Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

myjournal manuscript No. (will be inserted by the editor) On-board Vehicle Data Stream Monitoring using MineFleet and Fast Resource Constrained Monitoring of Correlation Matrices

Abstract This paper considers the problem of monitoring vehicle data streams in a resource-constrained environment. It particularly focuses on a monitoring task that requires frequent computation of correlation matrices using lightweight onboard computing devices. It motivates this problem in the context of the Mine-Fleet Real-Time system and offers a randomized algorithm for fast monitoring of correlation (FMC), inner product, and Euclidean distance matrices among others. Unlike the existing approaches that compute all the entries of these matrices from a data set, the proposed technique works using a divide-and-conquer approach. This paper presents a probabilistic test for quickly detecting whether or not a subset of coefficients contains a significant one with a magnitude greater than a user given threshold. This test is used for quickly identifying the portions of the space that contain significant coefficients. The proposed algorithm is particularly suitable for monitoring correlation and related matrices computed from continuous data streams.