Development of Multi-Objective Particle Swarm Optimisation (MOPSO) Strategy in Enhancing Interference Mitigation in Machine-to-Machine (M2M) Communication based on Fault-Clearance and Communication delay in Smart Grid Networks.

Abstract

The proliferation of connected devices has led to a paradigm shift in cellular standards, typified by the long-term evolution (LTE) standard. The fifth-generation (5G) standard supportsnumerouspromising mobile technologies, including Machineto-Machine (M2M) and Device-to-Device (D2D) communication, which enable the communication of a large number of intelligent devices and ubiquitous devices. The deployment of M2M devices in the application of smart grid (SG), specifically in power systems, has introduced new compromising challenges in the areas of resource allocation and interference management. The 5G cellular network’s qualityofservice (QoS) and performance deteriorate due to interference brought on by the reduced inter-cell distance and the smooth integration of heterogeneous devices.In this paper, an interference-aware multiobjective particle swarm optimisation(MOPSO)scheme is proposed for M2M communication in SG to mitigate the interference generated as a result of the localisation of M2M devices on the grid. In order to evaluate performance, the MOPSO approach was developed and implemented for smart grid situations based on pre-fault, during-fault, and post-optimisation conditions. The initial step was to use the multi-objective particle swarm optimisation (MOPSO) algorithm to optimise the smart grid network in order to decrease grid interference. According to simulation results, under different pre-fault and postoptimisation settings, the system throughput and signal-to-interference-to-noise ratio (SINR) were greatly increased by 32.69 and 21.94%, respectively. Furthermore, by using MOPSO, the fault clearance time was reduced by 106.06%, reducing the amount of time needed to clear an impending fault with interference. Additionally, the smart grid network’s power loss was improved and maintained at levels comparable to those of the pre-fault conditions. In the subsequent step, the performance of the developed MOPSO technique was compared to that of the non-dominated sorting genetic algorithm (NSGA-II) in terms of convergence in fault clearance time, SINR, and throughput. Simulation results indicated that, in comparison to NSGAII’s performance, MOPSO throughput and SINR were enhanced by 5.93%, 4.65%, and 0.96%, respectively. In comparison to NSGA-II, the proposed MOPSO converges to the optimal solution more quickly for the various objective functions. The findings provided by the developed MOPSO demonstrate that it can efficiently compete with similar algorithms when tackling problems involving interference optimisation algorithms.