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Energy Efficient Mobile Harvesting Scheme for Clustered SDWSN with Beamforming Technique

Subaselvi Sundarraj*, Gunaseelan Konganathan

Department of Electronics and Communication Engineering, College of Engineering Guindy, Anna University, Chennai, 600025, Tamilnadu, India

* Corresponding Author: Subaselvi Sundarraj. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1197-1213.


Software Defined Wireless Sensor Networks (SDWSN) provides a centralized scheduling algorithm to decrease energy consumption compared to WSN. The sensor nodes have a finite battery capacity in the SDWSN that reduces the lifetime of the nodes. To harvest energy for energy depleted nodes without interfering with the eventful data transfer in the clustered SDWSN, an energy efficient mobile harvesting scheme with the Multiple Input Single Output (MISO) beamforming technique is proposed. The mobile harvesting scheme transfer the energy to the energy starving node and the beamforming algorithm which transmits the energy in the desired direction increases the lifetime of the nodes. The Secondary Cluster Head (SCH) node in a cluster is selected based on Particle Swarm Optimization (PSO) with the distance between mobile harvester and sensor node, the energy consumption rate of a node and lifetime of a nodes constraints for harvesting energy without interrupting data transfer to sink node in clustered SDWSN. The distributed scheduling algorithm and efficient path planning decrease the energy consumption of mobile harvester in the network. The optimization problem is formulated with Signal to Noise Ratio (SNR) and transmit power constraints to increase the harvested energy for sensor nodes in the network. The simulation results show the enhanced performance of the proposed algorithm compared to the existing algorithm in terms of residual energy, end to end delay, throughput, travel distance, service time and charging delay.


Cite This Article

S. Sundarraj and G. Konganathan, "Energy efficient mobile harvesting scheme for clustered sdwsn with beamforming technique," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 1197–1213, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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