Inferring Person-to-person Proximity Using WiFi Signals

Abstract

Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches-including RFID badges or Bluetooth scanning-offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply.

GroupSense: A Lightweight Framework for Group Identification

“…On the other side, Bluetooth-based study [29] is one of the earliest attempts for localization in indoor environments. However, Bluetooth scanning is power hungry [10]. Moreover, many of the Android smartphones (starting from versions 4.4) have partial support for Bluetooth Low-Energy (BLE), which are capable of only detecting other BLE devices [16].…”

“…In this line, we capture different types of real-life meeting group scenarios such as outdoor roadside informal meeting; informal outdoor cafe meet, formal and informal laboratory meeting, and classroom interaction as shown in Figure 1. Das Identification of a meeting group primarily relies on the location proximity [9], [10] of the group members, which (apparently) can be conceptualized as a localization problem [11], [12]. In that direction, prior art explores the following three modalities -GPS, Bluetooth, and WiFi for identification of the location similarity in supervised [10] as well as unsupervised [13] manner.…”

“…Das Identification of a meeting group primarily relies on the location proximity [9], [10] of the group members, which (apparently) can be conceptualized as a localization problem [11], [12]. In that direction, prior art explores the following three modalities -GPS, Bluetooth, and WiFi for identification of the location similarity in supervised [10] as well as unsupervised [13] manner. In our context of group detection, vanilla localization based solutions demand high accuracy, which pushes the system towards complex processing.…”

“…The proposed method is lightweight, unsupervised, hence equipped to detect instantaneously formed groups, without any pre-training. First, we determine the co-located population using standard localization techniques [10], [19]. In our implementation, we relied on the WiFi-based proximity; nevertheless, this can be extended to Bluetooth and GPS based techniques as well.…”

Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces

“…a More recently, Sapiezynski [28] proposed a model to infer person-to-person proximity by using WiFi signals. Sekara and Lehmann [29] studied how to deploy the electronic datasets as a valid proxy for real life social interactions, and found that the strength of electronic signals can be used to distinguish between strong and weak friendship ties.…”