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EventMe: Location-Based Event Content Distribution through Human Centric Device-to-Device Communications

机译:EventMe:通过以人为中心的设备到设备通信进行基于位置的事件内容分发

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Location-based information dissemination has become increasingly popular in the recent years. Extensive research work has been done on the matching of interested parties to event information via publish/subscribe systems. However, the rich content types of such location-specific data, especially when the data are presented in multimedia form, requires efficient methods with low cost to transfer the content to the subscribers. In this paper, the potential of utilising human centric device-to-device (D2D) communications to disseminate location-based event content is investigated. The human centric D2D data dissemination process is formulated as a task assignment problem, which can be modelled as a Integer Quadratically Constrained Quadratic Programming (IQCQP) problem. Since the IQCQP problem is in general NP-hard, a sub- optimal polynomial framework named EventMe is proposed, which is able to compute a solution with guaranteed lower bounds on data distribution capacity in terms of throughput. Through extensive evaluation using several real world datasets, it has shown that EventMe is able to improve the network throughput by 100%-500% compared to baseline methods. A prototype is developed and shows that it is practical to implement EventMe on mobile devices by generating minimal control data overhead.
机译:近年来,基于位置的信息传播变得越来越流行。通过发布/订阅系统,已经进行了广泛的研究工作,以将感兴趣的各方与事件信息进行匹配。但是,这种特定于位置的数据的丰富的内容类型,特别是当数据以多媒体形式呈现时,需要有效的方法以低成本将内容传送给订户。在本文中,研究了利用以人为中心的设备到设备(D2D)通信来传播基于位置的事件内容的潜力。以人为中心的D2D数据分发过程被表述为任务分配问题,可以将其建模为整数二次约束二次规划(IQCQP)问题。由于IQCQP问题通常是NP难的,因此提出了一个名为EventMe的次优多项式框架,该框架能够计算出在吞吐量方面具有保证的数据分配容量下限的解决方案。通过使用多个实际数据集进行的广泛评估,结果表明,EventMe与基准方法相比,可以将网络吞吐量提高100 \%-500 \%。开发了一个原型,它表明通过生成最少的控制数据开销在移动设备上实现EventMe是可行的。

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