NOMA-Enabled Cooperative Computation Offloading for Blockchain-Empowered Internet of Things: A Learning Approach

Blockchain technologies allow the Internet of Things (IoT) to build trust among various interest parties. For the resource-limited IoT devices, offloading computation-intensive tasks (blockchain verification and mining tasks, and data process tasks) to edge servers for execution is considered as a promising solution in mobile-edge computing. However, conventional methods (such as linear programming or game theory) for the computation offloading problem cannot achieve long-term performance while the existing deep reinforcement learning (DRL)-based algorithms suffer from slow convergence, lack of robustness, and unstable performance. In this article, we propose a multiagent DRL framework to achieve long-term performance for cooperative computation offloading, in which a scatter network is adopted to improve its stability and league learning is introduced for agents to explore the environment collaboratively for fast convergence and robustness. First, we study the nonorthogonal multiple access-enabled cooperative computation offloading problem and formulate the joint problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Second, to avoid useless exploration and unstable performance, we initially train an intelligent agent represented by scatter networks using conventional expert strategies. Third, in order to enhance the performance, we subsequently establish a hierarchical league where agents collaborate with others to explore the environment. Finally, our experimental results demonstrate that our algorithm could perform better in terms of reducing energy cost and delay cost, and shortening almost 60% of the training time compared with the state-of-the-art approaches.

Automated summary

This article introduces a multiagent deep reinforcement learning framework to address the cooperative computation offloading problem, by using scatter networks and league learning to improve stability and performance.