Optimizing energy efficiency and prolonging lifetime in WSNs, using the genetic algorithm and predictive coding approach

Abstract

Wireless Sensor Networks (WSNs) have revolutionized monitoring and tracking applications across various domains. These networks consist of small sensor nodes responsible for collecting and transmitting data to a central base station for analysis and decision-making. However, the pervasive challenge in WSNs is the limited energy resources of these nodes, which significantly impacts their operational lifetime. This study addresses the critical issue of energy efficiency in WSNs by proposing an innovative energy-efficient routing protocol. The primary objective is to prolong the network’s operational lifetime while maintaining data quality and coverage. To achieve this, the research integrates Genetic Algorithms (GAs) to optimize routing decisions and employs Predictive Coding (PC) to enhance data transmission efficiency. Building on the foundation of Genetic Algorithms, the routing protocol optimizes communication paths by intelligently selecting routes that minimize energy consumption. Predictive Coding augments this approach by compressing data before transmission, effectively reducing the energy overhead associated with data transfer. The motivation for this research stems from the fact that existing energy-efficient routing protocols often struggle to comprehensively address the diverse applications and varying energy constraints of WSNs. In response, this study seeks to provide a more robust and adaptable routing protocol that can better manage energy consumption and extend network longevity The proposed protocol’s effectiveness is rigorously validated through extensive simulations conducted in Matlab. Evaluation metrics encompass factors such as dead nodes, throughput, energy consumption, and network delay. The results demonstrate a marked advantage over the standard Leach-CR routing protocol. The new protocol achieves significant energy savings and leads to an extended operational lifespan of wireless sensor networks. In comparison to existing protocols, the integrated approach of Genetic Algorithms and Predictive Coding offers unparalleled benefits, showcasing its potential for revolutionizing energy management in WSNs. The implications are far-reaching, as this research holds promise for enhancing network efficiency and sustainability across practical applications. In conclusion, this research contributes to the advancement of WSNs by offering a comprehensive the solution to the energy efficiency challenge. By seamlessly integrating Genetic Algorithms and Predictive Coding, the proposed protocol demonstrates its potential to revolutionize energy management and extend the operational lifespan of wireless sensor networks, ultimately benefiting a wide array of practical applications.

Date	18 Dec 2023
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Michel Kadoch (Supervisor)