跨设备类型和时空粒度的人体运动的实用预测
Practical Prediction of Human Movements Across Device Types and Spatiotemporal Granularities
摘要：
理解和预测移动性对于未来移动边缘缓存和网络的设计和评估至关重要。因此，在过去的十年中，对人类流动性预测的研究引起了极大的关注。利用信息理论的概念和机器学习方法，早期的研究表明，人类行为是高度可预测的。尽管现有的研究，需要更多的调查，以捕捉固有的流动性特征约束的可预测性，并探索更多的维度（如设备类型）和时空粒度，特别是随着人类行为和技术的变化。我们分析了覆盖16个月的具有精细空间粒度（AP级别）的广泛纵向数据集。该研究表明，设备类型是影响可预测性的重要因素。智能手机等超便携设备具有“移动”使用模式（因此被称为“长笛”），而笔记本电脑则是“坐着使用”（被称为“大提琴”）。本研究的目标是调查实际的预测机制，以量化可预测性作为人类移动建模的一个方面，跨时间，空间和设备类型。我们应用我们的系统分析，从一个大的大学校园的无线痕迹。我们比较了几种算法使用不同程度的时间和空间粒度的两种模式的设备;长笛与大提琴。通过我们的分析，我们量化了长笛的流动性如何比大提琴的流动性更难预测。此外，这种模式在不同的时空粒度和不同的方法（马尔可夫链、神经网络/深度学习、基于熵的估计器）中是一致的。这项工作证实了可预测性作为人类移动性的一个重要方面的重要性，直接应用于预测缓存，用户行为建模和移动模拟。
英文摘要：
Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on prediction of human mobility has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, and to explore more dimensions (e.g. device types) and spatio-temporal granularities, especially with the change in human behavior and technology. We analyze extensive longitudinal datasets with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
