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SCPS: A Social-Aware Distributed Cyber-Physical Human-Centric Search Engine

机译:SCPS:一种社交意识的分布式以物理为中心的以人为中心的搜索引擎

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Advances in ubiquitous sensing, computing and wireless communication technologies are leading to the development of cyber-physical systems (CPS), which promise to revolutionize the way we interact with the physical world. CPS applications, such as healthcare monitoring, may involve many users and objects scattered over a wide area. One critical function of CPS is object search in the physical world through the cyber sphere that enables interaction between the cyber and physical spheres. Some of the previously proposed physical object search engines use RFID tracking, and others collect the information of object locations into a hierarchical centralized server. The difficulty of widely deploying RFID devices, the centralized search, and the need for periodical location information collection prevent CPS from achieving higher scalability and efficiency. To deal with this problem, we propose a Social-aware distributed Cyber-Physical human-centric Search engine (SCPS) that leverages the social network formed by wireless device users for object search. Without requiring periodical location information collection, SCPS locates objects held by users based on the routine user movement pattern. Moreover, using a social-aware Bayesian network, it can accurately predict the users’ locations even at the occurrence of exceptional (i.e., non-routine) events (e.g., raining) that break user movement pattern. Thus, SCPS is more advantageous than all previous social network based works which assume that user behaviors always follow a certain pattern. Further, SCPS conducts the search in a fully distributed manner by relying on a distributed hash table (DHT) structure. As a result, SCPS achieves high scalability, efficiency and location accuracy. Extensive real-trace driven simulation results show the superior performance of SCPS compared to other representative search methods including a hierarchical centralized method, a decentralized method, and two social network based me- hods. The results also show the effectiveness of different components of SCPS.
机译:无处不在的传感,计算和无线通信技术的进步正在导致网络物理系统(CPS)的发展,这有望彻底改变我们与物理世界交互的方式。 CPS应用程序(例如医疗保健监控)可能涉及分散在广阔区域中的许多用户和对象。 CPS的一项关键功能是通过网络领域在物理世界中进行对象搜索,从而实现网络领域与物理领域之间的交互。某些先前提出的物理对象搜索引擎使用RFID跟踪,而其他一些则将对象位置的信息收集到分层的集中式服务器中。广泛部署RFID设备的困难,集中式搜索以及对定期位置信息收集的需求使CPS无法实现更高的可伸缩性和效率。为解决此问题,我们提出了一种社交感知的分布式以人为本的网络物理搜索引擎(SCPS),该引擎利用无线设备用户形成的社交网络进行对象搜索。 SCPS不需要定期收集位置信息,而是根据常规用户移动模式来定位用户持有的对象。此外,使用具有社交意识的贝叶斯网络,即使在发生打破用户移动模式的异常(即非常规)事件(例如下雨)时,它也可以准确地预测用户的位置。因此,SCPS比所有以前的基于社交网络的工作(假定用户行为始终遵循某种模式)更具优势。此外,SCPS依靠分布式哈希表(DHT)结构以完全分布式的方式进行搜索。结果,SCPS实现了高可伸缩性,效率和位置准确性。与其他有代表性的搜索方法(包括分层集中式方法,分散式方法和两个基于社交网络的方法)相比,广泛的由实迹驱动的模拟结果显示出SCPS的优越性能。结果还显示了SCPS不同组件的有效性。

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