Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the effective implementation of the iterative proportional fitting procedure
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Belief Propagation and Revision in Networks with Loops
Belief Propagation and Revision in Networks with Loops
Fault Tolerance in Collaborative Sensor Networks for Target Detection
IEEE Transactions on Computers
Robust distributed estimation in sensor networks using the embedded polygons algorithm
Proceedings of the 3rd international symposium on Information processing in sensor networks
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Distributed Fault-Tolerant Detection in Wireless Sensor Networks
IEEE Transactions on Computers
Spatio-temporal sampling rates and energy efficiency in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Network correlated data gathering with explicit communication: NP-completeness and algorithms
IEEE/ACM Transactions on Networking (TON)
Learning static object segmentation from motion segmentation
Learning static object segmentation from motion segmentation
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Consistency-driven data quality management of networked sensor systems
Journal of Parallel and Distributed Computing
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
IEEE Transactions on Signal Processing
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Tree-based reparameterization framework for analysis of sum-product and related algorithms
IEEE Transactions on Information Theory
IEEE Communications Magazine
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Power, spatio-temporal bandwidth, and distortion in large sensor networks
IEEE Journal on Selected Areas in Communications
Near-optimal reinforcement learning framework for energy-aware sensor communications
IEEE Journal on Selected Areas in Communications
RETRACTED: Impacts of sensor node distributions on coverage in sensor networks
Journal of Parallel and Distributed Computing
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We propose a systematic approach, based on probabilistic graphical model, to infer missing observations in Wireless Sensor Networks (WSNs) for sustaining environmental monitoring. This enables us to effectively address two critical challenges in WSNs: (a) energy-efficient data gathering through planned energy-saving sleep cycles and (b) sensor-node failure tolerance in harsh environments. In our approach, we model the spatial correlations in a sensor network as a pairwise Markov Random Field (MRF). Our MRF model is constructed from historical data using Iterative Proportional Fitting (IPF). Then Loopy Belief Propagation (LBP) is employed to estimate the missing data at the data sink. We demonstrate our approach using real-world sensed data on 32 × 32 grids. Empirical results show our approach can achieve the high rates of estimation accuracy (e.g. 65 90% for soil moisture data), even when the unobserved nodes consist of more than 50% of the total sensing nodes.