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REVIEW 4 major objections 5 minor 31 references

Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read TOICA couples D3QN offloading with knapsack channel allocation to cut delay-energy cost under mobility and task dependencies.

desk verdict Solid incremental MEC paper with a real but fixable channel-allocation inconsistency; deserves peer review. read the letter →

arxiv 2505.04272 v1 pith:YUEI7A5I submitted 2025-05-07 cs.NI eess.SP

Tianyi Shi , Tiankui Zhang , Jonathan Loo , Rong Huang , Yapeng Wang This is my paper · ORCID
classification cs.NIeess.SP
keywords mobileedgecomputingtaskoffloadingchannelallocationdeepreinforcementlearningD3QNgroupedknapsackproblemdependenciesdelay-energytrade-off
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

This paper tackles task offloading and channel allocation together in a mobile edge computing network where terminals move and applications have dependent tasks. It claims that jointly optimizing the two decisions—offloading choices via a D3QN agent and subchannel counts via a grouped-knapsack dynamic program—yields lower long-term delay-energy cost than existing dependency-aware offloading schemes, greedy offloading, and nearest-server offloading. The paper's contribution is a decomposition that keeps the two decisions coupled through the reward signal rather than solving them as one hard combinatorial problem. If right, the work matters because it offers a real-time-capable, scalable way to allocate communication and computation resources under both spatial mobility and temporal task dependencies.

What carries the argument

The engine is a two-level decomposition. First, tasks are ordered by a priority rule: each task's priority is its estimated average processing cost plus, for non-exit tasks, the largest priority among its successors; ordering by this priority turns the directed acyclic graph into a topological sequence. Second, given offloading decisions, each edge server solves a grouped knapsack problem—groups are terminals, items are possible subchannel counts, weights are counts, values are communication costs—via dynamic programming, returning the number of subchannels per terminal. Third, each terminal runs a D3QN agent whose action is local execution or one of the edge servers, state is position plus task data and computation size, and reward is $\tanh(\text{cost}_{n,i,0}-\text{cost}_{n,i,m'})$, where the offloaded cost is computed from the allocation returned by the channel allocator. The channel allocation result enters the reward, which is how channel allocation and offloading are coupled.

What would settle it

Replace DCA's final random assignment of specific subchannels with a channel-state-aware assignment under the same knapsack counts and rerun the simulations; if the cost drops materially, the current allocator is not optimal under the paper's own heterogeneous channel model in equation (6).

Watch

Extended reading notes

Core claim

The paper claims that in a multi-user, multi-server MEC network with moving terminals and dependent application tasks, the joint problem of where to offload each task and how to assign uplink subchannels can be split into two coupled pieces. A priority rule based on estimated average processing cost turns each terminal's task graph into a topological execution order. Given the offloading choices, each edge server allocates subchannel counts by solving a grouped knapsack problem with dynamic programming; the resulting allocation feeds into the reward of a D3QN agent that chooses, for each task, between local execution and one of the edge servers. The paper reports that this TOICA algorithm converges to the lowest delay-energy cost among the compared schemes, tracks the trade-off coefficient $\omega$ between delay and energy, and remains convergent in networks with 3, 6, and 9 edge servers.

Load-bearing premise

The load-bearing premise is that, once the number of subchannels for each terminal is fixed, the identity of those subchannels does not affect the result—the algorithm assigns them randomly—even though the model gives different subchannels different channel gains and interference levels.

Editorial extensions

If this is right

  • If TOICA works as claimed, MEC operators can tune one weight $\omega$ to shift system behavior between delay-critical and energy-critical applications without changing the algorithm.
  • Larger bandwidth and finer subchannel granularity (16 vs 8 subchannels) lower the weighted cost for the proposed scheme.
  • Increasing edge-server computing capacity reduces the cost of all schemes, with TOICA remaining the lowest in the reported experiments.
  • The policy can be trained offline and then deployed online, with simulated per-task decisions around 36.8 ms, inside the 10–100 ms range cited for autonomous driving and real-time video analytics.
  • In larger networks with 3, 6, and 9 edge servers, the training still converges, and adding servers under fixed terminal load reduces the weighted cost.

