Cluster Based Hidden Markov Model For Path Improvement In Mobile Wireless Sensor Network

In order to carry out some operation, the radio propagation wave is used by a wireless system that is dependent on a network of nodes. Wireless networks are frequently thought of when discussing the telecommunications network. By definition, a wireless system is a collection of components (or subsystems) that can perform some set of tasks through the use of electromagnetic wave propagation and does so in a reliable manner. Starting in the 1980s, wireless cellular networks relied on a centralized infrastructure. Roaming users maintain a constant connection to the wireless network at all times via access points. Mobile wireless sensor networks (MWSN) have had a mobility management strategy described for them utilizing a Hidden Markov Model (HMM). The metrics Received Signal Strength Indication (RSSI), Link Loss, and Expected Transmission Count are used to foretell a node's mobility (ETX). To begin, we set up a prediction timer during which we record both the received signal strength indication (RSSI) and the link failure rate (Link Loss). The HMM-based predictions are activated when either the RSSI or the Link Loss decreases from their previous values. If not, the timer will automatically reset and a new one will begin counting down.