ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks

Chang Liu; Lin Zhang; Muhammad Umar Farooq Qaisar; Paolo Bellavista; Shamsher Ullah; Shehzad Ashraf Chaudhry

ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks

T0 review · reviewed 2026-07-30 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Coupling ISAC mobility estimates to a four-factor charging queue and urgency-weighted partial hover lets a UAV serve more sensors with less flight waste and lower delay.

desk verdict Solid, incremental systems paper: a clean training-free ISAC–queue–partial-charge loop with real engineering care, but the headline gains rest on thin, under-documented baseline comparisons. read the letter →

arxiv 2607.23572 v1 pith:KCICE5KJ submitted 2026-07-26 cs.NI

Muhammad Umar Farooq Qaisar , Lin Zhang , Paolo Bellavista , Shehzad Ashraf Chaudhry , Shamsher Ullah , Chang Liu This is my paper · ORCID

classification cs.NI

keywords wirelessrechargeablesensornetworksUAVchargingpowertransferintegratedsensingandcommunicationon-demandschedulingpartialpriorityqueue

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Sensor networks die when batteries run out; a UAV that flies out and wirelessly recharges them can keep them alive, but only if it chooses the right nodes, in the right order, for the right amount of hover time under tight energy and flight limits. This paper argues that the missing piece is a closed loop: the base station keeps a priority queue scored by residual energy, traffic load, estimated travel time, and heading alignment, while integrated sensing and communication continuously refreshes the UAV’s distance, speed, and position so those scores stay honest as the vehicle moves. Scheduling decisions therefore reshape the flight path, and path updates reorder the queue. Limited hover time is then split across the queue by criticality rather than fully topping up one node at a time. In simulations against two strong baselines, the design uses more of the UAV’s energy for actual charging, flies shorter routes, and cuts average charging delay across networks of 100–500 nodes.

What carries the argument

The ISAC-assisted prioritized charging queue with bidirectional coupling: composite priority from urgency (energy + load) minus mobility cost (travel time) plus heading alignment, refreshed online by fused ISAC range/Doppler estimates, plus event-driven reallocation of a fixed hover budget proportional to normalized urgency weights.

What would settle it

Re-run the same 100–500 node scenarios with a position- and attitude-dependent WPT model (or a hardware testbed) and check whether energy-usage efficiency, path length, and charging delay still beat CGDA-Q and MA-DDQN; if received power varies sharply with small hover errors, the claimed gains should collapse.

Watch

Extended reading notes

Core claim

An ISAC-enabled on-demand UAV charging framework (OD-UCS) that bidirectionally couples a four-attribute priority queue—residual energy, traffic load, estimated travel time, and flight-direction alignment—with Kalman-fused ISAC state estimates and urgency-weighted partial hover-time allocation consistently achieves higher energy-usage efficiency, shorter travel distance, and lower charging delay than CGDA-Q and MA-DDQN under the same single-UAV WRSN conditions.

Load-bearing premise

Once the UAV is close enough above a node, wireless charging power is treated as essentially constant, so hover time maps straight into energy delivered and the partial-time splits remain meaningful.

Editorial extensions

If this is right

Base stations can keep return-to-depot safety inside the queue admission check rather than as a post-tour fix, so every admitted tour remains flyable.
Partial charging with a built-in safety margin serves more nodes per mission without trapping them in near-depletion cycles.
Event-driven ISAC updates and O(K log K) reordering stay cheap enough for networks of a few hundred nodes with only a small sensing subset.
Practitioners can tune the four priority weights (roughly 0.4/0.2/0.25/0.15) without large performance swings so long as urgency stays dominant.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same bidirectional queue-plus-sensing loop could transfer to multi-UAV fleets if conflict-free trajectory slots replace the single heading-alignment term.
If ISAC measurement quality drops, the independent urgency score and return-to-depot gate still give a usable degraded mode—suggesting the design is more fault-tolerant than pure learning-based chargers.
Security of charging requests and ISAC observables becomes a first-order deployment issue once the queue trusts those inputs to reorder flights.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Punchline: this is a competent cs.NI systems piece that packages known pieces—multi-attribute priority, partial hover allocation, return-to-depot admission, ISAC state fusion—into one closed loop (OD-UCS) with explicit bidirectional coupling between the queue and the trajectory. That integration is the real contribution, not a new theory result.

