Rajkumar Buyya, PhD, is Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems Laboratory, University of Melbourne, Australia and founding CEO of Manjrasoft. Dr. Buyya is author of several works including Mastering Cloud Computing and Editor-in-Chief of Wiley Software: Practice and Experience Journal. Satish Narayana Srirama, PhD, is a Research Professor and head of the Mobile & Cloud Lab, Institute of Computer Science, University of Tartu, Estonia. He is editor of Wiley Software: Practice and Experience Journal and has co-authored over 120 scientific publications.

• List of Contributors xixPreface xxiiiAcknowledgments xxviiPart I Foundations 11 Internet of Things (IoT) and New Computing Paradigms 3Chii Chang, Satish Narayana Srirama, and Rajkumar Buyya1.1 Introduction 31.2 Relevant Technologies 61.3 Fog and Edge Computing Completing the Cloud 81.3.1 Advantages of FEC: SCALE 81.3.2 How FEC AchievesThese Advantages: SCANC 91.4 Hierarchy of Fog and Edge Computing 131.5 Business Models 161.6 Opportunities and Challenges 171.7 Conclusions 20References 212 Addressing the Challenges in Federating Edge Resources 25Ahmet Cihat Baktir, Cagatay Sonmez, CemErsoy, Atay Ozgovde, and Blesson Varghese2.1 Introduction 252.2 The Networking Challenge 272.3 The Management Challenge 342.4 Miscellaneous Challenges 402.5 Conclusions 45References 453 Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges 51Guto Leoni Santos,Matheus Ferreira, Leylane Ferreira, Judith Kelner, Djamel Sadok, Edison Albuquerque, Theo Lynn, and Patricia Takako Endo3.1 Introduction 513.2 Methodology 523.3 Integrated C2F2T Literature by Modeling Technique 553.4 Integrated C2F2T Literature by Use-Case Scenarios 653.5 Integrated C2F2T Literature by Metrics 683.6 Future Research Directions 723.7 Conclusions 73Acknowledgments 74References 754 Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds 79Adel Nadjaran Toosi, RedowanMahmud, Qinghua Chi, and Rajkumar Buyya4.1 Introduction 794.2 Background 804.3 Network Slicing in 5G 834.4 Network Slicing in Software-Defined Clouds 874.5 Network Slicing Management in Edge and Fog 914.6 Future Research Directions 934.7 Conclusions 96Acknowledgments 96References 965 Optimization Problems in Fog and Edge Computing 103Zoltán Ádám Mann5.1 Introduction 1035.2 Background / RelatedWork 1045.3 Preliminaries 1055.4 The Case for Optimization in Fog Computing 1075.5 Formal Modeling Framework for Fog Computing 1085.6 Metrics 1095.6.5 Further Quality Attributes 1125.7 Optimization Opportunities along the Fog Architecture 1135.8 Optimization Opportunities along the Service Life Cycle 1145.9 Toward a Taxonomy of Optimization Problems in Fog Computing 1155.10 Optimization Techniques 1175.11 Future Research Directions 1185.12 Conclusions 119Acknowledgments 119References 119Part II Middlewares 1236 Middleware for Fog and Edge Computing: Design Issues 125Madhurima Pore, Vinaya Chakati, Ayan Banerjee, and Sandeep K. S. Gupta6.1 Introduction 1256.2 Need for Fog and Edge Computing Middleware 1266.3 Design Goals 1266.4 State-of-the-Art Middleware Infrastructures 1286.5 System Model 1296.6 Proposed Architecture 1316.7 Case Study Example 1366.8 Future Research Directions 1376.9 Conclusions 139References 1397 A Lightweight Container Middleware for Edge Cloud Architectures 145David von Leon, LorenzoMiori, Julian Sanin, Nabil El Ioini, Sven Helmer, and Claus Pahl7.1 Introduction 1457.2 Background/RelatedWork 1467.3 Clusters for Lightweight Edge Clouds 1497.4 Architecture Management – Storage and Orchestration 1527.5 IoT Integration 1597.6 Security Management for Edge Cloud Architectures 1597.7 Future Research Directions 1657.8 Conclusions 166References 1678 Data Management in Fog Computing 