Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN

• Main Authors: Ahmad Ali, Yu Ming, Tapas Si, Saima Iram, Sagnik Chakraborty
• Format: Article
• Language: English
• Published: MDPI AG 2018-03-01
• Series: Information
• Subjects: WRSN; WSN; FWA-ATF; swarm intelligence; vacation time; energy minimization
• Online Access: http://www.mdpi.com/2078-2489/9/3/60

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.