Data aggregation point placement optimization in Smart Metering Networks

Abstract

This study explores the application of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) within the framework of smart grids (SG), specifically for the optimal placement of data aggregation points (DAPs) across a network of 150 Z-wave smart meters distributed within various smart cities. The investigation aims to identify which of the two- optimization strategies offers a more cost-efficient solution while evaluating their performance in terms of transmission average latency (AL) and execution time (ET) efficiency. The results indicate that although ACO slightly edges out PSO in reducing overall costs in networks with a higher complexity and more DAPs, PSO demonstrates superior performance in execution speed, lower AL, and total cost, underscoring its viability for swift integration in smart metering infrastructures.