Offloading Decision Making Strategies and Applications in Mobile Edge Computing

Mobile edge computing (MEC), as a primary characteristic of fifth-generation (5G) networks, is a critical computing measure for providing a highly distributed computing environment in applications such as virtual reality enhancement, smart cities, connected vehicles and healthcare, etc. Intelligent computational offloading decision-making strategies in MEC can effectively schedule computational tasks for remote applications and decrease latency and energy consumption in dealing with tasks. As a notable application in MEC, this study concentrates on intelligent offloading decision-making strategies and their applications in MEC, where theoretical methods in game-theoretical modelling are investigated and formulated by considering various metrics such as latency constraints, energy consumption, the revenue of network operators, etc. Based on the foundation in modelling, efficient and effective solutions are explored to solve game-theoretical models in different application scenarios. More specifically, the main work and contributions in this thesis are listed as follows: • We investigate game-theoretical modelling and consider it as the primary method to formulate offloading decision-making strategies. The related metrics such as latency and energy consumption are integrated into a two-stage game theory framework for UAV scheduling. The optimal decision searching is developed with the objectives of reputation increase and energy conservation of UAVs. The profit of a network operator is maximized in the game-playing process and the network economy research is further explored. • To extend the exploration of offloading decision-making strategies in the network economy of MEC, we bridge the gap between revenues of network operators with offloading decision-making strategies. Revenues are realistic and ultimate goals of network operators in competitive markets. Therefore, a pricing scheme combined with the aims of reduction and restriction of energy consumption and latency is studied for revenue maximization of service operators. • Besides, we develop the offloading decision-making strategy for a complicated application scenario, where revenue maximization of network operators in maritime communications assisted by hybrid satellite-UAV-terrestrial networks is elucidated. In this work, we first formulate a two-stage game model considering the cost of offloading and the revenue of network operators. Then we conduct an equilibrium analysis to verify the existence of the game model and clarify the process of bridging machine learning to solve the game theoretical model. • In addition, we further explore a more effective solution to solve the formulated game-theoretical model by considering scalable offloading decision-making strategies in vehicular edge computing (VEC) along with the number increase in edge vehicles. A neural network is developed for solving the game-theoretical model which is built for the scenario with a single vehicle, and then an approach integrated with transfer learning is formulated to extend the offloading decision strategy to a large-scale vehicle scenario, by which the scalable optimization of game-theoretical offloading for VEC can be reached