171Tina Samizadeh Nikoui, Amir Masoud Rahmani, and Hooman Tabarsaied8.1 Introduction 1718.2 Background 1728.3 Fog Data Management 1748.4 Future Research and Direction 1868.5 Conclusions 186References 1889 Predictive Analysis to Support Fog Application Deployment 191Antonio Brogi, Stefano Forti, and Ahmad Ibrahim9.1 Introduction 1919.2 Motivating Example: Smart Building 1939.3 Predictive Analysis with FogTorch 1979.4 Motivating Example (continued) 2069.5 Related Work 2079.6 Future Research Directions 2149.7 Conclusions 216References 21710 Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems 223Melody Moh and Robinson Raju10.1 Introduction 22310.2 Background 23410.3 Survey of ML Techniques for Defending IoT Devices 24210.4 Machine Learning in Fog Computing 24810.4.1 Introduction 24810.5 Future Research Directions 25210.6 Conclusions 252References 253Part III Applications and Issues 25911 Fog Computing Realization for Big Data Analytics 261Farhad Mehdipour, Bahman Javadi, AniketMahanti, and Guillermo Ramirez-Prado11.1 Introduction 26111.2 Big Data Analytics 26211.3 Data Analytics in the Fog 26711.4 Prototypes and Evaluation 27211.4.1 Architecture 27211.4.2 Configurations 27411.5 Case Studies 27711.6 Related Work 28211.7 Future Research Directions 28711.8 Conclusions 287References 28812 Exploiting Fog Computing in Health Monitoring 291Tuan Nguyen Gia and Mingzhe Jiang12.1 Introduction 29112.2 An Architecture of a Health Monitoring IoT-Based System with Fog Computing 29312.3 Fog Computing Services in Smart E-Health Gateways 29712.4 System Implementation 30412.5 Case Studies, Experimental Results, and Evaluation 30812.6 Discussion of Connected Components 31312.7 Related Applications in Fog Computing 31312.8 Future Research Directions 31412.9 Conclusions 314References 31513 Smart Surveillance Video Stream Processing at the Edge for Real-Time Human Objects Tracking 319Seyed Yahya Nikouei, Ronghua Xu, and Yu Chen13.1 Introduction 31913.2 Human Object Detection 32013.3 Object Tracking 32713.4 Lightweight Human Detection 33513.5 Case Study 33713.6 Future Research Directions 34213.7 Conclusions 343References 34314 Fog Computing Model for Evolving Smart Transportation Applications 347M. Muzakkir Hussain,Mohammad Saad Alam, and M.M. Sufyan Beg14.1 Introduction 34714.2 Data-Driven Intelligent Transportation Systems 34814.3 Mission-Critical Computing Requirements of Smart Transportation Applications 35114.4 Fog Computing for Smart Transportation Applications 35414.5 Case Study: Intelligent Traffic Lights Management (ITLM) System 35914.6 Fog Orchestration Challenges and Future Directions 36214.7 Future Research Directions 36414.8 Conclusions 369References 37015 Testing Perspectives of Fog-Based IoT Applications 373Priyanka Chawla and Rohit Chawla15.1 Introduction 37315.2 Background 37415.3 Testing Perspectives 37615.4 Future Research Directions 39315.5 Conclusions 405References 40616 Legal Aspects of Operating IoT Applications in the Fog 411G. Gultekin Varkonyi, Sz. Varadi, and Attila Kertesz16.1 Introduction 41116.2 RelatedWork 41216.3 Classification of Fog/Edge/IoT Applications 41316.4 Restrictions of the GDPR Affecting Cloud, Fog, and IoT Applications 41416.5 Data Protection by Design Principles 42516.6 Future Research Directions 43016.7 Conclusions 430Acknowledgment 431References 43117 Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit 433Redowan Mahmud and Rajkumar Buyya17.1 Introduction 43317.2 iFogSim Simulator and Its Components 43517.3 Installation of iFogSim 43617.4 Building Simulation with iFogSim 43717.5 Example Scenarios 43817.6 Simulation of a Placement Policy 45017.7 A Case Study in Smart Healthcare 46117.8 Conclusions 463References 464Index 467