Pith. sign in

REVIEW 3 major objections 4 minor 33 references

Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A single closed-form extraction rate minimizes expected latency in Raft-blockchain edge computing for connected vehicles.

desk verdict The claimed closed-form optimal extraction rate is new, but the model behind it is flawed and the simulation contradicts it. read the letter →

arxiv 2506.14208 v1 pith:7DYCCLBP submitted 2025-06-17 cs.NI

Cui Zhang , Maoxin Ji , Qiong Wu , Pingyi Fan , Qiang Fan This is my paper · ORCID
classification cs.NI
keywords BlockchainEdgeComputingInternetofVehicleslatencyoptimizationRaftconsensusdataextractionrateconvexsystemdelay
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 tries to show that securing vehicle edge computing with a Raft-consensus blockchain does not necessarily force a large latency penalty, because the expected end-to-end delay is a convex function of the rate at which vehicles extract data. The paper derives a closed-form optimal extraction rate $\lambda^*$ that depends only on the number of vehicles served per base station and the collision timeout, not on the number of servers or how many are under attack. If the derivation is correct, a vehicle can set its extraction rate to $\lambda^*$ and obtain the minimum expected system delay, and the same tuning rule can be updated online when the network configuration changes. This matters because blockchain security and ultra-low latency are usually in tension, and a simple rate rule would let both coexist.

What carries the argument

The load-bearing object is the exponential collision term $e^{B\lambda}$ inside the delay expression. It comes from the collision probability $p_c = 1 - e^{-\lambda M(M-1)\tau_c/2}$, where $\tau_c$ is the packet collision timeout and $B = M(M-1)\tau_c/2$; this makes the expected number of retransmissions grow exponentially in $\lambda$. Differentiating the convex delay function with respect to $\lambda$ converts the minimization into solving the quadratic $BT_{ec}\lambda^2 + B\lambda - 1 = 0$, whose positive root is $\lambda^*$.

What would settle it

A discrete-event simulation of $M$ independent Poisson transmitters, each extracting at rate $\lambda$ and retransmitting after a collision, can measure the collision probability directly and compare it with $1 - e^{-\lambda M(M-1)\tau_c/2$; the same simulation can find the empirical minimizer of expected delay and compare it with $\lambda^*$ from Eq. (24) under the paper's Table 2 settings. If the measured collision curve or the measured optimum deviates from the formula, the claimed closed-form optimization is not confirmed.

Watch

Extended reading notes

Core claim

The paper's central claim is that the expected system delay in a Raft-blockchain edge computing network for the Internet of Vehicles is minimized by a closed-form vehicle data extraction rate. With $B = M(M-1)\tau_c/2$, the delay takes the convex form $E[T] = \frac{A}{1-p_d}\left[\left(T_{ec}+\frac{1}{\lambda}\right)e^{B\lambda} + T_{si}\right]$, and its unique positive minimizer is $\lambda^* = \frac{-B+\sqrt{B^2+4BT_{ec}}}{2BT_{ec}}$. The paper argues that this optimum is independent of the number of servers $N$ and of attack intensity $a$, which affect only the multiplicative prefactor $A$, so a vehicle can simply measure $M$ and set its extraction rate to $\lambda^*$.

Load-bearing premise

The result rests on the assumed formula for how often vehicle transmissions collide, which treats each pair of vehicles' sending times as independent; if that collision formula is not exact, the optimal extraction rate changes.

Editorial extensions

If this is right

  • Setting each vehicle's data extraction rate to $\lambda^*$ yields the minimum expected system delay for the given number of vehicles per station and collision timeout.
  • The optimal rate depends on $M$ but not on the number of servers $N$ or the attack intensity $a$, so the same per-station tuning rule remains optimal even as servers are attacked or added.
  • Because the delay function is convex, the optimum can be re-computed and applied online whenever the network configuration $M$ changes.
  • Latency rises steeply on both sides of $\lambda^*$, since small rates inflate the extraction wait $1/\lambda$ while large rates inflate collision-induced retransmissions.
  • The closed-form optimum gives network designers a direct latency floor for a given vehicle density, which can be compared against ultra-low-latency budget requirements.

