Bio-inspired Algorithms in the Optimisation of Wireless Sensor Networks

TL;DR

This paper reviews bio-inspired algorithms like GA, PSO, and ACO for optimizing wireless sensor networks, focusing on enhancing lifetime, coverage, and energy efficiency through multi-objective optimization techniques.

Contribution

It provides a comprehensive review of bio-inspired algorithms applied to WSN optimization, highlighting their effectiveness in multi-objective problems.

Findings

Bio-inspired algorithms improve WSN lifetime and coverage.

Multi-objective optimization effectively balances energy and connectivity.

Genetic Algorithms, PSO, and ACO are most commonly used methods.

Abstract

WSN are a growing technology in industrial and personal use fields. The Quality of Service (QoS) of WSN is associated to the architecture of WSN nodes and network design. In this work, the composition of the nodes and network is analysed. The success of WSN is related to the maximisation of the lifetime and coverage of the device, allied to the minimisation of energy consumption and number of nodes, guaranteeing a good network connectivity and high transmission. The most common WSN issues are presented and reviewed. The most suitable optimisation technique is Multi-objective (MOO) which is exemplified in this work from complex multi-objective functions which include several WSN problems. The second part of this review focus on bio-inspired algorithms in WSN optimisation: Genetic Algorithms (GA), Particles Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO). Other less common methods are also present and related to WSN issues.