ArticleSensors (Basel, Switzerland)2018
RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Novel Energy Replenishment Algorithm to Increase the Network Performance of Rechargeable Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2024Article
- Attention-Shared Multi-Agent Actor-Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks.Entropy (Basel, Switzerland) · 2022Article
- On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks.Sensors (Basel, Switzerland) · 2022Article
- Mobile Charging Strategy for Wireless Rechargeable Sensor Networks.Sensors (Basel, Switzerland) · 2022Article
- Nanosystems, Edge Computing, and the Next Generation Computing Systems.Sensors (Basel, Switzerland) · 2019Review
- A Type of Annulus-Based Energy Balanced Data Collection Method in Wireless Rechargeable Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
- A High-Efficiency Data Collection Method Based on Maximum Recharging Benefit in Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
- A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue of edge networks. In real-world applications of edge computing, sensor nodes usually have different task burdens due to the environmental impact, which results in a dynamic change of the energy consumption rate at different nodes. Therefore, the traditional periodical charging mode cannot meet the nodes charging demand that have dynamic energy consumption. In this paper, we propose a real-time on-demand charging scheduling scheme (RCSS) under the condition of limited mobile charger capacity. In the process of building the charging path, RCSS adequately considers the dynamic energy consumption of different node, and puts forward the next node selection algorithm. At the same time, a method to determine the feasibility of charging circuit is also proposed to ensure the charging efficiency. During the charging process, RCSS is based on adaptive charging threshold to reduce node mortality. Compared with existing approaches, the proposed RCSS achieves better performance in the number of survival nodes, the average service time and charging efficiency.
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Registered trials
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