scholarly journals RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1601 ◽  
Author(s):  
Ping Zhong ◽  
Yiwen Zhang ◽  
Shuaihua Ma ◽  
Xiaoyan Kui ◽  
Jianliang Gao
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Scheduling Scheme ◽  
On Demand ◽  
Wireless Rechargeable Sensor Networks

Real-Time Routing Based on On-Demand Multi-Hop Lookahead in Wireless Sensor Networks

2011 ◽  
Vol E94-B (2) ◽  
pp. 569-572
Author(s):  
Soochang PARK ◽  
Euisin LEE ◽  
Juhyun JUNG ◽  
Sang-Ha KIM
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Wireless Sensor ◽  
On Demand

scholarly journals A Fuzzy Logic-Based On-Demand Charging Algorithm for Wireless Rechargeable Sensor Networks With Multiple Chargers

2020 ◽  
Author(s):  
Abhinav Tomar
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Performance Metrics ◽  
Network Partitioning ◽  
On Demand ◽  
Network Parameters ◽  
Comparison Results ◽  
Wireless Rechargeable Sensor Networks ◽  
Joint Consideration

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 parameters while making scheduling decisions and even they overlook<br>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 fairly balance their workload. We next adopt the fuzzy logic which blends various network parameters 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<br>performance compared to the state-of-the-art schemes with respect to various performance metrics.<br>

A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks

2021 ◽  
Vol 110 ◽  
pp. 102278 ◽  
Author(s):  
Xianbo Cao ◽  
Wenzheng Xu ◽  
Xuxun Liu ◽  
Jian Peng ◽  
Tang Liu
Keyword(s):  
Reinforcement Learning ◽  
Sensor Networks ◽  
On Demand ◽  
Wireless Rechargeable Sensor Networks
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