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REVIEW 5 major objections 7 minor 49 references

UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain

T0 review · 5 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A blockchain-hosted smart contract and decentralized multi-agent reinforcement learning form a single framework that couples UAV relay selection with UAV coordination in Internet of Vehicles networks.

desk verdict A solid framework integration for UAV-assisted IoV with blockchain-based two-sided relay selection and decentralized PPO coordination, but the headline stability claim is unmeasured, the evaluation is not end-to-end, and the blockchain's 'trusted' selection assumes honest self-reporting. read the letter →

arxiv 2502.15713 v1 pith:3RY4EXYB submitted 2025-01-22 cs.NI cs.AI

Ahmed Alagha , Maha Kadadha , Rabeb Mizouni , Shakti Singh , Jamal Bentahar , Hadi Otrok This is my paper · ORCID
classification cs.NIcs.AI
keywords InternetofVehiclesUAVrelayselectionMulti-AgentDeepReinforcementLearningBlockchainProximalPolicyOptimizationNetworkcoverageandconnectivity
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 argues that relay selection and relay coordination in UAV-assisted Internet of Vehicles should be coupled and executed transparently, rather than treated as separate centralized steps. To that end, it proposes a three-part framework: a blockchain-hosted smart contract that matches vehicles to UAV relays using two-sided quality metrics (QoU and QoV), a decentralized multi-agent deep reinforcement learning model using PPO that controls UAV mobility after selection, and IPFS storage for the trained models. The reported evaluations show the selection mechanism improves average QoU and QoV compared with a nearest-neighbor style baseline, and the MDRL coordination maintains high coverage and connectivity while scaling to more UAVs where a centralized DRL benchmark degrades.

What carries the argument

The load-bearing objects are the two-sided quality metrics: QoU (available bandwidth, battery level, reputation, and inverse distance, weighted and normalized) computed by each vehicle for candidate UAVs, and QoV (requested bandwidth, payment per Mbps, reputation, and inverse distance) used by UAVs to rank proposers, with a smart contract allocation that sorts proposals by QoV over the square root of requested bandwidth. Coordination rides on a Markov Game formulation with a team reward $R_t = (\text{coverage} - 1) + (\text{connectivity} - 1)$, trained by Proximal Policy Optimization under centralized-learning/decentralized-execution, where each UAV's observations are four grid maps reduced into local windows and global downsampled maps and fed to a LeNet-style CNN actor-critic.

What would settle it

Run the selection mechanism with agents that inflate their reported bandwidth or reputation by 10 to 20 percent and check whether the allocation shifts toward the dishonest agents; if the matched set changes materially, the claimed transparent and trusted selection does not protect against false reporting.

Watch

Extended reading notes

Core claim

The central claim is that a smart contract can run a two-sided relay selection in which each vehicle proposes the UAV with the highest Quality-of-UAV, and each UAV accepts vehicles ranked by QoV divided by the square root of requested bandwidth until its bandwidth is exhausted. Once selected, the UAVs are coordinated by a shared multi-agent PPO policy, trained with central learning and decentralized execution, using CNN-processed local and global grid observations and a team-based reward that penalizes loss of coverage or connectivity. The paper reports that this integrated design yields higher average QoU and QoV for the matched pairs, keeps coverage high as the number of vehicles grows, and reaches full connectivity within about 5% of an episode on average, while a centralized DRL baseline struggles as team size increases.

Load-bearing premise

The selection's trustworthiness assumes vehicles and UAVs report their location, bandwidth, energy, reputation, and payment truthfully, because the smart contract reads these values from the chain without verification.

Editorial extensions

If this is right

  • If the framework works as described, operators get an auditable, on-chain record of which relay was selected and why, addressing traceability gaps in server-run or node-run selection.
  • Decentralized execution keeps per-agent model size at 1.692M trainable parameters regardless of team size, while a centralized agent grows from 1.692M to 9.31M parameters as UAVs increase from two to five, so the approach scales to larger teams.
  • The team reward pushes UAVs to reach full inter-UAV connectivity within about five timesteps of an episode while preserving coverage of assigned vehicles, even as vehicle counts increase.
  • Smart contract gas costs for registration, proposal submission, and zone allocation are low enough that the authors judge the framework feasible on a consortium blockchain such as Quorum.

