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Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN

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School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
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Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, Bankura, West Bengal 722146, India
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School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China
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Authors to whom correspondence should be addressed.
Information 2018, 9(3), 60; https://doi.org/10.3390/info9030060
Received: 26 January 2018 / Revised: 7 March 2018 / Accepted: 8 March 2018 / Published: 9 March 2018
Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs). Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF). In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN) backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle. View Full-Text
Keywords: WRSN; WSN; FWA-ATF; swarm intelligence; vacation time; energy minimization WRSN; WSN; FWA-ATF; swarm intelligence; vacation time; energy minimization
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Ali, A.; Ming, Y.; Si, T.; Iram, S.; Chakraborty, S. Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN. Information 2018, 9, 60.

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