{"id":"4eb092b3-7acf-415c-a502-532d9f521523","arxiv_id":"2504.15577","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"A DQN scheduler with edge-node state aggregation and a collaboration graph is claimed to reduce IoT energy consumption and latency, but the edge-collaboration contribution is never ablated.","lead":"This paper combines a Deep Q-Network with edge nodes that share device states to schedule IoT tasks, and reports lower energy use and latency than four fixed policies on FastBee data. The headline result rests on one comparison table with no code, data, or error bars, and no test of the edge mechanism in isolation.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Missing ablation: the paper never compares DQN+Edge against DQN without the collaboration graph, so the central claim that the graph drives the Table 1 gains is unsupported.","rationale":"I read the paper in good faith as proposing a DQN-based IoT scheduler whose distinctive contribution is the edge collaboration mechanism and collaborative graph for state aggregation. For that central claim to hold, the graph must add value beyond simply giving the DQN more state features. The experimental section compares only against non-learning heuristics and never against a DQN variant without the collaboration graph, so the reported gains are confounded. The missing ablation is load-bearing rather than cosmetic: the plausible alternative explanation is that a standard DQN with the same state features would achieve equal or better results. Supporting weaknesses include the unreported reward weights and the absence of run-level statistics, but those are secondary to the missing control. The paper's own conclusion lists limitations such as the linear reward and limited scalability, yet it does not acknowledge that the main mechanism has not been isolated. This agrees with the reader's weakest-assumption analysis, and the verdict should remain REJECT because the evidence does not support the central claim as stated.","tokens_in":6373,"tokens_out":5065,"duration_ms":47017,"concrete_test":"Re-run the exact training pipeline on the same FastBee-derived dataset under three DQN conditions: (1) DQN + edge collaborative graph as proposed; (2) DQN with a flat state vector containing the same neighbor information but no graph structure or edge aggregation; and (3) DQN with local device state only. Keep network architecture, replay buffer, exploration schedule, reward weights, and number of episodes identical across conditions, and report per-seed means and standard deviations over at least 10 seeds for energy, delay, and utilization. If condition (1) is not statistically better than condition (2) on all three metrics, the claim that the collaboration graph enhances performance is not supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is not merely that DQN+Edge beats four heuristic schedulers; it is that the edge collaboration mechanism and collaborative graph 'further enhance information sharing and policy complementarity among devices.' In Section III.B, Table 1 compares DQN+Edge only against Local, Edge-only, Random, and Round-Robin, all non-learning heuristics. There is no control condition in which the same DQN is trained with the same state information but without the graph-based edge aggregation, such as a flat concatenated neighbor-state vector or a purely local-state DQN. Consequently, the reported improvements in average energy consumption (31.96 mWh), processing delay (55.3 ms), and edge resource utilization (88.5%) could be entirely due to the DQN's learning capability rather than to the collaboration mechanism. The reward weights alpha, beta, and gamma in Section II are also unreported, so the objective actually optimized is not fully specified, and no run counts, seeds, or variance are given. The conclusion honestly admits that the reward is a linear combination without multi-objective trade-offs and that larger-scale validation remains future work, but it does not flag the absence of an ablation of the paper's own claimed contribution. Because the mechanism that defines the novelty is never isolated, the paper provides no empirical support for its central attribution.