Reading between the lines

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

  • Going beyond the paper, the same convex-delay structure would generalize to other consensus mechanisms that impose a fixed per-epoch election overhead; only the expression for the expected election delay would change, not the quadratic form of the optimization.
  • A practical extension would make the tuning rule local: a base station broadcasts $M$ and $\tau_c$, and each vehicle computes $\lambda^*$ itself, avoiding the need for a centralized optimizer.
  • One could test whether adapting $\lambda$ dynamically as $M$ changes during a drive-by scenario outperforms keeping a fixed rate, using the paper's delay formula as the performance baseline.
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

3 major / 4 minor

Summary. The paper proposes a security framework that integrates the Raft consensus mechanism with edge computing for Internet of Vehicles (IoV), models the expected system latency E[T] as a function of the vehicle data extraction rate λ, and derives a closed-form optimal rate λ* given by Eq. (24) via convex optimization. The paper claims that setting λ to λ* minimizes system latency, and presents simulations that it says confirm the theoretical optimum.

Significance. If the derivation were correct, the paper would offer a simple, closed-form rule for choosing a data extraction rate that minimizes latency in a blockchain-protected IoV edge computing system, which could be a useful engineering guideline for 5G and future networks. The paper has a clear system model and the algebraic minimization from the delay expression to Eq. (24) is internally consistent. However, the core collision probability model is not justified, and the reported simulation optima do not match the derived formula, so the central contribution is not currently established.

major comments (3)
  1. [§5.2, Eqs. (11)-(12)] The collision probability derivation is incorrect and load-bearing. The event in Eq. (11) is defined over ordered pairs with Pr((Tm1 - Tm2) > τc). For two independent Exp(λ) inter-arrival times, this probability is 0.5 e^{-λ τc}, not e^{-λ τc}; multiplying over all ordered pairs would give M(M-1) factors, not the M(M-1)/2 that appears in Eq. (12). Moreover, the no-collision event for M vehicles is not the product of independent pairwise events: in a superposition of M Poisson streams of rate λ, collisions are governed by the total rate Mλ, and the probability that no two events fall within τc does not factor as a product of pairwise tail probabilities. Since B = M(M-1)τc/2 enters directly into the optimum λ* in Eq. (24), this error invalidates the main theoretical result.
  2. [§6, Fig. 3] The simulation results contradict Eq. (24). Using the Table 2 parameters (Tec = 10, τc = 0.1), Eq. (24) gives λ* ≈ 0.95 for M = 2, λ* ≈ 0.53 for M = 3, and λ* ≈ 0.36 for M = 4. The paper reports optimal points of 0.31, 0.23, and 0.18 for M = 2, 3, 4, respectively. This is a substantial discrepancy, and it means the statement that the curves 'align with our theoretical derivations' is not supported. The simulation does not validate the proposed formula.
  3. [§3.2, Eqs. (3)-(4)] The non-homogeneous Poisson process formulas are incorrect. The first-arrival time CDF should be P(T1 ≤ t1) = 1 − exp(−∫_0^{t1} Λ(x) dx), not 1 − exp(−∫_0^{t1} e^{−Λ(x)} dx). Similarly, Eq. (4) should condition on T1 = t1 and use ∫_{t1}^{t2} Λ(x) dx inside the exponential. These equations are not used later in the latency derivation, but they are presented as part of the system model and are mathematically wrong.
minor comments (4)
  1. [§5.1, Eq. (7)] The expression for Pp appears to contain a repeated term 'Q(η, ρη) − Q(η, ρη)' and the definition is unclear; the Bessel function J0 is introduced but does not appear in the expression.
  2. [§5.3, Algorithm 2] The 'real-time data extra algorithm' is essentially a single assignment (λ ← λ*) and does not describe how λ* is recomputed when N or M changes beyond a jump; the pseudocode is too trivial to be informative.
  3. [Footnote 1] The GitHub link provided in the introduction points to a repository named 'BVIB-for-Data-Extraction-Based-on-Mutual-Information-in-the-IoV', which appears unrelated to the present paper's topic of Raft-blockchain edge computing; this should be corrected or the link should be verified.
  4. [Throughout] There are several typographical and presentation issues, including 'V ANET' in Section 2, 'transmition' in Fig. 2, and the text in Section 5.3 referring to '−AE^{−Bλ}/((1−pd)λ^2)' as 'always non-zero' when the correct nonzero factor is A e^{Bλ}/((1−pd)λ^2); these should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: lambda* is the analytic minimizer of the paper's own closed-form delay expression, not a fitted parameter or a definitionally recycled quantity.