Reading between the lines

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

  • The trust and transparency claim depends on the reliability of data written to the blockchain; if vehicles or UAVs can submit inflated bandwidth, reputation, or payment values, the ranking and allocation could be biased, so adding verification oracles or slashing mechanisms would be a natural extension.
  • The coordination model assumes a 2D plane with no obstacles or signal interference; extending the observation maps to include no-fly zones, building footprints, or 3D altitude constraints would test whether the learned policy transfers to realistic urban environments.
  • Because trained models live on IPFS and are assigned by identifier through the smart contract, the same architecture could be reused for other mobile relay or crowdsensing settings where selection and post-selection coordination must be coupled and auditable.
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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

5 major / 7 minor

Summary. This manuscript proposes a three-component framework for UAV-assisted Internet of Vehicles: (i) a blockchain-hosted two-sided relay selection mechanism based on new Quality-of-UAV (QoU) and Quality-of-Vehicle (QoV) metrics; (ii) a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) coordination model, trained with PPO under centralized learning and decentralized execution, with CNN policies and reduced local/global observations; and (iii) an IPFS/Solidity implementation for storing and distributing trained models. The evaluation consists of a static selection experiment against a nearest-neighbor matching variant, MDRL learning and coverage/connectivity experiments against centralized DRL and static placement, and a gas-cost analysis of the smart contract. The paper claims that the integrated framework improves relay stability, maximizes coverage and connectivity, and is scalable and cost-efficient.

Significance. If substantiated, this is a meaningful architectural contribution. The paper is among the first to couple blockchain-hosted two-sided relay selection with decentralized DRL-based UAV coordination, and the gas-cost analysis gives practical deployment information that is often missing in such designs. The observation-reduction design and the team reward are sensible, and the scalability comparison against centralized DRL is supported by parameter and FLOP counts. The strengths are the detailed system design, the explicit Markov-game formulation, and the fact that the evaluation is based on new simulations rather than on fitting parameters to pre-existing results. However, the current evidence does not validate the strongest advertised claims: relay stability is never measured, the blockchain trust model assumes honest reporting without any adversary treatment, and the selection and coordination components are evaluated separately. No code artifact or machine-checked proof is provided, so reproducibility rests on pseudocode and parameter tables.

