Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones
Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones
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DOI：
10.1109/tnet.2015.2421897
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发表时间：
2016-06-01
影响因子：
3.7
通讯作者：
Tang, Jian
中科院分区：
文献类型：
--
作者：
Yang, Dejun;Xue, Guoliang;Tang, Jian
智能手机是可编程的，并配备了一套便宜但功能强大的嵌入式传感器，如加速度计，数字罗盘，陀螺仪，GPS，麦克风和摄像头。这些传感器可以共同监测各种人类活动和周围环境。Crowdsensing是一种新的模式，它利用无处不在的智能手机来感知，收集和分析数据，超越了以前可能的规模。利用众包感知系统，众包者可以招募智能手机用户来提供感知服务。现有的众测应用和系统缺乏能够吸引更多用户参与的良好激励机制。为了解决这个问题，我们设计了激励机制的人群感知。我们考虑两个系统模型：以众包为中心的模型，其中众包者提供由参与用户共享的奖励，以及以用户为中心的模型，其中用户对他们将收到的支付有更多的控制权。对于以众包为中心的模型，我们使用Stackelberg博弈设计了一个激励机制，其中众包是领导者，而用户是追随者。我们展示了如何计算唯一的Stackelberg均衡，在该均衡下众包者的效用最大化，并且没有用户可以通过单方面偏离其当前策略来提高其效用。对于以用户为中心的模型，我们设计了一个基于拍卖的激励机制，这是计算效率，个人理性，有利可图的，和真实的。通过大量的模拟，我们评估的性能和验证我们的激励机制的理论属性。
Smartphones are programmable and equipped with a set of cheap but powerful embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsensing is a new paradigm which takes advantage of the pervasive smartphones to sense, collect, and analyze data beyond the scale of what was previously possible. With the crowdsensing system, a crowdsourcer can recruit smartphone users to provide sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for crowdsensing. We consider two system models: the crowdsourcer-centric model where the crowdsourcer provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design an incentive mechanism using a Stackelberg game, where the crowdsourcer is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.