Reading between the lines

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

  • Editorial inference: since DCA randomizes which subchannels a terminal receives after fixing the count, a frequency-aware assignment within the same knapsack solution would likely reduce cost further; this is testable by replacing the random draw with a greedy assignment and rerunning the comparison.
  • Editorial inference: the same decomposition—priority ordering for dependency constraints, a knapsack for resource-count decisions, and a DRL agent for discrete choices—could carry over to other resource dimensions, such as computation shares or cache slices, where count decisions separate from item identity.
  • Editorial inference: the 36.8 ms-per-task figure comes from a laptop CPU inside the simulator; measuring the online decision phase on representative edge hardware would show whether the 10–100 ms real-time target is met outside the simulated environment.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper studies joint task offloading and subchannel allocation in a multi-user multi-server MEC network with terminal mobility and dependent tasks modeled as DAGs. It formulates a long-term delay-energy weighted cost minimization problem (14), decouples task dependencies via a priority rule based on average cost, and proposes TOICA, which combines a dynamic programming-based channel allocation algorithm (DCA) built on a grouped knapsack model and a D3QN-based offloading decision algorithm (DTO). The authors claim that TOICA achieves the lowest delay-energy cost among the compared baselines in simulations. The manuscript includes a system model with co-channel interference and I/O interference, a complexity analysis of DCA, and simulation results over varying bandwidth, edge server capacity, and network scale.

Significance. If the claims hold, the paper would make a useful contribution to MEC resource management by jointly addressing spatial dynamics, task dependencies, and channel-aware offloading with a learning-based method. The explicit modeling of co-channel interference, I/O interference, and DAG scheduling, together with the integrated D3QN/knapsack architecture, is a plausible and relevant direction. The paper also provides a complexity analysis and evaluates adaptability to different weights and network sizes. However, the central algorithmic claim rests on the correctness of DCA, and the current presentation leaves several load-bearing gaps in that algorithm's definition and in the empirical validation.

