Uncertain reasoning using time-dynamic markov random field for sensor-network applications
Document Type
Conference Proceeding
Publication Date
1-1-2015
Abstract
Sensor networks are often used in environment monitoring. We consider uncertain reasoning in sensor network-based monitoring, in particular, in detecting and tracking plumes under heavy noise. We extend Markov random field to a new time-dynamic Markov random field (TD-MRF) and use it to model the environment. We provide an algorithm for inferring, based on TD-MRF, the plume situation in the environment given noisy sensor output. Our experiments showed that TD-MRF and our inference framework can lead to better detection and tracking results even when there is a high level of noise.
Publication Source (Journal or Book title)
Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015
First Page
588
Last Page
593
Recommended Citation
Shakya, S., & Zhang, J. (2015). Uncertain reasoning using time-dynamic markov random field for sensor-network applications. Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015, 588-593. Retrieved from https://repository.lsu.edu/eecs_pubs/2788
