Clustered Crowd GPS for Privacy Valuing Active Localization

Abstract

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.