Abstract
Mobile chargers have greatly promoted the wireless rechargeable sensor networks (WRSNs). While most recent works have focused on recharging the WRSNs in an on-demand fashion, little attention has been paid on joint consideration of multiple mobile chargers (MCs) and multi-node energy transfer for determining the charging schedule of energy-hungry nodes. Moreover, most of the schemes leave out the contemplation of multiple network attributes while making scheduling decisions and even they overlook the issue of ill-timed charging response to the nodes with uneven energy consumption rates. In this paper, we address the aforesaid issues together and propose a novel scheduling scheme for on-demand charging in WRSNs. We first present an efficient network partitioning method for distributing the MCs so as to evenly balance their workload. We next adopt the fuzzy logic which blends various network attributes for determining the charging schedule of the MCs. We also formulate an expression to determine the charging threshold for the nodes that vary depending on their energy consumption rate. Extensive simulations are conducted to demonstrate the effectiveness and competitiveness of our scheme. The comparison results reveal that the proposed scheme improves charging performance compared to the state-of-the-art schemes with respect to various performance metrics.
Highlights
Sensor networks (WSNs) have gained substantial research interest due to wide range of applications, including precision agriculture, habitat and environmental monitoring, IoT-based smart cities, industrial manufacturing, and so on [1]
This paper proposes an on-demand multi-node charging scheme with multiple mobile chargers (MCs) to address the following fundamental issues: 1) How to distribute the multiple MCs in the network without overlapping their charging regions so that their charging workload are evenly balanced? 2) How to ensure that a sensor node will not be recharged by more than one MC at any time? 3) How to make wise and efficient scheduling decisions by blending multiple network attributes, such as residual energy (RE), distance to MC (D), energy consumption rate (ECR), and critical node density
We mainly focus on three performance metrics, namely energy usage efficiency, survival rate, and average charging latency in order to judge the efficacy of the proposed scheme
Summary
Abhinav Tomar , Member, IEEE, Lalatendu Muduli , and Prasanta K. While most recent works have focused on recharging the WRSNs in an on-demand fashion, little attention has been paid on joint consideration of multiple mobile chargers (MCs) and multi-node energy transfer for determining the charging schedule of energy-hungry nodes. Most of the schemes leave out the contemplation of multiple network attributes while making scheduling decisions and even they overlook the issue of ill-timed charging response to the nodes with uneven energy consumption rates. We address the aforesaid issues together and propose a novel scheduling scheme for on-demand charging in WRSNs. We first present an efficient network partitioning method for distributing the MCs so as to evenly balance their workload. We adopt the fuzzy logic which blends various network attributes for determining the charging schedule of the MCs. We formulate an expression to determine the charging threshold for the nodes that vary depending on their energy consumption rate.
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