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a Deep Q-Network (DQN)-based scheduler for IoT devices, augmented by an edge-collaboration mechanism in which edge nodes aggregate neighbor-device states into a collaborative graph. The method is described through a state-action-reward formulation, a DQN with a target network and replay buffer, and a graph-conditioned Q-function. Experiments using FastBee platform data compare the proposed “DQN + Edge” scheme against four non-learning baselines (local-only, edge-only, random, round-robin), reporting average energy consumption, processing delay, and edge-resource utilization. Additional experiments describe robustness under heterogeneous task loads and the effect of state-space dimension on convergence. The paper concludes that the proposed method outperforms all baselines and that the collaboration graph enhances information sharing and policy complementarity.","tokens_in":6563,"tokens_out":3538,"duration_ms":33860,"significance":"If properly validated, a DRL-based edge-collaboration scheduler that demonstrably reduces energy and latency while improving utilization would be a useful contribution to IoT resource management. However, the manuscript in its current form does not provide that validation: the central mechanism it introduces is never isolated experimentally, the quantitative evidence is a single table of averages without variance, and the algorithmic description is partially corrupted in the PDF. The significance of the work is therefore not yet established; the paper is more a research proposal than a completed study.","major_comments":[{"comment":"The central claim of the paper is that the edge collaboration mechanism, embodied in the collaborative graph and the neighbor-state aggregation, drives the reported improvements. This claim is never tested. All baselines in Table 1 are non-learning heuristics (Local, All Edge, Random, Static Round-Robin); there is no control condition such as a DQN with a flat state vector that excludes the graph aggregation, or a DQN operating on purely local state. Consequently, the differences in Table 1 (e.g., 31.96 vs. 39.85 mWh average energy) could be entirely due to DQN's learning capability rather than to the edge-collaboration mechanism. This absence of an ablation leaves the paper's main contribution unsupported.","section":"Section III.B, Table 1"},{"comment":"The experimental evaluation reports single average numbers with no indication of variance, number of runs, random seeds, or statistical tests. The paper characterizes the results as “significant” and “the best” without any measure of uncertainty, so the reader cannot judge whether the differences in Table 1 are meaningful. Figures 2 and 3 are described only in prose; no actual data values, axes, or error bars are provided. This level of reporting is insufficient to support the robustness and convergence claims made in Sections III.B and IV.","section":"Section III.B, Table 1 and Figures 2-3"},{"comment":"The paper is not reproducible. The reward expression, the Q-value update, and the loss function appear in a corrupt mathematical rendering (for example, “tttt UDEr γβα ⬀＋−＋−＝” and “)',(max),( 1' asQrasQ tattt ⬀＋＋＝ γ”), making it impossible to extract the actual equations. Even if the rendering were corrected, the reward weights α, β, γ and the discount factor γ are never specified. The reward is a weighted linear combination of the same energy, delay, and utilization metrics used for evaluation, so the unreported weights are essential for knowing exactly what objective was optimized and whether the evaluation is independent of that objective.","section":"Section II, reward and Bellman equations"},{"comment":"The experimental methodology is severely under-specified. The paper does not state the number of devices, the size and split of the dataset, the number of training episodes, the neural network architecture, the learning rate, the replay buffer size, the target-network update frequency, or any other hyperparameters. Without these details, the experiments described in Section III cannot be reproduced, and the reader cannot assess whether the training and evaluation protocols are sound. This is a load-bearing omission for a purely empirical paper.","section":"Section III.A, experimental setup"}],"minor_comments":[{"comment":"The mathematical expressions for the reward, the Bellman update, and the loss function are corrupted in the PDF; they must be re-typeset properly so that the equations are readable.","section":"Abstract and Section II"},{"comment":"References [16] and [17] are cited for the “Random Scheduling Strategy” and “Static round-robin scheduling,” but the cited papers (on edge computing with AI and sparse trace ratio LDA) do not appear to describe these scheduling strategies. Please use appropriate references or describe the baselines directly.","section":"Section III.B, references [16] and [17]"},{"comment":"The text contains numerous grammar and phrasing issues, such as “the experience weight coefficient” and “tr” appearing in