Energy-Aware Performance Evaluation of WSNs using Fuzzy Logic
Santosh Kumar Bharti1, Shashi Kant Dargar2, Abha Nyati3
1Santosh Bharti, Department of E & C Engineering, Sir Padampat Singhania University, India.
2Shashi Kant Dargar, Department of E & C Engineering, Sir Padampat Singhania University, India.
3Abha Nyati Dargar, Scholar, Department of E & C Engineering, Pacific University, Paher, India.
Manuscript received on March 22, 2013. | Revised Manuscript received on April 18, 2013. | Manuscript published on April 30, 2013. | PP: 83-87 | Volume-2, Issue-4, April 2013. | Retrieval Number: D1293042413/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless Sensor Networks (WSNs) are being used to form large, dense networks for the purpose of long term environmental sensing and data collection. Unfortunately, these networks are typically deployed in remote environment where energy source are limited. WSN’s, present a new generation of real-time embedded systems with limited computation, energy and memory resources that are being used in a wide variety of applications where traditional networking infrastructure is practically infeasible. Appropriate cluster-head node election can drastically reduce the energy consumption and enhance the lifetime of the network. In this paper, a fuzzy based energy-aware sensor network communication protocol is developed based on three descriptors – energy, concentration and centrality. Further we have to compared fuzzy based approach with other popular protocol LEACH and improved hierarchy scheme DBS. Simulation shows that depending upon network configuration, a substantial increase in network lifetime can be accomplished as compared to probabilistically selecting the nodes as cluster-heads using only local information.
Keywords: Wireless Microsensor networks, LEACH, cluster head election, distance based Segmentation, Fuzzy C-Mean Algorithms.