major comments (4)
  1. [Section III-A, Eq. (6), Algorithm 1] The DCA algorithm's random assignment of specific subchannels is internally inconsistent with the channel model in Eq. (6). The rate r_up_{n,m}[t] depends on the specific channel gain h_{n,m,k}[t] and on the interference pattern created by which terminals share each subchannel, yet the knapsack DP in Section III-A optimizes only over the number z of subchannels per terminal, and then 'different subchannels are randomly assigned to MTs according to the quantity requirements.' Two allocations with the same count vector can have very different communication times and therefore very different costs under Eqs. (7)-(9). As written, the DP minimizes a count-based proxy, not the actual objective (14), so the reported performance gain may be an artifact of the random tie-breaking rather than of the claimed channel-aware allocation. The authors should either define v_{n,z} using an explicit averaging assumption over subchannel realizations and validate that assumption, or modify DCA to optimize over specific subchannel assignments.
  2. [Section III-A, Eq. (15)] The item value v_{n,z} in the grouped knapsack formulation is never defined. The text calls it 'the resulting communication cost' but provides no formula connecting z to the data load, channel gains, or interference terms of Section II. Without an explicit expression for v_{n,z}, the DP's optimality claim for the channel allocation subproblem is not verifiable, and the mapping from the original problem to the GKP instance is incomplete. A precise definition of v_{n,z} is essential for the paper's central claim that DCA optimally allocates channels for a given offloading decision.
  3. [Section III-A, NP-hardness discussion] The NP-hardness argument is logically reversed. The paper states that 'our channel allocation subproblem can be directly mapped to a GKP instance in polynomial time' and concludes that problem (14) inherits NP-hardness. Reducing the subproblem to GKP shows that the subproblem is no harder than GKP, not that GKP (or the original problem) is NP-hard. To establish NP-hardness, the authors need a polynomial-time reduction from a known NP-hard problem (e.g., GKP) to problem (14). This should be corrected either by supplying a proper reduction or by softening the claim to 'the problem is combinatorial and presumably NP-hard, motivating a heuristic approach.'
  4. [Section IV, Figs. 5-9] The simulation results are reported without error bars, confidence intervals, or multiple random seeds. Figures 5 and 9 show single convergence traces, and Figures 6-8 compare costs without any statistical measure. Since the DCA step involves random subchannel assignment and the environment includes Rayleigh fading and random mobility, the reported costs are random variables, and the claim that 'TOICA conducts the least cost among all the algorithms' may not be robust to different realizations. The authors should run multiple independent trials and report mean plus/minus standard deviation (or confidence intervals), and preferably use a paired statistical test for the cost comparisons.
minor comments (5)
  1. [Abstract and Section III-B] The term 'duel DQN' is a typographical error; it should be 'dueling DQN' (and similarly in the phrase 'duel DQN' in Section III-B).
  2. [Eq. (14a) and Eq. (14)] The definition of D[t] in (14a) uses a max over tasks, while the objective sums D[t] over t. Since each task is assumed to finish within one slot, it would be clearer to define D[t] as the maximum completion time among tasks executed in slot t and to state explicitly that only one task per terminal is active per slot.
  3. [Eqs. (4)-(5)] The summations over t in Eqs. (4) and (5) are confusing because on_{i,m}[t] is an indicator of offloading at slot t, but the model states each task completes in a single slot. It would help to clarify whether the sum is over the slot in which the task is processed, and to simplify the notation accordingly.
  4. [Algorithm 1 and Section IV] In Algorithm 1, the backtracking step sets 'j←K' and uses a loop over 'reverse(Z_n)' but the condition and the break logic are not fully specified; also the final line 'Assign subchannels according to alloc' hides the random assignment step that is critical to the cost evaluation. The pseudocode should be tightened to match the described procedure.
  5. [Fig. 5 and Fig. 9] The y-axis of Fig. 5 is 'cumulative rewards,' but the text describes cost comparisons; consider labeling the axes consistently and explaining the relationship between the accumulated reward and the cost metric used in the other figures.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the training reward and evaluation cost share the same cost model by design, but the comparisons are against external baselines and no load-bearing claim reduces to its own input.

full rationale

I walked the paper's derivation chain: the cost model in (11), the joint objective in (14), the DAG priority rule in (13), the grouped-knapsack channel allocation in (15), and the D3QN reward in (18). The reward r_n[t] = tanh(cost_{n,i,0} - cost_{n,i,m'}) is a monotone transform of the per-task cost difference, so the training signal and the evaluation metric come from the same cost model; this is standard reward design for an optimization problem, not a fitted input renamed as a prediction. No parameter is fitted to the benchmark cost, and the main claims are supported by comparisons with external baselines (HRROGA, SEG+DCA, ON+DCA) that are not derived from the proposed method. The only self-citations are background resource-allocation works ([9], [11]) and are not load-bearing for the central derivation; the NP-hardness argument is a conventional reduction to the well-known grouped knapsack problem rather than a self-citation chain. The weakest point in the paper is that DCA solves a count-based knapsack and then 'different subchannels are randomly assigned to MTs according to the quantity requirements', which is inconsistent with the channel-dependent rate in (6); this is a model/objective mismatch that could undermine the optimality claim, but it is not circularity because v_{n,z} is not defined in terms of the final realized cost and no result is shown to equal its input by construction. Under the required standard, no circular step can be exhibited, so the circularity score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central result rests on a standard system model plus several hand-chosen parameters (omega, zeta, alpha, D3QN hyperparameters) and heuristic modeling assumptions. The I/O interference formula and the priority rule are ad hoc constructions. No new physical or algorithmic entities are postulated.

