Clustered Crowd GPS for Privacy Valuing Active Localization
Clustered Crowd GPS for Privacy Valuing Active Localization
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DOI：
10.1109/access.2018.2830300
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发表时间：
2018-04
期刊：
影响因子：
3.9
通讯作者：
F. Yucel;E. Bulut
中科院分区：
文献类型：
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作者：
F. Yucel;E. Bulut
随着具有BLE能力的移动的设备的激增和信标技术的引入，基于众包的方法最近已经出现作为用于丢失的对象或个人（例如，儿童或老人）。通过将负担得起的信标标签附加到它们，护理对象可以由附近的用户设备跟踪和定位。虽然人群GPS服务最近得到普及，但它还没有扩展到被动模式之外，在被动模式中，在后台实现定位而不干扰用户的移动性。在本文中，我们研究了通过人群GPS服务的丢失物体的定位在一个积极的方式。我们建议在一个信标标签网络的基础上，他们可以从对方那里得到的好处，他们丢失的物品的本地化用户聚类。一个新的度量被开发来量化这个好处，然后可以提供大部分的总的可能的好处给对方的用户被分组在一起，使他们可以提供主动本地化服务，只有最有益的用户给他们。用户的聚类是基于两个贪婪启发式算法和遗传算法。利用合成数据和基于真实的位置的社交网络数据集进行了广泛的仿真结果。结果表明，在不同的用户数和组下的用户的有效划分，同时重视用户的隐私在其最大限度地限制用户之间的交互次数。
With the proliferation of mobile devices having BLE capability and the introduction of Beacon technology, crowdsourcing-based approaches have recently emerged as a promising solution for localization of lost objects or individuals (e.g., children or elders). By attaching affordable Beacon tags to them, objects of care could be tracked and localized by user devices in the proximity. While crowd GPS service has gained popularity recently, it has not extended beyond passive mode in which localization is achieved in the background without intruding the mobility of users. In this paper, we study the localization of lost objects through the crowd GPS service in an active manner. We propose clustering users in a Beacon tag network based on the benefits they can receive from each other in terms of the localization of their lost items. A new metric is developed to quantify this benefit and the users that can provide most of the total possible benefits to each other are then grouped together so that they can provide active localization service for only the users most beneficial to them. The clustering of users is achieved based on both a greedy heuristic based algorithm and a genetic algorithm. Extensive simulation results are conducted utilizing both synthetic data and real location based social network datasets. The results show the effective partitioning of the users under different user counts and groups while valuing the privacy of users at its maximum by limiting the number of interactions between users.