Zheng Yang
Yunhao Liu
ABSTRACT
Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.
- M. Azizyan, I. Constandache, and R. Roy Choudhury. Surroundsense: mobile phone localization via ambience fingerprinting. In Proceedings of ACM MobiCom, pages 261--272, 2009. Google Scholar
Digital Library
- P. Bahl and V. N. Padmanabhan. RADAR: an in-building RF-based user location and tracking system. In Proceedings of IEEE INFOCOM, volume 2, pages 775--784, 2000.Google ScholarCross Ref
- A. Bjorck. Numerical methods for least squares problems. Number 51. Society for Industrial Mathematics, 1996.Google Scholar
- I. Borg and P. Groenen. Modern multidimensional scaling: Theory and applications. Springer Verlag, 2005.Google Scholar
- Y. Chen, D. Lymberopoulos, J. Liu, and B. Priyantha. Fm-based indoor localization. In Proceedings of the ACM MobiSys 2012, MobiSys '12, pages 169--182, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan. Indoor localization without the pain. In Proceedings of ACM MobiCom, pages 173--184, 2010. Google ScholarDigital Library
- T. Cormen. Introduction to algorithms. The MIT press, 2001. Google ScholarDigital Library
- J. Costa, N. Patwari, and A. Hero III. Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Transactions on Sensor Networks (TOSN), 2(1):39--64, 2006. Google ScholarDigital Library
- B. Ferris, D. Fox, and N. Lawrence. Wifi-slam using gaussian process latent variable models. In Proceedings of IJCAI, pages 2480--2485, 2007. Google ScholarDigital Library
- R. Floyd. Algorithm 97: shortest path. Communications of the ACM, 5(6):345, 1962. Google ScholarDigital Library
- W. G. Griswold, P. Shanahan, S. W. Brown, R. Boyer, M. Ratto, R. B. Shapiro, and T. M. Truong. ActiveCampus: experiments in community-oriented ubiquitous computing. Computer, 37(10):73--81, 2004. Google ScholarDigital Library
- J. Huang, D. Millman, M. Quigley, D. Stavens, S. Thrun, and A. Aggarwal. Efficient, generalized indoor wifi graphslam. In IEEE International Conference on Robotics and Automation, ICRA 2011, Shanghai, China, 9-13 May 2011, pages 1038--1043. IEEE, 2011.Google ScholarCross Ref
- A. Jimenez, F. Seco, C. Prieto, and J. Guevara. A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. In Intelligent Signal Processing, IEEE International Symposium on, pages 37--42, 2009.Google Scholar
- J. Koo and H. Cha. Autonomous construction of a WiFi access point map using multidimensional scaling. Pervasive Computing, pages 115--132, 2011. Google ScholarDigital Library
- A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, I. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, et al. Place lab: Device positioning using radio beacons in the wild. Pervasive Computing, pages 301--306, 2005. Google ScholarDigital Library
- H. Lim, L. C. Kung, J. C. Hou, and H. Luo. Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure. Wireless Networks, 16(2):405--420, 2010. Google ScholarDigital Library
- J. MacQueen et al. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, page 14, 1967.Google Scholar
- D. Madigan, E. Einahrawy, R. P. Martin, W. H. Ju, P. Krishnan, and A. S. Krishnakumar. Bayesian indoor positioning systems. In Proceedings of IEEE INFOCOM, volume 2, pages 1217--1227, 2005.Google ScholarCross Ref
- M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit. Fastslam: A factored solution to the simultaneous localization and mapping problem. In Proceedings of AAAI, pages 593--598, 2002. Google ScholarDigital Library
- L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil. LANDMARC: indoor location sensing using active RFID. Wireless Networks, 10(6):701--710, 2004. Google ScholarDigital Library
- D. Niculescu and B. Nath. Ad hoc positioning system (APS) using AOA. In Proceedings of IEEE INFOCOM, volume 3, pages 1734--1743, 2003.Google ScholarCross Ref
- T. Opsahl, F. Agneessens, and J. Skvoretz. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3):245--251, 2010.Google ScholarCross Ref
- J. Park, B. Charrow, D. Curtis, J. Battat, E. Minkov, et al. Growing an organic indoor location system. In Proceedings of ACM MobiSys, pages 271--284, 2010. Google ScholarDigital Library
- N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In Proceedings of ACM MobiCom, pages 32-43, 2000. Google ScholarDigital Library
- T. Pulkkinen, T. Roos, and P. Myllymaki. Semi-supervised learning for wlan positioning. Artificial Neural Networks and Machine Learning, pages 355--362, 2011. Google ScholarDigital Library
- A. Rai, R. Sen, K. K. Chintalapudi, and V. Padmanabhan. Zee: Zero-effort crowdsourcing for indoor localization. In Proceedings of ACM MobiCom, 2012. Google ScholarDigital Library
- S. Sen, B. Radunovic, R. R. Choudhury, and T. Minka. You are facing the mona lisa: spot localization using phy layer information. In Proceedings of the ACM MobiSys 2012, MobiSys '12, pages 183--196, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- Y. Shang and W. Ruml. Improved MDS-based localization. In Proceedings of IEEE INFOCOM, volume 4, pages 2640--2651, 2004.Google ScholarCross Ref
- Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz. Localization from mere connectivity. In Proceedings of ACM MobiHoc, pages 201--212, 2003. Google ScholarDigital Library
- S. Thrun, W. Burgard, and D. Fox. Probabilistic Robotics. MIT Press, 2005.Google Scholar
- D. Turner, S. Savage, and A. Snoeren. On the empirical performance of self-calibrating wifi location systems. In Local Computer Networks (LCN), 2011 IEEE 36th Conference on, pages 76--84, oct. 2011. Google ScholarDigital Library
- H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. Choudhury. No need to war-drive: Unsupervised indoor localization. In Proceedings of ACM MobiSys, 2012. Google ScholarDigital Library
- O. Woodman and R. Harle. Pedestrian localisation for indoor environments. In Proceedings of ACM UbiComp, pages 114--123, 2008. Google ScholarDigital Library
- C. Wu, Z. Yang, Y. Liu, and W. Xi. WILL: wireless indoor localization without site survey. In INFOCOM, 2012 Proceedings IEEE, pages 64--72, Mar. 2012.Google Scholar
- M. Youssef and A. Agrawala. The horus WLAN location determination system. In Proceedings of ACM MobiSys, pages 205--218, 2005. Google ScholarDigital Library
- M. Youssef, A. Youssef, C. Rieger, U. Shankar, and A. Agrawala. Pinpoint: An asynchronous time-based location determination system. In Proceedings of ACM MobiSys, pages 165--176, 2006. Google ScholarDigital Library
- Z. Zhang, X. Zhou, W. Zhang, Y. Zhang, G. Wang, B. Y. Zhao, and H. Zheng. I am the antenna: accurate outdoor ap location using smartphones. In Proceedings of the 17th annual international conference on Mobile computing and networking, MobiCom '11, pages 109--120, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
Index Terms
- Locating in fingerprint space: wireless indoor localization with little human intervention
Recommendations
Locating using prior information: wireless indoor localization algorithm
SIGCOMM '13: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMMMost indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as ...
Locating using prior information: wireless indoor localization algorithm
Most indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as ...
Demo: ALPS -- The Acoustic Location Processing System
SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor SystemsWe demonstrate the Acoustic Location Processing System (ALPS), a platform that augments BLE proximity beacons with ultrasonic transmitters in a manner that allows for precise and robust indoor localization. {\em ALPS} uses Time-Difference-Of-Arrival (...
Comments