Pith. sign in

REVIEW 5 major objections 5 minor 2 cited by

Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning

T0 review · 5 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read This paper argues that blockchain-based access control and a double-dueling deep Q-network can jointly make IoT computation offloading secure and cost-efficient.

desk verdict The blockchain access-control demo is real, but the offloading optimization is broken as written: the objective does not depend on the offloading decisions, so the paper's central performance claim is unsupported. read the letter →

arxiv 1908.07466 v2 pith:GZOP3QPL submitted 2019-08-15 eess.SP

Dinh C. Nguyen , Pubudu N. Pathirana , Ming Ding , Aruna Seneviratne This is my paper · ORCID
classification eess.SP
keywords blockchaincomputationoffloadingdeepreinforcementlearningdoubleduelingDQNmobileedgecomputingaccesscontrolsmartcontractIoTnetworks
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 security and computation offloading in IoT edge-cloud networks can be handled in one framework: a blockchain smart contract decides which mobile devices may offload, and an advanced deep reinforcement learning agent—combining double and dueling Q-networks (ADRLO)—decides for each authorized device whether to send a task to the edge server or the cloud and how much computing and bandwidth to allocate. The goal is to minimize the long-term weighted sum of latency and energy cost across all devices. The paper reports that ADRLO achieves the lowest total cost against a plain DRL scheme, an all-edge scheme, and an all-cloud scheme, and that the smart contract rejects unauthorized offloading requests. If correct, the result suggests that untrusted IoT devices can get secure, cost-efficient offloading without relying on a centralized authority.

What carries the argument

The load-bearing mechanism is the ADRLO agent: a deep Q-network that combines double DQN—which uses one network to choose the best action and another to evaluate it, reducing overestimation—with a dueling architecture that splits $Q(s,a)$ into a state-value $V(s)$ and an action-advantage $A(a)$ before recombining them. The agent observes state $s=\{t_c,e_c,b_w\}$ (total offloading cost, available edge computation, available bandwidth), selects an action containing offloading decisions plus edge and bandwidth allocations, and receives reward $r(s,a)=-C(s,a)$. Around it, a smart contract with add-device, delete-device, policy-list, and penalty functions enforces access control so only registered devices can reach the offloading stage.

What would settle it

Take a single-device instance with fixed data size, channel gain, transmit power, and weights, enumerate all offloading and resource actions to find the exact minimum of the cost function, and compare that action with what ADRLO selects under its three-component state; any mismatch over a range of task sizes would show the state is not sufficient and the claimed optimal policy is unsupported.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes a joint access-control and offloading design for a mobile edge-cloud computation offloading (MECCO) system. Authorized devices are certified by checking a public key and device ID against a policy list in a smart contract; unauthorized requests are denied and penalized. For authorized devices, the offloading problem is formulated as minimizing $\sum_{n=1}^{N} C_n$ where $C_n = \beta_t T_n + \beta_e E_n$, over binary edge/cloud decisions, edge CPU allocation, and bandwidth allocation. The paper reports that its advanced double-dueling DQN algorithm (ADRLO) yields the lowest system cost under varying numbers of devices, task sizes, edge capacities, and total bandwidths, and that jointly including edge and bandwidth allocation is necessary to achieve those savings.

Load-bearing premise

The load-bearing premise is that the three-part state—current cost, remaining edge computation, and remaining bandwidth—contains enough information for the agent to learn an optimal offloading policy, even though the task data size, channel gain, transmit power, and cost weights that determine the reward are not part of the state.

Editorial extensions

If this is right

  • Authorized mobile devices can dynamically choose between edge and cloud execution depending on task size, so small tasks stay at the edge while large tasks go to the cloud.
  • The deep RL approach removes the need for prior knowledge of system statistics, because the agent learns an offloading policy from trial-and-error interactions.
  • Edge computation capacity and radio bandwidth should be allocated jointly with offloading decisions; dropping either allocation degrades total cost, as shown by the ablations in Fig. 10.
  • Blockchain-based access control replaces a single trusted authority with a distributed, auditable ledger of offloading transactions and device permissions.

