过滤Wi-Fi产生的人群运动数据

C. Chilipirea, Andreea-Cristina Petre, C. Dobre, M. Steen
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引用次数: 6

摘要

城市代表着拥有共同基础设施、共同社会群体和/或共同利益的一大群人。随着新技术的发展,当前城市的目标是成为所谓的智慧城市,其中这些大型建筑的所有小细节都得到控制,以更好地提高居民的生活质量。城市最重要的动力之一是交通,无论是车辆还是行人。因此,交通与城市内发生的所有其他活动密切相关。了解交通流量仍然是一个困难的过程,因为我们不仅要能够衡量有多少人在使用特定的路径,还要能够分析人们去哪里,什么时候去,同时仍然保持个人隐私。所有这些都必须以覆盖城市中大多数人(如果不是全部的话)的规模进行。随着智能手机普及率的大幅增长,我们可以肯定地认为,大部分城市人口在任何时候都随身携带至少一台支持Wi-Fi的设备。因为Wi-Fi设备有规律地传输信号,我们可以依靠这些设备在不显眼的情况下检测个人的活动,而无需识别或跟踪任何特定的个人。监控Wi-Fi频率的特殊传感器可以放置在城市各处,以收集数据,然后用于识别交通流量的模式。我们提供了一组过滤器,可用于最小化处理所需的数据量,并且不会对结果或可以从该数据中提取的信息产生负面影响。我们提出的部分滤波器可以部署在传感器级别,使整个系统更具可扩展性,而不同的部分可以在数据处理之前执行,从而实现实时信息提取和更广泛的时间和空间范围用于数据分析。其中一些滤波器是特定于Wi-Fi的,但其中一些可以应用于任何检测系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Filters for Wi-Fi Generated Crowd Movement Data
Cities represent large groups of people that share a common infrastructure, common social groups and/or common interests. With the development of new technologies current cities aim to become what is known as smart cities, in which all the small details of these large constructs are controlled to better improve the quality of life of its inhabitants. One of the important gears that powers a city is given by traffic, be it vehicular or pedestrian. As such traffic is closely related to all other activities that take place inside of a city. Understanding traffic is still a difficult process as we have to be able to not only measure it in the sense of how many people are using a particular path but also in analyzing where people are going and when, while still maintaining individual privacy. And all this has to be done at a scale that would cover most if not all individuals in a city. With the high increase in smartphones adoption we can reliably assume that a large part of the population in cities are carrying with them, at all times, at least one Wi-Fi enabled device. Because Wi-Fi devices are regularly transmitting signals we can rely on these devices to detect individual's movements unobtrusively without identifying or tracking any particular individual. Special sensors that monitor Wi-Fi frequencies can be placed around a city to gather data that can later be used to identify patterns in the traffic flows. We present a set of filters that can be used to minimize the amount of data needed for processing and without negatively impacting the result or the information that can be extracted from this data. Part of the filters we present can be deployed at the sensor level, making the entire system more scalable, while a different part can be executed before data processing thus enabling real time information extraction and a broader temporal and spatial range for data analysis. Some of these filters are particular to Wi-Fi but some of them can be applied to any detection system.
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