Currently, large amounts of Wi-Fi access logs are collected in diverse indoor environments, but cannot be widely used for ne-grained spatio-temporal analysis due to coarse positioning. We present a Log-based Di erential (D-Log) scheme for post-hoc localization based on di erentiated location estimates obtained from large-scale Access Point (AP) logs of WiFi connectivity traces, which can be used for data analysis and knowledge discovery of visitor behaviours. Speci cally, the location estimates are calculated by utilizing a combination of Received Signal Strength Indicator (RSSI) records from two neighbouring APs. D-Log exploits real-world industry WiFi logs where RSSI data sampled at low rates from single AP sources are recorded in each connectivity trace. The approach is independent of device and network infrastructure type. D-Log is evaluated using WiFi logs collected from controlled environment as well as real-world uncontrolled public indoor spaces, which includes discrete single-AP RSSI traces of around 100,000 mobile devices over a one-year period. The experiment results indicate that, despite of the challenges with the infrequent sampling rate and the limitations of the data that only records RSSI from single AP sources in each instance, D- Log performs comparatively well to the state-of-the-art RSSI-based localization methods and presents a viable alternative for many application areas where high-accuracy positioning infrastructure may
@article{DBLP:journals/percom/RenSTBCQS17,
author = {Yongli Ren and
Flora Dilys Salim and
Martin Tomko and
Yuntian Brian Bai and
Jeffrey Chan and
Kyle Kai Qin and
Mark Sanderson},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/journals/percom/RenSTBCQS17.bib},
doi = {10.1016/j.pmcj.2016.09.018},
journal = {Pervasive Mob. Comput.},
pages = {94--114},
timestamp = {Sat, 22 Feb 2020 00:00:00 +0100},
title = {D-Log: A WiFi Log-based differential scheme for enhanced indoor
localization with single RSSI source and infrequent sampling rate},
url = {https://doi.org/10.1016/j.pmcj.2016.09.018},
volume = {37},
year = {2017}
}
© 2021 Flora Salim - CRUISE Research Group.