Optimizing jointly mining decision and resource allocation in a MEC-enabled blockchain networks

In this paper, several recently published metaheuristic algorithms are adapted to optimize the NP-hard problem of jointly mining decision and resource allocation in mobile edge computing (MEC) enabled blockchain networks under two different encoding schemes. The first scheme represents individuals in a way that incorporates the mining decisions, transmission power, and computing resources of all miners for each individual, with mining decisions determined by a binary vector whose values indicate whether miners partake in mining or not. While, the second scheme makes each individual accountable for the transmission power and computing resources of each participant miner, treating all individuals as a sin-gular solution to the problem. Then the Nutcracker optimization algorithm and gradient-based optimizer are modified to propose two robust variants, MNOA and MGBO, respectively. We then combine MNOA and MGBO to create HNOA, which further optimizes the mining decision and resource allocation in this problem. HNOA and other variants are validated using nine instances with a range of 150 to 600 miners. HNOA is also compared to several competing optimizers to demonstrate its efficacy in terms of several performance metrics. The experimental findings show the superiority of the proposed algorithm. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper adapts several recently published metaheuristic algorithms to optimize the NP-hard problem of decision and resource allocation in mobile edge computing enabled blockchain networks. Different encoding schemes are used to represent the mining decisions, transmission power, and computing resources of the miners, and three algorithm variants are proposed for optimization.