Reading between the lines

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

  • The authors do not put the task data size, channel gain, transmit power, or cost weights into the state, so a natural extension is to test whether adding them changes the learned policy or lowers measured cost.
  • The paper's access-control experiment penalizes an unauthorized request only with a warning message; whether a real deterrent such as a fee or reputation loss would reduce repeated attacks is left untested.
  • Blockchain transaction and mining overhead is not included in the offloading cost model, so the reported cost savings likely do not include the security tax a real deployment would pay.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper proposes a secure computation-offloading framework for blockchain-based IoT networks, combining a smart-contract access-control mechanism with a deep reinforcement learning (DRL) offloading algorithm. The access-control part is implemented on a private Ethereum network and validated with two smartphones. The offloading part formulates a joint optimization problem (P1) over offloading decisions, edge computation resource, and radio bandwidth, and solves it with a double-dueling DQN algorithm (ADRLO). Numerical simulations compare ADRLO with regular DQN, edge-only, and cloud-only baselines, and report lower total cost. The core claims are that the smart contract rejects unauthorized offloading requests and that ADRLO learns an optimal offloading policy achieving the lowest system cost.

Significance. If the central claims were sound, the integration of blockchain-based access control with DRL-based offloading would be a plausible contribution to secure IoT edge-cloud systems. The access-control deployment and the honest reporting of the experimental setup are useful engineering pieces. However, the offloading model and the DRL formulation contain structural errors that invalidate the claimed optimality results: the objective in (P1) does not depend on the offloading decision variables, the cloud resource variable is unconstrained, the DRL state is not a sufficient statistic for the MDP, and the Bellman update equations use minimization where maximization is required. These are not local presentation issues; they undermine the core contribution, and the numerical results in Section VI-C cannot support the stated conclusions.

major comments (5)
  1. [Section IV-B3, Eqs. (5)-(6), (P1)] The cost function is independent of the offloading decision vector A. Equations (5) and (6) define T_n = T^e_n + T^c_n and E_n = E^e_n + E^c_n for every device, so the objective in (P1) sums edge and cloud costs regardless of whether the task is actually offloaded to the edge or the cloud. Since the binary variables alpha appear neither in the objective nor in constraints (C1)-(C6), every feasible A yields exactly the same cost, and minimizing over A is meaningless. The model must condition the latency and energy on the offloading decision, e.g., T_n = alpha^n_e T^e_n + alpha^n_c T^c_n, or otherwise make the objective depend on A.
  2. [Section IV-B3 and Section V-B1] The cloud resource variable f^c_n appears in T^c_n and E^c_n but is neither a decision variable nor bounded by any constraint. If f^c_n is treated as a variable, the cost can be driven to minus infinity by letting f^c_n go to infinity; if it is a fixed parameter, its value and feasible range are never specified. This makes (P1) ill-posed. The paper states that cloud resource allocation is 'ignored,' but the cost expressions still depend on f^c_n, so the model is incomplete.
  3. [Section V-B1, state definition] The state s = {tc, ec, bw} is not a sufficient statistic for the offloading MDP. The reward r(s,a) = -C(s,a) depends on the task data size D_n, the channel gain h_n, the transmit power p_n, the workload X_n, and the latency/energy weights, none of which appear in the state. Without these quantities, the agent cannot evaluate the reward associated with a state-action pair or predict future states, so the process is not Markov and the Q-learning convergence arguments do not apply. At a minimum, the state must include D_n, h_n, p_n, X_n, and the weights, or the authors must prove that the three listed components determine the full transition and reward structure.
  4. [Section V-B2, Eqs. (7)-(10) and Algorithm 2 line 12] The Q-learning update, the Bellman optimality equation, the DQN target, and the double-DQN target all use min over the next action, while the stated reward is r(s,a) = -C(s,a). With a reward to be maximized, the correct operator is max, and the greedy action selection in Algorithm 2 line 12 should be argmax, not argmin. The text in Section V-B2 even states pi*(s) = argmax Q*(s,a), so the equations are inconsistent with the surrounding prose. If the authors intended a cost-to-go with r = C, then the reward definition in Section V-B1 must be changed accordingly.
  5. [Section VI-C, Figs. 8-10] Because the objective in (P1) and the DRL reward are independent of the offloading decision A, the simulation results cannot demonstrate that ADRLO learns an optimal placement policy. The reported cost differences between ADRLO, DRLO, edge-only, and cloud-only baselines cannot be attributed to the learned offloading decisions under the model as written; they would need to be explained by the resource-allocation components, which are not the paper's claimed contribution. The evaluation therefore does not support the central conclusion.
minor comments (5)
  1. [Abstract vs. Section IV-B3] The abstract states that the optimization minimizes latency, energy, and smart-contract fee, but the cost function in Section IV-B3 contains only weighted latency and energy; no smart-contract fee appears in (P1). Please reconcile these statements.
  2. [References [37] and [38]] The reference numbering is inconsistent: Ethereum is cited as [37], but [37] is an IoT access-control paper by Lin et al., and reference [38] duplicates the content of [20]. Please recheck all citations and the bibliography.
  3. [Section IV-B3, cost weights] The text defines beta^n_t and beta^n_e with an index n, but the cost expression C_n = beta_t T_n + beta_e E_n drops the index. Please clarify whether the weights are per-device or global.
  4. [Algorithm 2, line 19] The target y^dou_j uses both theta and theta-prime, but the roles of the two networks are not clearly described in the text or pseudocode. A brief explanation of which network selects and which evaluates would improve readability.
  5. [Throughout] There are several typographical issues, including 'MEECO' for 'MECCO' in Section I and inconsistent use of 'DRL' and 'DLR' near Section V-B2. A careful proofreading pass is needed.