full rationale

The paper's derivation chain is self-contained: E[Tex]=1/lambda (Eq. 9) follows from the stated Poisson/exponential arrival model; the collision probability pc (Eq. 12) is a modeling assumption derived from pairwise separation of transmission times; E[T] is assembled from the named delay components in Eqs. (15)-(20); and lambda* (Eq. 24) is obtained as the closed-form solution of E'[T]=0 for the paper's own convex objective (Eqs. 21-22). No parameter is fitted to data and then renamed a prediction: lambda* is not calibrated from Fig. 3 or any other measurement. The only self-citation is the BVIB framing in Section 4 (reference [27]), and it is not load-bearing: the Raft mechanism is fully described within the paper, and the Section 5 algebra does not import any result from [27]. The self-referential structure in Eqs. (17)-(20), where E[Tele] depends on E[T], is an explicit fixed-point equation that is algebraically solved for E[T]; it is a modeling closure, not a hidden circular reduction. The reader-flagged problems -- the questionable derivation of Eq. (12) (the pairwise tail product and the M(M-1)/2 factor), the sign/consistency slips in Eqs. (3) and (22), and the mismatch between Fig. 3's reported optima (0.31, 0.23, 0.18) and Eq. (24) evaluated at Table 2 parameters (approximately 0.95, 0.53, 0.36) -- are concerns about mathematical correctness and about whether the simulation independently confirms the model. A failed external check is the opposite of circular self-confirmation; under the stated rules, mathematical errors and non-standard assumptions are correctness risks, not circularity. Because no claimed 'prediction' reduces by construction to its own inputs, and the self-citation is not load-bearing, the circularity score is 0.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The paper's central claim (lambda* minimizes E[T]) rests on a small set of modeling assumptions. Only two parameters, tau_c and Tec, affect the optimal rate itself; the other listed parameters affect the delay magnitude but not the optimizer. The most fragile premise is the collision probability formula, which is not rigorously derived. The paper introduces no new physical entities.

free parameters (8)
  • tau_c = 0.1 (simulation)
    Collision timeout, set to 3 time slots in the model and 0.1 in Table 2. Determines B and therefore lambda*.
  • Tec = 10
    Vehicle encoding delay, treated as a constant in Eq. (10) and set to 10 in simulation. Directly affects lambda*.
  • pd = 0.02
    Channel drop probability from Eq. (8), fixed as 0.02 in simulation. Affects E[T] but not lambda*.
  • tau_ele = 150
    Election duration in simulation, affects E[T] through Eq. (18) but not the optimizer.
  • Tterm = 2000
    Leader term duration in simulation, affects E[T] through Eq. (20) but not lambda*.
  • N = 10
    Number of base stations, set to 10 in simulation, affects E[T] via election overhead but not lambda*.
  • a = 2
    Number of attacked nodes, set to 2 in simulation, affects E[T] but not lambda*.
  • Tsi = 14 (Tdc+Tf+Tp)
    Sum of decoding, forwarding, and broadcast delays. Affects E[T] but not the optimizer.
assumptions (5)
  • domain assumption Vehicle data extraction is a homogeneous Poisson process with rate lambda.
    Section 3.2 uses a Poisson process to model data arrivals; this is an unvalidated modeling choice.
  • domain assumption The extraction delay is the inter-arrival time of the Poisson process, giving E[Tex] = 1/lambda.
    Section 5.2, Eq. (9). This links the decision variable lambda to the delay expression.
  • ad hoc to paper Collisions among M vehicles are independent events with probability pc = 1 - exp(-lambda M(M-1)tau_c/2).
    Section 5.2, Eqs. (11)-(12). Assumes independence of pairwise separation events and uses an unstated tail probability; not justified.
  • ad hoc to paper Raft election overhead per block is E[T]/Tterm * tau_ele * (1 + a/N).
    Section 5.2, Eqs. (17)-(18). Models expected elections per term and a linear attack factor; no validation.
  • domain assumption The channel drop probability pd from Eq. (8) is a constant.
    pd is imported from [32] and fixed in simulation; the channel state model is not re-derived.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles." pith.science (2026). https://pith.science/paper/7DYCCLBP

@misc{pith2026250614208,
  author       = {Pith},
  title        = {Pith review of: Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7DYCCLBP}},
  note         = {Machine review of arXiv:2506.14208}
}
read the original abstract

As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or even tampering, thereby creating serious security vulnerabilities. Blockchain technology can maintain a shared ledger among servers. In the Raft consensus mechanism, as long as more than half of the nodes remain operational, the system will not collapse, effectively maintaining the system's robustness and security. To protect vehicle information, we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing. To address the additional latency introduced by blockchain, we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency, ensuring that the system meets the requirements for low latency and high reliability. Simulation results demonstrate that the optimized data extraction rate significantly reduces system delay, with relatively stable variations in latency. Moreover, the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments, such as 5G and next-generation smart city systems.

