In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.

blockchain, computation offloading, deep reinforcement learning, Mobile edge computing
dx.doi.org/10.1109/TWC.2019.2956519
IEEE Transactions on Wireless Communications
Department of Systems and Computer Engineering

Guo, F. (Fengxian), Yu, F.R, Zhang, H. (Heli), Ji, H. (Hong), Liu, M. (Mengting), & Leung, V.C.M. (Victor C. M.). (2020). Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing. IEEE Transactions on Wireless Communications, 19(3), 1689–1703. doi:10.1109/TWC.2019.2956519