A Truthful Auction for Graph Job Allocation in Vehicular Cloud-Assisted Networks

Gao, Zhibin; Liwang, Minghui; Hosseinalipour, Seyyedali; Dai, Huaiyu; Wang, Xianbin

doi:10.1109/tmc.2021.3059803

Cited by 23 publications

(12 citation statements)

References 44 publications

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“…Specifically, the proposed Algorithm 1 and Algorithm 2 are abbreviated as "Opt" and "Subopt" for notational simplicity. Moreover, to better evaluate performance gains achieved by the proposed algorithms, while considering the characteristics of problem given in (10), four heuristic methods are considered as baselines [50]:…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

Cheng

Liwang

Xia

et al. 2022

IEEE Trans. Veh. Technol.

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Federated learning (FL) represents a promising distributed machine learning paradigm that allows smart devices to collaboratively train a shared model via providing local data sets. However, problems considering multiple co-existing FL services and different types of service providers are rarely studied. In this paper, we investigate a multiple FL service trading problem in Unmanned Aerial Vehicle (UAV)-aided networks, where FL service demanders (FLSDs) aim to purchase various data sets from feasible clients (smart devices, e.g., smartphones, smart vehicles), and model aggregation services from UAVs, to fulfill their requirements. An auction-based trading market is established to facilitate the trading among three parties, i.e., FLSDs acting as buyers, distributed located client groups acting as data-sellers, and UAVs acting as UAV-sellers. The proposed auction is formalized as a 0-1 integer programming problem, aiming to maximize the overall buyers' revenue via investigating winner determination and payment rule design. Specifically, since two seller types (data-sellers and UAV-sellers) are considered, an interesting idea integrating seller pair and joint bid is introduced, which turns diverse sellers into virtual seller pairs. Vickrey-Clarke-Groves (VCG)-based, and one-sided matchingbased mechanisms are proposed, respectively, where the former achieves the optimal solutions, which, however, is computationally intractable. While the latter can obtain suboptimal solutions that approach to the optimal ones, with low computational complexity, especially upon considering a large number of participants. Significant properties such as truthfulness and individual rationality are comprehensively analyzed for both mechanisms. Extensive experimental results verify the properties and demonstrate that our proposed mechanisms outperform representative methods significantly.

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Section: Simulation and Performance Evaluationmentioning

confidence: 99%

Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

Cheng

Liwang

Xia

et al. 2022

IEEE Trans. Veh. Technol.

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“…Ng et al [24] present a double auction to match vehicles' required computation resources with edge servers under coded distributed computing paradigms. Gao et al [25] propose a truthful auction mechanism to offload graph jobs efficiently under the vehicular cloud computing paradigm. The preceding works, however, are inapplicable to UAV networks with 3D mobility and complex aerial-ground dynamics.…”

Section: A Computation Offloading Mechanisms For Uavsmentioning

confidence: 99%

“…On-chain exchange (lines [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. For each task l,n , l ∈ G i , the winner i first computes its encrypted processing result with a symmetric key k l i , i.e., Enc k l i (result l i ).…”

Section: Seal: Our Detailed Construction a Framework Overviewmentioning

confidence: 99%

Towards Online Privacy-preserving Computation Offloading in Mobile Edge Computing

Pang

Wang

et al. 2022

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications

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Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)' computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework.Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs' task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving offchain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles' bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV's energy costs and expenses with low overheads.

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“…Resource trading mechanism: Existing works devoted to resource trading roughly fall into three categories: i) spot trading (also known as onsite trading), where players reach a trading agreement relying on current conditions (e.g., the current resource demand and supply, channel quality, etc. ), such as online game [13], [14], auction [15]- [17], and bilateral negotiation [18], [19]; ii) futures trading, where players sign a forward contract over buying or selling a certain amount of resources at a predetermined price in advance, that will be fulfilled during each trading in the future, where existing studies mainly investigate electricity market [20]- [22], spectrum resource trading [23], [24], and edge computing-assisted networks [5], [25]; and iii) resource trading in hybrid market where both futures and spot trading are allowed [26], [27]. In [13],…”

Section: B Related Workmentioning

confidence: 99%

“…In [16], Liwang et al studied a Vickrey-Clarke-Grovesbased reverse auction mechanism of vehicle-to-vehicle resource trading and suggested a unilateral matching-based mechanism. In [17], Gao et al developed a truthful auction under computing resource trading market via considering graph tasks, while providing both the optimal and an efficient sub-optimal algorithms. Shojaiemehr et al in [18] proposed a novel negotiation strategy to enhance the satisfaction of both trading parties while supporting negotiation of composite cloud service.…”

Section: Messous Et Al Investigated the Computation Offloading Proble...mentioning

confidence: 99%

Unifying Futures and Spot Market: Overbooking-Enabled Resource Trading in Mobile Edge Networks

Liwang¹,

Chen²,

Wang³

et al. 2021

Preprint

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Securing necessary resources for edge computing processes via effective resource trading becomes a critical technique in supporting computation-intensive mobile applications. Conventional onsite spot trading could facilitate this paradigm with proper incentives, which, however, incurs excessive decision-making latency/energy consumption, and further leads to underutilization of dynamic resources. Motivated by this, a hybrid market unifying futures and spot is proposed to facilitate resource trading among an edge server (seller) and multiple smart devices (buyers) by encouraging some buyers to sign a forward contract with seller in advance, while leaving the remaining buyers to compete for available resources with spot trading. Specifically, overbooking is adopted to achieve substantial utilization and profit advantages owing to dynamic resource demands. By integrating overbooking into futures market, mutually beneficial and risk-tolerable forward contracts with appropriate overbooking rate can be achieved relying on analyzing historical statistics associated with future resource demand and communication quality, which are determined by an alternative optimization-based negotiation scheme. Besides, spot trading problem is studied via considering uniform/differential pricing rules, for which two bilateral negotiation schemes are proposed by addressing both non-convex optimization and knapsack problems. Experiential results demonstrate that the proposed mechanism achieves mutually beneficial player's utilities, while outperforming baseline methods on critical indicators, e.g., decision-making latency, resource usage, etc.

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A Truthful Auction for Graph Job Allocation in Vehicular Cloud-Assisted Networks

Cited by 23 publications

References 44 publications

Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

Towards Online Privacy-preserving Computation Offloading in Mobile Edge Computing

Unifying Futures and Spot Market: Overbooking-Enabled Resource Trading in Mobile Edge Networks

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