›› 2014, Vol. 29 ›› Issue (4): 589-604.doi: 10.1007/s11390-014-1452-7

• Computer Networks and Distributed Systems • Previous Articles     Next Articles

Communicating Is Crowdsourcing：Wi-Fi Indoor Localization with CSI-Based Speed Estimation

Zhi-Ping Jiang1 (蒋志平), Wei Xi1,* (惠维), Member, CCF, ACM, IEEE Xiangyang Li1,2 (李向阳), Senior Member, IEEE, Member, ACM, Shaojie Tang3 (唐少杰), Member, IEEE Ji-Zhong Zhao1 (赵季中), Member, CCF, ACM, IEEE, Jin-Song Han1 (韩劲松), Member, CCF, ACM, IEEE, Kun Zhao1 (赵鲲), Zhi Wang1 (王志), and Bo Xiao4 (肖波)   

  1. 1. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710000, China;
    2. Department of Computer Science, Illinois Institute of Technology, Chicago, IL 60616, U.S.A.;
    3. Department of Information Science, University of Texas at Dallas, Richardson, TX, U.S.A.;
    4. Network Center, The Aviation University of Air Fone, Jinzhou 121000, China
  • Online:2014-07-05 Published:2014-07-05
  • About author:Zhi-Ping Jiang is a Ph.D candidate at Xi'an Jiaotong University, Xi'an. His research interests include localization, smart sensing, wireless communication, and image processing.
  • Supported by:

    The work is supported by the National Natural Science Foundation of China under Grant Nos. 61325013, 61190112, 61170216, and 61228202, the Natural Science Foundation of USA under Grant Nos. CNS-0832120, CNS-1035894, ECCS-1247944, and ECCS-1343306, the Fundamental Research Funds for the Central Universities of China under Project No. 2012jdgz02 (Xi'an Jiaotong University), and the Research Fund for the Doctoral Program of Higher Education of China under Project No. 20130201120016.

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Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, Wi-Fi fingerprint-based approaches are the most popular solutions, and the core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner, however the low participation rate blocks the practical use.
In this work, we propose a passive crowdsourcing CSI-based Indoor Localization scheme, C2IL. Despite a crowdsourcing-based approach, our scheme is totally transparent to client except the only requirement is to connect to our 802.11n APs. C2IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n Channel State Information (CSI). Knowing the walking speed of a client and its surrounding APs, a graph-matching algorithm is employed to extract the RSS fingerprints and establish the fingerprint map. In localization phase, we design a trajectory clustering-based localization algorithm to provide precise realtime indoor localization and tracking. We developed and deployed a practical working system of C2IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2m at 80% time in very complex indoor environment.

[1] Sen S, Choudhury R R, Nelakuditi S. SpinLoc: Spin once to know your location. In Proc. the 12th Workshop on Mobile Computing Systems & Applications, Feb. 2012, Article No.12.

[2] Rai A, Chintalapudi K K, Padmanabhan V N, Sen R. Zee: Zero-effort crowdsourcing for indoor localization. In Proc. the 18th International Conference on Mobile Computing and Networking, Aug. 2012, pp.293-304.

[3] Zhang L, Liu K, Jiang Y, Li X, Liu Y, Yang P. Montage: Combine frames with movement continuity for realtime multiuser tracking. In Proc. the 33rd Annual IEEE International Conference On Computer Communications, Apr. 27-May 2, 2014.

[4] Huang W, Xiong Y, Li X et al. Shake and walk: Acoustic direction finding and fine-grained indoor localization using smartphones. In Proc. the 33rd Annual IEEE International Conference On Computer Communications, Apr. 27-May 2, 2014.

[5] Yang Z, Wu C, Liu Y. Locating in fingerprint space: Wireless indoor localization with little human intervention. In Proc. the 18th International Conference on Mobile Computing and Networking, Aug. 2012, pp.269-280.

[6] Nandakumar R, Chintalapudi K K, Padmanabhan V N. Centaur: Locating devices in an office environment. In Proc. the 18th International Conference on Mobile Computing and Networking, Aug. 2012, pp.281-292.

[7] Liu H, Gan Y, Yang J, Sidhom S, Wang Y, Chen Y, Ye F. Push the limit of WiFi based localization for smartphones. In Proc. the 18th International Conference on Mobile Computing and Networking, Aug. 2012, pp.305-316.

[8] Rappaport T S. Wireless Communications: Principles and Practice. Prentice Hall PTR, New Jersey, USA, 1996.

[9] Liu J, Priyantha B, Hart T, Ramos H S, Loureiro A A, Wang Q. Energy efficient GPS sensing with cloud offloading. In Proc. the 10th ACM Conference on Embedded Network Sensor Systems, Nov. 2012, pp.85-98.

[10] Zhao J, Xi W, He Y et al. Localization of wireless sensor networks in the wild: Pursuit of ranging quality. IEEE/ACM Transactions on Networking, 2013, 21(1): 311-323.

[11] Yang Z, Liu Y, Li X. Beyond trilateration: On the localizability of wireless ad hoc networks. IEEE/ACM Transactions on Networking, 2010, 18(6): 1806-1814.

[12] Bo C, Ren D, Tang S et al. Locating sensors in the forest: A case study in Greenorbs. In Proc. the 31st Annual IEEE International Conference on Computer Communications, Mar. 2012, pp.1026-1034.

[13] Xi W, Zhao J, Liu X, Li X, Qi Y. EUL: An efficient and universal localization method for wireless sensor network. In Proc. the 29th IEEE International Conference on Distributed Computing Systems, Jun. 2009, pp.433-440.