major comments (5)
  1. [Abstract, §4.2] The abstract and §5 claim that the selection mechanism improves the stability of the selected relays, but Section 4.2 contains no stability measurement. Figures 5-6 report only the number of vehicles per UAV, the percentage of selected UAVs, and the average QoU/QoV values. These are static one-shot outcomes, and QoU/QoV in Eqs. (1)-(2) are the very quantities the mechanism maximizes, so comparing them against a benchmark shows that the optimizer improves its own objective, not that the resulting relays are stable or yield better network performance. A longitudinal metric is required, e.g., selection churn, reassignment rate, or the duration of assignments under vehicle mobility, ideally followed by coverage/connectivity results computed on the assignments produced by the selection mechanism. Without such evidence, the abstract's stability claim is unsupported.
  2. [§3.2, Tables 1-3, Algorithms 1-2] The blockchain-based selection is presented as transparent and trusted, but the trust claim is not established. The registration functions in Table 3 accept location, bandwidth, energy, reputation, and payment as self-reported values, and the reputation field is initialized but never updated or penalized. Algorithm 1 is executed off-chain by the vehicle, so the contract cannot verify that the submitted UAV is truly the one maximizing QoU; Algorithm 2 then ranks proposals by QoV/√RB computed from these unverified inputs. A vehicle can inflate RB or PayPerMbps, and a UAV can inflate AB or BL, to bias the ranking; the contract provides execution transparency but not data integrity. Since the evaluation in §4.2 uses honestly sampled uniform attributes, the reported QoU/QoV improvements and the subsequent coordination results assume an honest-reporting regime that is never stated or tested. Please add an explicit threat model with false-reporting rates, or a verification/reputation mechanism, or narrow the claim to transparent execution under honest reporting.
  3. [§4.1, §4.2, §4.3] Selection and coordination are never evaluated as an integrated pipeline. Section 4.2 evaluates the selection mechanism as a static allocation over the whole 50×50 km area with zones, while Section 4.3 trains MDRL per single zone with vehicles that are assumed to be assigned to each UAV based on the selection mechanism. No experiment executes Algorithms 1-2 and then uses their output to initialize the MDRL coordination task; nor is there a measurement of coverage or connectivity on the assignments produced by the blockchain component. The paper's central claim that the proposed selection and coordination mechanisms work together to improve stability and coverage is therefore not supported by the experiments. Please add an end-to-end evaluation, or explicitly present the results as component-level validations.
  4. [§4.1, Eq. (4)] The MDRL evaluation omits parameters that are essential to interpret the coverage and connectivity results. Coverage in Eq. (4) counts vehicles within the communication range of their allocated UAV, and connectivity requires a communication path between UAVs, but Table 5 does not list the UAV communication range, UAV speed, timestep duration, or the observation-window size n used in training. Figures 7-9 depend directly on these values, and the paper does not report sensitivity to them. Without the communication range and the other environment parameters, the learning curves and the final coverage/connectivity scores cannot be reproduced or compared with the benchmarks. Please specify these values and add a short sensitivity study.
  5. [§4.4, Algorithm 2, Table 3] Section 4.4 validates only the gas cost of the smart contract; it does not test whether the contract logic correctly implements Algorithms 1-2. This matters because the pseudocode and the function signature are inconsistent: Algorithm 2, line 5, pushes the address of u into UAVS electionList u, but the surrounding text and the data structure indicate this should be the address of the selected vehicle v; and Table 3 lists QoV as an input parameter of submitVehS election(), while §3.2 says the function calculates the QoV. A correctness test on the Solidity implementation, such as unit tests checking allocation outcomes against the algorithms, would resolve these discrepancies and substantiate the feasibility claim. This is especially important if the contract is meant to be the trust anchor of the system.
minor comments (7)
  1. [Algorithm 2] In line 5, the pushed address should be that of the vehicle v, not u; the pseudocode should match the text and the data structures.
  2. [Table 3, Algorithm 1] Please clarify whether QoV is computed on-chain or supplied by the vehicle; the function signature and the prose currently disagree.
  3. [§4.1, Eqs. (1)-(2)] The weights w1-w8 are free parameters set to 0.25 in Table 5, but no sensitivity analysis is reported; since the ranking depends on these weights, a brief sensitivity check would strengthen the selection results.
  4. [§3.3.1] The observation-reduction size n is described as a tunable hyperparameter, but its value is not reported; please state the value used in the experiments.
  5. [Figs. 5-9] The figures show no error bars or confidence intervals despite the text reporting averages over 5 iterations for selection and 20 experiments for Fig. 9; please add variance information.
  6. [Throughout] Typographical and formatting issues remain, including the UA V spacing artifacts in the abstract and body, the malformed URL in footnote 2, and inconsistent reference formatting; a careful copy edit is needed.
  7. [§5] The limitation paragraph is transparent about energy and obstacle assumptions, but those caveats should be reflected in the abstract so that the reader is not left with unqualified coverage and connectivity claims.

Circularity Check

1 steps flagged · score 2.0 of 10

No significant circularity; the only self-referential point is that the evaluation metrics mirror the optimization objectives, but the benchmarks and learned policies give independent content.

  1. self definitional [Section 3.3.4 (Eq. 4) and Section 4.3 (Fig. 9)]
    "Rt = (coverage− 1) + (connectivity− 1) ... Coverage reflects the portion of vehicles covered per step on average, while connectivity reflects the portion of steps (out of 100) where the UA Vs maintained full connectivity with each other."

    The reward that the PPO agents are trained to maximize is defined as coverage plus connectivity, and the evaluation in Section 4.3 measures exactly the same coverage and connectivity. Hence the headline claim that the MDRL method maximizes coverage and connectivity is partly a restatement of the training objective rather than an independent prediction. The circularity is mild because the policy is actually learned from experience and is compared against centralized DRL and static placement baselines on the same metric, so the comparison still has empirical content; the paper does not fit parameters and then call them predictions.

