Mobile Devices as an Infrastructure: A Survey of Opportunistic Sensing Technology

Now that billions of people carry sensor-enabled mobile devices (e.g., smartphones), employing powerful capability of such commercial mobile products has become a promising approach for large-scale environmental and human-behavioral sensing. Such a new paradigm of scalable context monitoring is known as opportunistic sensing, and has been successfully applied to a broad range of applications. In this paper, we briefly introduce basic architecture and building blocks on which these emerging systems are based, and then provide a survey of recent progress in the opportunistic sensing technology.

[1]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[3]  Ishwar K. Sethi,et al.  Classification of general audio data for content-based retrieval , 2001, Pattern Recognit. Lett..

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

[5]  Douglas A. Reynolds,et al.  An overview of automatic speaker recognition technology , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Bernt Schiele,et al.  Dead reckoning from the pocket - An experimental study , 2010, 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[7]  Joongheon Kim,et al.  Energy-efficient rate-adaptive GPS-based positioning for smartphones , 2010, MobiSys '10.

[8]  Zhigang Liu,et al.  Darwin phones: the evolution of sensing and inference on mobile phones , 2010, MobiSys '10.

[9]  Guobin Shen,et al.  BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices , 2007, SenSys '07.

[10]  Sung-Bae Cho,et al.  Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.

[11]  Helmut Hlavacs,et al.  Cellular data meet vehicular traffic theory: location area updates and cell transitions for travel time estimation , 2012, UbiComp '12.

[12]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[14]  Daniel Gatica-Perez,et al.  GroupUs: Smartphone Proximity Data and Human Interaction Type Mining , 2011, 2011 15th Annual International Symposium on Wearable Computers.

[15]  Byunghun Song,et al.  Surveillance Tracking System Using Passive Infrared Motion Sensors in Wireless Sensor Network , 2008, 2008 International Conference on Information Networking.

[16]  Ming-Syan Chen,et al.  ConvenienceProbe: A Phone-Based System for Retail Trade-Area Analysis , 2014, IEEE Pervasive Computing.

[17]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Klara Nahrstedt,et al.  Jyotish: A novel framework for constructing predictive model of people movement from joint Wifi/Bluetooth trace , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[19]  Peter A. Dinda,et al.  Indoor localization without infrastructure using the acoustic background spectrum , 2011, MobiSys '11.

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

[21]  Krista A. Ehinger,et al.  SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Eyal de Lara,et al.  Calibree: Calibration-Free Localization Using Relative Distance Estimations , 2009, Pervasive.

[23]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[24]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[25]  Antonio Torralba,et al.  Recognizing indoor scenes , 2009, CVPR.

[26]  Marco Gruteser,et al.  ParkNet: drive-by sensing of road-side parking statistics , 2010, MobiSys '10.

[27]  Wilhelm Stork,et al.  Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Patrick Robertson,et al.  Integration of foot-mounted inertial sensors into a Bayesian location estimation framework , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[29]  Hirozumi Yamaguchi,et al.  Detecting smoothness of pedestrian flows by participatory sensing with mobile phones , 2014, SEMWEB.

[30]  David Chu,et al.  On the feasibility of real-time phone-to-phone 3D localization , 2011, SenSys.

[31]  Aravind Srinivasan,et al.  eDiscovery: Energy efficient device discovery for mobile opportunistic communications , 2012, 2012 20th IEEE International Conference on Network Protocols (ICNP).

[32]  Hirozumi Yamaguchi,et al.  Clearing a Crowd: Context-Supported Neighbor Positioning for People-Centric Navigation , 2012, Pervasive.

[33]  Guangzhong Sun,et al.  Driving with knowledge from the physical world , 2011, KDD.

[34]  Yin Chen,et al.  FM-based indoor localization , 2012, MobiSys '12.

[35]  Romit Roy Choudhury,et al.  If you see something, swipe towards it: crowdsourced event localization using smartphones , 2013, UbiComp.

[36]  Wen Hu,et al.  Ear-phone: an end-to-end participatory urban noise mapping system , 2010, IPSN '10.

