BLOCKCHAIN-BASED EDGE COMPUTING: JOINT TASK OFFLOADING AND MINING WITH MULTI-AGENT REINFORCEMENT LEARNING

The integration of mobile edge computing (MEC) and blockchain enhances computing efficiency and security. This paper presents a novel cooperative task offloading and block mining (TOBM) approach in blockchain-based MEC. The system enables edge devices to offload tasks and participate in mining operations. To handle blockchain latency, a novel Proof-of-Reputation (PoR) consensus mechanism is introduced. A multi-objective function is developed to optimize system utility by managing offloading, channel selection, power allocation and computational resources. A multi-agent deep deterministic policy gradient (MA-DDPG) algorithm is used for optimization. A game-theoretic approach is applied to model competition among edge devices and establish a Nash equilibrium. Simulations demonstrate improved system utility compared to traditional approaches. The proposed TOBM framework provides efficient task allocation and computational resource management. It dynamically adapts to network conditions, reducing computational delays and enhancing overall performance. Blockchain security mechanisms prevent unauthorized data modifications, promoting data integrity and trustworthiness. The PoR consensus mechanism minimizes the verification time required for block mining, allowing for faster transactions and reduced network congestion. The proposed method enables edge devices to make intelligent task offloading decisions, optimizing computational efficiency while maintaining low energy consumption. The MA-DDPG algorithm effectively learns from network interactions, continuously improving decision-making policies. The results indicate that the system significantly outperforms existing solutions in terms of latency reduction, resource utilization and security enhancements. Future research directions include enhancing the PoR mechanism and exploring additional consensus models to improve scalability and performance.