full rationale

Score 2 reflects one mild self-referential point. The selection mechanism is evaluated on the QoU/QoV metrics that its algorithms explicitly maximize, and the MDRL reward is literally coverage plus connectivity, the same quantities used in the evaluation figures. These are not fitted inputs called predictions, and they are not the paper's main derivation: the benchmarks (NNM using the same metrics, centralized DRL, and static placement) provide independent comparisons, and the learning curves demonstrate actual convergence rather than a closed-form identity. The blockchain-related self-citations, e.g. [12] and [23], appear only in related-work positioning and are not load-bearing for the correctness of the proposed framework. The known limitation that the smart contract accepts self-reported attributes without verification is a threat-model and correctness gap, not a circularity. No equation in the paper is derived from itself, and no fitted parameter is disguised as a prediction.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The framework's central claims depend on hand-chosen metric weights, an unspecified observation-map size, and several simplifying mobility and communication assumptions. No new physical entities are introduced; QoU and QoV are scoring formulas, not entities. The blockchain trust feature requires accurate self-reporting, which is not enforced.

free parameters (2)
  • QoU/QoV weights w1-w8 = 0.25 each
    Hand-chosen equal weights in Eqs. 1 and 2; the paper notes weights can vary by application, so results are tied to this choice.
  • Observation reduction size n = not specified
    Declared a tunable hyperparameter in Section 3.3.1; no value or sensitivity analysis reported, so MDRL results depend on an undisclosed setting.
assumptions (5)
  • domain assumption The allocation runs at an instance of time where positions of vehicles and UAVs are constant despite their mobility.
    Stated in Section 3.2; permits static two-sided selection but prevents evaluation of relay stability over time, which the abstract nonetheless claims.
  • domain assumption UAVs operate in a 2D plane.
    Section 3.3.2 states 'For simplicity, it is assumed that the UAVs operate in a 2D plane'; altitude dynamics are ignored.
  • domain assumption Vehicles only move within the zone without leaving it.
    Section 3.3 states this assumption; handover and cross-zone mobility are excluded.
  • domain assumption The environment is idealized without obstacles or signal interference.
    Acknowledged in the Conclusion as a limitation; simulations assume ideal communication.
  • domain assumption Self-reported attributes in the smart contract are accurate and not manipulated.
    Section 3.2 computes QoU/QoV from blockchain-stored data with no verification mechanism; the trust/transparency guarantee depends on this.

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Pith. "Pith review of UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain." pith.science (2026). https://pith.science/paper/3RY4EXYB

@misc{pith2026250215713,
  author       = {Pith},
  title        = {Pith review of: UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3RY4EXYB}},
  note         = {Machine review of arXiv:2502.15713}
}
read the original abstract

This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for autonomous UAV coordination, and a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the decentralized coordination between them is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). The evaluation results demonstrate that the proposed selection and coordination mechanisms improve the stability of the selected relays and maximize the coverage and connectivity achieved by the UAVs.

Figures

Figures reproduced from arXiv: 2502.15713 by the authors.

Figure 1
Figure 1. The different layers of the system in the proposed framework to vehicles on top of Quorum consortium Blockchain. The proposed MDRL algorithm is used to train several models for different variations of the environment (different number of agents, number of vehicles, etc). Trained MDRL models are stored on the IPFS, which is a decentralized file storage system that efficiently handles large files. In IPFS, the data is… view at source ↗
Figure 2
Figure 2. The original observations acquired by a UAV, in a team of 3 UAVs, and their reduced counterparts (local in green and global in red). [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. The architecture used for the actor and critic networks. While both networks have the same architecture configuration, they are two separate [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The overall process diagram During the coordination phase, certain practical challenges, such as UAV battery drainage, can impact the sustain￾ability of the system. While the proposed framework focuses on optimizing coverage and connectivity through efficient coordinat…
Figure 5
Figure 5. Figure 5: Number of vehicles accepted per UAV and the percentage of selected UAVs. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Average QoU and QoV values achieve based on the selection mechanism. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: The average episodic reward throughout the learning for varying numbers of vehicles, for a scenario of (a) two UAVs, (b) three UAVs, (c) [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Comparison between the proposed MDRL and the benchmark for a scenario of 20 vehicles and a team of (a) two UAVs and (b) three UAVs. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: The coverage and connectivity achieved by di [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]

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Reference graph

Works this paper leans on

49 extracted references · 47 canonical work pages

  1. [10]

    Abualola, H

    H. Abualola, H. Otrok, H. Barada, M. Al-Qutayri, Y . Al-Hammadi, Matching game theoretical model for stable relay selection in a uav-assisted internet of vehicles, Vehicular Communications 27 (2021) 100290

    work page 2021

  2. [23]

