Abstract
The internal link performance inference has become an increasingly important issue in operating and evaluating a sensor network. Since it is usually impractical to directly monitor each node or link in the wireless sensor network, we consider the problem of inferring the internal link loss characteristics from passive end-to-end measurement in this paper. Specifically, the link loss performance inference based on the data aggregation is considered. Under the assumptions that the link losses are mutually independent, we formulate the problem of link loss estimation as a Bayesian inference problem and propose a Markov Chain Monte Carlo algorithm to solve it. Through the simulation, we can safely reach the conclusion that the internal link loss rate can be inferred accurately, comparable to the sampled internal link loss rate, and the simulation also shows that the proposed algorithm scales well according to the sensor network size.
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References
Coates, M., Hero III., A., Nowak, R., Yu, B.: Internet Tomography. IEEE Signal Processing Magazine 19(3), 47–65 (2002)
Zhu, W.: Using Bayesian network on network tomography. Computer Communications 26(2), 155–163 (2003)
Guo, D., Wang, X.: Bayesian inference of network loss and delay characteristics with applications to TCP performance prediction. IEEE Transactions on Signal Processing 51(8), 2205–2218 (2003)
Guo, D., Wang, X.: Bayesian network loss inference. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Apr. 2003, pp. 33–36. IEEE Computer Society Press, Los Alamitos (2003)
Ye, F., Luo, H., Cheng, J., Lu, S., Zhang, L.: A Two-tier Data Dissemination Model for Large-Scale Wireless Sensor Networks. In: Proceedings of ACM Mobicom 2002, Sep. 2002, pp. 148–159. ACM Press, New York (2002)
Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F.: Directed Diffusion for Wireless Sensor Networking. IEEE Trans. on Networking 11(1), 2–16 (2003)
Hartl, G., Li, B.: Loss inference in wireless sensor networks based on data aggregation. In: Third International Symposium on Information Processing in Sensor Networks, Apr. 2004, pp. 396–404 (2004)
Mao, Y., Kschischang, F.R., Li, B., S., P., Pasupathy, S.: A factor graph approach to link loss monitoring in wireless sensor networks. IEEE Journal on Selected Areas in Communications 23(4), 820–829 (2005)
Zhao, J., Ramesh, G., Deborah, E.: Sensor network tomography: Monitoring wireless sensor networks. Computer Communication Review 32(1), 64 (2002)
Zhao, J., Govindan, R., Estrin, D.: Residual energy scan for monitoring sensor networks. In: IEEE Wireless Communications and Networking Conference, vol. 1, Mar. 2002, pp. 356–362.
Zhao, J.: Measurement and Monitorin. In: Wireless Sensor Networks. Ph.D. Thesis, Department of Computer Science, University of Southern California (Dec. 2003)
Meng, X., Nandagopal, T., Li, L., Lu, S.: Contour maps: Monitoring and diagnosis in sensor networks. Computer Networks 50(15), 2820–2838 (2006)
Duffield, N., Horowitz, J., Presti, F.L., Towsley, D.: Multicast Topology Inference From Measured End-to-End Loss. IEEE Trans. on Information Theory 48(1), 26–45 (2002)
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Li, Y., Cai, W., Tian, G., Wang, W. (2007). Loss Tomography in Wireless Sensor Network Using Gibbs Sampling. In: Langendoen, K., Voigt, T. (eds) Wireless Sensor Networks. EWSN 2007. Lecture Notes in Computer Science, vol 4373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69830-2_10
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DOI: https://doi.org/10.1007/978-3-540-69830-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69829-6
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