Mobility Increases Localizability

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.

[1]  Fernando Seco Granja,et al.  Robust indoor positioning fusing PDR and RF technologies: The RFID and UWB case , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[2]  Reinhold Haux,et al.  A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Deborah Estrin,et al.  Discovering semantically meaningful places from pervasive RF-beacons , 2009, UbiComp.

[4]  Michael Trimble Dead Reckoning , 2006, Encyclopedia of Multimedia.

[5]  Krzysztof Janowicz,et al.  On the semantic annotation of places in location-based social networks , 2011, KDD.

[6]  Jie Yang,et al.  Push the limit of WiFi based localization for smartphones , 2012, Mobicom '12.

[7]  Philip Steadman Why are most buildings rectangular , 2006 .

[8]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[9]  G. Lachapelle,et al.  Assessment of Indoor Magnetic Field Anomalies using Multiple Magnetometers , 2010 .

[10]  Tom Minka,et al.  You are facing the Mona Lisa: spot localization using PHY layer information , 2012, MobiSys '12.

[11]  Swarun Kumar,et al.  Accurate indoor localization with zero start-up cost , 2014, MobiCom.

[12]  He Wang,et al.  I am a smartphone and i can tell my user's walking direction , 2014, MobiSys.

[13]  Agata Brajdic,et al.  Walk detection and step counting on unconstrained smartphones , 2013, UbiComp.

[14]  Valérie Renaudin,et al.  Step Length Estimation Using Handheld Inertial Sensors , 2012, Sensors.

[15]  Yuwei Chen,et al.  Accelerometer assisted robust wireless signal positioning based on a hidden Markov model , 2010, IEEE/ION Position, Location and Navigation Symposium.

[16]  Robert Harle,et al.  Pedestrian localisation for indoor environments , 2008, UbiComp.

[17]  V. M. Zat︠s︡iorskiĭ Kinematics of human motion , 1998 .

[18]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[19]  Kôiti Hasida,et al.  Rotation invariant feature extraction from 3-D acceleration signals , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

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

[22]  Liu Ming,et al.  Identification of Individual Walking Patterns Using Gait Acceleration , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[23]  Rodolfo Margaria,et al.  Biomechanics and Energetics of Muscular Exercise , 1976 .

[24]  Hojung Cha,et al.  Smartphone-based Wi-Fi pedestrian-tracking system tolerating the RSS variance problem , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[25]  Stefan Savage,et al.  On the empirical performance of self-calibrating WiFi location systems , 2011, 2011 IEEE 36th Conference on Local Computer Networks.

[26]  Kiyohito Yoshihara,et al.  An Integrated Location Method using Reference Landmarks for Dead Reckoning System , 2012, ICON 2012.

[27]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[28]  Yin Chen,et al.  Scalable and accurate indoor positioning on mobile devices , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[29]  Jian-Jiun Ding,et al.  Real time accelerometer-based gait recognition using adaptive windowed wavelet transforms , 2012, 2012 IEEE Asia Pacific Conference on Circuits and Systems.

[30]  Pei Zhang,et al.  SugarTrail: Indoor navigation in retail environments without surveys and maps , 2013, 2013 IEEE International Conference on Sensing, Communications and Networking (SECON).

[31]  Arvind Thiagarajan,et al.  Probabilistic models for mobile phone trajectory estimation , 2011 .

[32]  Gregory D. Abowd,et al.  Context-aware computing [Guest Editors' Intro.] , 2002, IEEE Pervasive Computing.

[33]  Sasu Tarkoma,et al.  Accelerometer-based transportation mode detection on smartphones , 2013, SenSys '13.

[34]  Robert P. Dick,et al.  Hallway based automatic indoor floorplan construction using room fingerprints , 2013, UbiComp.

[35]  Martin Klepal,et al.  A Backtracking Particle Filter for fusing building plans with PDR displacement estimates , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[36]  Yunhao Liu,et al.  WILL: Wireless indoor localization without site survey , 2012, 2012 Proceedings IEEE INFOCOM.

[37]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[38]  Guihai Chen,et al.  APT: Accurate outdoor pedestrian tracking with smartphones , 2013, 2013 Proceedings IEEE INFOCOM.

[39]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[40]  Yunhao Liu,et al.  Footprints elicit the truth: Improving global positioning accuracy via local mobility , 2013, 2013 Proceedings IEEE INFOCOM.

[41]  Yunhao Liu,et al.  Robust Trajectory Estimation for Crowdsourcing-Based Mobile Applications , 2014, IEEE Transactions on Parallel and Distributed Systems.

[42]  Shih-Hau Fang,et al.  Principal Component Localization in Indoor WLAN Environments , 2012, IEEE Transactions on Mobile Computing.

[43]  Moustafa Youssef,et al.  CrowdInside: automatic construction of indoor floorplans , 2012, SIGSPATIAL/GIS.

[44]  Henk L. Muller,et al.  Personal position measurement using dead reckoning , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[45]  Carsten Isert,et al.  Self-contained indoor positioning on off-the-shelf mobile devices , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[46]  Romit Roy Choudhury,et al.  Did you see Bob?: human localization using mobile phones , 2010, MobiCom.

[47]  Yunhao Liu,et al.  Locating in fingerprint space: wireless indoor localization with little human intervention , 2012, Mobicom '12.

[48]  Pedro José Marrón,et al.  A model for WLAN signal attenuation of the human body , 2013, UbiComp.

[49]  Bahram Honary,et al.  Indoor pedestrian displacement estimation using Smart phone inertial sensors , 2012 .

[50]  Guobin Shen,et al.  Walkie-Markie: Indoor Pathway Mapping Made Easy , 2013, NSDI.

