Localization of sensor nodes using Learning movement model in Wireless sensor networks

Abstract

Wireless Sensor Networks WSNs is a self organizing network that newlineconsists of many sensor nodes densely deployed in the area of interest newlineworking together to do some application specific task Each node in a sensor newlinenetwork is capable of sensing environmental condition within their range and newlineis typically equipped with a radio transceiver or other wireless communication newlinedevice a small micro controller and an energy source usually a battery With newlinethe advancement in related technologies it is possible to deploy deeply newlineembedded WSNs for various types of applications such as surveillance newlinedetection wildlife tracking virus monitoring military usages industrial newlinecontrol and monitoring home automation supply chain management newlineenvironmental sensing health monitoring etc Location awareness is highly newlinecritical for sensor networks Nevertheless based on the data collected from a newlinesensor network it is observed that the network is not always entirely newlinelocalizable By deriving the necessary and sufficient conditions for node newlinelocalizability it is possible to analyze and anticipate the number of nodes to newlinelocate in sparsely or moderately connected networks newlineLocalization refers to the ability of determining the position relative newlineor absolute of a sensor node with an acceptable accuracy The problem of newlinelocalization in WSNs has attracted extensive interest in the literature newlineLocalization typically relies on known positions and the unknown positions of newlinethe sensor node newline newline