[14] Bahl P, Padmanabhan V N. RADAR: An in-building RFbased user location and tracking system. In Proc. the 19th Annual IEEE International Conference on Computer Communications, Mar. 2000, pp.775-784.

[15] Youssef M, Agrawala A. The horus location determination system. Wireless Networks, 2008, 14(3): 357-374.

[16] Azizyan M, Constandache I, Roy Choudhury R. Surroundsense: Mobile phone localization via ambience fingerprinting. In Proc. the 15th Annual International Conference on Mobile Computing and Networking, Sept. 2009, pp.261-272.

[17] Sen S, Radunovic B, Choudhury R R, Minka T. You are facing the Mona Lisa: Spot localization using PHY layer information. In Proc. the 10th International Conference on Mobile Systems, Applications, and Services, Jun. 2012, pp.183-196.

[18] Constandache I, Choudhury R R, Rhee I. Towards mobile phone localization without war-driving. In Proc. the 29th Annual IEEE International Conference on Computer Communications, Mar. 2010, pp.2321-2329.

[19] Guha S, Plarre K, Lissner D, Mitra S, Krishna B, Dutta P, Kumar S. Autowitness: Locating and tracking stolen property while tolerating GPS and radio outages. ACM Transactions on Sensor Networks, 2012, 8(4): Article No.31.

[20] Jiang Z, Zhao J, Han J, Tang S, Zhao J, Xi W. Wi-Fi fingerprint based indoor localization without indoor space measurement. In Proc. the 10th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, Oct. 2013, pp.384-392.

[21] Halperin D, Hu W, Sheth A, Wetherall D. Tool release: Gathering 802.11n traces with channel state information. ACM SIGCOMM Computer Communication Review, 2011, 41(1): 53.

[22] Yang Z, Zhou Z, Liu Y. From RSSI to CSI: Indoor localization via channel response. ACM Computing Surveys, 2013, 46(2): Article No.25.

[23] Jiang Z, Zhao J, Li X et al. Rejecting the attack: Source authentication for Wi-Fi management frames using CSI information. In Proc. the 32nd Annual IEEE International Conference on Computer Communications, Apr. 2013, pp.25442552.

[24] Xi W, Zhao J, Li X et al. Electronic frog eye: Counting crowd using WiFi. In Proc. the 33rd Annual IEEE International Conference on Computer Communications, Apr. 2014.

[25] Han J, Qian C, Ma D et al. Twins: Device-free object tracking using passive tags. In Proc. the 33rd Annual IEEE International Conference on Computer Communications, Apr. 2014.

[26] Xiong J, Jamieson K. Arraytrack: A fine-grained indoor location system. In Proc. the 10th USENIX Conference on Networked Systems Design and Implementation, Apr. 2013, pp.71-84.

[27] Adib F, Katabi D. See through walls with WiFi! In Proc. the ACM SIGCOMM 2013 Conference, Aug. 2013, pp.75-86.

[28] Wang J, Katabi D. Dude, where's my card? RFID positioning that works with multipath and non-line of sight. In Proc. the ACM SIGCOMM 2013 Conference, Aug. 2013, pp.51-62.

[29] Adib F, Kabelac Z, Katabi D et al. 3D tracking via body radio reflections. In Proc. the 11th USENIX Conference on Networked Systems Design and Implementation, Apr. 2014, pp.317-329.

[30] Azemi G, Senadji B, Boashash B. Mobile unit velocity estimation based on the instantaneous frequency of the received signal. IEEE Transactions on Vehicular Technology, 2004, 53(3): 716-724.

[31] Mohanty S. VEPSD: A novel velocity estimation algorithm for next-generation wireless systems. IEEE Transactions on Wireless Communications, 2005, 4(6): 2655-2660.

[32] Pricope B, Haas H. Experimental validation of a new pedestrian speed estimator for OFDM systems in indoor environments. In Proc. the 54th IEEE Global Communications Conference, Dec. 2011.

[33] Cho M, Lee J, Lee K M. Reweighted random walks for graph matching. In Proc. the 11th European Conference on Computer Vision, Sept. 2010, Part 4, pp.492-505.

[34] Leordeanu M, Hebert M. A spectral technique for correspondence problems using pairwise constraints. In Proc. the 10th IEEE International Conference on Computer Vision, Oct. 2005, pp.1482-1489.

[35] Sklar B. Rayleigh fading channels in mobile digital communication systems. Part I. Characterization. Communications Magazine, IEEE, 1997, 35(7): 90-100.

[36] Sheriff I, Belding-Royer E. Multipath selection in multi-radio mesh networks. In Proc. the 3rd International Conference on Broadband Communications, Networks and Systems, Oct. 2006.

[37] Yang S C. OFDMA System Analysis and Design. Artech House, 2010.

[38] Hill D A. Electromagnetic Fields in Cavities: Deterministic and Statistical Theories. John Wiley & Sons, 2009.

[39] Groenen P J, Jajuga K. Fuzzy clustering with squared minkowski distances. Fuzzy Sets and Systems, 2001, 120(2): 227-237.

[40] Von Luxburg U. A tutorial on spectral clustering. Statistics and Computing, 2007, 17(4): 395-416.

[41] Weichsel P M. The Kronecker product of graphs. Proc. the American Mathematical Society, 1962, 13(1): 47-52.

[42] Doukas A, Kalivas G. Rician k factor estimation for wireless communication systems. In Proc. the 2nd International Conference on Wireless and Mobile Communications, Jul. 2006, Article No.69.
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[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
[10] Li Minghui;. CAD System of Microprogrammed Digital Systems[J]. , 1987, 2(3): 226 -235 .

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