A Survey on the Development of Wireless Sensor Network Challenges Using ML Algorithms

A wireless sensor network is a network that consists of a number of sensor nodes that are used to communicate wirelessly. These nodes are used to continuously monitor changes in the environment in a sensing area that are due to external factors, system design hardware, etc. Due to the limited power of wireless sensor networks and the constantly shifting topology of the network, attempts Common techniques for security, such as cryptography and key management, are useless. Machine learning(ML) algorithms can combine surveillance and intelligence-based decisions to deliver safety measures in this kind of system are one of the suggested methods. This information is given to a central processing area, which is a base station connected to the internet, through which a user can observe. WSN is a network that is an enabler for IOT Technology. In this review paper, we analyze over a two-decade time period in WSN issues or challenges faced, which can be reduced using different algorithms of machine learning. In this survey paper, some of the issues faced by WSN are Security, Routing, Clustering, Localization, Fault detection, Medium Access Control (MAC) protocols, etc., so by using ML algorithms, they are evaluated against the corresponding problems. Thus, this review paper provides an overall idea of ML algorithms for different issues faced by WSN.

I. Introduction

Action, temperature, light, and earthquakes are among the many informative physical world alternatives. It is important to collect data from wireless sensor networks (WSN), which provide an accessible architecture for massive environmental data. Autonomous sensors in space monitor their environment and jointly communicate their information to a primary node or base station over current networks that allow two-way data transmission. Monitoring and controlling sensor activity from node to base station [1] In Fig. 1, a sensor node detects, regulates, and responds to wirelessly send environmental data to stations. [1] Internet access lets the base station share data. AI was invented in the late 1950s. Subsets of AI include machine learning. [2]. across a range of industries, including healthcare, engineering, computing, etc., it can handle a variety of WSN challenges, including routing, security, localisation, coverage, connectivity, and congestion. Computer systems are capable of learning and adapting their behavior as a result. It creates a model by automatically,