Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.
College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2022 Apr 22;22(9):3217. doi: 10.3390/s22093217.
A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor-critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.
本研究提出了一种基于移动边缘计算（MEC）的区块链系统，用于物联网（IoT）网络中的安全数据存储和共享，MEC 充当覆盖系统，提供动态计算卸载服务。考虑到延迟敏感、资源有限和动态的 IoT 场景，设计了一种自适应的系统资源分配和计算卸载方案，以优化支持 MEC 的区块链系统的可扩展性性能，其中可扩展性被量化为 MEC 计算效率和区块链系统吞吐量。具体来说，我们联合优化计算卸载策略和块生成策略，以最大化支持 MEC 的区块链系统的可扩展性，同时保证数据安全性和系统效率。与忽略 IoT 网络中频繁的用户移动和动态任务要求的现有工作相比，联合性能优化方案被制定为马尔可夫决策过程（MDP）。此外，我们设计了一种基于深度确定性策略梯度（DDPG）的算法来解决 MDP 问题，并定义多个和可变数量的连续时隙作为决策时段来进行模型训练。具体来说，DDPG 可以解决具有连续动作空间的 MDP 问题，并且只需要一个直接的演员-评论家架构，使其适合解决支持 MEC 的区块链系统的动态性和复杂性。仿真结果表明，与基于深度 Q 网络（DQN）的方案和其他一些贪婪方案相比，所提出的方案在长期事务吞吐量方面具有性能提升。
