Fuzzy and probabilistic models of association information in sensor networks

Abstract

-The paper considers the problem of improving accuracy and reliability of measurement information acquired by sensor networks. It offers the way of integrating sensor measurement results with association information available or a priori derived at aggregating nodes. The models applied for describing both sensor results and association information are reviewed with consideration given to both neuro-fuzzy and probabilistic models and methods. The information sources, typically available in sensor systems, are classified according to the model (fuzzy or probabilistic), which seems more feasible to be applied. The integration problem is formulized as an optimization problem.

Accuracy Estimation for Sensor Systems

“…For measurements without error bounds reported, this step also generates the corresponding error estimations and thus parses the deterministic measurements into probabilistic ones. This can be achieved by various existing techniques, such as modeldriven approaches [7], [8], or approaches that leverage prior knowledge on sensor noise characteristics [9].…”

Uncertainty-aware Wireless Sensor Networks

“…If the difference of the two sums is greater than a threshold value, it indicates an attack. Reznik and Kreinovich (2004) investigate the issues for improving the reliability, accuracy and uncertainty management of the decisions based on the application of the meta-level models in sensor networks. The meta-level model represents a relationship or association between different sensors.…”