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

[38]  Kun Li,et al.  MAQS: a personalized mobile sensing system for indoor air quality monitoring , 2011, UbiComp '11.

[39]  Hirozumi Yamaguchi,et al.  Car-level congestion and position estimation for railway trips using mobile phones , 2014, UbiComp.

[40]  Eyal de Lara,et al.  GSM indoor localization , 2007, Pervasive Mob. Comput..

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

[42]  Mikkel Baun Kjærgaard,et al.  EnTracked: energy-efficient robust position tracking for mobile devices , 2009, MobiSys '09.

[43]  Ramesh Govindan,et al.  Cloud-enabled privacy-preserving collaborative learning for mobile sensing , 2012, SenSys '12.

[44]  Luca Benini,et al.  Tracking Motion Direction and Distance With Pyroelectric IR Sensors , 2010, IEEE Sensors Journal.

[45]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

[46]  Tarek F. Abdelzaher,et al.  GreenGPS: a participatory sensing fuel-efficient maps application , 2010, MobiSys '10.

[47]  Andrei Szabo,et al.  WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[48]  Alexandre M. Bayen,et al.  Estimating arterial traffic conditions using sparse probe data , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[49]  Qiang Yang,et al.  Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation , 2008, IEEE Transactions on Mobile Computing.

[50]  Li Deng,et al.  Challenges in adopting speech recognition , 2004, CACM.

[51]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[52]  Allison Woodruff,et al.  Common Sense Community: Scaffolding Mobile Sensing and Analysis for Novice Users , 2010, Pervasive.

[53]  M. Picheny,et al.  Comparison of Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences , 2017 .

[54]  Margaret Martonosi,et al.  Human mobility modeling at metropolitan scales , 2012, MobiSys '12.

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

[56]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[57]  Xing Xie,et al.  Sensing the pulse of urban refueling behavior , 2013, UbiComp.

[58]  Andreas Savvides,et al.  Tasking networked CCTV cameras and mobile phones to identify and localize multiple people , 2010, UbiComp.

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

[60]  Mikkel Baun Kjærgaard,et al.  Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones , 2012, UbiComp.

[61]  Wen Hu,et al.  Are you contributing trustworthy data?: the case for a reputation system in participatory sensing , 2010, MSWIM '10.

[62]  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).

[63]  Inseok Hwang,et al.  CoMon: cooperative ambience monitoring platform with continuity and benefit awareness , 2012, MobiSys '12.

[64]  Wei Pan,et al.  SoundSense: scalable sound sensing for people-centric applications on mobile phones , 2009, MobiSys '09.

[65]  Paul Lukowicz,et al.  Bluetooth based collaborative crowd density estimation with mobile phones , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[66]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[67]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.

[68]  S. Dixon.  ONSET DETECTION REVISITED , 2006 .

[69]  Mun Choon Chan,et al.  Low cost crowd counting using audio tones , 2012, SenSys '12.

[70]  Antonio Torralba,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition , 2022 .

[71]  Romit Roy Choudhury,et al.  EnLoc: Energy-Efficient Localization for Mobile Phones , 2009, IEEE INFOCOM 2009.

[72]  Davide Anguita,et al.  Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.

[73]  Oscar Mayora-Ibarra,et al.  Investigation of indoor localization with ambient FM radio stations , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

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

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

[76]  John Saunders,et al.  Real-time discrimination of broadcast speech/music , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[77]  Hirozumi Yamaguchi,et al.  A neighbor collaboration mechanism for mobile crowd sensing in opportunistic networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[78]  Sumit Basu.  A linked-HMM model for robust voicing and speech detection , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[79]  Ryosuke Shibasaki,et al.  A novel system for tracking pedestrians using multiple single-row laser-range scanners , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[80]  Malcolm Slaney,et al.  Construction and evaluation of a robust multifeature speech/music discriminator , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

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

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

[83]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[84]  Paul Lukowicz,et al.  Human tracking and identification using a sensitive floor and wearable accelerometers , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[85]  Salil S. Kanhere,et al.  A survey on privacy in mobile participatory sensing applications , 2011, J. Syst. Softw..

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