free parameters (4)
  • omega (delay-energy weight) = 0.5 in baseline, then varied
    Chosen by hand to balance delay and energy in the cost function (11); the paper shows sensitivity to it in Fig. 8.
  • zeta (VM I/O degradation factor) = 0.2
    Introduced ad hoc in equation (8) to model I/O interference among parallel VMs; value chosen without calibration.
  • alpha (path loss exponent) = 4
    Taken from common wireless assumptions (Table II), not fitted here.
  • D3QN hyperparameters (learning rate, discount factor, epsilon decay, batch size, target update frequency) = 0.0003, 0.99, 1.5e-5, 128, 30
    Manually selected for training stability, and the paper states distinct hyperparameters are used for different network scales (Fig. 9 discussion).
assumptions (5)
  • domain assumption Each task completes within a single time slot
    Section II-A states 'we assume that each task is completed within a single time slot', which underpins the slot-indexed formulation in equations (4)-(8).
  • domain assumption Downlink latency is negligible
    Section II-C justifies ignoring downlink time because output data is much smaller than input and downlink speeds are high; this assumption simplifies equation (7).
  • domain assumption Rayleigh fading modeled as independent exponential random variables
    Section II-C defines beta_k[t] as independent exponential with unit mean, a standard wireless channel model.
  • ad hoc to paper I/O interference among VMs scales as (1+zeta)^{N_m-1}
    Equation (8) introduces an exponential degradation factor with no empirical or theoretical derivation; it is constructed for this paper.
  • ad hoc to paper The average-cost priority ordering preserves task dependencies
    Section II-D asserts ranking tasks by P_{n,i} guarantees dependencies when scheduling topologically, but this is a heuristic claim and no proof is provided.

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Cite this review

Pith. "Pith review of Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks." pith.science (2026). https://pith.science/paper/YUEI7A5I

@misc{pith2026250504272,
  author       = {Pith},
  title        = {Pith review of: Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YUEI7A5I}},
  note         = {Machine review of arXiv:2505.04272}
}
read the original abstract

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multi-user multi-server MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy trade-off cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy trade-off cost and its adaptability for various applications.

Figures

Figures reproduced from arXiv: 2505.04272 by the authors.

Figure 1
Figure 1. Spatial and temporal dynamic MEC networks. average cost is designed to transform the tasks in DAGs into topological sequences, which guarantees the dependencies among tasks. Then, we formulate a long-term delay-energy trade-off cost minimization problem by jointly optimizing offloading decisions and channel allocation. • To deal with the above problem, we propose a D3QN￾based task offloading algorithm integrated mul… view at source ↗
Figure 2
Figure 2. DAG structure of task dependencies (Health monitoring appli￾cation example). vn,6. In this context, the output of data collection serves as input for data preprocessing, and the results of tasks like trend and anomaly detection are combined to generate the final user notification. The computational task vn,i can be described as a 2-tuple (bn,i, cn,i), in which elements represent the input data volume (in bits) and t… view at source ↗
Figure 3
Figure 3. Architecture of D3QN-based Task Offloading. that since an MT only performs offloading decisions for one current task indexed by i in each time slot t, and the related information of task vn,i is incorporated into the specific state expression, so i actually appear implicitly in the terms when designing the D3QN-based algorithm. The detailed principles are introduced in the following part. 1) State: The state of MT n… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The structure of the TOICA algorithm. rewards, which are returned to the agent as samples for network training and parameter updates. Subsequently, the environment transitions to the next state, and the process repeats for the next time slot. Within this framework, the…
Figure 5
Figure 5. Figure 5: displays the convergences of TOICA and DTO Only, in which each episode means the completion of a mobile appli￾cation for an MT. DTO Only indicates applying the proposed DTO to offload tasks while allocating resources in a random manner. It can be observed that with con…
Figure 6
Figure 6. Figure 6: Cost with varying bandwidth. 3 4 5 6 7 Server computation capacity/GHz 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Cost Valuation TOICA(K=16) TOICA HRROGA [8] TOICA-RA ON+DCA SEG [9] + DCA [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Cost with varying ES computation capability. task according to individual subtasks’ response ratio, reflecting a less tight relevance to the optimization goal in this work. TOICA-RA performs poorly due to its disregard for the inherent structural task dependencies, lea…

