Paper Info
Title
D-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate.
Abstract
Currently, large amounts of Wi-Fi access logs are collected in diverse indoor environments, but cannot be widely used for fine-grained spatio-temporal analysis due to coarse positioning. We present a Log-based Differential (D-Log) scheme for post-hoc localization based on differentiated 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. Specifically, 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 not be cost effective or where positioning applications are considered on legacy information infrastructure.
Year
DOI
Venue
2017
10.1016/j.pmcj.2016.09.018
Pervasive and Mobile Computing
Keywords
Field
DocType
RSSI,WiFi log,Localization
Computer science,Sampling (signal processing),Computer network,Exploit,Real-time computing,Mobile device,Signal strength,Knowledge extraction,Visitor pattern,Information infrastructure
Journal
Volume
ISSN
Citations 
37
1574-1192
6
PageRank 
References 
Authors
0.46
33
7
Name
Order
Citations
PageRank
Yongli Ren114223.56
Flora Dilys Salim212629.67
Martin Tomko384.21
Yuntian Brian Bai4142.96
Jeffrey Chan542736.55
Kyle Kai Qin660.46
Mark Sanderson73751341.56