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Real-World Spatio-Temporal Behavior Aware D2D Multicast Networks
IEEE Transactions on Network Science and Engineering ( IF 6.6 ) Pub Date : 2020-07-01 , DOI: 10.1109/tnse.2019.2947700
Mansi Peer , Vivek Ashok Bohara , Anand Srivastava

Studies show that real-world user mobility has certain spatio-temporal correlations. However, most of the existing works on device-to-device multicast (D2MD) networks use randomly generated user locations, thus fail to capture the real-world spatio-temporal behavior of the users. In this work, we propose a realistic performance evaluation of the D2MD network that takes into account the joint spatio-temporal correlations of the users in the network. The proposed performance evaluation will be beneficial in assessing the viability of the D2MD networks before their actual deployment. Further, in order to acquire the required joint spatio-temporal behavior, we propose two novel methods namely observed mobility exploitation (OME) and expected mobility exploitation (EME). In OME, the joint spatio-temporal behavior is extracted from the past location information of the users. On the other hand, in EME, the past location information of a set (i.e., the training set) of users, following an average spatial-temporal behavior similar to the intended users, is utilized to estimate the intended users’ joint spatio-temporal behavior. The realistic performance of two D2MD networks is evaluated for a tractable campus set-up in terms of content delivery time and data offload ratio. It has been shown that for the D2MD networks the data offloading varies spatio-temporally and goes up to 35%. Further, a comparison is drawn between the conventional cellular unicast and multicast networks, and the two D2MD networks.

中文翻译:

真实世界时空行为感知 D2D 多播网络

研究表明,现实世界的用户移动性具有一定的时空相关性。然而,大多数关于设备到设备组播(D2MD)网络的现有工作使用随机生成的用户位置,因此无法捕捉用户的真实世界时空行为。在这项工作中,我们提出了一个现实的 D2MD 网络性能评估,它考虑了网络中用户的联合时空相关性。拟议的性能评估将有助于在实际部署之前评估 D2MD 网络的可行性。此外,为了获得所需的联合时空行为,我们提出了两种新方法,即观察到的移动性开发(OME)和预期移动性开发(EME)。在欧米茄,从用户过去的位置信息中提取联合时空行为。另一方面,在 EME 中,一组用户(即训练集)的过去位置信息遵循与预期用户相似的平均时空行为,用于估计预期用户的联合时空行为。在内容交付时间和数据卸载率方面,针对易于处理的校园设置评估了两个 D2MD 网络的实际性能。已经表明,对于 D2MD 网络,数据卸载在时空上发生变化,最高可达 35%。此外,还比较了传统的蜂窝单播和多播网络,以及两种 D2MD 网络。遵循与预期用户相似的平均时空行为,用于估计预期用户的联合时空行为。在内容交付时间和数据卸载率方面,针对易于处理的校园设置评估了两个 D2MD 网络的实际性能。已经表明,对于 D2MD 网络,数据卸载在时空上发生变化,高达 35%。此外,还比较了传统的蜂窝单播和多播网络,以及两种 D2MD 网络。遵循与预期用户相似的平均时空行为,用于估计预期用户的联合时空行为。在内容交付时间和数据卸载率方面,针对易于处理的校园设置评估了两个 D2MD 网络的实际性能。已经表明,对于 D2MD 网络,数据卸载在时空上发生变化,最高可达 35%。此外,还比较了传统的蜂窝单播和多播网络,以及两种 D2MD 网络。已经表明,对于 D2MD 网络,数据卸载在时空上发生变化,最高可达 35%。此外,还比较了传统的蜂窝单播和多播网络,以及两种 D2MD 网络。已经表明,对于 D2MD 网络,数据卸载在时空上发生变化,最高可达 35%。此外,还比较了传统的蜂窝单播和多播网络,以及两种 D2MD 网络。
更新日期:2020-07-01
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