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Reference graph

Works this paper leans on

31 extracted references · 30 canonical work pages

  1. [1]

    Toward enabled industrial verticals in 5G: A survey on MEC-based approaches to provisioning and flexibility,

    F. Spinelli and V . Mancuso, “Toward enabled industrial verticals in 5G: A survey on MEC-based approaches to provisioning and flexibility,” IEEE Commun. Surv. Tutor . , vol. 23, no. 1, pp. 596–630, Firstquarter 2021

    work page 2021

  2. [2]

    Beyond the edge: An advanced exploration of reinforcement learning for mobile edge com- puting, its applications, and future research trajectories,

    N. Yang, S. Chen, H. Zhang, and R. Berry, “Beyond the edge: An advanced exploration of reinforcement learning for mobile edge com- puting, its applications, and future research trajectories,” IEEE Commun. Surv. Tutor ., vol. 27, no. 1, pp. 546–594, Feb. 2025

    work page 2025

  3. [3]

    A survey on mobile edge computing: The communication perspective,

    Y . Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surv. Tutor . , vol. 19, no. 4, pp. 2322–2358, Fourthquarter 2017

    work page 2017

  4. [4]

    How to share: Balancing layer and chain sharing in industrial microservice deployment,

    Y . Liu, B. Yang, Y . Wu, C. Chen, and X. Guan, “How to share: Balancing layer and chain sharing in industrial microservice deployment,” IEEE Trans. Serv. Comput. , vol. 16, no. 4, pp. 2685–2698, July 2023

    work page 2023

  5. [5]

    Service migration or task rerouting: A two-timescale online resource optimization for MEC,

    Y . Shi, C. Yi, R. Wang, Q. Wu, B. Chen, and J. Cai, “Service migration or task rerouting: A two-timescale online resource optimization for MEC,” IEEE Trans. Wirel. Commun. , vol. 23, no. 2, pp. 1503–1519, Feb. 2024

    work page 2024

  6. [6]

    Joint scheduling and offloading schemes for multiple interdependent compu- tation tasks in mobile edge computing,

    M. Guo, X. Hu, Y . Chen, Y . Yang, L. Zhang, and L. Chen, “Joint scheduling and offloading schemes for multiple interdependent compu- tation tasks in mobile edge computing,” IEEE Internet Things J. , vol. 11, no. 4, pp. 5718–5730, Feb. 2024

    work page 2024

  7. [7]

    Dependency-aware task scheduling and layer loading for mobile edge computing networks,

    M. Zhao, X. Zhang, Z. He, Y . Chen, and Y . Zhang, “Dependency-aware task scheduling and layer loading for mobile edge computing networks,” IEEE Internet Things J. , vol. 11, no. 21, pp. 34 364–34 381, Nov. 2024

    work page 2024

  8. [8]

    Dependency-aware computation offloading for mobile edge computing with edge-cloud cooperation,

    L. Chen, J. Wu, J. Zhang, H.-N. Dai, X. Long, and M. Yao, “Dependency-aware computation offloading for mobile edge computing with edge-cloud cooperation,” IEEE Trans. Cloud Comput. , vol. 10, no. 4, pp. 2451–2468, Oct. 2022

    work page 2022

Show all 31 references
  1. [9]

    Trajectory planning and resource allocation for multi-UA V cooperative computation,

    W. Xu, T. Zhang, X. Mu, Y . Liu, and Y . Wang, “Trajectory planning and resource allocation for multi-UA V cooperative computation,” IEEE Trans. Commun., vol. 72, no. 7, pp. 4305–4318, July 2024

    2024

  2. [10]

