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Efficient and Transparent Use of personal device storage in opportunistic data forwarding

机译:在机会数据转发中高效透明地使用个人设备存储

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We consider a growing research trend of using personal mobile devices for forwarding opportunistic network data. Because personal device storage is meant to support user applications, opportunistic networks must use it in a manner that remains completely transparent to the user. One way to make a device's storage use transparent is to allow priority access to the storage to user applications, even if the storage is currently occupied by network data yet to be forwarded. This means that data given to a device waiting to be forwarded can be overwritten by application data and may, thus, be lost. In this paper we consider random access memory (RAM) as the primary storage location in a mobile device. We propose three algorithms of different sophistications to answer the question of how much data should be moved when a contact opportunity arises between two devices in such a way to first maximise the data transferred while minimising the probability that this data will be overwritten when applications claim priority access. We collect 33 h of high-resolution RAM usage traces of two real smartphones over a 3-day period under a variety of usage scenarios to evaluate and compare the performances of the proposed algorithms. Surprisingly, we find that autoregression forecasting of RAM usage cannot outperform the simplest algorithm that greedily occupies all of the RAM that is found unused at the time of contact. We show that Bayesian inference is very effective in minimising the risk of data loss in such uncertain environments and significantly outperforms the greedy approach as well as autoregression forecasting. (C) 2015 Elsevier B.V. All rights reserved.
机译:我们认为使用个人移动设备转发机会网络数据的研究趋势正在增长。因为个人设备存储旨在支持用户应用程序,所以机会网络必须以对用户完全透明的方式使用它。使设备的存储使用透明的一种方法是允许用户应用程序优先访问该存储,即使该存储当前被尚待转发的网络数据所占用。这意味着提供给等待转发的设备的数据可能会被应用程序数据覆盖,因此可能会丢失。在本文中,我们将随机存取存储器(RAM)视为移动设备中的主要存储位置。我们提出了三种不同复杂度的算法,以回答在两个设备之间出现接触机会时应移动多少数据的问题,即首先最大化传输的数据,同时最小化当应用程序要求优先权时该数据将被覆盖的可能性访问。我们在各种使用情况下,在3天的时间内收集了两个真实智能手机的33小时高分辨率RAM使用轨迹,以评估和比较所提出算法的性能。令人惊讶的是,我们发现对RAM使用情况的自回归预测无法胜过贪婪地占用联系时发现未使用的所有RAM的最简单算法。我们表明,在这种不确定的环境中,贝叶斯推理在最小化数据丢失风险方面非常有效,并且明显优于贪婪方法和自回归预测。 (C)2015 Elsevier B.V.保留所有权利。

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