| With the advancement of AI and IoT technology,more and more applications appear in mobile terminals.However,due to the lack of storage,power,and computing resources for mobile devices,these applications are difficult to provide quality services for users.As an important technology with broad prospects,Mobile Edge Computing(MEC)allows resources for compute and storage to be deployed near mobile devices and solves problems such as insufficient computing and storage capacity of terminal devices.However,due to the changing usage scenarios of mobile applications,limited MEC computing resources will not be able to meet the further growth of computing demand,and offloading tasks to neighboring devices with D2D(Device-to-Device)communication can solve the problem of limited MEC resources.By combining D2D technology with MEC,computing resources can be shared between devices,and this short-range communication technology can reduce data transmission latency and saves energy,reduce network communication pressure,and improve the utilization rate of equipment computing resources.However,end users are often reluctant to participate in the sharing of computing resources due to the limitations of equipment resources and energy,as well as lack of security and privacy leakage.Therefore,it is necessary to design a reasonable incentive mechanism to motivate end users to participate in the sharing of computing resources.In this paper,considering the small amount of computing resources of terminal devices and the collaborative completion of computing tasks by multiple devices,this paper designs an incentive mechanism in the scenarios of single and multiple end users requesting computing resources in D2D-assisted MEC networks,and the research content is as follows:(1)To address the problem of incentivizing end-users to participate in resource sharing between a single requester and multiple providers,this paper proposes an incentive mechanism for joint computing resource trading and energy-efficient offloading.Firstly,a blockchainbased computing resource sharing architecture is designed to ensure user security and privacy.Then a resource trading model is constructed and a computing resource allocation and pricing algorithm based on Stakelberg game is designed so that both resource requesters and providers can get satisfactory utility through resource sharing.Finally,in order to minimize the energy consumption of the device,the computational offloading is constructed as a hybrid optimization problem under the premise of satisfying the participants’ utilities,and the problem is simplified by convex optimization KKT conditions,and an energy-efficient computational offloading algorithm is proposed.For the effectiveness of the above algorithm,simulation experiments are designed in this paper to compare the participant utility and device energy consumption derived from the algorithm proposed in this paper with the rest of the resource sharing mechanisms.To verify the low complexity of the energy-efficient computational offloading algorithm,the running time is compared with the traditional optimization algorithm.(2)To address the problem of incentivizing end-users to participate in multiple requesters and multiple providers resource sharing,this paper proposes a resource sharing incentive mechanism based on deep reinforcement learning and game theory.In this paper,we first design a blockchain-based computing resource sharing architecture for multiple requesting users,a resource allocation model and a resource pricing model,and construct an optimization problem with the objective of maximizing the overall revenue of users.This paper decomposes the optimization problem into two sub-problems and designs a two-layer optimization architecture.The first layer solves the transaction selection problem through deep reinforcement learning,and the second layer determines the resource allocation and resource pricing strategies through games,based on which a resource transaction algorithm(DRGT)based on deep reinforcement learning and game theory is proposed in this paper.Finally,the effectiveness of the proposed resource trading incentive mechanism is verified by simulation.The data from the experimental results show that the designed incentive mechanism quickly achieves an approximate optimal overall utility while satisfying the utilities of both requesters and providers,and can effectively encourage more users to participate in resource sharing in multiple requesters scenarios. |
