A Mobile Charger Scheduling Algorithm For Rechargeable Wireless Sensor Network Using Machine Learning.
Bhargavi Durseti, Varshini Adi, Savio Soni
Wireless Rechargeable Sensor Networks, Mobile Charger Vehicle, Reinforcement Learning.
The Wireless rechargeable sensor networks have benefited immensely from mobile chargers (WRSNs). While most current research has been on on-demand recharging of WRSNs, little attention has been made to the combined consideration of residual energies and priorities when defining the charging schedule of energy-hungry nodes. Furthermore, most schemes ignore the issue of ill-timed charging responses to nodes with unequal energy consumption rates when making scheduling decisions, and they also ignore the issue of considering numerous network properties when making scheduling decisions. We address the aforementioned challenges in this study and present a unique scheduling strategy for WRSN on-demand charging. For establishing the charging schedule of the mobile chargers, we first use fuzzy logic, which integrates numerous network data. Next, we present a memorization-based method for determining the best charging schedule for mobile chargers. To illustrate the efficacy and efficiency of our approach, we run extensive simulations. The comparative findings show that the suggested scheme outperforms the current state-of-the-art methods in terms of variety of performance.
Article Details
Unique Paper ID: 159200

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 626 - 635
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