Abstract
Symbolic location of a user, like a store name in a mall, is essential for context-based mobile advertising. Existing fingerprint-based localization using only a single phone is susceptible to noise, and has a major limitation in that the phone has to be held in the hand at all times. In this paper, we present SensOrchestra, a collaborative sensing framework for symbolic location recognition that groups nearby phones to recognize ambient sounds and images of a location collaboratively. We investigated audio and image features, and designed a classifier fusion model to integrate estimates from different phones. We also evaluated the energy consumption, bandwidth, and response time of the system. Experimental results show that SensOrchestra achieved 87.7% recognition accuracy, which reduces the error rate of single-phone approach by 2X, and eliminates the limitations on how users carry their phones. We believe general location or activity recognition systems can all benefit from this collaborative framework.
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References
Azizyan, M., Constandache, I., Roy Choudhury, R.: SurroundSense: mobile phone localization via ambience fingerprinting. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, pp. 261–272. ACM (2009)
Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 775–784. IEEE (2000)
Bao, X., Choudhury, R.R.: VUPoints: Collaborative Sensing and Video Recording through Mobile Phones. In: Proceedings of The First ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds, pp. 7–12 (2009)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus (2006)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 712–727 (2008)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., Srivastava, M.B.: Participatory sensing. In: Workshop on World-Sensor-Web (WSW 2006): Mobile Device Centric Sensor Networks and Applications, pp. 117–134 (2006)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chu, S., Narayanan, S., Kuo, C.-C.J.: Environmental sound recognition with time-frequency audio features. IEEE Transactions on Audio, Speech and Language Processing 17(6), 1142–1158 (2009)
Deng, Y., Manjunath, B.S., Kenney, C., Moore, M.S., Shin, H.: An efficient color representation for image retrieval. IEEE Transactions on Image Processing 10, 140–147 (2001)
Eronen, A.J., Peltonen, V.T., Tuomi, J.T., Klapuri, A.P., Fagerlund, S., Sorsa, T., Lorho, G., Huopaniemi, J.: Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing 14(1), 321–329 (2006)
Fang, Z., Guoliang, Z., Zhanjiang, S.: Comparison of different implementations of MFCC. Journal of Computer Science and Technology 16(6), 582–589 (2001)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 881–892 (2002)
Kuulusa, M., Bosch, G.: Nokia Energy Profiler Version 1.2 (2009), Software available at http://www.forum.nokia.com/Library/Tools_and_downloads/Other/Nokia_Energy_Profiler/
Lin, H., Zhang, Y., Griss, M., Landa, I.: WASP: An Enhanced Indoor Locationing Algorithm for a Congested Wi-Fi Environment. In: Fuller, R., Koutsoukos, X.D. (eds.) MELT 2009. LNCS, vol. 5801, pp. 183–196. Springer, Heidelberg (2009)
Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 37(6), 1067–1080 (2007)
Lu, H., Pan, W., Lane, N., Choudhury, T., Campbell, A.: SoundSense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 165–178. ACM, New York (2009)
Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In: SenSys 2008: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 337–350. ACM (2008)
Mun, M., Boda, P., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R.: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 55–68 (2009)
Narayanaswami, C., Coffman, D., Lee, M.C., Moon, Y.S., Han, J.H., Jang, H.K., McFaddin, S., Paik, Y.S., Kim, J.H., Lee, J.K., Park, J.W., Soroker, D.: Pervasive symbiotic advertising. In: HotMobile 2008: Proceedings of the 9th Workshop on Mobile Computing Systems and Applications, pp. 80–85. ACM (2008)
Partridge, K., Begole, B., Alto, P., Road, C.H.: Activity-based Advertising: Techniques and Challenges. In: Proceedings of the 1st Workshop on Pervasive Advertising, pp. 2–5 (2009)
Paxton, M., Benford, S.: Experiences of participatory sensing in the wild. In: Ubicomp 2009: Proceedings of the 11th International Conference on Ubiquitous Computing, pp. 265–274. ACM (2009)
Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: Proceedings of ACM International Conference on Mobile Computing and Networking, pp. 32–43 (2000)
Rimey, K.: Personal Distributed Information Store (PDIS) Project (2004), Software available at http://pdis.hiit.fi/pdis/download/
Sala, M.C., Partridge, K., Jacobson, L., Begole, J.: An Exploration into Activity-Informed Physical Advertising Using PEST. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 73–90. Springer, Heidelberg (2007), http://www.springerlink.com/index/U5692H972H232382.pdf
Scheible, J., Tuulos, V.: Mobile Python: Rapid prototyping of applications on the mobile platform. Wiley Publishing (2007)
Wang, Y., Lin, J., Annavaram, M., Jacobson, Q.A., Hong, J., Krishnamachari, B., Sadeh, N.: A framework of energy efficient mobile sensing for automatic user state recognition. In: MobiSys 2009: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 179–192. ACM (2009)
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Cheng, HT., Sun, FT., Buthpitiya, S., Griss, M. (2012). SensOrchestra: Collaborative Sensing for Symbolic Location Recognition. In: Gris, M., Yang, G. (eds) Mobile Computing, Applications, and Services. MobiCASE 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29336-8_11
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DOI: https://doi.org/10.1007/978-3-642-29336-8_11
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