The BLEBeacon dataset is a collection of Bluetooth Low Energy (BLE) advertisement packets/traces generated from BLE beacons carried by people following their daily routine inside a university building for a whole month. A network of Raspberry Pi 3 (RPi)-based edge devices were deployed inside a multi-floor facility continuously gathering BLE advertisement packets and storing them in a cloud-based environment. The focus is on presenting a real-life realization of a location-aware sensing infrastructure, that can provide insights for smart sensing platforms, crowd-based applications, building management, and user-localization frameworks.
Since 2019, the BLEBeacon dataset is part of the Community Resource for Archiving Wireless Data At Dartmouth (CRAWDAD): https://crawdad.org/unm/blebeacon/20190312/
To cite the dataset:
Dimitrios Sikeridis, Ioannis Papapanagiotou, Michael Devetsikiotis, CRAWDAD dataset unm/blebeacon (v. 2019‑03‑12), downloaded from https://crawdad.org/unm/blebeacon/20190312, Mar 2019.
Users carried off-the-shelf Gimbal Series 10 iBeacons that continuously transmit BLE advertisement packets. The periodic transmission rate for each beacon is set to 1 Hz, with omni-directional antenna propagation setting, and transmission power of 0 dBm. The backbone of the system is a network of Raspberry Pi 3 (RPi), able to collect all generated packets.
Regarding system operation two approaches were utilized in parallel:
- RSSI Report: all advertisement packet receptions from beacon devices are directly reported to a server with a message that contains the beacon/user ID, the packet's Received Signal Strength Indicator (RSSI), a reception timestamp, and finally the ID of the RPi that received the advertisement (Fig. 1).
- Check-In/Check-Out Report: each RPi scanner continuously manages a list of current occupants/users in its proximity. A check in-timestamp is created during the user's initial entry, and while this beacon is still being detected by the RPi, a last seen-timestamp is updated. When the beacon is no longer detected a Check-In/Check-Out report packet is created and sent to the server containing the beacon/user ID, the check in-timestamp, the last seen-timestamp, and finally the ID of the RPi (Fig. 2). A thirty-second period is used to ensure that the occupant exited the RPi proximity.
The BLEBeacon dataset contains two files, one with the trial readings from the RSSI report operation (RSSI Report file) and the other from the Check-In/Check-Out report operation (Check-In Check-Out Report file). The RSSI Report file contains the following entries:
- Entry_id: unique identifier of a packet in the dataset.
- Beacon_id: unique identifier of the occupant/beacon.
- RSSI: the Received Signal Strength Indicator (RSSI) in dB.
- Timestamp: Date (Month/Day/Year) and Unix time (Hour:Second) of the advertisement packet reception moment from the Rpi.
- RPi_id: RPi that received the packet.
The Check-In/Check-Out Report file contains Entry_id, Beacon_id, and RPi_id as described above with the addition of two entries namely:
- In_time: Date (Month/Day/Year) and Unix time (Hour:Second) of the moment a user enters the RPi's vicinity and the first advertisement packet is received.
- Out_time: Date (Month/Day/Year) and Unix time (Hour:Second) of the last advertisement packet received from the same user by the specific RPi.
Further information and a detailed description of the sensing infrastructure setup, the real-subject trial, and the BLEBeacon dataset can be found in: D. Sikeridis, I. Papapanagiotou, M. Devetsikiotis, "BLEBeacon: A Real-Subject Trial Dataset from Mobile Bluetooth Low Energy Beacons", arXiv preprint arXiv:1802.08782, 2018.
Publications related to the BLEBeacon dataset:
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D. Sikeridis, B.P. Rimal, I. Papapanagiotou, and M. Devetsikiotis, 2018. Unsupervised crowd-assisted learning enabling location-aware facilities. IEEE Internet of Things Journal, 5(6), pp.4699-4713. (Available in: https://ieeexplore.ieee.org/document/8305452)
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D. Sikeridis, M. Devetsikiotis, I. Papapanagiotou, "Occupant Tracking in Smart Facilities: An Experimental Study", IEEE GlobalSIP Symposium on Signal Processing for Smart Cities & Internet of Things, Nov. 2017, Montreal, Canada
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D. Sikeridis, M. Devetsikiotis, I. Papapanagiotou, "A Cloud-Assisted Infrastructure for Occupancy Tracking in Smart Facilities", IBM Cloud Academy Conference (ICA CON) 2017, Wroclaw, Poland
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M. Inaya, M. Meli, D. Sikeridis, and M. Devetsikiotis, "A Real-Subject Evaluation Trial for Location-Aware Smart Buildings", Conference on Computer Communications Workshops (INFOCOM WKSHPS), Atlanta, GA, USA, May 1-4, IEEE 2017
A detailed survey about this area:
- F. Zafari, I. Papapanagiotou, K. Christidis, "Micro-location for Internet of Things equipped Smart Buildings", IEEE Internet of Things Journal, pp. 96-112, Feb. 2016 (publisher link)
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