    Kadadha, R

    M. Kadadha, R. Mizouni, S. Singh, H. Otrok, A. Mourad, Crowdsourced vehicles and uavs for last-mile delivery application using blockchain- hosted matching mechanism, Vehicular Communications 47 (2024) 100761

    work page 2024

  3. [1]

    Hemmati, M

    A. Hemmati, M. Zarei, A. Souri, Uav-based internet of vehicles: A systematic literature review, Intelligent Systems with Applications 18 (2023) 200226

    work page 2023

  4. [2]

    Alagha, R

    A. Alagha, R. Mizouni, S. Singh, H. Otrok, A. Ouali, Sdrs: A stable data-based recruitment system in iot crowdsensing for localization tasks, Journal of Network and Computer Applications 177 (2021) 102968

    work page 2021

  5. [3]

    Alagha, S

    A. Alagha, S. Singh, H. Otrok, R. Mizouni, Rfls-resilient fault-proof localization system in iot and crowd-based sensing applications, Journal of Network and Computer Applications 170 (2020)

    work page 2020

  6. [4]

    Krishna, A survey uav-assisted vanet routing protocol, International Journal of Computer Science Trends and Technology (IJCST)–V ol 8 (2020)

    M. Krishna, A survey uav-assisted vanet routing protocol, International Journal of Computer Science Trends and Technology (IJCST)–V ol 8 (2020)

    work page 2020

  7. [5]

    C. Tang, X. Wei, C. Liu, H. Jiang, H. Wu, Q. Li, Uav-enabled social internet of vehicles: Roles, security issues and use cases, in: Y . Xiang, Z. Liu, J. Li (Eds.), Security and Privacy in Social Networks and Big Data, Springer Singapore, Singapore, 2020, pp. 153–163

    work page 2020

  8. [6]

    Y . Su, M. Liwang, Z. Chen, X. Du, Toward optimal deployment of uav relays in uav-assisted internet of vehicles, IEEE Transactions on Vehicular Technology 72 (2023) 13392–13405

    work page 2023

Show all 49 references
  1. [7]

    J. S. Ng, W. Y . B. Lim, H.-N. Dai, Z. Xiong, J. Huang, D. Niyato, X.-S. Hua, C. Leung, C. Miao, Joint auction-coalition formation framework for communication-efficient federated learning in uav-enabled internet of vehicles, IEEE Transactions on Intelligent Transportation Syst...

    2021

  2. [8]

    Abualola, H

    H. Abualola, H. Otrok, Stable coalitions for urban-vanet: A hedonic game approach, Vehicular Communications 30 (2021) 100355

    2021

  3. [9]

    H. Sami, R. Saado, A. E. Saoudi, A. Mourad, H. Otrok, J. Bentahar, Opportunistic uav deployment for intelligent on-demand iov service management, IEEE Transactions on Network and Service Management 20 (2023) 3428–3442

    2023

  4. [11]

    Nakamoto, Bitcoin: A peer-to-peer electronic cash system, Cryptography Mailing list at https: //metzdowd.com (2009)

    S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system, Cryptography Mailing list at https: //metzdowd.com (2009)

    2009

  5. [12]

    Kadadha, H

    M. Kadadha, H. Otrok, A blockchain-enabled relay selection for qos-olsr in urban vanet: A stackelberg game model, Ad Hoc Networks 117 (2021) 102502

    2021

  6. [13]

    Hammoud, H

    A. Hammoud, H. Sami, A. Mourad, H. Otrok, R. Mizouni, J. Bentahar, Ai, blockchain, and vehicular edge computing for smart and secure iov: Challenges and directions, IEEE Internet of Things Magazine 3 (2020) 68–73

    2020

  7. [14]

    Sharma, K

    S. Sharma, K. K. Ghanshala, S. Mohan, Blockchain-based internet of vehicles (iov): An e fficient secure ad hoc vehicular networking architecture, in: 2019 IEEE 2nd 5G World Forum (5GWF), pp. 452–457

    2019

  8. [15]

