Wireless Rechargeable Sensor Networks (WRSNs) overcome the energy limitations imposed by the traditional battery-powered Wireless Sensor Networks. WRSNs recharge the sensor nodes using single or multiple wireless mobile charger(s). But to find a charging schedule for the mobile charger to replenish the sensor nodes’ energy requires meticulous handling of the spatio-temporal constraints of the mobile charger(s). This paper presents an on-demand multi-node charging schedule for a mobile charger with finite battery capacity to reduce the dead periods of the sensor nodes while maintaining the mobile charger’s charging efficiency. A heuristic approach has been used to design the charging schedule. It is based on the parameters like sensors’ remaining energies, the mobile charger’s distance to the sensors, the mobile charger’s current energy level and the sensors’ energy consumption rates. It uses partial charging and the energy charging unit(s) is proportional to a sensor’s energy consumption rate. We perform extensive simulations to compare the effectiveness of the proposed algorithm with the two similar existing algorithms, MTSPC and CSD. The results show that our proposed algorithm RDPCS decreases the average total dead period by about <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="42979_2025_3679_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">$56\%$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>56</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> compared to MTSPC and by about <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="42979_2025_3679_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">$47\%$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>47</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> than that in CSD on average and achieves the similar charging efficiency of (32–33)% as attained by the two algorithms. The simulations prove that our proposed charging scheduling technique offers promising results and outperforms on average total dead period, tour length, number of dead nodes and certain other performance evaluation metrics.

A Heuristic Greedy Approach to Reduce the Dead Periods of the Sensor Nodes in an On-Demand WRSN

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发表日期：

2025年2月25日

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Sabah Tazeen,
Dinesh Dash

摘要

Wireless Rechargeable Sensor Networks (WRSNs) overcome the energy limitations imposed by the traditional battery-powered Wireless Sensor Networks. WRSNs recharge the sensor nodes using single or multiple wireless mobile charger(s). But to find a charging schedule for the mobile charger to replenish the sensor nodes’ energy requires meticulous handling of the spatio-temporal constraints of the mobile charger(s). This paper presents an on-demand multi-node charging schedule for a mobile charger with finite battery capacity to reduce the dead periods of the sensor nodes while maintaining the mobile charger’s charging efficiency. A heuristic approach has been used to design the charging schedule. It is based on the parameters like sensors’ remaining energies, the mobile charger’s distance to the sensors, the mobile charger’s current energy level and the sensors’ energy consumption rates. It uses partial charging and the energy charging unit(s) is proportional to a sensor’s energy consumption rate. We perform extensive simulations to compare the effectiveness of the proposed algorithm with the two similar existing algorithms, MTSPC and CSD. The results show that our proposed algorithm RDPCS decreases the average total dead period by about $56\%$ $56 %$ compared to MTSPC and by about $47\%$ $47 %$ than that in CSD on average and achieves the similar charging efficiency of (32–33)% as attained by the two algorithms. The simulations prove that our proposed charging scheduling technique offers promising results and outperforms on average total dead period, tour length, number of dead nodes and certain other performance evaluation metrics.