Circularity Check

1 steps flagged · score 6.0 of 10

The offloading-decision variables never enter the cost or reward, so the claimed learned edge-vs-cloud policy is a dummy variable; the placement part of the reported cost saving is vacuous by construction.

  1. self definitional [Section IV-B2, Eqs. (5)-(6), with Section V-B1, Reward definition]
    "According to (1)-(4), the computation latency and energy consumption of the MD n in our MECCO system can be expressed respectively as Tn =T e_n +T c_n, (5) En =Ee_n +Ec_n. (6) ... Accordingly, we can formulate the system reward as r(s,a) =−C(s,a)."

    Because Cn=βtTn+βeEn, and Tn and En add the edge and cloud terms for every MD, the binary offloading variables αe_n and αc_n appear only in constraints C1-C2 and never in the objective or reward. Thus C(s,a) is independent of the offloading-decision part of the action a; minimizing over A is vacuous, and r=-C provides no learning signal about where to offload. The paper's central claim that ADRLO learns the optimal edge-vs-cloud offloading policy is therefore not derived from the model: the placement component of the reported cost saving is an artifact of a cost function that does not depend on placement. In effect, the 'optimal offloading decision' is equivalent to an unconstrained dummy variable by construction.

full rationale

One concrete by-construction reduction exists in the model as written: the per-MD cost is the unweighted sum of the edge term and the cloud term, so the binary offloading decision A is absent from the objective and reward. Consequently, the claimed learned offloading policy cannot be the source of the reported cost improvements; that part of the result is vacuous by construction. I do not count the standard RL practice of training and evaluating on the same cost metric as circular by itself, because Figs. 8-10 compare against fixed baselines and thus give some independent content. The minor self-citation [15] for access-control efficiency is not load-bearing: Fig. 7 provides a direct experiment in this paper. The min-Q Bellman update with r=-C and the non-Markov state definition are serious correctness flaws, but they are internal inconsistencies rather than circular reductions, so they do not further raise the circularity score.

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

The central claim rests on the weighted-sum cost model, the sufficiency of the three-dimensional state, and Q-learning convergence; the cost model is mis-specified (edge and cloud costs summed for every task), the state sufficiency is asserted without proof, and the Bellman equations contain sign errors. No new physical or algorithmic entity is introduced, but several domain assumptions are unstated or unverified.

