In-Network Outlier Detection in Wireless Sensor Networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200

SEARCH: Evolution, and the Gene Expression . . .

This paper makes an effort to project the theoretical lessons of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework introduced elsewhere (Kargupta, 1995b) in the context of natural evolution and introduce the gene expression messy genetic algorithm (GEMGA)---a new generation of messy GAs that directly search for relations among the members of the search space. The GEMGA is an O(jj k (` +k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995a) (problems that can be solved by considering no higher than order-k relations) in sequence representation of length ` and alphabet set . Unlike the traditional evolutionary search algorithms, the GEMGA emphasizes the computational role of gene expression and uses a transcription operator to detect appropriate relations. Theoretical conclusions are also substantiated by experimental results for a test bed, comprised of different large, multimodal, scaled problems. 1 Introduction The ..

The Gene Expression Messy Genetic Algorithm

This paper introduces the gene expression messy genetic algorithm (GEMGA)---a new generation of messy GAs that directly search for relations among the members of the search space. The GEMGA is an O( k (` 2 + k)) sample complexity algorithm for the class of order-k delineable problems [6] (problems that can be solved by considering no higher than order-k relations). The GEMGA is designed based on an alternate perspective of natural evolution proposed by the SEARCH framework [6] that emphasizes the role of gene expression. The GEMGA uses the transcription operator to search for relations. This paper also presents the test results of the GEMGA for large multimodal order-k delineable problems. I. Introduction The field of evolutionary computation is deluged with many algorithms. Introducing yet another evolutionary algorithm demands a strong justification. The SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework, introduced elsewhere [6] offered an alternate persp..

Relation Learning In Gene Expression: Introns, Variable Length Representation, And All That

this paper we will primarily be concerned with tuples taken from space of n-ary Cartesian products of the search domain with itself

Search, polynomial complexity, and the fast messy genetic algorithm

Blackbox optimization--optimization in presence of limited knowledge about the objective function--has recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Simulated annealing, genetic algorithms, tabu search are some examples. Unfortuntely, each of these algorithms is creating a separate field in itself and their use in practice is often guided by personal discretion rather than scientific reasons. The primary reason behind this confusing situation is the lack of any comprehensive understanding about blackbox search. This dissertation takes a step toward clearing some of the confusion. The main objectives of this dissertation are: (1) present SEARCH (Search Envisioned As Relation & Class Hierarchizing)--an alternate perspective of blackbox optimization and its quantitative analysis that lays the foundation essential for transcending the limits of random enumerative search; (2) design and testing of the fast messy genetic algorithm.SEARCH is a general framework for understanding blackbox optimization in terms of relations, classes and ordering. The primary motivation comes from the observation that sampling in blackbox optimization is essentially an inductive process (Michalski, 1983) and in absence of any relation among the members of the search space, induction is no better than enumeration. The foundation of SEARCH is laid on a decomposition of BBO into relation, class, and sample spaces. An ordinal, probablistic, and approximate framework is developed on this foundation to identify the fundamental principles in-blackbox optimization, essential for transcending the limits of random enumerative search. Bounds on success probability and sample complexity ate derived. I explicitly consider specific blackbox algorithms like simulated annealing, genetic algorithms and demonstrate that the fundamental computations in all of them can be captured using SEARCH. SEARCH also offers an alternate perspective of natural evolution that establishes the computational role of gene expression (DNA $\to$ RNA $\to$ Protein) in evolution. This model of evolutionary computation hypothesizes a possible mapping of the decomposition is relation, class, and sample spaces of SEARCH into the transcriptional regulatory mechanisms, proteins, and DNA respectively. The second part of this dissertation starts by noting the limitations of simple GAs, which fail to properly search for relations and makes decision making very noisy by combining relation, class, and the sample spaces. Messy genetic algorithms (Goldberg, Korb, & Deb, 1989; Deb, 1991) are a rare class of algorithms that emphasize the search for relations. Despite this strength of messy GAs, they lacked complete benefits of implicit parallelism (Holland, 1975). The fast messy GA initiated by Goldberg, Deb, Kargupta, and Harik (1993) introduced some of the benefits of implicit parallelism in messy GA without sacrificing its other strengths very much. This dissertation investigates fast messy GAs and presents test results to demonstrate its performance for order-k delineable problems.U of I OnlyETDs are only available to UIUC Users without author permissio

Search, polynomial complexity, and the fast messy genetic algorithm

Blackbox optimization--optimization in presence of limited knowledge about the objective function--has recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Simulated annealing, genetic algorithms, tabu search are some examples. Unfortuntely, each of these algorithms is creating a separate field in itself and their use in practice is often guided by personal discretion rather than scientific reasons. The primary reason behind this confusing situation is the lack of any comprehensive understanding about blackbox search. This dissertation takes a step toward clearing some of the confusion. The main objectives of this dissertation are: (1) present SEARCH (Search Envisioned As Relation & Class Hierarchizing)--an alternate perspective of blackbox optimization and its quantitative analysis that lays the foundation essential for transcending the limits of random enumerative search; (2) design and testing of the fast messy genetic algorithm.SEARCH is a general framework for understanding blackbox optimization in terms of relations, classes and ordering. The primary motivation comes from the observation that sampling in blackbox optimization is essentially an inductive process (Michalski, 1983) and in absence of any relation among the members of the search space, induction is no better than enumeration. The foundation of SEARCH is laid on a decomposition of BBO into relation, class, and sample spaces. An ordinal, probablistic, and approximate framework is developed on this foundation to identify the fundamental principles in-blackbox optimization, essential for transcending the limits of random enumerative search. Bounds on success probability and sample complexity ate derived. I explicitly consider specific blackbox algorithms like simulated annealing, genetic algorithms and demonstrate that the fundamental computations in all of them can be captured using SEARCH. SEARCH also offers an alternate perspective of natural evolution that establishes the computational role of gene expression (DNA $\to$ RNA $\to$ Protein) in evolution. This model of evolutionary computation hypothesizes a possible mapping of the decomposition is relation, class, and sample spaces of SEARCH into the transcriptional regulatory mechanisms, proteins, and DNA respectively. The second part of this dissertation starts by noting the limitations of simple GAs, which fail to properly search for relations and makes decision making very noisy by combining relation, class, and the sample spaces. Messy genetic algorithms (Goldberg, Korb, & Deb, 1989; Deb, 1991) are a rare class of algorithms that emphasize the search for relations. Despite this strength of messy GAs, they lacked complete benefits of implicit parallelism (Holland, 1975). The fast messy GA initiated by Goldberg, Deb, Kargupta, and Harik (1993) introduced some of the benefits of implicit parallelism in messy GA without sacrificing its other strengths very much. This dissertation investigates fast messy GAs and presents test results to demonstrate its performance for order-k delineable problems.U of I OnlyETDs are only available to UIUC Users without author permissio