A stability-based group recruitment system for continuous mobile crowd sensing
A stability-based group recruitment system for continuous mobile crowd sensing
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DOI：
10.1016/j.comcom.2018.01.012
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发表时间：
2018-04-01
影响因子：
6
通讯作者：
Ouali, Anis
中科院分区：
文献类型：
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作者：
Azzam, Rana;Mizouni, Rabeb;Ouali, Anis
随着移动人群感知 (MCS) 的普及，许多响应不同传感请求的领域应用程序已经受益于感兴趣领域 (AoI) 参与者的可用性。这些请求通常被分类为一次性感测请求或连续感测请求。在前者中，需要从所招募的参与者的设备上进行一次性读取来回答请求，而在后者中，需要在给定的时间间隔内进行读取，这使得招募具有挑战性，特别是在考虑到参与者的流动性时。理想情况下，为给定的连续感知任务招募参与者的过程应确定回答感知请求的最佳参与者集，同时满足两个重要约束，包括（1）给定的信息质量（QoI）水平和2）给定的预算内。这种选择还对参数敏感，例如感知任务对 AoI 覆盖范围的要求以及参与者的移动性和分布。为了应对这一挑战，我们提出了一种新颖的、基于稳定性的连续感知群体招募系统（Stable-GRS），该系统采用遗传算法来考虑参与者的移动模式来选择参与者群体。所提出的系统选择 AoI 中能够达到一定 QoI 水平的最稳定的参与者群体，其中稳定性反映了该群体的时间和空间可用性。招聘过程是动态的；它涉及在整个感知期间添加和删除参与者以保持 QoI 要求。合作博弈论，特别是沙普利值，用于根据所选工人各自的贡献来奖励他们。使用现实数据集进行模拟，结果表明我们的方法优于基于个人的招聘系统 (IRS)，后者采用贪婪算法来招募参与者的所有关键绩效指标，例如 QoI 和成本。
With the proliferation of Mobile Crowd Sensing (MCS), many domain applications that answer different sensing requests, have been benefiting from the availability of participants in areas of interest (AoI). These requests have been commonly classified as one time sensing or continuous sensing requests. In the former, one-time reading from the devices of the recruited participants is needed to answer the request, while in the latter, readings are needed over a given time interval, making recruitment challenging, particularly when considering participants' mobility. Ideally, the process of recruiting participants for a given continuous sensing task should determine the best set of participants to answer the sensing requests, while satisfying two important constraints including (1) a given level of quality of information (QoI) and 2) within a given budget. This selection is also sensitive to parameters such as requirements of the sensing task with regards to the AoI coverage, and participants' mobility and distribution. To address this challenge, we propose a novel, stability-based group recruitment system for continuous sensing (Stable-GRS) that employs a genetic algorithm to select groups of participants considering their mobility patterns. The proposed system selects the most stable group of participants in the AoI that can achieve a certain level of QoI, where stability reflects the group's temporal and spatial availability. The process of recruitment is dynamic; it involves adding and removing participants throughout the sensing period to preserve the QoI requirement. Cooperative game theory, specifically the Shapley value, is used to reward selected workers based on their respective contribution. Simulations are conducted using real-life datasets and the results establish that our approach outperforms an individual-based recruitment system (IRS), which employs greedy algorithms to recruit participants for all key performance metrics, such as the QoI and costs.