Cooperative and Distributed Intelligent Computation in Fog Computing

<ol><li>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.&nbsp;<br></li></ol>