Research Article
Energy Harvesting Aware Clustering and Opportunistic Transmission with Fuzzy Petri Net Reasoning
@ARTICLE{10.4108/eai.17-9-2015.150283, author={Aya Mostafa and Khaled Hassan}, title={Energy Harvesting Aware Clustering and Opportunistic Transmission with Fuzzy Petri Net Reasoning}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={2}, number={5}, publisher={EAI}, journal_a={INIS}, year={2015}, month={9}, keywords={energy harvesting, fuzzy petri nets reasoning, wireless sensor networks, clustering algorithms}, doi={10.4108/eai.17-9-2015.150283} }
- Aya Mostafa
Khaled Hassan
Year: 2015
Energy Harvesting Aware Clustering and Opportunistic Transmission with Fuzzy Petri Net Reasoning
INIS
EAI
DOI: 10.4108/eai.17-9-2015.150283
Abstract
As a solution to the environmental and natural disaster monitoring and detection, we propose an energy efficient large-scale autonomous Wireless Sensor Networks (WSNs) that can withstand the limited battery lifetime problem. Hereto, an energy-harvesting (EH) aware clustering technique is introduced using a knowledge-based inference approach for selecting cluster heads using fuzzy petri nets with three level hierarchy. Our EH mechanism endorses the sensor nodes with hybrid sources EH giving a potential boost for the network life. The robustness of our WSN is addressed by introducing a primary and a secondary backups for each level to reduce the frequency of reclustering. Finally, an opportunistic energy harvesting aware transmission technique is presented to enhance the network life-time. The performance of this scheme is evaluated against the famous clustering techniques, where the proposed algorithms extend the WSN field life-time significantly.
Copyright © 2015 K. Hassan and A. Mostafa, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.