BLEMAT, the Bluetooth Low Energy Microlocation Asset Tracking is a semi space-agnostic, context-aware fog computing system that performs real-time indoor positioning, smoothing and filtering, fingerprinting and floor plan layout detection accompanied with various complex data analytics and forecasting tasks. BLEMAT is equipped with occupancy detection, neural network-based occupancy forecasting and patterns extraction algorithms, as well as codebase to build social relationships graphs, run social communities detection and track social communities evolution.
BLEMAT is deployed in a 5-building residential complex with cloud-managed networking and IoT infrastructure providing Internet access and IoT services. This is a repository of different positioning datasets obtained from one of deployment sites residential buildings. It contains original figures from BLEMAT scientific publications and a codebase for running various experiments on the data (SRCodeSamples). With respect to tenants privacy, only the Bluetooth data is made available, while the WiFi data is not disclosed. Furthermore, all timestamps and mac addresses of beacons have been scrambled. Note that some datasets are zipped (.7z format) to evade GitHub 100Mb file size limit.
Scientific publications related to BLEMAT:
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Pešić, Saša, et al. "Bluetooth low energy microlocation asset tracking (blemat) in a context-aware fog computing system." Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. 2018.
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Pešić, Saša, et al. "GEMAT-Internet of Things Solution for Indoor Security Geofencing." Proceedings of the 9th Balkan Conference on Informatics. 2019.
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Pešić, Saša, et al. "BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction." International Journal on Artificial Intelligence Tools 28.06 (2019): 1960005.