Open AccessDetection of Coverage Hole Nodes in Wireless Sensor Network using Artificial Intelligence
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i1095.0789s19
Subject(s) - wireless sensor network , computer science , artificial neural network , node (physics) , process (computing) , swarm intelligence , fitness function , computer network , vulnerability (computing) , energy (signal processing) , energy consumption , key distribution in wireless sensor networks , artificial intelligence , wireless network , wireless , machine learning , engineering , genetic algorithm , particle swarm optimization , computer security , telecommunications , electrical engineering , statistics , mathematics , structural engineering , operating system
Adequate coverage of the sensing field in Wireless sensor networks (WSNs) is critical to many applications. However, when one or more sensor nodes stop working due to energy exhaustion or physical damage, the network may experience overlay vulnerability. This can disrupt network connectivity and hinder performance. Therefore, it must be fixed automatically. To resolve this problem, swarm inspired Artificial Bee Colony (ABC) scheme in addition to the Artificial Neural Network (ANN) approach is used. The aim of ABC is to optimize the shortest path by selecting an appropriate fitness function and then identify holes using ANN. Before the detection of holes, ANN is trained as per the optimized properties of nodes that are as per the genuine nodes and coverage hole repair properties. Therefore during the testing process, ANN compares these properties with the stored properties and then identify the hole repair node. From the experiment, it has been analyzed that the energy consumption up to 23.88% is saved.