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An efficient on-demand charging schedule method in rechargeable sensor networks

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Abstract

Nowadays, wireless energy charging (WEC) is emerging as a promising technology for improving the lifetime of sensors in wireless rechargeable sensor networks (WRSNs). Using WEC, a mobile charger (MC) reliably supplies electric energy to the sensors. However, finding an efficient charging schedule for MC to charge the sensors is one of the most challenging issues. The charging schedule depends on remaining energy, geographical and temporal constraints, etc. Therefore, in this article, a novel efficient charging algorithm is proposed, such that the lifetime of the sensors in WRSN are increased. The proposed algorithm uses a multi-node MC that can charge multiple sensors at the same time. In this algorithm, the charging requests of the low-energy sensors are received by the MC. Then, a reduced number of visiting points are determined for the MC to visit them. The visiting points are within the charging range of one or more requesting sensors. Thereafter, an efficient charging schedule is determined using an adaptive fuzzy model. Sugeno-fizzy inference method (S-FIS) is being used as a fuzzy model. It takes remaining energy, node density, and distance to MC, as network inputs for making real-time decisions while scheduling. Through simulation experiments, it is finally shown that the proposed scheme has higher charging performance comparing to base-line charging schemes in terms of survival ratio, energy utilization efficiency, and average charging latency. In addition, ANOVA tests are conducted to verify the reported results.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar, 800005, India

    Naween Kumar, Dinesh Dash & Mukesh Kumar

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  1. Naween Kumar
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  2. Dinesh Dash
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  3. Mukesh Kumar
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Correspondence to Naween Kumar.

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Kumar, N., Dash, D. & Kumar, M. An efficient on-demand charging schedule method in rechargeable sensor networks. J Ambient Intell Human Comput 12, 8041–8058 (2021). https://doi.org/10.1007/s12652-020-02539-1

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