Active inference and dynamic Gaussian Bayesian networks for battery optimization in wireless sensor networks

AAAI Workshop - Technical Report

Volumn WS-16-01 - WS-16-15, Issue , 2016, Pages 241-247

  Komurlu, Caner ^a   Bilgic, Mustafa ^a  

^a ILLINOIS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; BIG DATA; BUDGET CONTROL; COGNITIVE SYSTEMS; COMPUTER GAMES; COMPUTER PROGRAMMING; COMPUTER SYSTEMS PROGRAMMING; DATA MINING; ELECTRIC BATTERIES; ELECTRIC POWER TRANSMISSION NETWORKS; ENERGY UTILIZATION; HYBRID SYSTEMS; POPULATION STATISTICS; SMART POWER GRIDS; SOLAR ENERGY; WIRELESS SENSOR NETWORKS;

BATTERY CONSUMPTION; BATTERY OPTIMIZATION; BUDGETARY CONSTRAINTS; GAUSSIAN BAYESIAN NETWORKS; POWER AWARE ROUTING PROTOCOLS; POWER SAVING MODES; PREDICTION ACCURACY; SENSOR READINGS;

POWER MANAGEMENT (TELECOMMUNICATION);

EID: 85017364003     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Ageev, A. A., and Sviridenko, M. I. 1999. Approximation algorithms for maximum coverage and max cut with given sizes of parts. In Integer Programming and Combinatorial Optimization. Springer. 17-30.
2. Akyildiz, I. F.; Su, W.; Sankarasubramaniam, Y.; and Cayirci, E. 2002. Wireless sensor networks: a survey. Computer networks 38(4):393-422.
3. Bilgic, M., and Getoor, L. 2009. Reflect and correct: A misclassification prediction approach to active inference. ACM Transactions on Knowledge Discovery from Data 3(4):1-32.
4. Bilgic, M., and Getoor, L. 2010. Active inference for collective classification. In Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track), 1652-1655.
5. Bilgic, M., and Getoor, L. 2011. Value of information lattice: Exploiting probabilistic independence for effective feature subset acquisition. Journal of Artificial Intelligence Research (JAIR) 41:69-95.
6. Castillo, E.; Gutiérrez, J.; Hadi, A.; and Solares, C. 1997. Symbolic propagation and sensitivity analysis in gaussian bayesian networks with application to damage assessment. Artificial Intelligence in Engineering 11(2):173-181.
7. Castillo, E.; Menéndez, J. M.; and Sánchez-Cambronero, S. 2008. Predicting traffic flow using bayesian networks. Transportation Research Part B: Methodological 42(5):482-509.
8. Chen, D.; Bilgic, M.; Getoor, L.; Jacobs, D.; Mihalkova, L.; and Yeh, T. 2011. Active inference for retrieval in camera networks. In Workshop on Person Oriented Vision.
9. Cooper, G. F., and Herskovits, E. 1992. A bayesian method for the induction of probabilistic networks from data. Machine learning 9(4):309-347.
10. Deshpande, A.; Guestrin, G; Madden, S. R.; Hellerstein, J. M.; and Hong, W. 2004. Model-driven data acquisition in sensor networks. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, 588-599. VLDB Endowment.
11. Elnahrawy, E., and Nath, B. 2004. Context-aware sensors. In Wireless Sensor Networks. Springer. 77-93.
12. Gilks, W. R.; Richardson, S.; and Spiegelhalter, D. J. 1996. Markov Chain Monte Carlo in Practice. Interdisciplinary Statistics. Chapman & Hall/CRC.
13. Hastings, W. K. 1970. Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1):97-109.
14. Koller, D., and Friedman, N. 2009. Probabilistic Graphical Models: Principles and Techniques. The MIT Press.
15. Krause, A., and Guestrin, C. 2005a. Near-optimal nonmyopic value of information in graphical models. In Annual Conference on Uncertainty in Artificial Intelligence.
16. Krause, A., and Guestrin, C. 2005b. Optimal nonmyopic value of information in graphical models - efficient algorithms and theoretical limits. In International Joint Conference on Artificial Intelligence, 1339-1345.
17. Krause, A., and Guestrin, C. 2009. Optimal value of information in graphical models. Journal of Artificial Intelligence Research 35:557-591.
18. Rasmussen, C. E. 2004. Gaussian processes in machine learning. In Advanced lectures on machine learning. Springer. 63-71.
19. Settles, B. 2012. Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool.
20. Slijepcevic, S., and Potkonjak, M. 2001. Power efficient organization of wireless sensor networks. In Communications, 2001. ICC 2001. IEEE International Conference on, volume 2, 412-476 vol. 2.
21. Wang, L., and Deshpande, A. 2008. Predictive modelingbased data collection in wireless sensor networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4913 LNCS: 34-51.