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.