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A novel efficient on demand charging schedule for rechargeable wireless sensor networks

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Abstract

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MCVs) are used for charging sensors using wireless energy charging (WEC) technology. In on demand charging, a sensor sends a charging request to the service station, and the MCV visits the sensor to transfer energy. The major drawbacks of using MCV-based WEC are its high energy consumption rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MCV reaches the requesting nodes to recharge them. We have adapted the partial charging scheme to serve a larger number of charging requests and have tried to reduce unnecessary movements of the MCV. In this article, our objectives are to improve the survival ratio and energy utilization efficiency of the MCV. Due to the mechanical movement of the MCV and its limited battery capacity, sometimes it is impossible to charge all the requesting sensors in a single charging round. Therefore, MCV takes multiple rounds to charge the sensors, and it is a continuous process to keep the network alive. We finally evaluate the performance of our proposed algorithm using extensive simulations and compare it with two other existing approaches. The simulation results show that our proposed algorithm improves the survival ratio by 30–40%, reduces the number of dead nodes significantly, and also improves the energy utilization efficiency of the MCV in comparison with existing methods.

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Authors and Affiliations

  1. Department of CSE, NIT Patna, Ashoke Rajpath, Patna, Bihar, 800005, India

    Anil Kumar Dudyala & Dinesh Dash

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  1. Anil Kumar Dudyala
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  2. Dinesh Dash
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Correspondence to Dinesh Dash.

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Anil Kumar Dudyala and Dinesh Dash contributed equally to this work.

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Dudyala, A.K., Dash, D. A novel efficient on demand charging schedule for rechargeable wireless sensor networks. Computing 105, 1697–1715 (2023). https://doi.org/10.1007/s00607-023-01170-0

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