The Experts below are selected from a list of 25569 Experts worldwide ranked by ideXlab platform
Chang Hu - One of the best experts on this subject based on the ideXlab platform.
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Truthful Incentive Mechanism for Nondeterministic Crowdsensing with Vehicles
IEEE Transactions on Mobile Computing, 2018Co-Authors: Mingjun Xiao, Liusheng Huang, Jie Wu, Chang HuAbstract:In this paper, we focus on the Incentive Mechanism design for a vehicle-based, nondeterministic crowdsensing system. In this crowdsensing system, vehicles move along their trajectories and perform corresponding sensing tasks with different probabilities. Each task may be performed by multiple vehicles jointly so as to ensure a high probability of success. Designing an Incentive Mechanism for such a crowdsensing system is challenging since it contains a non-trivial set cover problem. To solve this problem, we propose a truthful, reverse-auction-based Incentive Mechanism that includes an approximation algorithm to select winning bids with a nearly minimum social cost and a payment algorithm to determine payments for all participants. Moreover, we extend the problem to a more complex case in which the Quality of sensing Data (QoD) of each vehicle is taken into consideration. For this problem, we propose a QoD-aware Incentive Mechanism, which consists of a QoD-aware winning-bid selection algorithm and a QoD-aware payment determination algorithm. We prove that the proposed Incentive Mechanisms have truthfulness, individual rationality, and computational efficiency. Moreover, we analyze the approximation ratios of the winning-bid selection algorithms. The simulations, based on a real vehicle trace, also demonstrate the significant performances of our Incentive Mechanisms.
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Truthful Incentive Mechanism for vehicle-based nondeterministic crowdsensing
2016 IEEE ACM 24th International Symposium on Quality of Service (IWQoS), 2016Co-Authors: Chang Hu, Mingjun Xiao, Liusheng HuangAbstract:Nowadays, vehicles have shown great potential in crowdsensing. To guarantee a good Quality of Service (QoS), stimulating enough vehicles to participate in crowdsensing is very necessary. In this paper, we focus on the Incentive Mechanism design in the vehicle-based nondeterministic crowdsensing. Different from existing works, we take into consideration that each vehicle performs sensing tasks along some trajectories with different probabilities, and each task must be successfully performed with a joint probability no less than a threshold. Designing an Incentive Mechanism for such a nondeterministic crowdsensing system is challenging, which contains a non-trivial set cover problem with non-linear constraints. To solve the problem, we propose a truthful Incentive Mechanism based on reverse auction, including an approximation algorithm to select winning bids with a nearly minimum social cost, and a payment algorithm to determine the payments for all participants. Through theoretical analysis, we prove that our Incentive Mechanism is truthful and individual rational, and we give an approximation ratio of the winning bid selection algorithm. In addition, we conduct extensive simulations, based on a real vehicle trace, to validate the performances of the proposed Incentive Mechanism.
Limin Sun - One of the best experts on this subject based on the ideXlab platform.
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A Blockchain Based Truthful Incentive Mechanism for Distributed P2P Applications
IEEE Access, 2018Co-Authors: Yunhua He, Xiuzhen Cheng, Yan Liu, Chao Yang, Hong Li, Limin SunAbstract:In distributed peer-to-peer (P2P) applications, peers self-organize and cooperate to effectively complete certain tasks such as forwarding files, delivering messages, or uploading data. Nevertheless, users are selfish in nature and they may refuse to cooperate due to their concerns on energy and bandwidth consumption. Thus each user should receive a satisfying reward to compensate its resource consumption for cooperation. However, suitable Incentive Mechanisms that can meet the diverse requirements of users in dynamic and distributed P2P environments are still missing. On the other hand, we observe that Blcokchain is a decentralized secure digital ledger of economic transactions that can be programmed to record not just financial transactions and Blockchain-based cryptocurrencies get more and more market capitalization. Therefore in this paper, we propose a Blockchain based truthful Incentive Mechanism for distributed P2P applications that applies a cryptocurrency such as Bitcoin to incentivize users for cooperation. In this Mechanism, users who help with a successful delivery get rewarded. As users and miners in the Blockchain P2P system may exhibit selfish actions or collude with each other, we propose a secure validation method and a pricing strategy, and integrate them into our Incentive Mechanism. Through a game theoretical analysis and evaluation study, we demonstrate the effectiveness and security strength of our proposed Incentive Mechanism.
