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

Resource allocation for wildland fire suppression planning using a stochastic program

2011 Fall.Includes bibliographical references.Resource allocation for wildland fire suppression problems, referred to here as Fire-S problems, have been studied for over a century. Not only have the many variants of the base Fire-S problem made it such a durable one to study, but advances in suppression technology and our ever-expanding knowledge of and experience with wildland fire behavior have required almost constant reformulations that introduce new techniques. Lately, there has been a strong push towards randomized or stochastic treatments because of their appeal to fire managers as planning tools. A multistage stochastic program with variable recourse is proposed and explored in this paper as an answer to a single-fire planning version of the Fire-S problem. The Fire-S stochastic program is discretized for implementation according to scenario trees, which this paper supports as a highly useful tool in the stochastic context. Our Fire-S model has a high level of complexity and is parameterized with a complicated hierarchical cluster analysis of historical weather data. The cluster analysis has some incredibly interesting features and stands alone as an interesting technique apart from its application as a parameterization tool in this paper. We critique the planning model in terms of its complexity and options for an operational version are discussed. Although we assume no interaction between fire spread and suppression resources, the possibility of incorporating such an interaction to move towards an operational, stochastic model is outlined. A suppression budget analysis is performed and the familiar "production function" fire suppression curve is created, which strongly indicates the Fire-S model performs in accordance with fire economic theory as well as its deterministic counterparts. Overall, this exploratory study demonstrates a promising future for the existence of tractable stochastic solutions to all variants of Fire-S problems

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

Software tools for the cognitive development of autonomous robots

Robotic systems are evolving towards higher degrees of autonomy. This paper reviews the cognitive tools available nowadays for the fulfilment of abstract or long-term goals as well as for learning and modifying their behaviour.Peer ReviewedPostprint (author's final draft