Multi-Round Incentive Mechanism for Cold Start-Enabled Mobile Crowdsensing
Multi-Round Incentive Mechanism for Cold Start-Enabled Mobile Crowdsensing
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DOI：
10.1109/tvt.2021.3050339
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发表时间：
2021-01
影响因子：
6.8
通讯作者：
Yaguang Lin;Zhipeng Cai;Xiaoming Wang;Fei Hao;Liang Wang;Akshita Maradapu Vera Venkata Sai
中科院分区：
文献类型：
--
作者：
Yaguang Lin;Zhipeng Cai;Xiaoming Wang;Fei Hao;Liang Wang;Akshita Maradapu Vera Venkata Sai
移动的人群感知（MCS）已经成为执行大规模感知任务的新范例。已经提出了许多激励机制来鼓励用户参与监控监。然而，他们中的大多数人都忽略了MCS不可避免的冷启动阶段，MCS系统刚刚开始释放任务。而且，它们都采用单轮激励，没有考虑连续累积效应。针对MCS冷启动阶段参与者严重不足的问题，提出了多轮激励机制（MRIM）。MRIM基于货币激励，采用多轮合作，任务信息扩散和任务分配操作交替进行，即使没有系统预算约束的轮间耦合，这两个操作也是NP难问题。我们探索一种方法来预测用户参与任务的概率准确。此外，我们提出了一个有效的任务信息扩散算法，以最大限度地提高用户参与任务提交投标。提出了一种基于真实拍卖的快速任务分配算法，包括一个近似算法来解决单轮赢家选择和支付计算。在预算约束下，MRIM通过迭代地执行任务信息扩散和任务分配来最大化已完成任务的数量。我们还证明了MRIM也具有所需的属性，如计算效率，用户的理性，平台的盈利能力，价格真实性，这可以进一步保证MRIM的鲁棒性。在真实世界数据集上进行的大量模拟证明了MRIM的有效性。
Mobile CrowdSensing (MCS) has emerged as a novel paradigm for performing large-scale sensing tasks. Many incentive mechanisms have been proposed to encourage user participation in MCS. However, most of them ignore the inevitable cold start stage of MCS, where the MCS system has just begun releasing tasks. Also, they all adopt the single-round incentive without considerations of the continuous cumulative effect. Given the severe shortage of participants in the cold start stage of MCS, this paper proposes a Multi-Round Incentive Mechanism (MRIM). MRIM is based on monetary incentives by adopting multi-round cooperation and alternating between task information diffusion and task allocation operations, both of which are NP-hard problems even without inter-round coupling imposed by system budget constraints. We explore a method to predict the probability of users participating in tasks accurately. Furthermore, we present an efficient task information diffusion algorithm to maximize the number of users participating in tasks by submitting bids. We propose a fast task allocation algorithm based on truthful auction, comprising an approximation algorithm for solving the one-round winner selection and payment calculation. With budget constraints, MRIM maximizes the number of completed tasks by iteratively performing task information diffusion and task allocation. We also prove that MRIM also possesses desired properties such as computational efficiency, user rationality, platform profitability, and price truthfulness, which can further guarantee the robustness of MRIM. The extensive simulations conducted on real-world datasets have proved the efficiency of MRIM.