Jiming Chen - One of the best experts on this subject based on the ideXlab platform.
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REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing
IEEE Transactions on Information Forensics and Security, 2018Co-Authors: Zhikun Zhang, Shibo He, Jiming Chen, Junshan ZhangAbstract:Incentive Mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies assume a trustworthy fusion center (FC) in their co-design of Incentive Mechanism and privacy preservation. Very recent work has taken the step to relax the assumption on trustworthy FC and allowed participatory users (PUs) to randomly report their binary sensing data, whereas the focus is to examine PUs' equilibrium behavior. Making a paradigm shift, this paper aims to study the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such a scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer injecting larger noises for higher privacy-preserving levels (PPLs). To strike a good balance therein, we propose an efficient Incentive Mechanism named REAP to reconcile FC's aggregation accuracy and individual PU's data privacy. Specifically, we adopt the celebrated notion of differential privacy to quantify PUs' PPLs and characterize their impacts on FC's aggregation accuracy. Then, appealing to contract theory, we design an Incentive Mechanism to maximize FC's aggregation accuracy under a given budget. The proposed Incentive Mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control them. It can further overcome the information asymmetry problem, i.e., FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed Incentive Mechanism.
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promoting cooperation by the social Incentive Mechanism in mobile crowdsensing
IEEE Communications Magazine, 2017Co-Authors: Guang Yang, Shibo He, Jiming ChenAbstract:An Incentive Mechanism is important for mobile crowdsensing to recruit sufficient participants to complete large-scale sensing tasks with high quality. Previous Incentive Mechanisms have focused on quantifying participants' contribution to the quality of sensing and provide Incentives directly to them. In this article, we introduce a novel approach, called the social Incentive Mechanism, which, surprisingly, incentivizes the social friends of the participants who perform the sensing tasks. The basic idea is to leverage the social ties among participants to promote global cooperation. Since the Incentive that a participant receives largely relies on the behaviors of his/her social friends, participants have the motivation to impact their friends' behaviors through their social relationships in order to gain a higher payoff. This approach is applicable to many scenarios where the contributions to the quality of sensing among participants are interdependent, such as data aggregation. We have provided a case study which shows that the social Incentive Mechanism is more cost-effective than traditional Incentive Mechanisms.
Mingjun Xiao - One of the best experts on this subject based on the ideXlab platform.
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Truthful Incentive Mechanism for Nondeterministic Crowdsensing with Vehicles
IEEE Transactions on Mobile Computing, 2018Co-Authors: Mingjun Xiao, Liusheng Huang, Jie Wu, Chang HuAbstract:In this paper, we focus on the Incentive Mechanism design for a vehicle-based, nondeterministic crowdsensing system. In this crowdsensing system, vehicles move along their trajectories and perform corresponding sensing tasks with different probabilities. Each task may be performed by multiple vehicles jointly so as to ensure a high probability of success. Designing an Incentive Mechanism for such a crowdsensing system is challenging since it contains a non-trivial set cover problem. To solve this problem, we propose a truthful, reverse-auction-based Incentive Mechanism that includes an approximation algorithm to select winning bids with a nearly minimum social cost and a payment algorithm to determine payments for all participants. Moreover, we extend the problem to a more complex case in which the Quality of sensing Data (QoD) of each vehicle is taken into consideration. For this problem, we propose a QoD-aware Incentive Mechanism, which consists of a QoD-aware winning-bid selection algorithm and a QoD-aware payment determination algorithm. We prove that the proposed Incentive Mechanisms have truthfulness, individual rationality, and computational efficiency. Moreover, we analyze the approximation ratios of the winning-bid selection algorithms. The simulations, based on a real vehicle trace, also demonstrate the significant performances of our Incentive Mechanisms.