Figures

Figures reproduced from arXiv: 2506.14208 by the authors.

Figure 1
Figure 1. Environment. secure information transmission within the network. However, they do not address the time delay from the perspective of information transmission. This paper further investigates the relationship between vehicle data extraction rates and time delays, and optimizes the total system delay through convex optimization methods. 3 System Model In this section, we construct a network architecture that integrate… view at source ↗
Figure 2
Figure 2. Secure Framework Based on the Raft Consensus Mechanism. In our framework, all edge-deployed servers (e.g., base stations and RSUs) function as nodes, while vehicles act as clients that send requests. When a vehicle performs edge computing, the computational task is uploaded as a request to the leader. The leader maps this request to a log entry and replicates it to all follower nodes. Upon receiving the log, each fo… view at source ↗
Figure 3
Figure 3. latency with λ. simulated by disabling servers, where an intensity of 1 represents disabling one server, and an intensity of 2 represents disabling two servers. The figure shows that as the attack intensity increases, the system latency also increases continuously. Before the attack intensity reaches 5, the increase in latency is not significant. However, after this point, the latency sharply rises, exhibiting a tre… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Latency under attack. 7 Conclusion This paper proposes an edge computing architecture that integrates blockchain technology and designs an online optimization algorithm based on the relationship between vehicle data extraction rate and system latency, achieving the opt…
Figure 5
Figure 5. Figure 5: Comparison of average delay between Random Algorithm and Optimal Algorithm with different number of vehicles. Acknowledgement: We are grateful for the encouragement and support from our families and friends. Funding Statement: This work was supported in part by the Nat…
Figure 6
Figure 6. Figure 6: Latency with M and N. edge caching based on elastic federated and multi-agent deep reinforcement learning in next-generation networks. IEEE Transactions on Network and Service Management, 21(4):4179–4196, 2024. 5. Maoxin Ji, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, J…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

  1. [1]

    SDN/NFV-empowered future IoV with enhanced communication, computing, and caching

    Weihua Zhuang, Qiang Ye, Feng Lyu, Nan Cheng, and Ju Ren. SDN/NFV-empowered future IoV with enhanced communication, computing, and caching. Proceedings of the IEEE, 108(2):274–291, 2019

    work page 2019

  2. [2]

    Value matters: A novel value of information-based resource scheduling method for CA Vs.IEEE Transactions on Vehicular Technology, 2024

    Wei Wang, Nan Cheng, Mushu Li, Tingting Yang, Conghao Zhou, Changle Li, and Fangjiong Chen. Value matters: A novel value of information-based resource scheduling method for CA Vs.IEEE Transactions on Vehicular Technology, 2024

    work page 2024

  3. [3]

    Edgecooper: Network-aware cooperative lidar perception for enhanced vehicular awareness

    Guiyang Luo, Chongzhang Shao, Nan Cheng, Haibo Zhou, Hui Zhang, Quan Yuan, and Jinglin Li. Edgecooper: Network-aware cooperative lidar perception for enhanced vehicular awareness. IEEE Journal on Selected Areas in Communications, 2023

    work page 2023

  4. [4]

    Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, and Khaled B. Letaief. Cooperative 160 Not specified, 2024, vol., no. Figure 6: Latency with M and N. edge caching based on elastic federated and multi-agent deep reinforcement learning in next-generation networks. IEEE Transactions on Network and Service Management, 21(4):4179–4196, 2024

    work page 2024

  5. [5]

    Maoxin Ji, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, and Khaled B. Letaief. Graph neural networks and deep reinforcement learning based resource allocation for v2x communications. IEEE Internet of Things Journal, pages 1–1, 2024

    work page 2024

  6. [6]

    Kangwei Qi, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, and Khaled B. Letaief. Deep-reinforcement-learning-based aoi-aware resource allocation for ris-aided iov networks. IEEE Transactions on Vehicular Technology, pages 1–14, 2024

    work page 2024

  7. [7]

    Accessibility analysis and modeling for IoV in an urban scene

    Jiujun Cheng, Guiyuan Yuan, Mengchu Zhou, Shangce Gao, Cong Liu, Hua Duan, and QingTian Zeng. Accessibility analysis and modeling for IoV in an urban scene. IEEE Transactions on Vehicular Technology, 69(4):4246–4256, 2020

    work page 2020

  8. [8]