    Joint task offloading and radio resource management in stochastic MEC systems,

    T. H. Hoang, C. T. Nguyen, T. N. Do, and G. Kaddoum, “Joint task offloading and radio resource management in stochastic MEC systems,” IEEE Trans. Commun. , vol. 72, no. 5, pp. 2670–2686, May 2024

    2024

  3. [11]

    Dynamic multi-time scale user admission and resource allocation for semantic extraction in MEC systems,

    Y . Zheng, T. Zhang, and J. Loo, “Dynamic multi-time scale user admission and resource allocation for semantic extraction in MEC systems,” IEEE Trans. V eh. Technol., vol. 72, no. 12, pp. 16 441–16 453, Dec. 2023

    2023

  4. [12]

    Load balancing approaches in cloud and fog computing environments: A framework, classification, and systematic review,

    H. Shakeel and M. Alam, “Load balancing approaches in cloud and fog computing environments: A framework, classification, and systematic review,” Int. J. Cloud Appl. Comput. , vol. 12, pp. 1–24, Jan. 2022

    2022

  5. [13]

    Load and cost-aware min-min workflow scheduling algorithm for heterogeneous resources in fog, cloud, and edge scenarios,

    J. Bisht and V . Vampugani, “Load and cost-aware min-min workflow scheduling algorithm for heterogeneous resources in fog, cloud, and edge scenarios,” Int. J. Cloud Appl. Comput. , vol. 12, pp. 1–20, Jan. 2022

    2022

  6. [14]

    Sharing VM resources with using prediction of future user requests for an efficient load balancing in cloud computing environment,

    M. Elrotub, B. Ahmed, and G. Abdelouahed, “Sharing VM resources with using prediction of future user requests for an efficient load balancing in cloud computing environment,” Int. J. Softw. Sci. Comput. Intell., vol. 13, no. 2, pp. 37–64, Apr. 2021

    2021

  7. [15]

    Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms,

    I. A. Elgendy, W.-Z. Zhang, H. He, B. B. Gupta, and A. A. Abd El- Latif, “Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms,” Wirel. Netw., vol. 27, no. 3, pp. 2023–2028, Jan. 2021

    2023

  8. [16]

    Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber–physical systems,

    M. Xu, J. Peng, B. B. Gupta, J. Kang, Z. Xiong, Z. Li, and A. A. A. El- Latif, “Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber–physical systems,” Wirel. Netw., vol. 9, no. 22, pp. 22 095–22 108, Nov. 2022

    2022

  9. [17]

    Intelligent delay-aware partial computing task offloading for multiuser Industrial Internet of Things through edge computing,

    X. Deng, J. Yin, P. Guan, N. N. Xiong, L. Zhang, and S. Mumtaz, “Intelligent delay-aware partial computing task offloading for multiuser Industrial Internet of Things through edge computing,” IEEE Internet Things J., vol. 10, no. 4, pp. 2954–2966, Feb. 2023

    2023

  10. [18]

    Energy-efficient collaborative multi-access edge computing via deep reinforcement learning,

    L. Tan, Z. Kuang, J. Gao, and L. Zhao, “Energy-efficient collaborative multi-access edge computing via deep reinforcement learning,” IEEE Trans. Industr . Inform., vol. 19, no. 6, pp. 7689–7699, June 2023

    2023

  11. [19]

    V-D D3QN: the variant of double deep Q-learning network with dueling architecture,

    Y . Huang, G. Wei, and Y . Wang, “V-D D3QN: the variant of double deep Q-learning network with dueling architecture,” in 2018 37th Chinese Control Conference (CCC) , 2018, pp. 9130–9135

    2018

  12. [20]

    Deep-reinforcement-learning-based distributed computation offloading in vehicular edge computing networks,

    L. Geng, H. Zhao, J. Wang, A. Kaushik, S. Yuan, and W. Feng, “Deep-reinforcement-learning-based distributed computation offloading in vehicular edge computing networks,” IEEE Internet Things J. , vol. 10, no. 14, pp. 12 416–12 433, July 2023

