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arxiv: 2605.22490 · v1 · pith:IYRDVT6Qnew · submitted 2026-05-21 · 📡 eess.SP

UAV-based Energy-Efficient Data Collection in Smart Grids with ISAC QoS Guarantees

Yibin Xie , Jin Zhao , Indrakshi Dey , Nicola Marchetti This is my paper

Pith reviewed 2026-05-22 03:40 UTC · model grok-4.3

classification 📡 eess.SP
keywords UAVISACdata collectionsmart gridsdynamic line ratingenergy minimizationreinforcement learningsemidefinite relaxation
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The pith

UAVs with integrated sensing and communication reduce energy for smart grid data collection by up to 34.6 percent

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

This paper sets out to show that UAVs can collect monitoring data from power lines more efficiently when sensing and communication tasks are handled together rather than separately. It formulates the problem as minimizing total energy use subject to trajectory, scheduling, and quality-of-service constraints for both sensing and data transmission. A hybrid solver is introduced that lets reinforcement learning handle the path planning while a relaxation technique manages the difficult quality constraints. If successful, this would let drones stay aloft longer to cover more of the grid without missing the frequent updates needed for accurate line capacity estimates.

Core claim

The central discovery is that the joint energy minimization problem for UAV trajectories and collection scheduling under ISAC constraints can be solved effectively by a hybrid deep reinforcement learning and semidefinite relaxation algorithm, leading to lower energy consumption while satisfying the required quality of service for safe and timely dynamic line rating updates.

What carries the argument

Hybrid algorithm combining deep reinforcement learning for optimizing UAV trajectories and collection scheduling with semidefinite relaxation to manage non-convex ISAC quality of service constraints

If this is right

  • The proposed scheme reduces energy consumption by up to 34.6% compared with offline benchmarks.
  • The proposed scheme reduces energy consumption by about 2.2% compared with the separated sensing-and-communication baseline.
  • The scheme satisfies the minute-level timescale requirement of DLR.
  • ISAC QoS guarantees are maintained for cooperative UAV data collection.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could be applied to other UAV-based monitoring tasks in infrastructure where both sensing the environment and communicating data are necessary.
  • Real-world deployment would require accounting for unpredictable factors like wind that affect actual energy use beyond the simulations.
  • Joint design of sensing and communication may become standard for energy-limited aerial systems in time-sensitive applications.

Load-bearing premise

Semidefinite relaxation adequately solves the non-convex ISAC QoS constraints without causing performance loss or safety violations in the energy minimization problem.

What would settle it

Flight experiments demonstrating that the optimized trajectories violate the ISAC quality requirements or fail to collect data within the required minute timescale.

Figures

Figures reproduced from arXiv: 2605.22490 by Indrakshi Dey, Jin Zhao, Nicola Marchetti, Yibin Xie.

Figure 1
Figure 1. Figure 1: Overview of the system model. A. UAV mobility model During the mission, we consider a frame structure with time slot length τ . Each slot is indexed by t, and the slot set is denoted as T = {t | 1 ≤ t ≤ T}. Each UAV is set to fly at a fixed altitude z0 and the position of UAVs under a three￾dimensional Cartesian coordinate system in each slot can be denoted as qm[t] = [xm[t], ym[t], z0] T , and the overall… view at source ↗
Figure 2
Figure 2. Figure 2: Training convergence of the proposed DRL-SDR algorithm over five [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Total energy consumption vs. MD number [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Total energy consumption vs. Inter-UAV communication SINR. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

Dynamic line rating (DLR) is a methodology that requires timely monitoring data to determine the real-time ampacity of power lines. However, DLR monitoring devices (MD) are vulnerable to connectivity disruptions, leading to missing or delayed data. Although unmanned aerial vehicles (UAV) can enable resilient data collection from MD, their limited onboard energy challenges timely monitoring over extended transmission corridors with flight hazards. This paper proposes a cooperative UAV-based data collection framework with integrated sensing and communication (ISAC) to support timely DLR updates. In this framework, ISAC is employed to maintain the sensing and communication quality required for safe and cooperative UAV data collection. Accordingly, a joint energy minimization problem is formulated over UAV trajectories and collection scheduling under ISAC constraints. To solve it, a hybrid algorithm combining deep reinforcement learning (DRL) and semidefinite relaxation (SDR) is proposed, where DRL optimizes the trajectory and collection scheduling, while SDR is used to handle the non-convex ISAC constraints. Simulation results show that the proposed scheme reduces energy consumption by up to 34.6% compared with offline benchmarks and by about 2.2% compared with the separated sensing-and-communication baseline, while satisfying the minute-level timescale requirement of DLR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a cooperative UAV-based data collection framework for dynamic line rating (DLR) in smart grids that employs integrated sensing and communication (ISAC) to enforce QoS for safe operation. It formulates a joint energy-minimization problem over UAV trajectories and collection scheduling subject to ISAC constraints, and solves it with a hybrid algorithm that uses deep reinforcement learning (DRL) for trajectory and scheduling decisions while applying semidefinite relaxation (SDR) to the non-convex ISAC QoS terms. Simulation results are reported to show energy reductions of up to 34.6% relative to offline benchmarks and 2.2% relative to a separated sensing-and-communication baseline, while satisfying the minute-level DLR timescale.

