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ISAC-Assisted Wireless Rechargeable Sensor Networks with Multiple Mobile Charging Vehicles

Muhammad Umar Farooq Qaisar, Weijie Yuan, Paolo Bellavista, Guangjie Han, Adeel Ahmed

TL;DR

The paper tackles energy scarcity in IoT-based WRSNs by proposing an ISAC-assisted framework with multiple MCVs. It introduces a multi-metric, probabilistic charging strategy that balances load across MCV queues and enables partial charging, coupled with an ISAC-based mechanism to reduce travel time and prevent charging conflicts. Analytical descriptions and simulation results show improvements in Energy Usage Efficiency, Charging Delay, and Travel Distance over baseline approaches. This work advances practical, scalable, and energy-efficient WRSNs for large-scale IoT deployments by integrating sensing/comms resources with mobility and wireless charging.

Abstract

As IoT-based wireless sensor networks (WSNs) become more prevalent, the issue of energy shortages becomes more pressing. One potential solution is the use of wireless power transfer (WPT) technology, which is the key to building a new shape of wireless rechargeable sensor networks (WRSNs). However, efficient charging and scheduling are critical for WRSNs to function properly. Motivated by the fact that probabilistic techniques can help enhance the effectiveness of charging scheduling for WRSNs, this article addresses the aforementioned issue and proposes a novel ISAC-assisted WRSN protocol. In particular, our proposed protocol considers several factors to balance the charging load on each mobile charging vehicle (MCV), uses an efficient charging factor strategy to partially charge network devices, and employs the ISAC concept to reduce the traveling cost of each MCV and prevent charging conflicts. Simulation results demonstrate that this protocol outperforms other classic, cutting-edge protocols in multiple areas.

ISAC-Assisted Wireless Rechargeable Sensor Networks with Multiple Mobile Charging Vehicles

TL;DR

The paper tackles energy scarcity in IoT-based WRSNs by proposing an ISAC-assisted framework with multiple MCVs. It introduces a multi-metric, probabilistic charging strategy that balances load across MCV queues and enables partial charging, coupled with an ISAC-based mechanism to reduce travel time and prevent charging conflicts. Analytical descriptions and simulation results show improvements in Energy Usage Efficiency, Charging Delay, and Travel Distance over baseline approaches. This work advances practical, scalable, and energy-efficient WRSNs for large-scale IoT deployments by integrating sensing/comms resources with mobility and wireless charging.

Abstract

As IoT-based wireless sensor networks (WSNs) become more prevalent, the issue of energy shortages becomes more pressing. One potential solution is the use of wireless power transfer (WPT) technology, which is the key to building a new shape of wireless rechargeable sensor networks (WRSNs). However, efficient charging and scheduling are critical for WRSNs to function properly. Motivated by the fact that probabilistic techniques can help enhance the effectiveness of charging scheduling for WRSNs, this article addresses the aforementioned issue and proposes a novel ISAC-assisted WRSN protocol. In particular, our proposed protocol considers several factors to balance the charging load on each mobile charging vehicle (MCV), uses an efficient charging factor strategy to partially charge network devices, and employs the ISAC concept to reduce the traveling cost of each MCV and prevent charging conflicts. Simulation results demonstrate that this protocol outperforms other classic, cutting-edge protocols in multiple areas.
Paper Structure (20 sections, 5 figures, 1 table)

This paper contains 20 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. Network Model and Initialization
  5. Charging Model
  6. Mobility Model
  7. The Proposed Protocol
  8. Charging Load Strategy
  9. Residual Energy of Charging Sensor Device
  10. Distance from MCV to Charging Sensor Device
  11. Degree of Charging Sensor Device
  12. Charging Sensor Device Betweenness Centrality
  13. Charging Queue Metric
  14. Charging Factor Strategy
  15. ISAC-based MCV detection
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Wireless rechargeable sensor networks for IoT applications
  • Figure 2: The core architecture of the proposed novel scheme
  • Figure 3: The figure illustrates the steps involved in the charging factor strategy: 1) It starts with the charging sensors in the MCV queue. 2) The residual energy attribute of the charging sensor device is taken into consideration in the queue. 3) The residual energy values are then used to calculate the criticality values, representing the minimum and maximum criticality of the sensor devices. 4) These criticality values are then utilized in the calculation of the probabilistic weighted factor. 5) The charging control factor is obtained for each sensor device in the queue to regulate the charging process efficiently, adjusted to 10% of the residual energy priority for each sensor device. 6) Finally, the charging factor strategy is determined based on the above factors to enhance charging efficiency and ensure fair and successful partial charging.
  • Figure 4: MCV detection based on ISAC approach
  • Figure 5: Performance over number of devices