    2023

  13. [21]

    Edge intelligence: A computational task offloading scheme for dependent IoT application,

    H. Xiao, C. Xu, Y . Ma, S. Yang, L. Zhong, and G.-M. Muntean, “Edge intelligence: A computational task offloading scheme for dependent IoT application,” IEEE Trans. Wirel. Commun. , vol. 21, no. 9, pp. 7222– 7237, Sep. 2022

    2022

  14. [22]

    Dependent task scheduling and offloading for minimizing deadline violation ratio in mobile edge computing networks,

    S. Liu, Y . Yu, X. Lian, Y . Feng, C. She, P. L. Yeoh, L. Guo, B. Vucetic, and Y . Li, “Dependent task scheduling and offloading for minimizing deadline violation ratio in mobile edge computing networks,” IEEE J. Sel. Areas Commun. , vol. 41, no. 2, pp. 538–554, Feb. 2023

    2023

  15. [23]

    De- pendent task offloading for edge computing based on deep reinforcement learning,

    J. Wang, J. Hu, G. Min, W. Zhan, A. Y . Zomaya, and N. Georgalas, “De- pendent task offloading for edge computing based on deep reinforcement learning,” IEEE Trans. Comput. , vol. 71, no. 10, pp. 2449–2461, Oct. 2022

    2022

  16. [24]

    Multitask offloading strategy optimization based on directed acyclic graphs for edge computing,

    J. Chen, Y . Yang, C. Wang, H. Zhang, C. Qiu, and X. Wang, “Multitask offloading strategy optimization based on directed acyclic graphs for edge computing,” IEEE Internet Things J. , vol. 9, no. 12, pp. 9367– 9378, June 2022

    2022

  17. [25]

    Intelligent task offloading and resource allocation in digital twin based aerial computing AUTHOR et al.: TITLE 11 networks,

    H. Guo, X. Zhou, J. Wang, J. Liu, and A. Benslimane, “Intelligent task offloading and resource allocation in digital twin based aerial computing AUTHOR et al.: TITLE 11 networks,” IEEE J. Sel. Areas Commun. , vol. 41, no. 10, pp. 3095–3110, Oct. 2023

    2023

  18. [26]

    Wireless networks for mobile edge computing: Spatial modeling and latency analysis,

    S.-W. Ko, K. Han, and K. Huang, “Wireless networks for mobile edge computing: Spatial modeling and latency analysis,” IEEE Trans. Wirel. Commun., vol. 17, no. 8, pp. 5225–5240, Aug. 2018

    2018

  19. [27]

    Kellerer, U

    H. Kellerer, U. Pferschy, and D. Pisinger, Knapsack Problems. Springer, 2004

    2004

  20. [28]

    Human- level control through deep reinforcement learning,

    M. V olodymyr, K. Koray, S. David, A. A. Rusu, V . Joel, M. G. Bellemare, G. Alex, R. Martin, A. K. Fidjeland, and O. Georg, “Human- level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015

    2015

  21. [29]

    Snapdragon 865 5G Mobile Platform Product Brief,

    Qualcomm Technologies, Inc., “Snapdragon 865 5G Mobile Platform Product Brief,” https://www.qualcomm.com/snapdragon, 2019

    2019

  22. [30]

    Raspberry pi 4 model b technical specifica- tions,

    Raspberry Pi Foundation, “Raspberry pi 4 model b technical specifica- tions,” https://www.raspberrypi.com/products/raspberry-pi-4-model-b/ specifications/, Jun. 2019

    2019

  23. [31]

    NR; Physical channels and modulation (Release 18), TS 38.211, V18.5.0,

    3rd Generation Partnership Project (3GPP), “NR; Physical channels and modulation (Release 18), TS 38.211, V18.5.0,” https://www.3gpp.org/ ftp/Specs/archive/38\ series/38.211/, Jan. 2025. Tianyi Shi received the B.S. degree in Com- munication Engineering from Harbin Institute o...

    2002

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.