An efficient on-demand charging schedule method in rechargeable sensor networks
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ORIGINAL RESEARCH
An efficient on‑demand charging schedule method in rechargeable sensor networks Naween Kumar1 · Dinesh Dash1 · Mukesh Kumar1 Received: 2 April 2020 / Accepted: 5 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Wireless energy charging · Sensor lifetime · Mobile charger · Multi-node energy charging · Charging schedule · Sugeno FIS · Wireless rechargeable sensor network
1 Introduction Wireless sensor networks (WSNs) are widely used for a number of applications, namely in military surveillance (Prabhu et al. 2017), disaster forecasting (Khan et al. 2015), health-care monitoring (Kachuee et al. 2016), and IoTbased smart homes (Ransing and Rajput 2015), etc (GarcíaHernández et al. 2007). Based on the application needs, the sensors capture different types of data from the surroundings (Diallo et al. 2012). However, sensors usually have limited energy capacity batteries, which may not be feasible * Naween Kumar [email protected] Dinesh Dash [email protected] Mukesh Kumar [email protected] 1
Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar 800005, India
for the continuous operation of the network (Akyildiz et al. 2002). Therefore, the sensors’ energy conservation is of great importance for the long-term operation of WSNs. A simple way is the replacement of sensors’ batteries when they are exhausted. Nevertheless, for a large-scale WSN, this approach could be too expens
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