Abstract
Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Findsounds, http://www.findsounds.com
Beach, A., Gartrell, M., Akkala, S., Elston, J., Kelley, J., Nishimoto, K., Ray, B., Razgulin, S., Sundaresan, K., Surendar, B., Terada, M., Han, R.: Whozthat? evolving an ecosystem for context-aware mobile social networks. Netwrk. Mag. of Global Internetwkg 22(4), 50–55 (2008)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection for discrete sequences: A survey. IEEE Transactions on Knowledge and Data Engineering 24(5), 823–839 (2012)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)
Eskin, E.: Modeling system calls for intrusion detection with dynamic window sizes. In: Proc. DISCEX (2001)
Fawcett, T., Provost, F.: Activity monitoring: Noticing interesting changes in behavior. In: Proc. SIGKDD, pp. 53–62 (1999)
Ganti, R., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine 49(11), 32–39 (2011)
Goldstein, M., Dengel, A.: Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In: Stefan Advances in Konwledge Discovery and Data Mining. LNCS, pp. 577–593. Springer (206)
Le, V.D., Scholten, H., Havinga, P.: Flead: Online frequency likelihood estimation anomaly detection for mobile sensing. In: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, UbiComp 2013 Adjunct, pp. 1159–1166. ACM, New York (2013)
Lu, H., Yang, J., Liu, Z., Lane, N.D., Choudhury, T., Campbell, A.T.: The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys 2010, pp. 71–84. ACM, New York (2010)
Manevitz, L.M., Yousef, M.: One-class svms for document classification. J. Mach. Learn. Res. 2, 139–154 (2002)
Ng, A.: Machine learning, https://class.coursera.org/ml/lecture/97
Olson, D.L., Delen, D.: Advanced DataMining Techniques. Springer (2008)
Rachuri, K.K., Musolesi, M., Mascolo, C., Rentfrow, P.J., Longworth, C., Aucinas, A.: Emotionsense: A mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Ubicomp 2010, pp. 281–290. ACM, New York (2010)
Sherchan, W., Jayaraman, P.P., Krishnaswamy, S., Zaslavsky, A., Loke, S., Sinha, A.: Using on-the-move mining for mobile crowdsensing. In: Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (Mdm 2012), MDM 2012, pp. 115–124. IEEE Computer Society, Washington, DC (2012)
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: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, MobiSys 2009, pp. 179–192. ACM, New York (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Le, VD., Scholten, H., Havinga, P.J.M. (2014). Online Change Detection for Energy-Efficient Mobile Crowdsensing. In: Awan, I., Younas, M., Franch, X., Quer, C. (eds) Mobile Web Information Systems. MobiWIS 2014. Lecture Notes in Computer Science, vol 8640. Springer, Cham. https://doi.org/10.1007/978-3-319-10359-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-10359-4_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10358-7
Online ISBN: 978-3-319-10359-4
eBook Packages: Computer ScienceComputer Science (R0)