Abstract
We develop level set estimation algorithms for a novel low cost sensor network architecture, where sensors are mounted on agents moving without an explicit objective of sensing. A level set in a planar scalar field is the set of points with field values greater than or equal to a specified value. We model the problem as a classification problem and evaluate a heuristic to reduce the amount of communication assuming that the base station uses a Support Vector Machine classifier. We then develop a fully distributed, low complexity solution which uses opportunistic information exchange to estimate level set boundaries locally at nodes selected using leader election. We observe that the learning rates of the boundary in a locality is proportional to the complexity. Effectiveness of the proposed scheme is evaluated using simulations with data from both synthetic and measured fields. Random way point mobility model is used for node motion and trade off of accuracy and of coverage with communication costs is studied.
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References
Szexczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., Estrin, D.: Habitat monitoring with sensor networks. Communications of the ACM 47(6), 34–40 (2004)
Soreide, N.N., Woody, C., Holt, S.M.: Overview of ocean based buoys and drifters: Present applications and future needs. In: 16th Inter. Conf. on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology (2004)
Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L., Rubenstein, D.: Energy-efficient computing for wildlife tracking: Design trade offs and early experiences with zebranet. In: ASPLOS, San Jose, CA, October (2002)
Sikka, P., Corke, P.I., Overs, L.: Wireless Sensor Devices for Animal Tracking and Control. LCN (2004)
Solis, I., Obraczka, K.: Effcient Continuous Mapping in Sensor Networks Using Isolines. Mobiquitous (2005)
Meng, X., et al.: Contour maps: monitoring and diagnosis in sensor networks. Computer Networks: The IJCTNÂ 50(15) (October 2006)
Liao, P.-K., Chang, M.-K., Kuo, C.-C.J.: A distributed approach to contour line extraction using sensor networks. Vehicular Technology Conference (2005)
Buragohain, C., Gandhi, S., Hershberger, J., Suri, S.: Contour Approximation in Sensor Networks. In: Gibbons, P.B., Abdelzaher, T., Aspnes, J., Rao, R. (eds.) DCOSS 2006. LNCS, vol. 4026, Springer, Heidelberg (2006)
Tilak, S., Kolar, V., Abu-Ghazaleh, N.B., Kang, K.D.: Dynamic Localization Control for Mobile Sensor Networks. In: IEEE IWSEEASN 2005. IEEE International Workshop on Strategies for Energy Efficiency in Ad Hoc and Sensor Networks (2005)
Willet, R., Martin, A., Nowak, R.: Backcasting: Adaptive sampling for sensor networks. In: Proceedings of IPSN (2004)
Willet, R.M., Nowak, R.D.: Minimax Optimal Level Set Estimation, submitted to IEEE Transactions on Image Processing
Leonard, N., Zhang, F.: Generating Contour Plots Using Multiple Sensor Platforms. IEEE Swarm Intelligence Symposium (2005)
Singh, A., Nowak, R., Ramanathan, P.: Active Learning for Adaptive Mobile Sensing Networks, IPSN, Nashville, TN (2006)
Wang, K.-C., Ramananthan, P.: Collaborative sensing using sensors of uncoordinated mobility. In: Prasanna, V.K., Iyengar, S., Spirakis, P.G., Welsh, M. (eds.) DCOSS 2005. LNCS, vol. 3560, pp. 293–306. Springer, Heidelberg (2005)
Srinivasan, S., Ramamritham, K., Ramanathan, P.: Contour Estimation using Collaborating Mobile Sensors, DIWANS, Los Angeles (2006)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Schlkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT-Press (1999)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI (1995)
Teillet, P.M., et al.: A Framework For In-Situ Sensor Measurement Assimilation in Remote Sensing Applications. In: Proceedings of the 22nd Canadian Symposium on Remote Sensing, Sainte-Foy, Quebec, pp. 111–118 (2001)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel based learning methods. Cambridge University Press, Cambridge, UK (2000)
Hyytiä, E., Virtamo, J.: Random Waypoint Model in n-Dimensional Space. Operations Research Letters 33/6, 567–571 (2005)
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Gupta, G.R., Ramanathan, P. (2007). Level Set Estimation Using Uncoordinated Mobile Sensors. In: Kranakis, E., Opatrny, J. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2007. Lecture Notes in Computer Science, vol 4686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74823-6_8
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DOI: https://doi.org/10.1007/978-3-540-74823-6_8
Publisher Name: Springer, Berlin, Heidelberg
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