What it does well. The design is readable and deployable-minded: four normalized scores with stated default weights, event-driven reallocation before each hover, return-to-depot checked at enqueue rather than at tour end, and a short but honest overhead/scalability note (O(K log K) reorder, small ISAC subset). The weight-sensitivity sweep in §V-E is modest but useful—efficiency moves <5% over a sensible α range. Against pure DRL charging papers, the training-free, interpretable scheduler is a legitimate practical alternative. Related-work positioning is fair; self-cites to their POISED/ISAC-MCV line supply background rather than the measured gains.

Soft spots, in proportion. The comparative claim (better efficiency, shorter paths, lower delay vs CGDA-Q and MA-DDQN, Fig. 3) is the load-bearing result and is under-supported. No run counts, no error bars, curves look single-seed. CGDA-Q is forced to one UAV outside its multi-UAV design; MA-DDQN’s training, hyperparameters, and convergence in this single-UAV ISAC setting are never stated. A hand-tuned heuristic beating a handicapped or under-trained learner is a known artifact here. There is also no ablation that isolates ISAC-triggered reorder plus urgency-weighted splits from a plain residual-energy greedy queue, so even a real gap is hard to attribute. The constant 5 W WPT model is a shared abstraction—it hurts external validity more than the internal ranking. Priority is described in prose without a displayed equation; free parameters (weights, threshold, Kalman trust) are design knobs, not predictions.

Who it’s for: people building or comparing on-demand UAV chargers for WRSNs who want a transparent baseline rather than another DQN. It deserves a serious referee, not a desk reject—revise for stats, baseline fidelity, formula, and a small ablation. I would engage the work as related systems engineering, not as a must-cite foundation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: systems design with external baseline comparison; self-citations are background only

full rationale

OD-UCS is a systems framework (priority queue + ISAC state fusion + urgency-weighted partial charging) whose strongest claims are comparative simulation outcomes versus CGDA-Q and MA-DDQN on energy usage efficiency, travel distance, and charging delay (§V-B–D, Fig. 3). Those metrics are not defined in terms of the priority formula or the hand-chosen weights (α=0.4, β=0.2, γ=0.25, δ=0.15); the weights are explicit design knobs with a brief sensitivity sweep (§IV-A, §V-E), not fitted parameters renamed as predictions. Self-citations to the authors’ prior ISAC-WRSN line ([3], [7]) appear in related-work positioning and do not underwrite the measured gains or forbid alternatives via a uniqueness theorem. There is no self-definitional loop, no fitted-input-called-prediction, and no ansatz smuggled in as a forced result. The paper is self-contained against external benchmarks in the ordinary sense for a networking systems paper; circularity burden is nil.

Assumptions & free parameters 6 free parameters · 6 assumptions · 2 invented entities

Load-bearing content is almost entirely modeling choices and hand-set weights inside a discrete-event UAV-WRSN simulator. No formal theorem is proved; claims rest on domain abstractions (constant WPT rate, straight-line flight, usable ISAC delay/Doppler) and free priority/safety parameters. Invented pieces are design constructs (composite priority, time-allocation rule), not new physical entities.

free parameters (6)

Priority weights (urgency α, traffic β, travel-time γ, direction δ) = 0.4, 0.2, 0.25, 0.15
Hand-set to 0.4/0.2/0.25/0.15; only α is swept 0.2–0.6. Ordering and efficiency claims depend on these relative influences.
Charging-request residual-energy threshold = 30%
Nodes request charge below 30% battery; changes request arrival process and urgency distribution.
Effective WPT charging rate = 5 W
Abstracted received power after losses; directly scales energy delivered per hover second in all policies.
Partial-charging safety margin / demand definition
Demand is defined to push nodes “above a safety margin,” but the margin value is not numerically fixed; it shapes time splits and outage behavior.
ISAC/Kalman fusion trust parameters
Filter gain balances onboard reports vs ISAC measurements; covariances and node-subset size (n=3–5) are stated only qualitatively yet drive travel-time accuracy.
UAV energy and power model constants = 500 kJ; 150 W; 200 W; 20 m/s
Initial energy 500 kJ, flight 150 W, hover 200 W, vmax 20 m/s fix feasible tour length and return-to-depot admissions.

