Delay-Aware Incentive Mechanism for Crowdsourcing with Vehicles in Smart Cities
Delay-Aware Incentive Mechanism for Crowdsourcing with Vehicles in Smart Cities
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DOI：
10.1109/globecom38437.2019.9013829
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Xianhao Chen;Lan Zhang;B. Lin;Yuguang Fang
中科院分区：
文献类型：
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作者：
Xianhao Chen;Lan Zhang;B. Lin;Yuguang Fang
基于车辆的众包正在成为一个强大的范例，可以通过利用车辆的车载资源将密集型任务外包给车辆。在本文中，我们专注于激励车辆加入众包系统的问题。考虑到智慧城市中任务的各种延迟需求，设计了一种基于逆向拍卖的延迟感知车辆使用激励机制。具体地说，通过考虑任务延迟，我们的效用模型的服务请求者作为一个功能密切相关的发布的任务将完成。在我们的机制中，参与车辆不仅通过提交投标价格，还通过提交预计完成时间（ETC）来投标其首选任务。为了在预算约束下最大化服务请求者的效用，所提出的延迟感知机制被转换为具有背包约束的非单调子模块最大化问题。由于制定的问题的NP-困难，我们开发了一个近似算法的投标选择和支付确定，保证真实性，预算的可行性，个人的理性，盈利能力和计算效率。仿真结果验证了该激励机制的有效性。
Vehicle-based crowdsourcing is becoming a powerful paradigm that can outsource intensive tasks to vehicles by exploiting their on-board resources. In this paper, we focus on the problem of motivating vehicles to join the crowdsourcing system. Considering the various delay demands of tasks in smart cities, we design a delay-aware incentive mechanism to employ vehicles based on reverse auction. Specifically, by taking task delay into consideration, we model the utility of service requester as a function closely related to when its released tasks would be completed. In our mechanism, the participating vehicles bid for their preferred tasks by submitting not only the bidding prices, but also the estimated time of completion (ETC). To maximize the utility of the service requester under a budget constraint, the proposed delay-aware mechanism is cast as a nonmonotone submodular maximization problem with a knapsack constraint. Due to the NP-hardness of the formulated problem, we develop an approximate algorithm for bid selection and payment determination, which guarantees truthfulness, budget feasibility, individual rationality, profitability, and computational efficiency. Simulation results demonstrate the effectiveness of our proposed incentive mechanism.