Impact of Machine Learning-Based Routing Protocols for Efficient Data Transmission in Wireless Sensor Networks (Wsns)

Wireless Sensor Networks (WSNs) play a pivotal role in collecting and disseminating data in various applications, ranging from environmental monitoring to healthcare. The efficiency of data transmission in WSNs greatly depends on the routing protocols employed. In recent years, machine learning techniques have emerged as promising tools to enhance the performance of routing protocols in WSNs.This review paper aims to provide a thorough examination of the impact of machine learning-based routing protocols on the efficiency of data transmission within Wireless Sensor Networks. This research delves into the fundamental challenges faced by traditional routing protocols in WSNs, such as energy consumption, network congestion, and dynamic environmental conditions. Subsequently, this research explores the application of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning, in addressing these challenges.Through a comprehensive analysis of the existing literature, this research highlights the strengths and limitations of various machine learning-based routing protocols. Moreover, this research discusses their adaptability to dynamic network conditions, scalability, and ability to optimize resource utilization. The aim is to provide researchers, practitioners, and stakeholders with valuable insights into the current state-of-the-art machine learning-based routing protocols for WSNs

Computer network · Data transmission · Key distribution in wireless sensor networks · Routing protocol · Telecommunications · Wireless · Wireless network · Wireless sensor network · Computer Science · Energy Efficient Wireless Sensor Networks · Internet of Things and AI · IoT-based Smart Home Systems