Crescendo: An Infrastructure-free Ubiquitous Cellular Network-based Localization System

A ubiquitous outdoor localization system that is easy to deploy and works equally well for all mobile devices is highly-desirable. The GPS, despite its high accuracy, cannot be reliably used for this purpose since it is not available on low-end phones nor in areas with low satellite coverage. The application of classical fingerprinting approaches, on the other hand, is prohibited by excessive maintenance and deployment costs. In this paper, we propose Crescendo, a cellular network-based outdoor localization system that does not require calibration or infrastructure support. Crescendo builds on techniques borrowed from computational geometry to estimate the user's location. Specifically, given the network cells heard by the mobile device it leverages the Voronoi diagram of the network sites to provide an initial ambiguity area and incrementally reduces this area by leveraging pairwise site comparisons and visible cell information. Evaluation of Crescendo in both an urban and a rural area using real data shows median accuracies of 152m and 224m, respectively. This is an improvement over classical techniques by at least 18% and 15%, respectively.

[1]  Tao Gu,et al.  City-Scale Localization with Telco Big Data , 2016, CIKM.

[2]  Moustafa Youssef,et al.  The Diversity and Scale Matter: Ubiquitous Transportation Mode Detection Using Single Cell Tower Information , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[3]  Moustafa Youssef,et al.  TrueStory: Accurate and Robust RF-Based Floor Estimation for Challenging Indoor Environments , 2018, IEEE Sensors Journal.

[4]  Moustafa Youssef,et al.  Accurate and efficient map matching for challenging environments , 2014, SIGSPATIAL/GIS.

[5]  Moustafa Youssef,et al.  CellinDeep: Robust and Accurate Cellular-Based Indoor Localization via Deep Learning , 2019, IEEE Sensors Journal.

[6]  Moustafa Youssef,et al.  LaneQuest: An accurate and energy-efficient lane detection system , 2015, 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[7]  Mohamed Ibrahim,et al.  CellSense: An Accurate Energy-Efficient GSM Positioning System , 2011, IEEE Transactions on Vehicular Technology.

[8]  Jun Luo,et al.  WOLoc: WiFi-only outdoor localization using crowdsensed hotspot labels , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[9]  Ben Y. Zhao,et al.  Identifying Value in Crowdsourced Wireless Signal Measurements , 2017, WWW.

[10]  Moustafa Youssef,et al.  DeepLoc: a ubiquitous accurate and low-overhead outdoor cellular localization system , 2018, SIGSPATIAL/GIS.

[11]  Injong Rhee,et al.  Towards Mobile Phone Localization without War-Driving , 2010, 2010 Proceedings IEEE INFOCOM.

[12]  Ramesh Govindan,et al.  Energy-efficient positioning for smartphones using Cell-ID sequence matching , 2011, MobiSys '11.

[13]  Moustafa Youssef,et al.  Dejavu: an accurate energy-efficient outdoor localization system , 2013, SIGSPATIAL/GIS.

[14]  Hua Yang,et al.  Experimental Study of Telco Localization Methods , 2017, 2017 18th IEEE International Conference on Mobile Data Management (MDM).

[15]  Ning Xu,et al.  Mobile Localization Based on Received Signal Strength and Pearson's Correlation Coefficient , 2015, Int. J. Distributed Sens. Networks.

[16]  Mohamed Ibrahim,et al.  Enabling wide deployment of GSM localization over heterogeneous phones , 2013, 2013 IEEE International Conference on Communications (ICC).

[17]  Moustafa Youssef,et al.  Robust and ubiquitous smartphone-based lane detection , 2016, Pervasive Mob. Comput..

[18]  Moustafa Youssef,et al.  UPTIME: Ubiquitous pedestrian tracking using mobile phones , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[19]  Moustafa Youssef,et al.  Map++: A crowd-sensing system for automatic map semantics identification , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[20]  Ying Zhang,et al.  Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments , 2008, MobiCom 2008.

[21]  Moustafa Youssef,et al.  Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[22]  Moustafa Youssef,et al.  Accurate and Energy-Efficient GPS-Less Outdoor Localization , 2017, ACM Trans. Spatial Algorithms Syst..

[23]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[24]  Moustafa Youssef,et al.  SenseIO: Realistic Ubiquitous Indoor Outdoor Detection System Using Smartphones , 2018, IEEE Sensors Journal.

[25]  Moustafa Youssef,et al.  semMatch: road semantics-based accurate map matching for challenging positioning data , 2015, SIGSPATIAL/GIS.

[26]  Luis E. Ortiz,et al.  Network-side positioning of cellular-band devices with minimal effort , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[27]  Pravin Varaiya,et al.  RSSI-Fingerprinting-Based Mobile Phone Localization With Route Constraints , 2014, IEEE Transactions on Vehicular Technology.

[28]  Lukas Kencl,et al.  Active GSM cell-id tracking: "Where Did You Disappear?" , 2008, MELT '08.

[29]  Jun Luo,et al.  Learning-Based Outdoor Localization Exploiting Crowd-Labeled WiFi Hotspots , 2019, IEEE Transactions on Mobile Computing.

[30]  Mohamed N. El-Derini,et al.  GAC: Energy-Efficient Hybrid GPS-Accelerometer-Compass GSM Localization , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[31]  Per Enge,et al.  Special Issue on Global Positioning System , 1999, Proc. IEEE.

[32]  Moustafa Youssef,et al.  WiDeep: WiFi-based Accurate and Robust Indoor Localization System using Deep Learning , 2019, 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom.