Occupancy detection by multi-power bluetooth low energy beaconing

Self Cite

Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.

Section: Related Workmentioning

confidence: 99%

Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Baronti

Chessa

Mavilia

et al. 2018

Self Cite

“…Girolami et al propose a supervised occupancy detection by exploiting two different BLE transmitted signal strengths (i.e., −18 dBm and 3 dBm) [ 28 ]. Each tracked user needs to bring a BLE transmitter with him/her and BLE receivers are deployed in each room.…”

Section: Related Workmentioning

confidence: 99%

Multi-User Low Intrusive Occupancy Detection

Pratama

Widyawan

Lazovik

et al. 2018

Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach.

“…Since the emergence of this technology, several authors have already used Bluetooth beacons for indoor location or as part of different approaches to solve occupancy problems. For example, in [ 10 , 11 , 12 , 13 ] beacons were used to detect the presence of persons in a specific indoor region, although without detecting the precise number of individuals. Some authors [ 13 ] used a mobile app for their experiment, but it is only available for IOS devices.…”

Section: Introductionmentioning

confidence: 99%

A Tool to Calculate the Level of Occupancy in Indoor and Outdoor Spaces Using BLE and Open Data to Be Published in Real-Time

Sánchez

Gallinas

Beato

et al. 2020

The ability to know the precise level of occupancy in an indoor or outdoor space in real time could have multiple applications. It is a well-known problem for which a number of technologies have been proposed over time. The recent emergence of BLE beacon technology has provided a solution to the problem. This study presents a tool that uses beacons and user smartphones to determine the level of occupancy in indoor and outdoor spaces, providing real-time information that can be published as open data and subsequently used by other applications. The tool was tested in a university environment in real-life situations and has produced promising results in obtaining an occupancy count.