Enabling entity discovery in indoor commercial environments without pre-deployed infrastructure

Abstract

Finding entities of interest in indoor commercial places, such as the merchandise in supermarkets and shopping malls, is an essential issue for customers, especially when they are unfamiliar with an ad hoc indoor environment. This type of location-based indoor service requires comprehensive knowledge of indoor entities, including locations as well as their semantic information. However, the existing indoor localization approaches fail to directly localize these general entities without dedicated devices. This paper first focuses on the problem of discovering large-scale general entities of interest in indoor commercial spaces without pre-deployed infrastructure.We present a unique entity localization approach that leverages the localization results from multiple independent users to accurately determine the location of corresponding entities. Our key idea is to exploit the short-distance estimation with dead reckoning to guarantee the accuracy of entity localization. We develop a prototype system based on the crowdsourcing method, iScan, and test it in one of the biggest supermarkets in Changsha, China, to validate the performance of our design. Extensive experimental results show that our approach can achieve meter-level accuracy in a single day with 70 participants. Moreover, in a monthly evaluation with 500 effective participants, iScan discovered more than 200 entities and localized approximately 75% of them within 2 m.

Keywords

indoor localization / entity discovery / crowdsourcing system / location-based service

References

[1] Nandakumar R, Chintalapudi K K, Padmanabhan V N. Centaur: locating devices in an office environment. In: Proceedings of ACM International Conference on Mobile Computing and Networking. 2012, 281–292

[2] Ni L M, Liu Y H, Lau , Y C, Patil A P. LANDMARC: indoor location sensing using active RFID. Wireless Networks, 2004, 10(6): 701–710

[3] Xiong J, Jamieson K. ArrayTrack: a fine-grained indoor location system. In: Proceedings of USENIX Symposium on Networked Systems Design and Implementation. 2013, 71–84

[4] Liu K, Liu X, Li X. Guoguo: enabling fine-grained indoor localization via smartphone. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2013, 235–248

[5] Sen S, Lee J, Kim K H, Congdon P. Avoiding multipath to revive inbuilding WiFi localization. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2013, 249–262

[6] Bahl P, Padmanabhan V N. RADAR: an in-building RF-based user location and tracking system. In: Proceedings of IEEE International Conference on Computer Commnication. 2000, 775–784

[7] Chung J, Donahoe M, Schmandt C, Kim I J, Razavai P, Wiseman M. Indoor location sensing using geo-magnetism. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2011, 141–154

[8] Wang H, Sen S, Elgohary A, Farid M, Youssef M, Choudhury R R. No need to war-drive: unsupervised indoor localization. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2012, 197–210

[9] Newman N. Apple iBeacon technology briefing. Journal of Direct, Data and Digital Marketing Practice, 2014, 15(3): 222–225

[10] Macé S, Locteau H, Valveny E, Tabbone S. A system to detect rooms in architectural floor plan images. In: Proceedings of ACM International Workshop on Document Analysis Systems. 2010, 167–174

[11] Zhou P, Li M, Shen G. Use it free: instantly knowing your phone attitude. In: Proceedings of ACM International Conference on Mobile Computing and Networking. 2014, 605–616

[12] Kumar S, Gil S, Katabi D, Rus D. Accurate indoor localization with zero start-up cost. In: Proceedings of ACM International Conference on Mobile Computing and Networking. 2014, 483–494

[13] Cho D K, Mun M, Lee U, Kaiser W J, Gerla M. Autogait: a mobile platform that accurately estimates the distance walked. In: Proceedings of IEEE International Conference on Pervasive Computing and Communications. 2010, 116–124

[14] Crisan D, Doucet A. A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing, 2002, 50(3): 736–746

[15] Li F, Zhao C, Ding G, Gong J, Liu C, Zhao F. A reliable and accurate indoor localization method using phone inertial sensors. In: Proceedings of ACM Conference on Ubiquitous Computing. 2012, 421–430

[16] Zhou P F, Zheng Y Q, Li Z J, Li M, Shen G B. IODetector: a generic service for indoor outdoor detection. In: Proceedings of ACM International Conference on Embedded Network Sensor Systems. 2012, 113–126

[17] Abdelnasser H, Mohamed R, Elgohary A, Alzantot M F, Wang H, Sen S, Choudhury R R, Youssef M. SemanticSLAM: using environment landmarks for unsupervised indoor localization. IEEE Transactions on Mobile Computing, 2016, 15(7): 1770–1782

[18] Constandache I, Bao X, Azizyan M, Choudhury R R. Did you see Bob?: human localization using mobile phones. In: Proceedings of ACM International Conference on Mobile Computing and Networking. 2010, 149–160

[19] Shen G, Chen Z, Zhang P, Moscibroda T, Zhang Y. Walkie-Markie: indoor pathway mapping made easy. In: Proceedings of USENIX Symposium on Networked Systems Design and Implementation. 2013, 85–98

[20] Yang Z, Wu C S, Zhou Z M, Zhang X L, Wang X, Liu Y H. Mobility increases localizability: a survey on wireless indoor localization using inertial sensors. ACM Computing Surveys, 2015, 47(3): 54

[21] Azizyan M, Constandache I, Roy Choudhury R. SurroundSense: mobile phone localization via ambience fingerprinting. In: Proceedings of ACMInternational Conference onMobile Computing and Networking. 2009, 261–272

