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Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks

Author

Listed:
  • Nelofar Aslam

    (School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Kewen Xia

    (School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Muhammad Tafseer Haider

    (Department of Computer Science and Engineering, University of Engineering and Technology (UET), Lahore 54890, Pakistan)

  • Muhammad Usman Hadi

    (Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, 40136 Bologna, Italy)

Abstract

Wireless sensor networks (WSNs), built from many battery-operated sensor nodes are distributed in the environment for monitoring and data acquisition. Subsequent to the deployment of sensor nodes, the most challenging and daunting task is to enhance the energy resources for the lifetime performance of the entire WSN. In this study, we have attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimizes energy consumption and prolongs the overall network lifetime of WSNs. Initially, a wireless portable charging device (WPCD) is assumed which periodically travels on our proposed routing path among the nodes of the WSN to decrease their charge cycle time and recharge them with the help of wireless power transfer (WPT). Further, a scheduling scheme is proposed which creates clusters of WSNs. These clusters elect a cluster head among them based on the residual energy, buffer size, and distance of the head from each node of the cluster. The cluster head performs all data routing duties for all its member nodes to conserve the energy supposed to be consumed by member nodes. Furthermore, we compare our technique with the available literature by simulation, and the results showed a significant increase in the vacation time of the nodes of WSNs.

Suggested Citation

  • Nelofar Aslam & Kewen Xia & Muhammad Tafseer Haider & Muhammad Usman Hadi, 2017. "Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks," Future Internet, MDPI, vol. 9(4), pages 1-21, September.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:4:p:54-:d:112988
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    Cited by:

    1. Zhihao Hao & Dianhui Mao & Bob Zhang & Min Zuo & Zhihua Zhao, 2020. "A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain," IJERPH, MDPI, vol. 17(7), pages 1-18, March.

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