assumptions (6)

domain assumption Within an effective charging region, delivered energy increases approximately in proportion to hover time at a near-constant rate.
Stated in §III-B and §IV-C; underpins linear time-to-energy mapping for partial charging.
domain assumption UAV motion is straight-line point-to-point at constant vmax between stops, with known or calibratable propulsion/hover power.
§III-C mobility model; travel-time and energy feasibility checks depend on it.
domain assumption ISAC delay- and Doppler-like observables from a small geometry-selected sensor subset, fused by a Kalman-type filter, yield travel-time and heading estimates accurate enough to usefully reorder the queue.
§IV-B; central to the claimed bidirectional coupling under mobility uncertainty.
domain assumption Nodes periodically report residual energy and traffic load truthfully enough for the base station’s global queue.
§III-A network model; priority scores are only as good as these reports.
domain assumption Return-to-depot energy from any candidate, including hover and safety reserve, can be estimated before enqueueing so unsafe nodes are deferred.
§III-C and §IV-A admission rule integrated into queue construction.
ad hoc to paper Composite priority is a normalized weighted combination where energy urgency and load increase priority, travel time decreases it, and heading alignment increases it.
§IV-A design choice; no optimality proof, only simulation comparison.

invented entities (2)

OD-UCS composite priority queue (four-attribute urgency/cost score with ISAC-triggered reorder)
purpose: Rank charging requests online so scheduling shapes trajectory and ISAC updates reshape the schedule.
Specific attribute set and bidirectional coupling are the paper’s main design artifact; no external optimality or field evidence.
Urgency-weighted time-allocated partial charging rule
purpose: Split limited mission hover budget across the current queue by residual-energy and load criticality.
Event-driven reallocation formula is introduced here; behavior validated only in the authors’ simulator.

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Cite this review

Pith. "Pith review of ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks." pith.science (2026). https://pith.science/paper/KCICE5KJ

@misc{pith2026260723572,
  author       = {Pith},
  title        = {Pith review of: ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KCICE5KJ}},
  note         = {Machine review of arXiv:2607.23572}
}

read the original abstract

Unmanned aerial vehicles (UAVs) equipped with wireless power transfer (WPT) extend the lifetime of wireless rechargeable sensor networks (WRSNs) by delivering energy on demand. This article presents an integrated sensing and communication (ISAC)-enabled on-demand UAV charging framework coordinated by a central base station. A prioritized charging queue captures node urgency and service cost through residual energy, traffic load, estimated UAV travel time, and flight-direction alignment. This bidirectional coupling ensures that scheduling decisions shape the UAV trajectory, while updated mobility estimates from ISAC dynamically reorder the queue. ISAC-assisted estimation of UAV distance, speed, and position updates travel-time predictions under mobility uncertainty. A time-allocated partial charging policy distributes limited hover time across queued nodes according to criticality. Simulations show gains in energy usage efficiency, travel distance, and charging delay compared with representative baselines. We discuss deployment considerations, including computational overhead, scalability, and parameter selection, to aid practitioners evaluating the framework for IoT scenarios.

Figures

Figures reproduced from arXiv: 2607.23572 by the authors.

**Figure 2.** Overview of the BS-orchestrated ISAC-enabled on-demand UAV charging framework for WRSNs: (left) system model [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Performance over sensor nodes. robust to moderate weight perturbations. The largest performance drop occurs at very low α (≤ 0.2), where urgency is under-weighted and critical nodes are deprioritized, this boundary case reinforces the design rationale for setting α relatively high. VI. CONCLUSION AND FUTURE DIRECTIONS This article has presented an ISAC-enabled, on-demand UAV charging framework for wireless recharge… view at source ↗

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Reference graph

Works this paper leans on

15 extracted references

[1]

Sdorp: Sdn based opportunistic routing for asynchronous wireless sensor networks,

M. U. Farooq, X. Wang, A. Hawbani, L. Zhao, A. Al-Dubai, and O. Busaileh, “Sdorp: Sdn based opportunistic routing for asynchronous wireless sensor networks,”IEEE Transactions on Mobile Computing, vol. 22, no. 8, pp. 4912–4929, 2022

2022
[2]

Resilient sensor data dissemination to mitigate link faults in iot networks with long-haul optical wires for power transmission grids,

B. Zhou, C. Wu, Q. Yang, Y . Qian, and Y . Nie, “Resilient sensor data dissemination to mitigate link faults in iot networks with long-haul optical wires for power transmission grids,”IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15 919–15 939, 2024

