Energy savings and coverage optimization in edge WiFi sensor deployment for buildings: A multi-objective evolutionary approach

Abstract

Edge sensor nodes are used to ensure informed decisions in several fields, including smart buildings, supply chain management, sustainability, mobile robotics in industry and logistics, and applications, including the Internet of Things (IoT). However, designing an optimal and cost-effective deployment of edge sensor nodes in a complex environment with different types of walls and interferences poses a significant challenge. Traditional methodologies rely on trial and error, which can lead to non-optimal solutions and ignore the network’s efficiency and sustainability issues, such as energy consumption and quality of service (QoS). This paper proposes a two-stage strategy for deploying edge sensor nodes using multi-objective evolutionary algorithms (MOEA) that consider the topology of the environment, including walls and doors, which impact the network’s QoS. The first stage involves using a single-solution-based metaheuristic (S-metaheuristic) to generate an initial population. The second stage involves integrating the population into a population-based metaheuristic (P-metaheuristic) to find the optimal sensor positioning and communication strategy. The computational experiments demonstrate the superiority of the proposed approach compared to traditional methods that rely on random generation of the initial population in terms of energy consumption and area coverage.