    S. Tu, H. Yu, A. Badshah, M. Waqas, Z. Halim, I. Ahmad, Secure internet of vehicles (iov) with decentralized consensus blockchain mechanism, IEEE Transactions on Vehicular Technology 72 (2023) 11227–11236

    2023

  9. [16]

    Alladi, V

    T. Alladi, V . Chamola, N. Sahu, M. Guizani, Applications of blockchain in unmanned aerial vehicles: A review, Vehicular Communications 23 (2020) 100249

    2020

  10. [17]

    M. M. Islam, M. T. R. Khan, M. M. Saad, M. A. Tariq, D. Kim, Dynamic positioning of uavs to improve network coverage in vanets, Vehicular Communications 36 (2022) 100498

    2022

  11. [18]

    Sedjelmaci, M

    H. Sedjelmaci, M. A. Messous, S. M. Senouci, I. H. Brahmi, Toward a lightweight and efficient uav-aided vanet, Transactions on Emerging Telecommunications Technologies 30 (2019) e3520

    2019

  12. [19]

    A. Raza, S. H. R. Bukhari, F. Aadil, Z. Iqbal, An uav-assisted vanet architecture for intelligent transportation system in smart cities, International Journal of Distributed Sensor Networks 17 (2021) 15501477211031750

    2021

  13. [20]

    Samir, D

    M. Samir, D. Ebrahimi, C. Assi, S. Sharafeddine, A. Ghrayeb, Leveraging uavs for coverage in cell-free vehicular networks: A deep reinforcement learning approach, IEEE Transactions on Mobile Computing 20 (2021) 2835–2847

    2021

  14. [21]

    T. Yuan, C. E. Rothenberg, K. Obraczka, C. Barakat, T. Turletti, Harnessing uavs for fair 5g bandwidth allocation in vehicular communication via deep reinforcement learning, IEEE Transactions on Network and Service Management 18 (2021) 4063–4074

    2021

  15. [22]

    O. S. Oubbati, M. Atiquzzaman, A. Baz, H. Alhakami, J. Ben-Othman, Dispatch of uavs for urban vehicular networks: A deep reinforcement learning approach, IEEE Transactions on Vehicular Technology 70 (2021) 13174–13189. 20

    2021

  16. [24]

    Kadadha, S

    M. Kadadha, S. Singh, R. Mizouni, H. Otrok, A context-aware blockchain-based crowdsourcing framework: Open challenges and opportunities, IEEE Access (2022)

    2022

  17. [25]

    Samir, S

    M. Samir, S. Sharafeddine, C. Assi, T. M. Nguyen, A. Ghrayeb, Trajectory planning and resource allocation of multiple uavs for data delivery in vehicular networks, IEEE Networking Letters 1 (2019) 107–110

    2019

  18. [26]

    N. Lin, L. Fu, L. Zhao, G. Min, A. Al-Dubai, H. Gacanin, A novel multimodal collaborative drone-assisted vanet networking model, IEEE Transactions on Wireless Communications 19 (2020) 4919–4933

    2020

  19. [27]

    S. A. Hadiwardoyo, C. T. Calafate, J.-C. Cano, K. Krinkin, D. Klionskiy, E. Hernández-Orallo, P. Manzoni, Three dimensional uav positioning for dynamic uav-to-car communications, Sensors 20 (2020) 356

    2020

  20. [28]

    G. A. Ahmed, T. R. Sheltami, A. S. Mahmoud, M. Imran, M. Shoaib, A novel collaborative iod-assisted vanet approach for coverage area maximization, IEEE Access 9 (2021) 61211–61223

    2021

  21. [29]

    Noh, H.-B

    S.-C. Noh, H.-B. Jeon, C.-B. Chae, Energy-e fficient deployment of multiple uavs using ellipse clustering to establish base stations, IEEE Wireless Communications Letters 9 (2020) 1155–1159

    2020

  22. [30]

    Samir, D

    M. Samir, D. Ebrahimi, C. Assi, S. Sharafeddine, A. Ghrayeb, Trajectory planning of multiple dronecells in vehicular networks: A reinforcement learning approach, IEEE Networking Letters 2 (2020) 14–18

    2020

  23. [31]

    Damani, Z

    M. Damani, Z. Luo, E. Wenzel, G. Sartoretti, Primal _2: Pathfinding via reinforcement and imitation multi-agent learning-lifelong, IEEE Robotics and Automation Letters 6 (2021) 2666–2673