2024
[3]

Poised: Probabilistic on-demand charging scheduling for isac-assisted wrsns with multiple mobile charging vehicles,

M. U. F. Qaisar, W. Yuan, P. Bellavista, F. Liu, G. Han, R. S. Zakariyya, and A. Ahmed, “Poised: Probabilistic on-demand charging scheduling for isac-assisted wrsns with multiple mobile charging vehicles,”IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 10 818–10 834, 2024

2024
[4]

Wireless rechargeable sensor net- works: Energy provisioning technologies, charging scheduling schemes, and challenges,

S. A. Aziz, X. Wang, A. Hawbani, B. Qureshi, S. H. Alsamhi, A. Alabsi, L. Zhao, A. Al-Dubai, and A. Ismail, “Wireless rechargeable sensor net- works: Energy provisioning technologies, charging scheduling schemes, and challenges,”IEEE Transactions on Sustainable Computing, vol. 10, no. 5, pp. 873–890, 2025

2025
[5]

Collaborative hybrid charging scheduling in wireless rechargeable sensor networks,

J. Chen, C. W. Yu, and R.-H. Cheng, “Collaborative hybrid charging scheduling in wireless rechargeable sensor networks,”IEEE Transac- tions on Vehicular Technology, vol. 71, no. 8, pp. 8994–9010, 2022

2022
[6]

Uav dispatch planning for a wireless rechargeable sensor network for bridge monitoring,

C. Zhao, Y . Wang, X. Zhang, S. Chen, C. Wu, and K. L. Teo, “Uav dispatch planning for a wireless rechargeable sensor network for bridge monitoring,”IEEE Transactions on Sustainable Computing, vol. 8, no. 2, pp. 293–309, 2022

2022
[7]

Isac- assisted wireless rechargeable sensor networks with multiple mobile charging vehicles,

M. U. F. Qaisar, W. Yuan, P. Bellavista, G. Han, and A. Ahmed, “Isac- assisted wireless rechargeable sensor networks with multiple mobile charging vehicles,”IEEE Internet of Things Magazine, vol. 7, no. 6, pp. 80–86, 2024

2024
[8]

Uav-enabled integrated sensing and communication: Op- portunities and challenges,

K. Meng, Q. Wu, J. Xu, W. Chen, Z. Feng, R. Schober, and A. L. Swindlehurst, “Uav-enabled integrated sensing and communication: Op- portunities and challenges,”IEEE Wireless Communications, vol. 31, no. 2, pp. 97–104, 2023

2023

Show all 15 references

[9]

Maximizing energy efficiency of period-area coverage with a uav for wireless rechargeable sensor networks,

C. Lin, S. Hao, W. Yang, P. Wang, L. Wang, G. Wu, and Q. Zhang, “Maximizing energy efficiency of period-area coverage with a uav for wireless rechargeable sensor networks,”IEEE/ACM Transactions on Networking, vol. 31, no. 4, pp. 1657–1673, 2022

2022
[10]

A reinforcement learning-based energy charging strategy for agricultural internet of things with multi-uav-assisted wrsn,

J. Chen, X. Li, B. Cai, J. He, Y . Ma, and J. Liu, “A reinforcement learning-based energy charging strategy for agricultural internet of things with multi-uav-assisted wrsn,”IEEE Internet of Things Journal, vol. 12, no. 23, pp. 49 022–49 035, 2025

2025
[11]

Drl-based charging strategy optimization for irs-assisted uav in wireless rechargeable sensor networks,

X. Liu, C. Zhao, S. Chen, T. Wang, and F. Chen, “Drl-based charging strategy optimization for irs-assisted uav in wireless rechargeable sensor networks,”ACM Transactions on Sensor Networks, vol. 22, no. 3, pp. 1–38, 2026

2026
[12]

Green laser-powered uav far-field wireless charging and data backhauling for a large-scale sensor network,

X. Ma, X. Liu, and N. Ansari, “Green laser-powered uav far-field wireless charging and data backhauling for a large-scale sensor network,” IEEE Internet of Things Journal, vol. 11, no. 19, pp. 31 932–31 946, 2024

2024
[13]

Dy- namic charging strategy optimization for uav-assisted wireless recharge- able sensor networks based on deep q-network,

