A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime

Abstract

Wireless sensor networks (WSNs) grapple with a challenging and pivotal issue: how to maximize the network’s lifespan. To improve the quality of service (QoS) and extend the life of the network, there has been a lot of effort made in this area in recent years. The sensor nodes of a WSN are autonomous, dispersed devices that gather and direct data to a central hub, or “Base Station”, using wireless connections without any central coordinator. These networks have less processing power, memory capacity, power supply, and so on, which limits their range and battery life. Numerous studies preceding our research have proposed a myriad of strategies to enhance network longevity. In a WSN, information is relayed from one node to the next until it reaches the base station. Most nodes may be reliably expected to operate for the duration of their batteries. These strategies encompass reducing energy consumption, minimizing latency, load balancing, clustering, efficient data aggregation, and curtailing data transmission delays. WSNs may alter dynamically as a result of internal or external circumstances, necessitating a depreciating dispensable redesign of the network. Because the networks in classic WSN techniques are expressly programmed, it is difficult for them to respond dynamically. Machine learning (ML) approaches can be used to respond appropriately in order to overcome such circumstances. Machine learning is the process of acting without human involvement or reprogramming in order to learn from experiences. This paper presents a review of different ML-based algorithms for WSNs together with their benefits, limitations, and parameters that affect network longevity.