[51]  Richard P. Martin,et al.  Detecting driver phone use leveraging car speakers , 2011, MobiCom.

[52]  Kenichi Yamazaki,et al.  Gait analyzer based on a cell phone with a single three-axis accelerometer , 2006, Mobile HCI.

[53]  Sebastian Tilch,et al.  Survey of optical indoor positioning systems , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[54]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[55]  Eckehard Steinbach,et al.  Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning , 2014, UbiComp.

[56]  Angelo M. Sabatini,et al.  A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[57]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[58]  共立出版株式会社 コンピュータ・サイエンス : ACM computing surveys , 1978 .

[59]  Wolfram Burgard,et al.  Particle Filters for Mobile Robot Localization , 2001, Sequential Monte Carlo Methods in Practice.

[60]  권기영,et al.  Inertial Measurement Unit를 이용한 관절 가동 범위 측정 , 2014 .

[61]  Albrecht Schmidt,et al.  There is more to context than location , 1999, Comput. Graph..

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

[63]  Seth J. Teller,et al.  Online pose classification and walking speed estimation using handheld devices , 2012, UbiComp '12.

[64]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[65]  Quentin Ladetto,et al.  On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering , 2000 .

[66]  Fanglin Chen,et al.  CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones , 2013, MobiSys.

[67]  Yunhao Liu,et al.  Sherlock: Micro-Environment Sensing for Smartphones , 2014, IEEE Transactions on Parallel and Distributed Systems.

[68]  Anshul Kumar,et al.  Strap-down Pedestrian Dead-Reckoning system , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[69]  Mo Li,et al.  IODetector: a generic service for indoor outdoor detection , 2012, SenSys '12.

[70]  Yiqiang Chen,et al.  Power-efficient access-point selection for indoor location estimation , 2006, IEEE Transactions on Knowledge and Data Engineering.

[71]  Injong Rhee,et al.  FM-based indoor localization via automatic fingerprint DB construction and matching , 2013, MobiSys '13.

[72]  Song Han,et al.  WheelLoc: Enabling continuous location service on mobile phone for outdoor scenarios , 2013, 2013 Proceedings IEEE INFOCOM.

[73]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[74]  Moustafa Youssef,et al.  No need to war-drive: unsupervised indoor localization , 2012, MobiSys '12.

[75]  M. Bernardine Dias,et al.  Robust Indoor Localization on a Commercial Smart Phone , 2012, ANT/MobiWIS.

[76]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[77]  Hojung Cha,et al.  Smartphone-based pedestrian tracking in indoor corridor environments , 2011, Personal and Ubiquitous Computing.

[78]  Min Gao,et al.  FILA: Fine-grained indoor localization , 2012, 2012 Proceedings IEEE INFOCOM.

[79]  Valérie Renaudin,et al.  Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users , 2013, Sensors.

[80]  Xiaolin Li,et al.  Guoguo: enabling fine-grained indoor localization via smartphone , 2013, MobiSys '13.

[81]  Cecilia Mascolo,et al.  EmotionSense: a mobile phones based adaptive platform for experimental social psychology research , 2010, UbiComp.

[82]  Paul Congdon,et al.  Avoiding multipath to revive inbuilding WiFi localization , 2013, MobiSys '13.

[83]  H. Weinberg Using the ADXL202 in Pedometer and Personal Navigation Applications , 2002 .

[84]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.

[85]  Qiang Yang,et al.  Transferring Localization Models over Time , 2008, AAAI.

[86]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[87]  Sneha Kumar Kasera,et al.  Robust location distinction using temporal link signatures , 2007, MobiCom '07.

[88]  A. Ruina,et al.  Multiple walking speed-frequency relations are predicted by constrained optimization. , 2001, Journal of theoretical biology.

[89]  Haiyun Luo,et al.  Zero-Configuration, Robust Indoor Localization: Theory and Experimentation , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[90]  Hojung Cha,et al.  Automatically characterizing places with opportunistic crowdsensing using smartphones , 2012, UbiComp.

[91]  WangXu,et al.  Mobility Increases Localizability , 2015 .

[92]  William J. Kaiser,et al.  AutoGait: A mobile platform that accurately estimates the distance walked , 2010, 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[93]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[94]  Mahesh K. Marina,et al.  HiMLoc: Indoor smartphone localization via activity aware Pedestrian Dead Reckoning with selective crowdsourced WiFi fingerprinting , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[95]  P. Dourish,et al.  Context-Aware Computing , 2001 .

[96]  Shaojie Tang,et al.  Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation , 2013, Journal of Computer Science and Technology.

[97]  F. Seco,et al.  A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[98]  Juha Röning,et al.  Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data , 2012, Int. J. Interact. Multim. Artif. Intell..

[99]  P. Barralon,et al.  Walk Detection With a Kinematic Sensor: Frequency and Wavelet Comparison , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[100]  Mo Li,et al.  How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing , 2012, IEEE Transactions on Mobile Computing.

[101]  Patrick Robertson,et al.  FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors—Hitchhiking on Human Perception and Cognition , 2012, Proceedings of the IEEE.

[102]  Yunhao Liu,et al.  MoLoc: On Distinguishing Fingerprint Twins , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.

[103]  Jun Sun,et al.  Social-Loc: improving indoor localization with social sensing , 2013, SenSys '13.

[104]  Xing Xie,et al.  Learning location naming from user check-in histories , 2011, GIS.

[105]  Pei Zhang,et al.  Headio: zero-configured heading acquisition for indoor mobile devices through multimodal context sensing , 2013, UbiComp.