Significance. If the simulation results prove reliable, the work contributes a practical joint-optimization approach that combines DRL trajectory planning with SDR-based constraint handling for energy-efficient UAV data collection under ISAC QoS guarantees. The hybrid solver and the explicit comparison against both offline and separated baselines are positive elements; however, the overall significance remains moderate because the central claims rest entirely on simulation outcomes without reported real-world validation, error bars, or sensitivity checks against modeling assumptions.

major comments (2)
  1. [hybrid algorithm description] The hybrid DRL+SDR algorithm (described in the optimization and solution sections) applies semidefinite relaxation to the non-convex ISAC QoS constraints, yet provides no analysis of solution rank (e.g., whether the obtained covariance matrices are rank-1), randomization gap, or post-processing feasibility rate. Because the headline energy-reduction figures (34.6% and 2.2%) and the QoS-satisfaction claim are obtained from this solver, the absence of tightness verification directly affects the validity of both the safety guarantees and the performance comparisons.
  2. [simulation results] The simulation results that underpin the energy savings and DLR timescale compliance are presented without details on Monte-Carlo repetitions, confidence intervals, or sensitivity to key modeling parameters (channel models, UAV energy consumption coefficients, or ISAC QoS thresholds). This weakens the support for the central claim that the proposed scheme simultaneously reduces energy and meets minute-level requirements.
minor comments (2)
  1. Notation for the ISAC QoS constraints and the DRL state-action space could be clarified with an explicit table of symbols to improve readability.
  2. [abstract] The abstract states the energy reductions but does not mention the number of Monte-Carlo trials or the specific simulation parameters used to obtain the 34.6% and 2.2% figures.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We have prepared point-by-point responses to the major concerns and will revise the paper to incorporate additional analysis and details as outlined below.

read point-by-point responses

  1. Referee: The hybrid DRL+SDR algorithm (described in the optimization and solution sections) applies semidefinite relaxation to the non-convex ISAC QoS constraints, yet provides no analysis of solution rank (e.g., whether the obtained covariance matrices are rank-1), randomization gap, or post-processing feasibility rate. Because the headline energy-reduction figures (34.6% and 2.2%) and the QoS-satisfaction claim are obtained from this solver, the absence of tightness verification directly affects the validity of both the safety guarantees and the performance comparisons.

    Authors: We acknowledge that the manuscript does not include an explicit analysis of SDR solution rank, randomization gap, or post-processing feasibility. To address this gap and strengthen the support for the QoS guarantees and performance claims, we will add a dedicated discussion in the revised solution section. This will report the empirical rank distribution of the covariance matrices obtained from the SDR step, the frequency with which rank-1 solutions occur, and the feasibility rate after randomization, based on the same simulation conditions used for the reported results. revision: yes

  2. Referee: The simulation results that underpin the energy savings and DLR timescale compliance are presented without details on Monte-Carlo repetitions, confidence intervals, or sensitivity to key modeling parameters (channel models, UAV energy consumption coefficients, or ISAC QoS thresholds). This weakens the support for the central claim that the proposed scheme simultaneously reduces energy and meets minute-level requirements.

    Authors: We agree that the simulation section would benefit from greater statistical rigor. The current manuscript presents aggregate performance figures without specifying the number of Monte-Carlo repetitions, variability measures, or sensitivity checks. In the revised manuscript we will expand the simulation results section to include the number of independent runs, confidence intervals or standard deviations for the key metrics (energy consumption and QoS satisfaction), and sensitivity plots or tables with respect to channel models, UAV energy coefficients, and ISAC QoS thresholds. revision: yes

Circularity Check

0 steps flagged

No significant circularity in formulation or solver chain

full rationale

The paper formulates a standard joint energy-minimization problem over UAV trajectories and scheduling subject to ISAC QoS constraints, then applies a hybrid DRL-plus-SDR solver. DRL handles the trajectory/scheduling decisions while SDR relaxes the non-convex QoS terms; both are external, well-established techniques whose correctness does not depend on any parameter fitted inside this work or on a self-citation chain. The reported energy reductions are obtained from simulation comparisons against independent benchmarks and baselines, not from any quantity that is defined in terms of itself or statistically forced by an internal fit. The derivation therefore remains self-contained against external methods and empirical validation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on abstract only; the approach relies on standard optimization assumptions for handling non-convexity and simulation-based evaluation without introducing new entities or many free parameters.

axioms (1)
  • domain assumption ISAC QoS constraints admit effective semidefinite relaxation for tractable solving
    Invoked to convert the joint energy minimization into a solvable form within the hybrid DRL-SDR algorithm.

pith-pipeline@v0.9.0 · 5764 in / 1224 out tokens · 49405 ms · 2026-05-22T03:40:47.412625+00:00 · methodology

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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

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