The Role of Machine Learning in Advancing IoT and WSNs
Navneet Kumar, Subhash C. Pandey · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2025
Artificial intelligence (AI) and machine learning (ML) approaches can effectively manage the automated operations of IoT nodes in intelligent cities. Key IoT applications in smart cities include intellectual buildings, waste management, traffic monitoring, and healthcare.However, the design of WSN-based IoT (WSN-IoT) faces several challenges, including Web range and connectivity problems, power consumption, bandwidth requirements, maximizing network lifetime, transmission protocols, and advanced infrastructure development.One of the most significant challenges for wireless sensor networks (WSNs) is extending their lifespan.Over the past few years, considerable research has been focused on improving network longevity and quality of service (QoS).A WSN's sensor nodes are independent, distributed devices that use wireless connections to collect and route data to a central hub, also known as a "Base Station," without the need for a central supervisor.These networks' range and battery life are constrained by their lower processor, memory, power supply, and other capabilities.To overcome such situations, machine learning (ML) methods can be applied to react correctly.The practice of functioning without human intervention or reprogramming to learn from experiences is known as machine learning.The proliferation of Internet of Things (IoT) based Wireless Sensor Networks (WSNs) has triggered a paradigm shift in the business, necessitating the use of dependable and efficient routing techniques.Preliminaries in Machine Learning for WSN-IoT, IOT protocols, and several ML-based methods are reviewed in this study.