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Human-centric Data Dissemination in the IoP
ACM Transactions on Autonomous and Adaptive Systems ( IF 2.7 ) Pub Date : 2020-02-10 , DOI: 10.1145/3366372
Matteo Mordacchini 1 , Marco Conti 1 , Andrea Passarella 1 , Raffaele Bruno 1
Affiliation  

Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed “Internet of People” paradigm, accessing relevant data dynamically generated in the environment nearby is one of the key services. Moreover, personal mobile devices become proxies of their human users while exchanging data in the cyber world and, thus, largely use ONs and D2D communications for exchanging data directly. Recently, researchers have successfully demonstrated the viability of embedding human cognitive schemes in data dissemination algorithms for ONs. In this article, we consider one such scheme based on the recognition heuristic, a human decision-making scheme used to efficiently assess the relevance of data. While initial evidence about its effectiveness is available, the evaluation of its behaviour in large-scale settings is still unsatisfactory. To overcome these limitations, we have developed a novel hybrid modeling methodology that combines an analytical model of data dissemination within small-scale communities of mobile users, with detailed simulations of interactions between different communities. This methodology allows us to evaluate the algorithm in large-scale city- and countrywide scenarios. Results confirm the effectiveness of cognitive data dissemination schemes, even when content popularity is very heterogenous.

中文翻译:

IoP 中以人为中心的数据传播

使用设备到设备 (D2D) 通信和机会网络 (ON) 的数据管理是以人为中心的普遍互联网服务的主要焦点之一。在最近提出的“人联网”范式中,访问在附近环境中动态生成的相关数据是关键服务之一。此外,个人移动设备在网络世界中交换数据时成为其人类用户的代理,因此大量使用 ON 和 D2D 通信直接交换数据。最近,研究人员已经成功地证明了将人类认知方案嵌入到 ON 的数据传播算法中的可行性。在本文中,我们考虑了一种基于识别启发式的方案,这是一种用于有效评估数据相关性的人类决策方案。虽然可以获得关于其有效性的初步证据,但对其在大规模环境中的行为的评估仍然不能令人满意。为了克服这些限制,我们开发了一种新颖的混合建模方法,该方法将移动用户小规模社区内的数据传播分析模型与不同社区之间交互的详细模拟相结合。这种方法使我们能够在大规模城市和全国范围内评估算法。结果证实了认知数据传播方案的有效性,即使内容流行度非常不同。我们开发了一种新颖的混合建模方法,将小规模移动用户社区内的数据传播分析模型与不同社区之间交互的详细模拟相结合。这种方法使我们能够在大规模城市和全国范围内评估算法。结果证实了认知数据传播方案的有效性,即使内容流行度非常不同。我们开发了一种新颖的混合建模方法,将小规模移动用户社区内的数据传播分析模型与不同社区之间交互的详细模拟相结合。这种方法使我们能够在大规模城市和全国范围内评估算法。结果证实了认知数据传播方案的有效性,即使内容流行度非常不同。
更新日期:2020-02-10
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