[22] Bisio I, Lavagetto F, Marchese M, Sciarrone A. Energy efficient WiFibased fingerprinting for indoor positioning with smartphones. In: Proceedings of IEEE Globecom Workshops. 2013, 4639–4643

[23] Bisio I, Cerruti M, Lavagetto F, Marchese M, Pastorino M, Randazzo A, Sciarrone A. A trainingless WiFi fingerprint positioning approach over mobile devices. IEEE Antennas andWireless Propagation Letters, 2014, 13(1): 832–835

[24] Bisio I, Lavagetto F, Marchese M, Sciarrone A. Smart probabilistic fingerprinting for WiFi-based indoor positioning with mobile devices. Pervasive and Mobile Computing, 2016, 31: 107–123

[25] Chen Y, Lymberopoulos D, Liu J, Priyantha B. FM-based indoor localization. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2012, 169–182

[26] Chung J, Donahoe M, Schmandt C, Kim I J, Razavai P, Wiseman M. Indoor location sensing using geo-magnetism. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2011, 141–154

[27] Sen S, Radunovic B, Choudhury R R, Minka T. You are facing the Mona Lisa: spot localization using phy layer information. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2012, 183–196

[28] Yang Z, Wu C S, Liu Y H. Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of ACMInternational Conference onMobile Computing and Networking. 2012, 269–280

[29] Rai A, Chintalapudi K K, Padmanabhan V N, Sen R. Zee: zero-effort crowdsourcing for indoor localization. In: Proceedings of ACM International Conference on Mobile Computing and Networking. 2012, 293–304

[30] Wu K S, Xiao J, Yi Y W, Gao M, Ni L M. Fila: fine-grained indoor localization. In: Proceedings of IEEE International Conference on Computer Commnication. 2012, 2210–2218

[31] Xiao J, Yi Y W, Wang L, Li H C, Zhou , Z M, Wu K S, Ni L M. Nom- Loc: calibration-free indoor localization with nomadic access points. In: Proceedings of IEEE International Conference on Distributed Computing Systems. 2014, 587–596

[32] Manweiler J G, Jain P, Choudhury R R. Satellites in our pockets: an object positioning system using smartphones. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2012, 211–224

[33] Shangguan L F, Zhou Z M, Yang Z, Liu K B, Li Z J, Zhao X B, Liu Y H. Towards accurate object localization with smartphones. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(10): 2731–2742

[34] Shangguan L F, Li Z J, Yang Z, Li M, Liu Y H. Otrack: order tracking for luggage in mobile RFID systems. In: Proceedings of IEEE International Conference on Computer Commnication. 2013, 3066–3074

[35] Zou Y P, Xiao J, Han J S, Wu K S, Li Y, Ni L M. Grfid: a device-free rfid-based gesture recognition system. IEEE Transactions on Mobile Computing, 2017, 16(2): 381–393

[36] Zou Y P, Wang G H, Wu K S, Ni L M. SmartScanner: know more in walls with your smartphone! IEEE Transactions on Mobile Computing, 2016, 15(11): 2865–2877

[37] Aly H, Basalamah A, Youssef M. Map++: a crowd-sensing system for automatic map semantics identification. In: Proceedings of IEEE International Conference on Sensing, Communication, and Networking. 2014, 546–554

[38] Luo C, Hong H, Cheng L, Sankaran K, Chan M C. iMap: automatic inference of indoor semantics exploiting opportunistic smartphone sensing. In: Proceedings of IEEE International Conference on Sensing, Communication, and Networking. 2015, 489–497

[39] Yang D J, Xue G L, Fang X, Tang J. Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: Proceedings of ACM International Conference onMobile Computing and Networking. 2012, 173–184

[40] Zhang X, Xue G L, Yu R Z, Yang D J, Tang J. Truthful incentive mechanisms for crowdsourcing. In: Proceedings of IEEE International Conference on Computer Commnication. 2015, 2830–2838

[41] Gordon M, Zhang L, Tiwana B, Dick R, Mao Z M, Yang L. PowerTutor: a power monitor for android-based mobile platforms. An Android Application, 2013

[42] Wang X G. Deep learning in object recognition, detection, and segmentation. Foundations and Trends in Signal Processing, 2016, 8(4): 217–382

[43] Elhamshary M, Youssef M, Uchiyama A, Yamaguchi H, Higashino T. TransitLabel: a crowd-sensing system for automatic labeling of transit stations semantics. In: Proceedings of ACM International Conference on Mobile Systems, Applications, and Services. 2016, 193–206

[44] Liu C H, Zhang L, Liu Z Q, Liu K B, Li X Y, Liu Y H. Lasagna: towards deep hierarchical understanding and searching over mobile sensing data. In: Proceedings of ACM International Conference on Mobile Computing and Networking. 2016, 334–347

[45] Wang Y X, Wu K S, Ni LM. Wifall: device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 2017, 16(2): 581–594

[46] Bisio I, Lavagetto F, Marchese M, Sciarrone A. GPS/HPS-and Wi-Fi fingerprint-based location recognition for check-in applications over smartphones in cloud-based LBSs. IEEE Transactions on Multimedia, 2013, 15(4): 858–869

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature