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Passive Loss Inference in Wireless Sensor Networks Using EM Algorithm
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Wireless Sensor Network·Volume 02 (2010)·Pages 512–519·Published 8 July 2010·DOI10.4236/wsn.201027063
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Abstract
Wireless Sensor Networks (WSNs) are mainly deployed for data acquisition, thus, the network performance can be passively measured by exploiting whether application data from various sensor nodes reach the sink. In this paper, therefore, we take into account the unique data aggregation communication paradigm of WSNs and model the problem of link loss rates inference as a Maximum-Likelihood Estimation problem. And we propose an inference algorithm based on the standard Expectation-Maximization (EM) techniques. Our algorithm is applicable not only to periodic data collection scenarios but to event detection scenarios. Finally, we validate the algorithm through simulations and it exhibits good performance and scalability.
KeywordsWireless Sensor NetworksPassive MeasurementNetwork TomographyData AggregationEM Algorithm
- C. G. Vehbi and P. H. Gerhard, “Industrial Wireless SenSor Networks: Challenges, Design Principles, and Technical Approaches,” IEEE Transactions on Industrial Electronics, Vol. 56, No. 10, October 2009, pp. 4258-4265.
- J. A. Stankovic, “When Sensor and Actuator Networks Cover the World,” ETRI Journal, Vol. 30, No. 5, October 2008, pp. 627-633.
- A. Willig, “Recent and Emerging Topics in Wireless Industrial Communications: A Selection,” IEEE Transactions on Industrial Informatics, Vol. 4, No. 2, May 2008, pp. 102-124.
- E. Fasolo, M. Rossi, J. Widmer and M. Zorzi, “In-Network Aggregation Techniques for Wireless Sensor Networks: A Survey,” IEEE Wireless Communications, Vol. 14, No. 2, 2007, pp. 70-86.
- K. S. Low, W. N. N. Win and M. J. Er, “Wireless Sensor Networks for Industrial Environments,” Proceedings of International Conference on Computational Intelligence for Modelling, Control and International Conference on Automation and Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Vol. 2, 2005, pp. 271276.
- G. J. McLachlan and T. Krishnan, “The EM Algorithm and Extensions,” 2nd Edition, John Wiley and Sons, Inc, New York, 2008.
- R. Castro, M. Coates, G. Liang, R. Nowak and B. Yu, “Network Tomography: Recent Developments,” Statistical Science, Vol. 19, No. 3, 2004, pp. 499-517.
- B. Tian, D. Nick, P. Francesco Lo and T. Don, “Network Tomography on General Topologies,” ACM SIGMETRICS Performance Evaluation Review, Vol. 30, No. 1, 2002, pp. 21-30.
- S. Rost and H. Balakrishnan, “Memento: A Health Monitoring System for Wireless Sensor Networks,” Proceedings of 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, Santa Clara, Vol. 3, 2006, pp. 575-584.
- X. Meng, T. Nandagopal, L. Li and S. Lu, “Contour Maps: Monitoring and Diagnosis in Sensor Networks,” Computer Networks, Vol. 50, No. 15, 2006, pp. 28202838.
- N. Ramanathan, K. Chang, R. Kapur, L. Girod and E. Kohler, “Sympathy for the Sensor Network Debugger,” Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys), San Diego, 2005, pp. 255-267.
- S. Gupta, R. Zheng and A. M. K. Cheng, “ANDES: An Anomaly Detection System for Wireless Sensor Networks,” Proceedings of IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS), Pisa, 2007, pp. 1-9.
- A. Meier, M. Motani, S. Hu and K. Simon, “DiMo: Distributed Node Monitoring in Wireless Sensor Networks,” Proceedings of International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Vancouver, 2008, pp. 117-121.