Incentive-Driven Computation Task Allocation for Integrated Edge Computing and Energy Feeding
Biling Zhang, Jiahua Liu, Zhengyang Jiao, Yunpeng Wang, Caili Guo, Zhu Han · IEEE Transactions on Cognitive Communications and Networking · 2025
This paper considers a novel scenario where the service provider encounters an insufficiency of computation and energy resources simultaneously. To complete the task on time, the service provider has to perform partial computation offloading and energy fetching, exploiting the idle resources of nearby edge nodes. However, computation offloading and energy fetching incur costs which are interdependent. On the other hand, the edge nodes are selfish and need an incentive, but the information between the service provider and the edge nodes is asymmetric. Therefore, how to offload the task and fetch energy while stimulating the edge nodes is a challenge. To minimize the expenditure of service provider, we investigate the task allocation problem where the incentive problem is evolved and formulated with contract theory. Noticing that the task allocation problem and the incentive problem are interdependent and cannot be solved through traditional optimization approaches, we decouple the problems and first theoretically derive the expression of feasible contracts for the given allocation scheme. Then we propose a low complexity sparrow algorithm to find the sub-optimal solution to the task allocation problem, with which the desired contracts are obtained. Simulations show that the proposed scheme achieves superior performance in terms of total expenditure, potential computing resource and grid power reduction.