformatics 533\u003c\/p\u003e \u003cp\u003e21.3.1 Bioinformatics Tools Deployed as SaaS 533\u003c\/p\u003e \u003cp\u003e21.3.2 Bioinformatics Platforms Deployed as PaaS 535\u003c\/p\u003e \u003cp\u003e21.3.3 Bioinformatics Tools Deployed as IaaS 535\u003c\/p\u003e \u003cp\u003e21.4 Fog Computing 537\u003c\/p\u003e \u003cp\u003e21.5 Fog Computing for Bioinformatics Applications 539\u003c\/p\u003e \u003cp\u003e21.5.1 Real-Time Microorganism Detection System 541\u003c\/p\u003e \u003cp\u003e21.6 Conclusion 543\u003c\/p\u003e \u003cp\u003eReferences 543\u003c\/p\u003e \u003cp\u003eIndex 547\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407081382231,"sku":"9781119551690","price":108.86,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119551690.jpg?v=1730498109","url":"https:\/\/bookcurl.com\/products\/fog-computing-9781119551690","provider":"Book Curl","version":"1.0","type":"link"}