Blockchain-Based Edge Collaboration With Incentive Mechanism for MEC-Enabled VR Systems

This work investigates the secure resource collaboration among selfish edge servers for multi-access edge computing (MEC)-enabled VR systems in a dynamic scenario. Due to the time-varying and stochastic nature of VR user requests, the edge servers usually have significant differences in workload. To this end, we first propose a type judgment method to perceive their service capability and divide them into two types, i.e., the requesting node (RN) with a poor service capability and the cooperative node (CN) with a powerful service capability. To promote collaboration among self-interest nodes, we then model the competitive interactions among RNs and CNs as a multi-leader and multi-follower Stackelberg game. For the RN (as the leader), we design a novel pricing strategy based on deep reinforcement learning (DRL) to motivate CNs to provide resource assistance. Meanwhile, an optimal selling strategy for the CN (as the follower) is presented to maximize its payoffs from the network. To overcome the security problem during the resource collaboration, we finally introduce the blockchain as a secure and trusted platform for resource publishing and trading, where an efficient consensus mechanism called Proof-of-Trust (PoT) is developed to improve the performance of blockchain. The simulation results show that the proposed approach achieves superior performance.