Energy-Efficient and Robust In-Network Inference in Wireless Sensor Networks

IEEE Transactions on Cybernetics

Volumn 45, Issue 10, 2015, Pages 2105-2118

  Zhao, Wei ^a   Liang, Yao ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

Belief propagation (BP); energy efficiency; graphical modeling; in network inference; multiresolution inference; wavelets; Wireless sensor networks (WSNs)

Indexed keywords

ENERGY EFFICIENCY; INFORMATION THEORY;

BELIEF PROPAGATION; BUILDING INFORMATION MODEL - BIM; DATA-DRIVEN APPROACH; DISTRIBUTED DETECTION; ENERGY EFFICIENT; INFORMATION MODELING; LARGE-SCALE WIRELESS SENSOR NETWORKS; WSN APPLICATIONS;

WIRELESS SENSOR NETWORKS;

EID: 84911071750     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2014.2365541     Document Type: Article

References (43)

1. R. Nowak, U. Mitra, R. Willett, "Estimating inhomogeneous fields using wireless sensor networks," IEEE J. Sel. Areas Commun., vol. 22, no. 6, pp. 999-1006, Aug. 2004.
2. A. Oka and L. Lampe, "Energy efficient distributed filtering with wireless sensor networks," IEEE Trans. Signal Process., vol. 56, no. 5, pp. 2062-2075, May 2008.
3. C. Crick and A. Pfeffer, "Loopy belief propagation as a basis for communication in sensor networks," in Proc. 19th Conf. Uncertainty Artif. Intell., Acapulco, Mexico, 2003, pp. 159-166.
4. L. Chen, M. J. Wainwright, M. Cetin, A. Willsky, "Multitargetmultisensor data association using the tree-reweighted max-product algorithm," Proc. SPIE Aerosense Conf., vol. 5096, Aug. 2003, pp. 127-138.
5. J. M. F. Moura, J. Lu, M. Kleiner, "Intelligent sensor fusion: A graphical model approach," in Proc. Int. Conf. Acoust. Speech Signal Process., Piscataway, NJ, USA, 2003, pp. 733-736.
6. L. Chen, M. J. Wainwright, M. Cetin, A. S. Willsky, "Data association based on optimization in graphical models with application to sensor networks," Math. Comput. Model., vol. 43, nos. 9-10, pp. 1114-1135, 2006.
7. M. Cetin, L. Chen, J. W. Fisher, "Distributed fusion in sensor network: A graphical model perspective," IEEE Signal Process. Mag., vol. 23, no. 4, pp. 42-55, Jul. 2006.
8. A. T. lhler, J. W. Fisher, III, R. L. Moses, A. S. Willsky, "Nonparametric belief propagation for self-localization of sensor networks," IEEE J. Sel. Areas Commun., vol. 23, no. 4, pp. 809-819, Apr. 2005.
9. M. A. Paskin and C. E. Guestrin, "Robust probabilistic inference in distributed systems," in Proc. 20th Conf. Uncertainty Artif. Intell., Banff, AB, Canada, 2004, pp. 436-445.
10. A. T. Ihler, J. W. Fisher, III, R. L. Moses, A. S. Willsky, "Nonparametric belief propagation for self-calibration in sensor networks," IEEE J. Sel. Areas Commun., vol. 23, no. 4, pp. 809-819, Apr. 2005.
11. A. Anandkumar, J. E. Yukich, L. Tong, A. Swami, "Energy scaling laws for distributed inference in random fusion networks," IEEE J. Sel. Areas Commun., vol. 27, no. 7, pp. 1203-1217, Sep. 2009.
12. A. Dogandzic and B. Zhang, "Distributed estimation and detection for sensor networks using hidden Markov random field models," IEEE Trans. Signal Process., vol. 54, no. 8, pp. 3200-3215, Aug. 2006.
13. J. Schiff, D. Antonelli, A. G. Dimakis, D. Chu, M. J. Wainwright, "Robust message-passing for statistical inference in sensor networks," in Proc. Int. Symp. Inf. Process. Sensor Netw. (IPSN), Cambridge, MA, USA, 2007, pp. 109-118.
14. D. Bickson, D. Dolev, Y. Weiss, "Modified belief propagation algorithm for energy saving in wireless and sensor networks," Dept. Comput. Sci. Eng., Hebrew Univ. Jerusalem, Jerusalem, Israel, Tech. Rep. TR-2005-85, 2005.
15. T. Anker, D. Dolev, B. Hod, "Belief propagation in wireless sensor networks-A practical approach," Wireless Algorithms, Systems, Applications, vol. 5258. Berlin, Germany: Springer, 2008, pp. 466-479.
16. W. Zhao and Y. Liang, "W-LBP: Wavelet-based loopy belief propagation for wireless sensor networks," in Proc. 3rd Int. Conf. Sensor Technol. Appl. (SENSORCOMM), Athens, Greece, 2009, pp. 617-622.
17. R. Lasowski, A. Tevs, M. Wand, H.-P. Seidel, "Wavelet belief propagation for large scale inference problems," in Proc. Comput. Vis. Pattern Recognit. (CVPR), Providence, RI, USA, 2011, pp. 1921-1928.