the reward description. The paper would benefit from a careful language edit.","section":"Throughout"},{"comment":"The figures are mentioned but not actually included in the manuscript text; the reader only sees the authors' verbal descriptions. The figures should be included so that the trends claimed (energy increase with load, convergence slowdown with state dimension) can be inspected.","section":"Figures 2 and 3"},{"comment":"The conclusion honestly acknowledges future work on multi-objective rewards and scalability, but it does not mention the absence of an ablation of the edge-collaboration mechanism. This omission is surprising given that the collaboration mechanism is the paper's stated novelty.","section":"Section IV"}],"recommendation":"reject","confidential_remarks":"The manuscript is far from meeting the standards of a peer-reviewed venue. The central contribution is not experimentally isolated, the quantitative evidence is a single table with no statistical support, and the method description is partially unreadable. In my view this is not a matter of minor revision: the paper would need a new experimental design with ablations, repeated runs, and full hyperparameter reporting to become evaluable, so I recommend rejection. I have no concerns about novelty disclosure, but the reference list contains many works from a narrow cluster of journals that appear only tangentially related to the paper's topic."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"First off: the one thing that is actually new here is the collaborative graph that lets edge nodes aggregate neighbor states into a conditional Q-function. That is the claimed contribution. But the paper never tests it. There is no DQN-without-graph baseline, no flat-state DQN, no comparison to any existing DRL scheduler. The Table 1 gains against Local, Edge-only, Random, and Round-Robin could come entirely from the DQN's learning. So the central attribution is unsupported.\n\nWhat the paper does well: the setup is standard and readable, the FastBee data are real and described enough to be plausible, and the authors are honest in the conclusion that the reward is a linear combination and that larger-scale validation is future work. That honesty is worth something. The four heuristics are reasonable lower-bound baselines.\n\nThe soft spots, in order of importance: (1) the missing ablation is load-bearing, not cosmetic; (2) no variance, run counts, or statistical tests—everything is a single average; (3) the reward weights alpha, beta, gamma and the training hyperparameters are unreported, so the objective actually optimized is under-specified; (4) no code or data are released, so the numbers cannot be checked; (5) the broken math rendering is largely a text-conversion artifact, but it does hide the exact Bellman and loss equations. The citation pattern is loaded with the authors' own prior work in low-visibility venues, which I would not score as fraud, but it does not add credibility.\n\nThe central claim is not supported by the evidence. That is the kind of flaw that a major revision can fix: run the DQN without the graph, add error bars over seeds, report the weights, release the code. The paper is not irredeemable, and it is not a takedown; it is an under-tested idea with a plausible mechanism.\n\nWho should read it: people working on DRL for edge scheduling might use the Table 1 numbers as a data point, but the method section is too vague to reproduce from the text alone.\n\nWould I referee it? Yes, I'd send it to peer review rather than desk reject, because the missing ablation is fixable and the real data give it a concrete anchor. But I would expect a major revision, not acceptance. If it comes back with only cosmetic changes, reject.","headline":"The collaborative-graph mechanism that defines the paper's novelty is never isolated from the DQN, so the headline gains are not attributable to the paper's own contribution.","tokens_in":7172,"tokens_out":2362,"would_cite":false,"duration_ms":21687,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Pairing a Deep Q-Network with edge-node collaboration cuts IoT energy use to 31.96 mWh while lifting edge utilization to 88.5%.","keywords":["Deep Q-Networks","edge computing","IoT scheduling","energy efficiency optimization","collaborative graph","task offloading","resource utilization","reinforcement learning"],"falsifier":"Run an ablation on the FastBee data using the same DQN architecture and hyperparameters but replace the collaboration graph with a flat state vector containing the same neighbor information; if energy consumption stays near 31.96 mWh, latency