    Cui Zhang, Wenjun Zhang, Qiong Wu, Pingyi Fan, Qiang Fan, Jiangzhou Wang, and Khaled B. Letaief. Distributed deep reinforcement learning based gradient quantization for federated learning enabled vehicle edge computing. IEEE Internet of Things Journal, pages 1–1, 2024

    work page 2024

Show all 33 references
  1. [9]

    Space/aerial- assisted computing offloading for IoT applications: A learning-based approach.IEEE Journal on Selected Areas in Communications, 37(5):1117–1129, 2019

    Nan Cheng, Feng Lyu, Wei Quan, Conghao Zhou, Hongli He, Weisen Shi, and Xuemin Shen. Space/aerial- assisted computing offloading for IoT applications: A learning-based approach.IEEE Journal on Selected Areas in Communications, 37(5):1117–1129, 2019

    2019

  2. [10]

    A blockchain- based authentication scheme and secure architecture for IoT-enabled maritime transportation systems

    Peiying Zhang, Yaqi Wang, Gagangeet Singh Aujla, Anish Jindal, and Yasser D Al-Otaibi. A blockchain- based authentication scheme and secure architecture for IoT-enabled maritime transportation systems. IEEE Transactions on Intelligent Transportation Systems, 24(2):2322–2331, 2022

    2022

  3. [11]

    Characterizing user association patterns for optimizing small-cell edge system performance

    Fan Wu, Feng Lyu, Huaqing Wu, Ju Ren, Yaoxue Zhang, and Xuemin Shen. Characterizing user association patterns for optimizing small-cell edge system performance. IEEE Network, 37(3):210–217, 2022

    2022

  4. [12]

    End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

    Min Wang, Ziyi Gao, Haoyuan Wang, Sibo Qiao, and Jinhai Wang. End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network. Simulation Modelling Not specified, 2024, vol., no. 17 Practice and Theory, 138:103031, 2025

    2024

  5. [13]

    In search of an understandable consensus algorithm

    Diego Ongaro and John Ousterhout. In search of an understandable consensus algorithm. In 2014 USENIX Annual Technical Conference (USENIX ATC 14), pages 305–319, Philadelphia, PA, June 2014. USENIX Association

    2014

  6. [14]

    Heterogeneous system list scheduling algorithm based on improved optimistic cost matrix

    Min Wang, Haoyuan Wang, Sibo Qiao, Jiawang Chen, Qin Xie, and Cuijuan Guo. Heterogeneous system list scheduling algorithm based on improved optimistic cost matrix. Future Generation Computer Systems, 164:107576, 2025

    2025

  7. [15]

    Secure data sharing: Blockchain-enabled data access control framework for IoT

    Xin Wei, Yong Yan, Shaoyong Guo, Xuesong Qiu, and Feng Qi. Secure data sharing: Blockchain-enabled data access control framework for IoT. IEEE Internet of Things Journal, 9(11):8143–8153, 2021

    2021

  8. [16]

    Secure computation offloading in blockchain based IoT networks with deep reinforcement learning

    Dinh C Nguyen, Pubudu N Pathirana, Ming Ding, and Aruna Seneviratne. Secure computation offloading in blockchain based IoT networks with deep reinforcement learning. IEEE Transactions on Network Science and Engineering, 8(4):3192–3208, 2021

    2021

  9. [17]

    UA V-assisted computation offloading toward energy-efficient blockchain operations in internet of things

    Xunqiang Lan, Xiao Tang, Ruonan Zhang, Wensheng Lin, and Zhu Han. UA V-assisted computation offloading toward energy-efficient blockchain operations in internet of things. IEEE Wireless Communi- cations Letters, 12(8):1469–1473, 2023

    2023

  10. [18]

    Vikas Hassija, Vinay Chamola, Guangjie Han, Joel J. P. C. Rodrigues, and Mohsen Guizani. Dagiov: A framework for vehicle to vehicle communication using directed acyclic graph and game theory. IEEE Transactions on Vehicular Technology, 69(4):4182–4191, 2020

    2020

  11. [19]

    Crt-biov: A cognitive radio technique for blockchain-enabled internet of vehicles

    Geetanjali Rathee, Farhan Ahmad, Fatih Kurugollu, Muhammad Ajmal Azad, Razi Iqbal, and Muhammad Imran. Crt-biov: A cognitive radio technique for blockchain-enabled internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(7):4005–4015, 2021