18. W. Zhao and Y. Liang, "Inference in wireless sensor networks based on information structure optimization," in Proc. 37th IEEE Conf. Local Comput. Netw. (LCN), Clearwater, FL, USA, 2012, pp. 555-562.
19. (2014, Sept.). Intel Lab Data. [Online]. Available: http://db.csail.mit.edu/labdata/labdata.html
20. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA, USA: Morgan Kaufmann, 1988.
21. M. I. Jordan, Ed., Learning in Graphical Models. London, U.K.: MIT Press, 1998.
22. K. P. Murphy, Y. Weiss, M. I. Jordan, "Loopy belief propagation for approximate inference: An empirical study," in Proc. Uncertainty Artif. Intell., Stockholm, Sweden, 1999, pp. 467-475.
23. Y. Weiss, "Belief propagation and revision in networks with loops," AI Lab., Massachusetts Inst. Technol., Tech. Rep. TR-1616, 1997.
24. D. J. C. MacKay, R. McEliece, J. J.-F. Cheng, "Turbo decoding as an instance of Pearl's 'belief propagation' algorithm," IEEE J. Sel. Areas Commun., vol. 16, no. 2, pp. 140-152, Feb. 1998.
25. W. T. Freeman, E. C. Pasztor, O. T. Carmichael, "Learning low-level vision," Int. J. Comput. Vis., vol. 40, no. 1, pp. 25-47, 2000.
26. M. D. Penrose, Random Geometric Graphs, vol. 5. Oxford, U.K.: Oxford Univ. Press, 2003.
27. F. Xue and P. R. Kumary, "The number of neighbors needed for connectivity of wireless networks," Wireless Netw., vol. 10, no. 2, pp. 169-181, 2004.
28. P. Balister, B. Bollobás, A. Sarkar, M. Walters, "Connectivity of random k-nearest-neighbour graphs," Adv. Appl. Probab., vol. 37, no. 1, pp. 1-24, 2005.
29. Y. Zhou, "Structure learning of probabilistic graphical models: a comprehensive survey," arXiv preprint arXiv:1111.6925, 2011.
30. P. Ravikumar, M. J. Wainwright, J. D. Lafferty, "High-dimensional Ising model selection using l1-regularized logistic regression," Ann. Statist., vol. 38, no. 3, pp. 1287-1319, 2010.
31. M. J. Wainwright, "Sharp thresholds for high-dimensional and noisy sparsity recovery using l1-constrained quadratic programming (Lasso)," IEEE Trans. Inf. Theory, vol. 55, no. 5, pp. 2183-2202, May 2009.
32. M. Schmidt, K. Murphy, G. Fung, R. Rosales, "Structure learning in random fields for heart motion abnormality detection," in Proc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Anchorage, AK, USA, 2008, pp. 1-8.
33. L. A. Goodman, Analyzing Qualitative/Categorical Data: Log-Linear Models and Latent Structure Analysis. Cambridge, MA, USA: Abt Books, 1978.
34. R. Christensen, Log-linear Model and Logistic Regression. New York, NY, USA: Springer, 1997.
35. M. Yan and Y. Lin, "Model selection and estimation in regression with grouped variables," J. Royal Statist. Soc. B, vol. 68, no. 1, pp. 49-67, 2006.
36. M. J. Wainwright, T. S. Jaakkola, A. S. Willsky, "Tree based reparameterization framework for analysis of sum-product and related algorithms," IEEE Trans. Inf. Theory, vol. 49, no. 5, pp. 1120-1146, 2003.
37. S. G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674-693, Jul. 1989.
38. G. Tolle et al., "A macroscope in the redwoods," in Proc. ACM Int. Conf. Embedded Netw. Sensor Syst. (SenSys), San Diego, CA, USA, 2005, pp. 51-63.
39. C. J. Willmott and K. Matsuura, "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance," Clim. Res., vol. 30, no. 1, pp. 79-82, 2005.
40. Y. S. Chiou and F. Tsai, "A reduced-complexity data-fusion algorithm using belief propagation for location tracking in heterogeneous observations," IEEE Trans. Cybern., vol. 44, no. 6, pp. 922-935, Jun. 2014.
41. S. Seifzadeh, B. Khaleghi, F. Karray, "Distributed soft-dataconstrained multi-model particle filter," IEEE Trans. Cybern., Jun. 2014. Doi: 10.1109/TCYB.2014.2326549.
42. J. Xue, N. Zheng, J. Geng, X. Zhong, "Tracking multiple visual targets via particle-based belief propagation," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 1, pp. 196-209, Feb. 2008.
43. K. LeBlanc and A. Saffiotti, "Multirobot object localization: A fuzzy fusion approach," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 5, pp. 1259-1276, Oct. 2009.