Smart Behavioral Analytics over a Low‐Cost IoT Wi‐Fi Tracking Real Deployment

Abstract

In a more and more urbanized World, the so-called Smart Cities need to be driven by the principles of efficiency and sustainability. Information and Communications Technologies and, in particular, the Internet of Things will play a key role on this, since they will allow monitoring and optimizing all the municipal services that exist and shall exist. People flow monitoring stands out in this context due to its wide range of applications, spanning from monitoring transport infrastructure to physical security applications. There are different techniques to perform people flow monitoring, presenting pros and cons, as in any other engineering problem. Typically, the options that provide the most accurate results are also the most expensive ones, whereas there are cases where presence detection in given areas is enough and cost is a limiting factor. The main goal of this paper is to prove that a minimal deployment of sensors, combined with the adequate analysis and visualization algorithms, can render useful results. In order to achieve this goal, a dataset is used with 1-year data from a real infrastructure composed of 9 Wi-Fi tracking sensors deployed in the Telecommunications Engineering School of Universidad Politécnica de Madrid, which is visited by 4000 people daily and covers 1.8 hectares. The data analysis includes time and occupancy, position of people, and identification of common behaviors, as well as a comparison of the accuracy of the considered solution with actual data and a video monitoring system available at the library of the school. The obtained insights can be used for optimizing the management and operation of the school, as well as for other similar infrastructures and, in general, for other kind of applications which require not very accurate people flow monitoring at low cost.

The Future of Emerging IoT Paradigms: Architectures and Technologies

“…Using Wi-Fi to monitor is easier and able to provide more useful data since the MAC addresses can be collected and used as a means of identifying individual users and creating unique profiles for each one. This is exactly how the data is collected in [17] is the architectural paradigm from which this paper is approaching behavioral monitoring. Monitoring using Wi-Fi is typically done by two different methods.…”

“…The architecture for an IoT topology with the capability to monitor behaviors can be built in multiple ways and using various techniques. As explained in [17], there are two main groups of techniques: those that are not based on Wi-Fi tracking and those that are. In the context of this piece, only Wi-Fi based techniques will be discussed.…”

“…With this configuration, Wi-Fi enabled sensors can send measurements and receive commands over SSH sessions. In the scenario provided by [17], privacy of the data sent across the architecture has been covered by having the Wi-Fi sensors use an MD-5 hash function and salting the MAC addresses. Figure 10 provides a visual illustration of the architecture used in [17] and the direction of data flow starting at the sensors, through UPM IP network and directly into the backend services.…”

“…In the environment constructed by [17], data acquisition was handled by a low-cost Raspberry Pi board and a USB dongle antenna. It should be noted that the dongle connected to the Raspberry Pi must be set to monitor mode so that it knows how to operate.…”

“…Lastly, the architecture poses issues because some users could configure their devices to perform MAC spoofing. [17] explains that the effects of this behavior, however, are negligible because doing this could result in connectivity problems for the user.…”