Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities
Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities
复制标题
智慧城市车辆众包的时效性激励机制
DOI：
10.1109/tmc.2021.3052963
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发表时间：
2021-01
影响因子：
7.9
通讯作者：
Xianhao Chen;Lan Zhang;Yawei Pang;B. Lin;Yuguang Fang
中科院分区：
文献类型：
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作者：
Xianhao Chen;Lan Zhang;Yawei Pang;B. Lin;Yuguang Fang
车辆众包是一种很有前途的模式，它利用车辆强大的车载功能来执行智能城市中的各种任务。为了实现这一愿景，一个设计良好的激励机制是必不可少的，以刺激车辆的参与。在本文中，我们提出了一个时间意识的车辆众包激励机制，考虑到车辆的不确定行程时间。鉴于交通条件的随机性，我们推导出一个易于处理的任务延迟的概率分布的离散时间交通模型的基础上的表达式。通过利用反向拍卖框架，我们的效用模型的服务请求者作为一个功能的不确定的任务延迟和支付。为了在预算约束下最大化请求者的效用，我们将机制设计转化为背包约束下的非单调子模块最大化问题。在此基础上，我们提出了一种真实的预算效用最大化拍卖（TBUMA），它是真实的，预算可行的，有利可图的，个人理性和计算效率。通过广泛的跟踪模拟，我们证明了我们提出的激励机制的有效性。
Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle’s uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms of uncertain task delay and incurred payment. To maximize the requester’s utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop a truthful budgeted utility maximization auction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.