Open AccessNode Localization using Naive Bayes Classifier and Trilateral Algorithm
Author(s) -
K. Madhumathi,
T. Suresh,
R. Maruti
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5604.029320
Subject(s) - wireless sensor network , computer science , node (physics) , trilateration , naive bayes classifier , data mining , bayes' theorem , sensor node , classifier (uml) , computer network , algorithm , key distribution in wireless sensor networks , real time computing , artificial intelligence , wireless , wireless network , bayesian probability , support vector machine , engineering , telecommunications , structural engineering
Node localization is an important problem considered among the researchers in the area of Wireless Sensor Networks (WSN). The WSN is formed by a group of sensor nodes having limited energy and other resources that transfers data among each other or to a base station in an ad-hoc fashion. The estimation of the geo location (co-ordinates in the two-dimensional space) of the sensor nodes is essential for ensuring the QoS within the network. The different applications of WSN require varied level of accuracy in the estimation of the location of the sensor nodes. Different localization schemes are adopted in the literature for better estimation of the node location and each of them has both merits and demerits. This paper focuses on analyzing the different node localization mechanism used in the WSN and to identify various issues and challenges in the estimation of the node location. This paper also proposes an optimal approach with less computational effort and high accuracy in prediction based on trilateration algorithm and the RSSI (Received Signal Strength Indicator) values extracted from the target nodes antennas. The network is segmented in to different blocks of unequal size and the block number in which the node is present will be identified using the naive bayes classifier.