A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning
2021
Sensors
In wireless rechargeable sensor networks (WRSNs), a mobile charger (MC) moves around to compensate for sensor nodes' energy via a wireless medium. In such a context, designing a charging strategy that optimally prolongs the network lifetime is challenging. This work aims to solve the challenges by introducing a novel, on-demand charging algorithm for MC that attempts to maximize the network lifetime, where the term "network lifetime" is defined by the interval from when the network starts till
doi:10.3390/s21165520
pmid:34450962
pmcid:PMC8401319
fatcat:yjefmrtigjfadhdoxfpniwquim