Fog Computing: Concepts & Recent Advances -- 1.1 Introduction -- 1.2 Fog Computing Architectures -- 1.2.1 Hierarchical Architecture Model -- 1.2.2 Layered Architecture Model -- 1.3 Computation Offloading in Fog Computing Architectures -- 1.4 Key Technologies for Future Fog Computing Architectures -- 1.4.1 Communication and Networking Technologies -- 1.4.2 Virtualization Technologies -- 1.4.3 Storage Technologies -- 1.4.4 Privacy and Data Security Technologies -- 1.5 Conclusions -- 2 Applications of Fog Computing -- 2.1 Introduction -- 2.2 Typical Applications of Fog Computing -- 2.2.1 Healthcare -- 2.2.2 Smart Cities -- 2.2.3 Smart Grid -- 2.2.4 Industrial Robotics and Automation in Smart Factories -- 2.2.5 Agriculture -- 2.2.6 Logistics and Supply Chains -- 2.3 Summary and Conclusions -- -- 3 Cooperation for Distributed Task Offloading in Fog Computing Networks -- 3.1 Introduction -- 3.2 System Model -- 3.2.1 Fog Computing Networks -- 3.2.2 Computation Tasks -- 3.2.3 Computation Offloading Model -- 3.3 Cooperation-based Task Offloading Models -- 3.4 Open Research Issues -- 3.4.1 Data Fragmentation -- 3.4.2 Distribution of Fog Networks -- 3.4.3 Advances of Distributed Algorithms -- 3.4.4 Comprehensive Performance Analysis -- 3.5 Conclusions -- -- 4 Fog Resource Aware Framework for Adaptive Task Offloading in Fog-based IoT Systems -- 4.1 Introduction -- 4.2 Related Works -- 4.3 System Model and Problem Formulation -- 4.3.1 System Model -- 4.3.2 Problem Formulation 4.4 FRATO: Fog Resource Aware Task Offloading Framework -- 4.4.1 Offloading Strategies for Minimizing Service Provisioning Delay -- 4.4.2 Mathematical Formulation of FRATO -- 4.4.3 Solution Deployment Analysis -- 4.5 Distributed Resource Allocation in Fog -- 4.5.1 Task Priority-based Resource Allocation -- 4.5.2 Maximal Resource Utilization based Allocation -- 4.6 Simulation and Performance Evaluation -- 4.6.1 Simulation Environment Setup -- 4.6.2 Comparative Approaches -- 4.6.3 Evaluation and Analysis -- 4.6.4 Further Analysis of Computation Time and Complexity -- 4.7 Conclusions -- 4.8 Future Works -- 4.8.1 Data Fragmentation -- 4.8.2 Distribution of Fog Networks -- 4.8.3 Advance of Optimization Algorithms -- 4.8.4 Comprehensive Performance Analysis -- -- 5 Dynamic Collaborative Task Offloading in Fog computing Systems -- 5.1 Introduction -- 5.2 Related Works -- 5.3 System Model and Problem Formulation -- 5.3.1 System Model -- 5.3.2 Computation Task Model -- 5.3.3 Problem Formulation -- 5.4 Optimization Problem for Minimization of Task Execution Delay -- 5.5 Simulation and Performance Evaluation -- 5.5.1 Simulation Environment Setup -- 5.5.2 Evaluation and Analysis -- 5.6 Conclusions and Future Works -- 6 Fundamentals of Matching Theory -- 6.1 Introduction -- 6.2 Basic Concepts and Terminologies -- 6.3 Classification -- 6.3.1 One-to-One (OTO) Matching -- 6.3.2 Many-to-One (MTO) Matching -- 6.3.3 Many-to-Many (MTM) Matching -- 6.3.4 Variants of Matching Models -- 6.4 Matching Algorithms -- 6.5 Conclusions -- 7 Matching Theory for Distributed Computation Offloading in Fog Computing Systems -- 7.1 Introduction -- 7.2 System and Offloading Problem Description -- 7.2.1 System Model -- 7.2.2 Computation Tasks -- 7.2.3 Computation Offloading Models -- 7.2.4 Optimization Problems of Computational Offloading -- 7.3 Proposed Matching-based Models for Distributed Computation -- 7.3.1 One-to-One (OTO) Matching -- 7.3.2 Many-to-One (MTO) Matching7 -- 7.3.3 Many-to-Many (MTM) Matching -- 7.4 Challenges and Open Research Issues -- 7.4.1 Matching With Dynamics -- 7.4.2 Matching with Groups -- 7.4.3 Matching with Externality -- 7.4.4 Security and Privacy of Data and End Users -- 7.4.5 New Offloading Application Scenarios -- 7.4.6 Application of AI and ML-Based Techniques -- 7.5 Conclusions -- 8 Distributed Computation Offloading Frameworks for Fog Networks -- 8.1 Introduction -- 8.2 Preliminary and Related Works -- 8.2.1 Preliminary of Many-to-One (M2O) Matching Model -- 8.2.2 Related Works -- 8.3 System Model -- 8.3.1 Fog Computing Networks -- 8.3.2 Computation Offloading Model -- 8.4 Problem Formulation -- 8.5 Description of DISCO Framework -- 8.5.1 Overview -- 8.5.2 PL Construction -- 8.5.3 Matching Algorithms -- 8.5.4 Optimal Task Offloading and Communication Scheduling Algorithm -- 8.5.5 Stability Analysis -- 8.6 Simulations and Performance Evaluation -- 8.6.1 Simulation Environment Setup -- 8.6.2 Evaluation and Analysis -- 8.7 Conclusions -- 9 Reinforcement Learning-based Resource Allocations in Fog Networks -- 9.1 Introduction -- 9.2 Fog Computing Environment -- 9.2.1 System Model -- 9.2.2 Resource Allocation Problems in Fog Computing Systems -- 9.3 Reinforcement Learning -- 9.3.1 Basic Concepts -- 9.3.2 Taxonomy of RL Algorithms -- 9.4 RL based Algorithms for Resource Allocation in FC Systems -- 9.4.1 Resource Sharing and Management -- 9.4.2 Task Scheduling -- 9.4.3 Task Offloading and Redistribution -- 9.5 Challenges and Open Issues of RL-based Resource Allocations -- 9.5.1 RL-related Challenges -- 9.5.2 Fog Computing Environment related Challenges -- 9.5.3 Computation Task related Challenges -- 9.6 Conclusions and Discussions -- -- -- 10 Bandit Learning and Matching based Distributed Task Offloading in Fog Networks -- 10.1 Introduction -- 10.2 Bacground and Related Works -- 10.2.1 One-to-One Matching-based Task Offloading -- 10.2.2 Bandit Learning-based Computation Offloading -- 10.3 System Model -- 10.3.1 Fog Computing networks -- 10.3.2 Computation Offloading Model -- 10.4 Design of BLM-DTO Algorithm -- 10.4.1 OTO Matching Model for Computation Offloading -- 10.4.2 Multi-Player Multi-Armed Bandit with TS -- 10.5 Simulation Results and Evaluation Analysis -- 10.5.1 Simulation Environment Configuration -- 10.5.2 Comparative Evaluation and Analysis -- 10.6 Conclusions and Discussions. .