Open AccessAN ENHANCED CLUSTERING APPROACH FOR ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORKS
Author(s) -
Harpinderjeet Kaur Sekhon,
Mohita
Publication year - 2018
Publication title -
international journal of computer and technology
Language(s) - English
Resource type - Journals
ISSN - 2277-3061
DOI - 10.24297/ijct.v17i1.7136
Subject(s) - wireless sensor network , computer science , energy consumption , node (physics) , cluster analysis , key distribution in wireless sensor networks , computer network , routing protocol , mobile wireless sensor network , efficient energy use , sensor node , routing (electronic design automation) , distributed computing , wireless , wireless network , engineering , telecommunications , structural engineering , machine learning , electrical engineering
Energy consumption is the core issue in wireless sensor networks (WSN). To generate a node energy model that can accurately reveal the energy consumption of sensor nodes is an extremely important part of protocol development, system design and performance evaluation in WSNs. In this paper, by studying component energy consumption in different node states and within state transitions, the authors present the energy models of the node core components, including processors, RF modules and sensors. One of the major issues in wireless sensor networks is developing a routing protocol which has a significant impact on the overall lifetime of the sensor network. The network area is divided into same sized small–small regions. Sensor nodes are randomly deployed in each predefined sub-area. Each region will have its region head (RH) and multiple member nodes. The member nodes in a specific region will send the data to the RH. RH within the region will be elected by distributed mechanism and will be based on fuzzy variables. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, transmission tuning algorithm for cluster-based WSNs has been proposed to balance the load among cluster heads that fall in different regions. This algorithm is applied prior to a cluster algorithm to improve the performance of the clustering algorithm without affecting the performance of individual sensor nodes.