    2021

  24. [32]

    Bayerlein, M

    H. Bayerlein, M. Theile, M. Caccamo, D. Gesbert, Multi-uav path planning for wireless data harvesting with deep reinforcement learning, IEEE Open Journal of the Communications Society 2 (2021) 1171–1187

    2021

  25. [33]

    Alagha, R

    A. Alagha, R. Mizouni, J. Bentahar, H. Otrok, S. Singh, Multi-agent deep reinforcement learning with demonstration cloning for target localization, IEEE Internet of Things Journal (2023)

    2023

  26. [34]

    W. Lee, T. Kim, Multi-agent reinforcement learning in controlling offloading ratio and trajectory for multi-uav mobile edge computing, IEEE Internet of Things Journal (2023)

    2023

  27. [35]

    Jiang, S

    B. Jiang, S. N. Givigi, J.-A. Delamer, A marl approach for optimizing positions of vanet aerial base-stations on a sparse highway, IEEE Access 9 (2021) 133989–134004

    2021

  28. [36]

    Accessed: 27-09-2023

    Quorum: a permissioned implementation of ethereum supporting data privacy, 2018. Accessed: 27-09-2023

    2018

  29. [37]

    Accessed: 2024-11-21

    Performance evaluation of the quorum blockchain platform, arXiv:1809.03421 (2018). Accessed: 2024-11-21

    2018 arXiv

  30. [38]

    Gronauer, K

    S. Gronauer, K. Diepold, Multi-agent deep reinforcement learning: a survey, Artificial Intelligence Review (2021) 1–49

    2021

  31. [39]

    Alagha, J

    A. Alagha, J. Bentahar, H. Otrok, S. Singh, R. Mizouni, Blockchain-assisted demonstration cloning for multi-agent deep reinforcement learning, IEEE Internet of Things Journal (2023)

    2023

  32. [40]

    Alagha, H

    A. Alagha, H. Otrok, S. Singh, R. Mizouni, J. Bentahar, Blockchain-based crowdsourced deep reinforcement learning as a service, Available at SSRN 4753209 (2024)

    2024

  33. [41]

    Schulman, et al., Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347 (2017)

    J. Schulman, et al., Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347 (2017)

    2017 arXiv

  34. [42]

    Baker, I

    B. Baker, I. Kanitscheider, T. Markov, Y . Wu, G. Powell, B. McGrew, I. Mordatch, Emergent tool use from multi-agent autocurricula, in: 2020 Proc. Int. Conf. on Learning Representations (ICLR)

    2020

  35. [43]

    Alagha, S

    A. Alagha, S. Singh, R. Mizouni, J. Bentahar, H. Otrok, Target localization using multi-agent deep reinforcement learning with proximal policy optimization, Future Generation Computer Systems 136 (2022) 342–357

    2022

  36. [44]

    Alagha, R

    A. Alagha, R. Mizouni, S. Singh, J. Bentahar, H. Otrok, Adaptive target localization under uncertainty using multi-agent deep reinforcement learning with knowledge transfer, Available at SSRN 4872731 (2024)

    2024

  37. [45]

    LeCun, et al., Lenet-5, convolutional neural networks, URL: http: //yann

    Y . LeCun, et al., Lenet-5, convolutional neural networks, URL: http: //yann. lecun. com/exdb/lenet 20 (2015) 14

    2015

  38. [46]

    M. Fan, Y . Wu, T. Liao, Z. Cao, H. Guo, G. Sartoretti, G. Wu, Deep reinforcement learning for uav routing in the presence of multiple charging stations, IEEE Transactions on Vehicular Technology 72 (2022) 5732–5746

    2022

  39. [47]

    C. H. Liu, C. Piao, J. Tang, Energy-e fficient uav crowdsensing with multiple charging stations by deep learning, in: IEEE INFOCOm 2020-IEEE conference on computer communications, IEEE, pp. 199–208

    2020

  40. [48]

    R. H. Dehejia, S. Wahba, Propensity score-matching methods for nonexperimental causal studies, Review of Economics and statistics 84 (2002) 151–161

    2002

  41. [49]

    R. S. Sutton, A. G. Barto, Reinforcement learning: An introduction, MIT press, 2018. 21

    2018

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.