Identifying Value in Crowdsourced Wireless Signal Measurements

Zhijing Li, Ana Nika, Xinyi Zhang, Yanzi Zhu, Yuanshun Yao, Ben Y. Zhao, Haitao Zheng
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
While crowdsourcing is an attractive approach to collect large-scale wireless measurements, understanding the quality and variance of the resulting data is difficult. Our work analyzes the quality of crowdsourced cellular signal measurements in the context of basestation localization, using large international public datasets (419M signal measurements and ∼1M cells) and corresponding ground truth values. Performing localization using raw received signal strength (RSS) data produces poor results
more » ... and very high variance. Applying supervised learning improves results moderately, but variance remains high. Instead, we propose feature clustering, a novel application of unsupervised learning to detect hidden correlation between measurement instances, their features, and localization accuracy. Our results identify RSS standard deviation and RSSweighted dispersion mean as key features that correlate with highly predictive measurement samples for both sparse and dense measurements respectively. Finally, we show how optimizing crowdsourcing measurements for these two features dramatically improves localization accuracy and reduces variance.
doi:10.1145/3038912.3052563 dblp:conf/www/LiNZZYZZ17 fatcat:p473oxsfr5he7pfmre3psxt6aa