near 55.3 ms, and utilization near 88.5%, the paper's specific claim that the graph structure drives coordination is falsified. The comparison would also be settled by fixing $\\alpha$, $\\beta$, and $\\gamma$ and re-running the baselines under a full sweep of weights.","tokens_in":6104,"feed_emoji":"🔋","tokens_out":7353,"duration_ms":62152,"temperature":0.7,"pith_summary":"This paper proposes a scheduling method for energy-constrained Internet-of-Things devices that pairs a Deep Q-Network (DQN) with edge-node coordination. The claim is that letting edge nodes aggregate nearby device states into a collaboration graph, and feeding that neighbor context into each device's Q-function, lets the scheduler choose between local processing, edge offloading, and delayed processing so that the whole fleet uses less energy. On FastBee platform data, the method reports average energy consumption of 31.96 mWh, processing delay of 55.3 ms, and edge resource utilization of 88.5%, beating local-only, all-edge, random, and round-robin baselines on all three measures. A sympathetic reader would take the paper's point to be that lightweight edge collaboration plus deep reinforcement learning can handle dynamic multi-device workloads better than static or single-device policies.","feed_headline":"Edge-aware DQN scheduling cuts IoT energy 45%","feed_subtitle":"Collaboration-graph scheduling hits 31.96 mWh average use, 55.3 ms delay, 88.5% edge utilization.","key_machinery":"The load-bearing object is the collaboration graph $G(V, E)$ maintained by edge nodes, which represent devices as vertices and adjacency as edges and supply the set $N^i_t$ of neighbor states used in the conditional Q-function $Q(s^i_t, a^i_t \\mid N^i_t)$. Its job is to make each device's action choice responsive to the state of other devices, turning a local reinforcement-learning problem into a coordinated one. The training machinery is standard DQN: an MLP approximates $Q(s, a)$, experience replay decouples correlated samples, a target network stabilizes the Bellman updates, and the reward is a weighted combination of energy consumption, processing delay, and edge resource utilization. The paper reports that state-space dimension strongly affects convergence speed, with dimensions 4 and 8 converging quickly and dimension 64 much slower.","core_discovery":"The central discovery is a joint scheduling scheme whose decision-making is state-aware at two levels: the DQN gives each device a learned mapping from its own state to actions, and the edge collaboration graph conditions that mapping on the states of neighboring devices, yielding a conditional Q-function $Q(s^i_t, a^i_t \\mid N^i_t)$. With this scheme, the paper reports energy consumption falling from 58.42 mWh under local-only processing to 31.96 mWh, processing delay from 83.7 ms to 55.3 ms, and edge utilization rising from 12.4% to 88.5%. The paper attributes the improvement to the graph's enabling of information sharing and policy complementarity among devices, and to the DQN's ability to adapt to changing task loads.","pith_inferences":["Because the paper never compares DQN with the collaboration graph against DQN with a flat state vector, the distinct contribution of the graph structure is untested; the 31.96 mWh result could in principle come from the DQN alone.","The reward weights $\\alpha$, $\\beta$, and $\\gamma$ are described as tunable but their chosen values are never reported, so the quoted numbers are one point on a three-way Pareto frontier; different weights would likely change which strategy wins.","The evaluation uses recorded FastBee data, so the method has not yet faced live wireless contention, device failure, or packet loss; a small testbed deployment would be the natural check on whether the simulated gains survive real network conditions.","The neighbor-conditioned Q-function is a representation rather than a DQN-specific trick, so the same collaboration graph could be grafted onto policy-gradient or multi-agent methods if scaling becomes a bottleneck."],"forward_implications":["Replacing local-only processing with the reported DQN-plus-edge scheduler cuts average device energy from 58.42 mWh to 31.96 mWh in the same workload, roughly a 45% reduction.","The scheduler holds its advantage as task load rises: the DQN-plus-edge curve stays below all baselines under low, medium, and high load, while random scheduling fluctuates most.","State-space design is a practical lever: low and medium state dimensions converge rapidly, while dimension 64 slows learning markedly, so compact state representations should be preferred when deploying the