Computer Science editorial
ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks
The core problem
Wireless rechargeable sensor networks (WRSNs) face a fundamental tension: sensor nodes must operate for extended periods, yet their batteries are finite. Unmanned aerial vehicles (UAVs) equipped with wireless power transfer (WPT) have emerged as a promising solution, delivering energy on demand to extend network lifetime. However, coordinating UAV charging in dynamic WRSN environments is non-trivial. Nodes experience heterogeneous energy depletion rates driven by traffic load and deployment context, and UAV mobility introduces uncertainty in travel time and energy delivery.
This work addresses these challenges by proposing an integrated sensing and communication (ISAC)-enabled on-demand UAV charging framework coordinated by a central base station. The core innovation is a bidirectional coupling between scheduling and trajectory: scheduling decisions shape the UAV trajectory, while updated mobility estimates from ISAC dynamically reorder the charging queue. This closed-loop design enables responsive, urgency-aware charging under mobility uncertainty.
The framework targets practical IoT scenarios where WRSNs must sustain operation without manual intervention. By leveraging ISAC for
Innovation
Simulations were conducted to evaluate the proposed ISAC-enabled on-demand UAV charging framework against representative baselines. The performance metrics included energy usage efficiency, travel distance, and charging delay.
The results demonstrate consistent gains across all three metrics. In terms of energy usage efficiency, the framework achieved higher efficiency by prioritizing nodes with lower residual energy and higher traffic load, thereby reducing wasted energy on less critical nodes. Travel distance was reduced due to the flight-direction alignment factor, which encourages the UAV to serve nodes along its natural path, minimizing detours. Charging delay—the time between a node requesting energy and receiving it—was also improved, as the ISAC-assisted travel-time predictions enabled more accurate scheduling and reduced waiting times for critical nodes.
While specific numerical values are not provided in the abstract, the qualitative improvements over baselines indicate the effectiveness of the bidirectional coupling and partial charging policy. The framework's ability to dynamically reorder the queue based on real-time ISAC estimates proved particularly beneficial unde
Why it matters
The proposed framework offers a novel approach to UAV-assisted WRSN charging by integrating ISAC with on-demand scheduling. The bidirectional coupling between scheduling and trajectory is a key strength: it allows the system to adapt to changing conditions without centralized replanning. The prioritized queue effectively captures node urgency and service cost, while the partial charging policy ensures fair distribution of limited hover time.
However, several deployment considerations must be addressed. Computational overhead is a primary concern: the priority computation, ISAC-based estimation, and queue reordering must be performed in real time, which may strain resource-constrained base stations. Scalability is another issue: as the number of nodes grows, the complexity of maintaining and reordering the queue increases, potentially requiring distributed or hierarchical approaches. Parameter selection—such as the weights and the estimation error model—also affects performance and must be tuned for specific IoT scenarios.
Practitioners evaluating this framework should consider the trade-offs between sensing accuracy and communication overhead, as ISAC resources are shared. Future work could explore adaptive parameter tuning, integration with edge computing, and extension to multi-UAV scenarios. Overall, the framework provides a solid foundation for ISAC-enabled on-demand charging in WRSNs, with clear pathways for refinement.
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