free parameters (2)
  • Latency/energy weights beta^t, beta^e = not reported
    The weighted cost Cn=beta^t*Tn+beta^e*En requires per-MD weights in [0,1]; the simulation never states these values, yet the reported total cost depends on them.
  • DRL hyperparameters (learning rate, discount factor, exploration schedule, network architecture, replay memory size… = not reported
    Algorithm 2 lists these only symbolically (alpha, gamma, epsilon, D, M); no concrete values are given, so the simulation results are not reproducible and the reported cost depends on unstated choices.
assumptions (5)
  • standard math Q-learning converges to the optimal Q-function with probability one over infinite visits (Sutton and Barto).
    Invoked in Section V-B2 to justify the DRL approach; the paper's Eq. (8) writes min over next actions instead of max, so the cited convergence theorem is misapplied.
  • domain assumption All MDs and the wireless network remain stationary during the offloading period.
    Stated in Section IV-B1; this makes the problem a static snapshot and conflicts with the claimed long-term dynamic DRL adaptation.
  • domain assumption Task data size Dn and CPU cycles Xn are fixed and known via program profilers.
    Stated in Section IV-B1; the DRL state omits them, so the agent does not actually observe these quantities when choosing actions.
  • ad hoc to paper Blockchain access control incurs no latency, energy, or monetary cost in the offloading optimization.
    The abstract mentions smart contract fee, but the body's objective (P1) only includes latency and energy; the blockchain phase and offloading phase are treated as sequential with no overhead modeled.
  • ad hoc to paper State s={tc,ec,bw} is a sufficient statistic for the offloading MDP.
    Defined in Section V-B1 with no justification; task sizes, channel gains, and weights are absent, so the reward is not a deterministic function of the state and action.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning." pith.science (2026). https://pith.science/paper/GZOP3QPL

@misc{pith2026190807466,
  author       = {Pith},
  title        = {Pith review of: Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GZOP3QPL}},
  note         = {Machine review of arXiv:1908.07466}
}
read the original abstract

For current and future Internet of Things (IoT) networks, mobile edge-cloud computation offloading (MECCO) has been regarded as a promising means to support delay-sensitive IoT applications. However, offloading mobile tasks to the cloud is vulnerable to security issues due to malicious mobile devices (MDs). How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem. In this paper, we investigate simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain. First, to improve the offloading security, we propose a trustworthy access control using blockchain, which can protect cloud resources against illegal offloading behaviours. Then, to tackle the computation management of authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions, the allocation of computing resource and radio bandwidth, and smart contract usage. This optimization problem aims to minimize the long-term system costs of latency, energy consumption and smart contract fee among all MDs. To solve the proposed offloading problem, we develop an advanced deep reinforcement learning algorithm using a double-dueling Q-network. Evaluation results from real experiments and numerical simulations demonstrate the significant advantages of our scheme over existing approaches.

Figures

Figures reproduced from arXiv: 1908.07466 by the authors.

Figure 1
Figure 1. The proposed MECCO architecture [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 3
Figure 3. Workflow of access control [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 5
Figure 5. Access control experiment [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
Figures from the paper (5 more)
Figure 7
Figure 7. Figure 7: Illustrations of access control results on smartphones with blockchain: a) Device registration form with Ethereum account, [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]
Figure 6
Figure 6. Figure 6: Offloading access and transactions are recorded and shown on the web interface for [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]
Figure 8
Figure 8. Figure 8: Total offloading cost versus number of MDs and tasks. [PITH_FULL_IMAGE:figures/full_fig_p025_8.png]
Figure 9
Figure 9. Figure 9: Total offloading cost versus resource allocation. [PITH_FULL_IMAGE:figures/full_fig_p025_9.png]
Figure 10
Figure 10. Figure 10: Total costs of offloading schemes. gradually with the increment of the total system bandwidth. The reason behind this observation that bandwidth allocation is always significant in improving the data transmission rate between MDs and edge-cloud server. As a result, th…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning

    eess.SP 2019-08 conditional novelty 4.0 of 10

    A DQN-based offloading policy for mobile blockchain miners reduces simulated latency and energy costs and increases a formula-based privacy score compared with fixed offload/no-offload baselines.

  2. Integration of Blockchain and Cloud of Things: Architecture, Applications and Challenges

    cs.CR 2019-08 conditional novelty 3.0 of 10

    A comprehensive survey of blockchain-Cloud of Things integration that categorizes architectures, applications, platforms, and open challenges.