method.","An edge node that aggregates neighbor states can serve as the coordination point for action selection without requiring a central cloud, which is what enables the low-latency scheduling."],"supporting_citations":[{"why":"Supplies the reinforcement-learning framework that the DQN update rule follows.","marker":"[10]"},{"why":"Motivates the experience-replay sampling strategy used for stable convergence.","marker":"[11]"},{"why":"Supports the neural approximation of the Q-function across dynamic input distributions.","marker":"[12]"},{"why":"Supports the adaptive weighting strategy used in the distributed edge mechanism.","marker":"[13]"},{"why":"Backs the modular, self-supervised components that enable local edge inference without central dependency.","marker":"[14]"},{"why":"Motivates the lightweight coordination protocols that let heterogeneous devices interact through edge nodes.","marker":"[15]"},{"why":"Provides the random scheduling strategy used as a baseline in Table 1.","marker":"[16]"},{"why":"Provides the static round-robin scheduling strategy used as a baseline in Table 1.","marker":"[17]"}],"fun_headline_variants":["Edge-graph DQN scheduling cuts IoT energy 45%","Graph-aware DQN trims IoT power use by 45%","Edge-coordinated DQN slashes IoT latency 34%","State-aware scheduling lifts edge utilization to 88.5%","DQN plus edge graph: 45% energy savings in IoT scheduling"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The results stand on the assumption that the edge collaboration graph, rather than the DQN's neural-network engineering or the unstated reward weights, is what produces the reported gains, and that the gains generalize beyond the single FastBee dataset.","fun_headline_variants_meta":{"raw":{"variants":["Edge-graph DQN scheduling cuts IoT energy 45%","Graph-aware DQN trims IoT power use by 45%","Edge-coordinated DQN slashes IoT latency 34%","State-aware scheduling lifts edge utilization to 88.5%","DQN plus edge graph: 45% energy savings in IoT scheduling"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00111,"raw_usage":{"total_tokens":4610,"prompt_tokens":918,"completion_tokens":3692,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":534,"completion_tokens_details":{"reasoning_tokens":3614}},"tokens_in":534,"tokens_out":3692,"duration_ms":26253,"temperature":1.0,"reasoning_tokens":3614,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T11:22:38.273408+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run an ablation on the FastBee data using the same DQN architecture and hyperparameters but replace the collaboration graph with a flat state vector containing the same neighbor information; if energy consumption stays near 31.96 mWh, latency near 55.3 ms, and utilization near 88.5%, the paper's specific claim that the graph structure drives coordination is falsified. The comparison would also be settled by fixing $\\alpha$, $\\beta$, and $\\gamma$ and re-running the baselines under a full sweep of weights.","supporting_citations":[{"cited_title":"Dynamic Distributed Scheduling for Data Stream Computing: Balancing Task Delay and Load Efficiency,","cited_arxiv_id":null,"evidence_quote":"Supplies the reinforcement-learning framework that the DQN update rule follows."},{"cited_title":"Multivariate Time Series Forecasting and Classification via GNN and Transformer Models,","cited_arxiv_id":null,"evidence_quote":"Motivates the experience-replay sampling strategy used for stable convergence."},{"cited_title":"Improved Transformer for Cross-Domain Knowledge Extraction with Feature Alignment,","cited_arxiv_id":null,"evidence_quote":"Supports the neural approximation of the Q-function across dynamic input distributions."},{"cited_title":"Mining Multimodal Data with Sparse Decomposition and Adaptive Weighting,","cited_arxiv_id":null,"evidence_quote":"Supports the adaptive weighting strategy used in the distributed edge mechanism."},{"cited_title":"A Visual Communication Optimization Method for Human- Computer Interaction Interfaces Using Fuzzy Logic and Wavelet Transform,","cited_arxiv_id":null,"evidence_quote":"Motivates the lightweight coordination protocols that let heterogeneous devices interact through edge nodes."},{"cited_title":"Edge computing with artificial intelligence: A machine learning perspective,","cited_arxiv_id":null,"evidence_quote":"Provides the random scheduling strategy used as a baseline in Table 1."},{"cited_title":"Sparse trace ratio LDA for supervised feature selection,","cited_arxiv_id":null,"evidence_quote":"Provides the static round-robin scheduling strategy used as a baseline in Table 1."}],"review_version":1}