Using Bluetooth Low Energy Advertisements for the Detection of People Temporal Proximity Patterns

Abstract

The pervasive presence of smartphones has emerged as one of the key elements for sensing people contextual information. Their sensors and communication capabilities can be used to gather a huge amount of data. Such capabilities have made it possible to compose profiles of people by relating different parameters such as time and location. This paper contributes in this sense by providing the basis for the composition of temporal proximity patterns—when and whom people share their time with each other. For this purpose, the Bluetooth Low Energy (BLE) advertisement protocol was used. The contribution of this work departs from that of those who use BLE technology focused on measuring the intensity of the signals to, for example, determine distances. In this field, a huge amount of work has been already done with very interesting results. Instead, in this work, BLE is used to emit and sense the presence of people. A set of algorithms are then used inside the smartphones to analyse the data gathered and to detect proximity patterns between people. This scenario avoids the difficulties that appear in other works—like those focused on people positioning—derived from the lack of precision of the sensors and the differences between BLE chipsets. Tests to evaluate the consumption, precision, and reliability of using this technology, together with the proposed algorithms, confirmed the feasibility of the approach. In addition, the proposal has proved very useful for the automatic construction of social networks based on physical closeness of people.