Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach

Abstract

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.

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

“…Recently, DRL has been discussed in literature as an efficient approach for multi-objective optimization particularly in blockchain-enabled systems. Works such as in [30] and [31] proposed the use of DRL-based optimization schemes for resource management in blockchain-based mobile edge computing (MEC) systems. The authors in [32] proposed a multi-agent reinforcement learning framework for resource management in blockchain-enabled digital twin IoT systems.…”

A blockchain‐enabled security management framework for mobile edge computing

“…A survey article about fog computing security and privacy is presented by Alzoubi et al 12 They say blockchain provides a secure, trusted, and efficient computing environment for the development of fog applications. The article 33 proposes a deep deterministic policy gradient (DDPG) algorithm to solve the Markov decision process (MDP) problem in a MEC‐enabled blockchain system. The algorithm uses a variable number of consecutive time slots as a decision epoch for model training.…”

Recent Advances of Blockchain and Its Applications

“…Those service providers that are qualified to conduct the off-loaded tasks may earn some profit or reward for providing computation service. In the process of computation offloading in edge computing, it is critical to dynamically make optimal offloading decisions minimize the communication delay, energy consumption spent on the devices, and the throughput of data storage on blockchain [135] .…”

“…Though abundant off-loading optimization methods have been developed, it is hard to evaluate how good the outcome as well as to compare these methods. To address this issue, Qu et al [135] proposed ChainFL, that is a lightweight simulation platform for building a test edge computing environment which also supports federated learning and blockchain technology.…”