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Unformatted text preview: 9 Information Fusion for Wireless Sensor Networks: Methods, Models, and Classifications EDUARDO F. NAKAMURA Analysis, Research and Technological Innovation Center FUCAPI Federal University of Minas Gerais UFMG ANTONIO A. F. LOUREIRO Federal University of Minas Gerais UFMG and ALEJANDRO C. FRERY Federal University of Alagoas UFAL Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and as- sessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks. Categories and Subject Descriptors: I.2.6 [ Artificial Intelligence ]: Learning; I.2.8 [ Artificial Intelli- gence ]: Problem Solving, Control Methods, and Search; I.2.9 [ Artificial Intelligence ]: Robotics Sensors ; I.5.1 [ Pattern Recognition ]: Models; I.5.2 [ Pattern Recognition ]: Methodology; I.5.4 [ Pattern Recogni- tion ]: Applications; C.2.1 [ Computer-Communication Networks ]: Network Architecture and Design Distributed networks, Network topology ; C.2.4 [ Computer-Communication Networks ]: Distributed Sys- tems; C.3 [ Special-Purpose and Application-Based Systems ]: Real-time and embedded systems, Signal processing systems ; H.4.2 [ Information Systems Applications ]: types of Systems Decision support General Terms: Algorithms, Design, Measurement Additional Key Words and Phrases: Information fusion, data aggregation, data fusion, wireless sensor net- works, architectures and models Authors addresses: E. F. Nakamura, Analysis, Research and Technological Innovation Center (FUCAPI), Av. Gov. Danilo de Matos Areosa, 381, DI, 69075-351, Manaus, AM, Brazil; email: firstname.lastname@example.org; A. A. F. Loureiro, Department of Computer Science, Federal University of Minas Gerais, Av. Antonio Carlos, 6627, 31270-010, Belo Horizonte, MG, Brazil; email: email@example.com; A. C. Frery, Department of Infor- mation Technology, Federal University of Alagoas; Campus A. C. Simoes, BR 104 Norte km 97, Tabuleiro do Martins, 57072-970, Maceio, AL, Brazil; email: firstname.lastname@example.org. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copy-that copies show this notice on the first page or initial screen of a display along with the full citation....
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