Reference graph

Works this paper leans on

53 extracted references · 52 canonical work pages · cited by 2 Pith papers

  1. [15]

    Nguyen, Pubudu N

    Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, and Aruna Seneviratne, ”Blockchain for Secure EHRs Sharing of Mobile Cloud based E-health Systems,” IEEE Access, vol. 7, pp. 66792-66806, 2019

    work page 2019

  2. [1]

    Al-Fuqaha et al., ”Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials , vol

    A. Al-Fuqaha et al., ”Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials , vol. 17, no. 4, pp. 2347-2376, 2015

    work page 2015

  3. [2]

    S. M. Riazul Islam, Daehan Kwak, MD. Humaun Kabir, Mahmud Hossain, and Kyung-Sup Kwak, ”The Internet of Things for Health Care: A Comprehensive Survey,” IEEE Access, vol. 3, pp. 678-708, 2015

    work page 2015

  4. [3]

    Zanella et al., ”Internet of Things for Smart Cities,” IEEE IoT Journal , vol

    A. Zanella et al., ”Internet of Things for Smart Cities,” IEEE IoT Journal , vol. 1, no. 1, pp. 22-32, Feb. 2014

    work page 2014

  5. [4]

    Karthik Kumar, Jibang Liu, Yung-Hsiang Lu, and Bharat Bhargava, ”A Survey of Computation Offloading for Mobile Systems” Mob. Netw. Appl., pp. 129-140, 2013

    work page 2013

  6. [5]

    Mach and Z

    P. Mach and Z. Becvar, ”Mobile Edge Computing: A Survey on Architecture and Computation Offloading,” IEEE Communications Surveys & Tutorials , vol. 19, no. 3, pp. 1628-1656, 2017

    work page 2017

  7. [6]

    Y . Mao, C. You et al., ”A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322-2358, 2017

    work page 2017

  8. [7]

    B. P. Rimal et al., ”Mobile-edge computing vs. centralized cloud computing in fiber-wireless access networks,” in IEEE Conference on Computer Communications Workshops , pp. 991-996, 2016

    work page 2016

Show all 53 references
  1. [8]

    Zhou et al.,”A Survey on Mobile task offloading Technologies,” IEEE Access, vol

    H. Zhou et al.,”A Survey on Mobile task offloading Technologies,” IEEE Access, vol. 6, pp. 5101-5111, 2018

    2018

  2. [9]

    et al.,”Mobile cloud computing for computation offloading: Issues and challenges,” Appl

    Akherfi, K. et al.,”Mobile cloud computing for computation offloading: Issues and challenges,” Appl. Comput. Inform. , pp 116, 2018. 29

    2018

  3. [10]

    Zhang, B

    J. Zhang, B. Chen, Y . Zhao, X. Cheng and F. Hu, ”Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues,” IEEE Access, vol. 6, pp. 18209-18237, 2018

    2018

  4. [11]

    Xiao and Y

    Z. Xiao and Y . Xiao, ”Security and Privacy in Cloud Computing,” IEEE Communications Surveys & Tutorials , vol. 15, no. 2, pp. 843-859, 2013

    2013

  5. [12]

    T. M. Fernndez-Carams et al., ”A Review on the Use of Blockchain for the Internet of Things,” IEEE Access, vol. 6, pp. 32979-33001, 2018

    2018

  6. [13]

    S. Huh, S. Cho and S. Kim, ”Managing IoT devices using blockchain platform,” in 19th International Conference on Advanced Communication Technology (ICACT) , 2017, pp. 464-467

    2017

  7. [14]

    Wood, ”Ethereum: A secure decentralised generalised transaction ledger,” Yellow Paper

    G. Wood, ”Ethereum: A secure decentralised generalised transaction ledger,” Yellow Paper. [Online]. Available: https://ethereum.github.io/yellowpaper/paper.pdf

  8. [16]

    H. G. Do and W. K. Ng, ”Blockchain-Based System for Secure Data Storage with Private Keyword Search,” in IEEE World Congress on Services (SERVICES) , Honolulu, HI, 2017, pp. 90-93

    2017

  9. [17]

    Ronghua Xu, Yu Chen, Erik Blasch, and Genshe Chen, ”BlendCAC: A Smart Contract Enabled Decentralized Capability- Based Access Control Mechanism for the IoT,” Computers, 2018

    2018

  10. [18]

    C. H. Liu, Q. Lin and S. Wen, ”Blockchain-enabled Data Collection and Sharing for Industrial IoT with Deep Reinforcement Learning,” IEEE Transactions on Industrial Informatics , 2019

    2019

  11. [19]