N. Liu, J. Zhang, C. Luo, J. Cao, Y . Hong, Z. Chen, and T. Chen, “Dy- namic charging strategy optimization for uav-assisted wireless recharge- able sensor networks based on deep q-network,”IEEE Internet of Things Journal, vol. 11, no. 12, pp. 21 125–21 134, 2023

2023
[14]

Dynamic charging and path planning for uav-powered rechargeable wsns using multi-agent deep reinforcement learning,

M. L. Betalo, S. Leng, A. M. Seid, H. N. Abishu, A. Erbad, and X. Bai, “Dynamic charging and path planning for uav-powered rechargeable wsns using multi-agent deep reinforcement learning,”IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 15 610–15 626, 2025

2025
[15]

A non-cooperative pricing strategy for uav-enabled charging of wireless sensor network,

A. K. Gupta and M. R. Bhatnagar, “A non-cooperative pricing strategy for uav-enabled charging of wireless sensor network,”IEEE Transac- tions on Green Communications and Networking, vol. 9, no. 2, pp. 459–470, 2024

2024

Pith tools

Reviewed July 30, 2026 · model on record in the stance chip above.

[1] [1]

Sdorp: Sdn based opportunistic routing for asynchronous wireless sensor networks,

M. U. Farooq, X. Wang, A. Hawbani, L. Zhao, A. Al-Dubai, and O. Busaileh, “Sdorp: Sdn based opportunistic routing for asynchronous wireless sensor networks,”IEEE Transactions on Mobile Computing, vol. 22, no. 8, pp. 4912–4929, 2022

2022

[2] [2]

Resilient sensor data dissemination to mitigate link faults in iot networks with long-haul optical wires for power transmission grids,

B. Zhou, C. Wu, Q. Yang, Y . Qian, and Y . Nie, “Resilient sensor data dissemination to mitigate link faults in iot networks with long-haul optical wires for power transmission grids,”IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15 919–15 939, 2024

2024

[3] [3]

Poised: Probabilistic on-demand charging scheduling for isac-assisted wrsns with multiple mobile charging vehicles,

M. U. F. Qaisar, W. Yuan, P. Bellavista, F. Liu, G. Han, R. S. Zakariyya, and A. Ahmed, “Poised: Probabilistic on-demand charging scheduling for isac-assisted wrsns with multiple mobile charging vehicles,”IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 10 818–10 834, 2024

2024

[4] [4]

Wireless rechargeable sensor net- works: Energy provisioning technologies, charging scheduling schemes, and challenges,

S. A. Aziz, X. Wang, A. Hawbani, B. Qureshi, S. H. Alsamhi, A. Alabsi, L. Zhao, A. Al-Dubai, and A. Ismail, “Wireless rechargeable sensor net- works: Energy provisioning technologies, charging scheduling schemes, and challenges,”IEEE Transactions on Sustainable Computing, vol. 10, no. 5, pp. 873–890, 2025

2025

[5] [5]

Collaborative hybrid charging scheduling in wireless rechargeable sensor networks,

J. Chen, C. W. Yu, and R.-H. Cheng, “Collaborative hybrid charging scheduling in wireless rechargeable sensor networks,”IEEE Transac- tions on Vehicular Technology, vol. 71, no. 8, pp. 8994–9010, 2022

2022

[6] [6]

Uav dispatch planning for a wireless rechargeable sensor network for bridge monitoring,

C. Zhao, Y . Wang, X. Zhang, S. Chen, C. Wu, and K. L. Teo, “Uav dispatch planning for a wireless rechargeable sensor network for bridge monitoring,”IEEE Transactions on Sustainable Computing, vol. 8, no. 2, pp. 293–309, 2022

2022

[7] [7]

Isac- assisted wireless rechargeable sensor networks with multiple mobile charging vehicles,

M. U. F. Qaisar, W. Yuan, P. Bellavista, G. Han, and A. Ahmed, “Isac- assisted wireless rechargeable sensor networks with multiple mobile charging vehicles,”IEEE Internet of Things Magazine, vol. 7, no. 6, pp. 80–86, 2024

2024

[8] [8]

Uav-enabled integrated sensing and communication: Op- portunities and challenges,

K. Meng, Q. Wu, J. Xu, W. Chen, Z. Feng, R. Schober, and A. L. Swindlehurst, “Uav-enabled integrated sensing and communication: Op- portunities and challenges,”IEEE Wireless Communications, vol. 31, no. 2, pp. 97–104, 2023

2023