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Truthful Incentive Mechanism for vehicle-based nondeterministic crowdsensing
2016 IEEE ACM 24th International Symposium on Quality of Service (IWQoS), 2016Co-Authors: Chang Hu, Mingjun Xiao, Liusheng HuangAbstract:Nowadays, vehicles have shown great potential in crowdsensing. To guarantee a good Quality of Service (QoS), stimulating enough vehicles to participate in crowdsensing is very necessary. In this paper, we focus on the Incentive Mechanism design in the vehicle-based nondeterministic crowdsensing. Different from existing works, we take into consideration that each vehicle performs sensing tasks along some trajectories with different probabilities, and each task must be successfully performed with a joint probability no less than a threshold. Designing an Incentive Mechanism for such a nondeterministic crowdsensing system is challenging, which contains a non-trivial set cover problem with non-linear constraints. To solve the problem, we propose a truthful Incentive Mechanism based on reverse auction, including an approximation algorithm to select winning bids with a nearly minimum social cost, and a payment algorithm to determine the payments for all participants. Through theoretical analysis, we prove that our Incentive Mechanism is truthful and individual rational, and we give an approximation ratio of the winning bid selection algorithm. In addition, we conduct extensive simulations, based on a real vehicle trace, to validate the performances of the proposed Incentive Mechanism.
Yunhua He - One of the best experts on this subject based on the ideXlab platform.
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A Blockchain Based Truthful Incentive Mechanism for Distributed P2P Applications
IEEE Access, 2018Co-Authors: Yunhua He, Xiuzhen Cheng, Yan Liu, Chao Yang, Hong Li, Limin SunAbstract:In distributed peer-to-peer (P2P) applications, peers self-organize and cooperate to effectively complete certain tasks such as forwarding files, delivering messages, or uploading data. Nevertheless, users are selfish in nature and they may refuse to cooperate due to their concerns on energy and bandwidth consumption. Thus each user should receive a satisfying reward to compensate its resource consumption for cooperation. However, suitable Incentive Mechanisms that can meet the diverse requirements of users in dynamic and distributed P2P environments are still missing. On the other hand, we observe that Blcokchain is a decentralized secure digital ledger of economic transactions that can be programmed to record not just financial transactions and Blockchain-based cryptocurrencies get more and more market capitalization. Therefore in this paper, we propose a Blockchain based truthful Incentive Mechanism for distributed P2P applications that applies a cryptocurrency such as Bitcoin to incentivize users for cooperation. In this Mechanism, users who help with a successful delivery get rewarded. As users and miners in the Blockchain P2P system may exhibit selfish actions or collude with each other, we propose a secure validation method and a pricing strategy, and integrate them into our Incentive Mechanism. Through a game theoretical analysis and evaluation study, we demonstrate the effectiveness and security strength of our proposed Incentive Mechanism.
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a bitcoin based Incentive Mechanism for distributed p2p applications
Wireless Algorithms Systems and Applications, 2017Co-Authors: Yunhua He, Hong Li, Xiuzhen ChengAbstract:The effectiveness of distributed Peer-to-Peer (P2P) applications heavily relies on the cooperation of mobile users. Each user should receive a satisfying reward to compensate its resource consumption for cooperation. However, suitable Incentive Mechanisms that can meet the diverse requirements of users in dynamic and distributed P2P environments are still missing. Therefore in this paper, we propose a Bitcoin based Incentive Mechanism for distributed P2P applications that applies the basic idea of Bitcoin to incentivize users for cooperation. In this Mechanism, users who help with a successful delivery get rewarded. Through a game theoretical analysis and evaluation study, we demonstrate the effectiveness and security strength of our proposed Incentive Mechanism.