    O. J. A. Pinno, A. R. A. Gregio and L. C. E. De Bona, ”ControlChain: Blockchain as a Central Enabler for Access Control Authorizations in the IoT,” in IEEE Global Communications Conference , pp. 1-6, 2017

    2017

  12. [20]

    Satoh, ”Toward Access Control Model for Context-Aware Services Offloaded to Cloud Computing,” in IEEE 35th Symposium on Reliable Distributed Systems Workshops (SRDSW) , pp

    I. Satoh, ”Toward Access Control Model for Context-Aware Services Offloaded to Cloud Computing,” in IEEE 35th Symposium on Reliable Distributed Systems Workshops (SRDSW) , pp. 7-12, 2016

    2016

  13. [21]

    J. Xu, L. Chen, K. Liu and C. Shen, ”Designing Security-Aware Incentives for Computation Offloading via Device-to- Device Communication,” IEEE Transactions on Wireless Communications , vol. 17, no. 9, pp. 6053-6066, Sept. 2018

    2018

  14. [22]

    He et al., ”Energy-Efficient MobileEdge Computation Offloading for Applications with Shared Data,” in IEEE Global Communications Conference (GLOBECOM) , 2018, pp

    X. He et al., ”Energy-Efficient MobileEdge Computation Offloading for Applications with Shared Data,” in IEEE Global Communications Conference (GLOBECOM) , 2018, pp. 1-6

    2018

  15. [23]

    Lyu et al., ”Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds,” IEEE Transactions on Vehicular Technology, vol

    X. Lyu et al., ”Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds,” IEEE Transactions on Vehicular Technology, vol. 66, no. 4, pp. 3435-3447, April 2017

    2017

  16. [24]

    Zheng et al., ”Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach,” IEEE Transactions on Mobile Computing , vol

    J. Zheng et al., ”Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach,” IEEE Transactions on Mobile Computing , vol. 18, no. 4, pp. 771-786, 1 April 2019

    2019

  17. [25]

    J. Du, L. Zhao, J. Feng and X. Chu, ”Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee,” IEEE Trans. on Communications, vol. 66, no. 4, pp. 1594-1608, April 2018

    2018

  18. [26]

    Comsa et al., ”Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management,” IEEE Trans

    I. Comsa et al., ”Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management,” IEEE Trans. on Network and Service Management , vol. 15, no. 4, pp. 1661-1675, 2018

    2018

  19. [27]

    R. S. Sutton, A. G. Barto et al., ”Reinforcement learning: An introduction,” MIT press, 1998

    1998

  20. [28]

    Arulkumaran, M

    K. Arulkumaran, M. P. Deisenroth, M. Brundage and A. A. Bharath, ”Deep Reinforcement Learning: A Brief Survey,” IEEE Signal Processing Magazine , vol. 34, no. 6, pp. 26-38, Nov. 2017

    2017

  21. [29]

    Kavukcuoglu, D

    Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al., ”Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, p. 529, 2015

    2015

  22. [30]

    Chen et al., ”Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning,” IEEE Internet of Things Journal , 2019

    X. Chen et al., ”Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning,” IEEE Internet of Things Journal , 2019. 30

    2019

  23. [31]

    Min et al., ”LearningBased Computation Offloading for IoT Devices With Energy Harvesting,” IEEE Transactions on Vehicular Technology, vol

    M. Min et al., ”LearningBased Computation Offloading for IoT Devices With Energy Harvesting,” IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1930-1941, Feb. 2019

    1930

  24. [32]

    Y . Cui, Y . Liang and R. Wang, ”Resource Allocation Algorithm With Multi-Platform Intelligent Offloading in D2D-Enabled Vehicular Networks,” IEEE Access, vol. 7, pp. 21246-21253, 2019

    2019

  25. [33]

    X. Chen, L. Jiao, W. Li and X. Fu, ”Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing,” IEEE/ACM Transactions on Networking , vol. 24, no. 5, pp. 2795-2808, October 2016

    2016

  26. [34]

    Shibin and G

    D. Shibin and G. J. W. Kathrine, ”A comprehensive overview on secure offloading in mobile cloud computing,” in 4th International Conference on Electronics and Communication Systems (ICECS) , Coimbatore, 2017, pp. 121-124

    2017

  27. [35]

