Secure Computation Offloading in Blockchain Based IoT Networks With Deep Reinforcement Learning

For current and future Internet of Things (IoT) networks, mobile edge-cloud computation offloading (MECCO) has been regarded as a promising means to support delay-sensitive IoT applications. However, offloading mobile tasks to the cloud gives rise to new security issues due to malicious mobile devices (MDs). How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem. In this paper, we investigate simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain. First, to improve the offloading security, we propose a trustworthy access control mechanism using blockchain, which can protect cloud resources against illegal offloading behaviours. Then, to tackle the computation management of the authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions, the allocation of computing resource and radio bandwidth, and smart contract usage. This optimization problem aims to minimize the long-term system costs of latency, energy consumption and smart contract fee among all MDs. To solve the proposed offloading problem, we develop an advanced deep reinforcement learning algorithm using a double-dueling Q-network. Evaluation results from real experiments and numerical simulations demonstrate the significant advantages of our scheme over the existing approaches.

Automated summary

The paper addresses security and computation offloading problems in a multi-user MECCO system with blockchain. A trustworthy access control mechanism using blockchain is proposed to improve offloading security, while an advanced deep reinforcement learning algorithm is developed to solve the optimization problem of offloading decisions and resource allocation for authorized MDs. Evaluation results demonstrate the significant advantages of the proposed scheme over existing approaches.