    2021

  12. [20]

    Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles

    Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, and Yan Zhang. Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Transactions on Vehicular Technology, 69(4):4298–4311, 2020

    2020

  13. [21]

    A blockchain-based efficient, secure and anonymous conditional privacy-preserving and authentication scheme for the internet of vehicles

    Kashif Naseer Qureshi, Luqman Shahzad, Abdelzahir Abdelmaboud, Taiseer Abdalla Elfadil Eisa, Bandar Alamri, Ibrahim Tariq Javed, Arafat Al-dhaqm, and Noël Crespi. A blockchain-based efficient, secure and anonymous conditional privacy-preserving and authentication scheme for th...

    2022

  14. [22]

    An iov-pbft consensus-based blockchain for collaborative congestion avoidance and simulation test

    Cheng Li, Hao Wu, Lei Xiong, and Yunlong Lu. An iov-pbft consensus-based blockchain for collaborative congestion avoidance and simulation test. Wireless Communications and Mobile Computing, 2022

    2022

  15. [23]

    Blockchain unleashed: Empowering information- centric network computing in cloud, fog, and edge services

    Pranav Zambre, Mitali Panchal, and Ankit Chouhan. Blockchain unleashed: Empowering information- centric network computing in cloud, fog, and edge services. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pages 1–6, 2023

    2023

  16. [24]

    A security architecture of vanet based on blockchain and mobile edge computing

    XiaoDong Zhang, Ru Li, and Bo Cui. A security architecture of vanet based on blockchain and mobile edge computing. In 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN), pages 258–259, 2018

    2018

  17. [25]

    Model checking the efficiency of blockchain-based edge computing network

    Kai Zheng, Xiang Yao, Zhe Zhang, Liyou Fang, and Xin Huang. Model checking the efficiency of blockchain-based edge computing network. In 2021 20th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), pages 168–171, 2021

    2021

  18. [26]

    Resource allocation of iot systems integrated with blockchain and mobile edge computing

    Zihan Bai, Jianxiong Wan, Leixiao Li, Chuyi Liu, and Mingda Duan. Resource allocation of iot systems integrated with blockchain and mobile edge computing. In 2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC), pages 377–382, 2022

    2022

  19. [27]

    Blockchain-enabled variational information bottleneck for iot networks

    Qiong Wu, Le Kuai, Pingyi Fan, Qiang Fan, Junhui Zhao, and Jiangzhou Wang. Blockchain-enabled variational information bottleneck for iot networks. IEEE Networking Letters, 6(2):92–96, 2024

    2024

  20. [28]

    Zhongbang Yao, Junfeng Jiang, Pingyi Fan, Zhigang Cao, and V .O.K. Li. A neighbor-table-based multipath routing in ad hoc networks. In The 57th IEEE Semiannual Vehicular Technology Conference,

  21. [29]

    Reconfigurable holographic surfaces for ultra-massive MIMO in 6G: Practical design, optimization and implementation

    Ruoqi Deng, Yutong Zhang, Haobo Zhang, Boya Di, Hongliang Zhang, H Vincent Poor, and Lingyang Song. Reconfigurable holographic surfaces for ultra-massive MIMO in 6G: Practical design, optimization and implementation. IEEE Journal on Selected Areas in Communications, 41(8):2367...

    2023

  22. [30]

    Hybrid near-far field channel estimation for holographic MIMO communications

    Shaohua Yue, Shuhao Zeng, Liang Liu, Yonina C Eldar, and Boya Di. Hybrid near-far field channel estimation for holographic MIMO communications. IEEE Transactions on Wireless Communications, 2024

    2024

  23. [31]

    Improving multipath TCP performance over wifi and cellular networks: An analytical approach

    Shiva Raj Pokhrel and Michel Mandjes. Improving multipath TCP performance over wifi and cellular networks: An analytical approach. IEEE Transactions on Mobile Computing, 18(11):2562–2576, 2019

    2019

  24. [32]

    Federated learning with blockchain for autonomous vehicles: Analysis and design challenges

    Shiva Raj Pokhrel and Jinho Choi. Federated learning with blockchain for autonomous vehicles: Analysis and design challenges. IEEE Transactions on Communications, 68(8):4734–4746, 2020

    2020

  25. [2003]

    180 Not specified, 2024, vol., no

    VTC 2003-Spring., volume 3, pages 1739–1743 vol.3, 2003. 180 Not specified, 2024, vol., no

    2003

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.