    Han et al., ”Energy Efficient Secure Computation Offloading in NOMA-based mMTC Networks for IoT,” IEEE Internet of Things Journal , 2019

    S. Han et al., ”Energy Efficient Secure Computation Offloading in NOMA-based mMTC Networks for IoT,” IEEE Internet of Things Journal , 2019

    2019

  28. [36]

    Elgendy et al., ”An Efficient and Secured Framework for Mobile Cloud Computing,” IEEE Trans. on Clou. Comput., 2019

    2019

  29. [37]

    Lin et al., ”Local Authentication and Access Control Scheme in M2M Communications With Computation Offloading,” IEEE Internet of Things Journal , vol

    Y . Lin et al., ”Local Authentication and Access Control Scheme in M2M Communications With Computation Offloading,” IEEE Internet of Things Journal , vol. 5, no. 4, pp. 3209-3219, Aug. 2018

    2018

  30. [38]

    I. Satoh, ”Toward Access Control Model for Context-Aware Services Offloaded to Cloud Computing,” in IEEE 35th Symposium on Reliable Distributed Systems Workshops (SRDSW) , Budapest, 2016, pp. 7-12

    2016

  31. [39]

    Tu and Y

    S. Tu and Y . Huang, ”Towards efficient and secure access control system for mobile cloud computing,” China Communications, vol. 12, no. 12, pp. 43-52, December 2015

    2015

  32. [40]

    T. Li et al., ”On Efficient Offloading Control in Cloud Radio Access Network with Mobile Edge Computing,” in IEEE 37th International Conference on Distributed Computing Systems , 2017, pp. 2258-2263

    2017

  33. [41]

    Y . Zhu, H. Hu, G. Ahn, D. Huang and S. Wang, ”Towards temporal access control in cloud computing,” in Proceedings IEEE INFOCOM, Orlando, FL, 2012, pp. 2576-2580

    2012

  34. [42]

    Mora-Gimeno FJ, Mora-Mora H, Marcos-Jorquera D, V olckaert B, ”A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust,” Sensors Journal. 2018

    2018

  35. [43]

    Muthanna Ammar et al., ”Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain,” Journal of Sensor and Actuator Networks , 2019

    2019

  36. [44]

    Liu et al., ”Computation Offloading and Content Caching in Wireless Blockchain Networks With Mobile Edge Computing,” IEEE Trans

    M. Liu et al., ”Computation Offloading and Content Caching in Wireless Blockchain Networks With Mobile Edge Computing,” IEEE Trans. on Vehicular Technology, vol. 67, no. 11, pp. 11008-11021, Nov. 2018

    2018

  37. [45]

    Van Hasselt et al., Deep reinforcement learning with double q-learning,” in Conf

    H. Van Hasselt et al., Deep reinforcement learning with double q-learning,” in Conf. Artificial Intell., pp. 2094-2100, 2016

    2016

  38. [46]

    Wang et al., ”Dueling network architectures for deep reinforcement learning,” arXiv:1511.06581, 2015

    Z. Wang et al., ”Dueling network architectures for deep reinforcement learning,” arXiv:1511.06581, 2015

    2015 arXiv

  39. [47]

    [Online]

    Amazon Web Services (AWS) - Cloud Computing Services. [Online]. Available: https://aws.amazon.com/

  40. [48]

    [Online]

    Solidity 0.5.3 documentation. [Online]. Available: https://solidity.readthedocs.io/en/develop/

  41. [49]

    [Online]

    Ethereum on Android. [Online]. Available: https://github.com/ethereum/go-ethereum/wiki/Ethereum-on-Android

  42. [50]

    [Online]

    Lightweight Java library for Ethereum clients. [Online]. Available: https://github.com/web3j/web3j

  43. [51]

    Martn, A

    A. Martn, A. Ashish et al., ”TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

    2015

  44. [52]

    Nguyen, Pubudu N

    Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, and Aruna Seneviratne, ”Secrecy Performance of the UA V enabled Cognitive Relay Network,” in IEEE 3rd Int. Conf. on Communication and Information Systems , Dec. 2018

    2018

  45. [53]

    Azade Fotouhi, Haoran Qiang, Ming Ding et al., ”Survey on UA V Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges,” IEEE Communications Surveys & Tutorials , 2019

    2019

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.