An adaptive on-demand charging scheme for rechargeable wireless sensor networks

Zhansheng Chen, Hong Shen, Tingmei Wang, Xiaofan Zhao

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In view of the stability and reliability of energy supply, distinct from the time-varying and uncertainty of energy harvesting systems, adopting mobile vehicles to replenish energy of sensors has become a research hotspot. While some existing studies on the mobile recharging problem ignored the limited energy capacity carried by mobile vehicle and the difference in energy consumption rates of sensors, in this work, we propose an adaptive real-time on-demand charging scheduling scheme that maximizes energy efficiency (CSS-MEE) for Rechargeable Wireless Sensor Networks. In CSS-MEE, we aim to achieve a compromise between maximizing charging energy efficiency and maximizing charging throughput for solving the on-demand mobile charging problem. Due to the limited energy capacity of the mobile charger, CSS-MEE uses both full-charge mode and adaptive charging mode, depending on the number of charging requests. It combines charging node selection with dispatch path feasibility determination, which takes into account the location-generated charging cost and energy-driven charging priority, to ensure the charging efficiency. Extensive simulations are conducted to demonstrate the advantages of CSS-MEE. Compared with existing approaches, simulation results show that CSS-MEE achieves better performance in terms of charging throughput, average charging latency, charge scheduling times, and charging efficiency.

Original languageEnglish
Article numbere6136
JournalConcurrency Computation Practice and Experience
Issue number2
Publication statusPublished - 25 Jan 2022
Externally publishedYes


  • charging efficiency
  • charging latency
  • charging throughput
  • on-demand mobile charging
  • rechargeable wireless sensor networks


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