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dc.contributor.authorSridhar, Prasanna
dc.date.accessioned2007-09-09T06:03:51Z
dc.date.available2007-09-09T06:03:51Z
dc.date.issued2007-09-09T06:03:51Z
dc.date.submittedJuly 2007
dc.identifier.urihttp://hdl.handle.net/1928/3280
dc.description.abstractThe primary idea behind deploying sensor networks is to utilize the distributed sensing capability provided by tiny, low powered and low cost devices. Multiple sensing devices can be used cooperatively and collaboratively to capture events or monitor space more effectively than a single sensing device. The realm of applications envisioned for sensor networks is diverse including military, aerospace, industrial, commercial, environmental and health monitoring. Typical examples include: traffic monitoring of vehicles, crossborder infiltration-detection and assessment, military reconnaissance and surveillance, target tracking, habitat monitoring and structure monitoring, to name a few. Most of the applications envisioned with sensor networks demand highly reliable, accurate and fault-tolerant data acquisition process. The integrity of data alone can have tremendous effects on the performance of any data acquisition system. Due to the low manufacturing cost, the sensors lend themselves to be deployed in large numbers with a high spatial distribution. Such a large deployment scheme often generates enormous amount of data that needs to be efficiently summarized and delivered for analysis and processing. In-network data compression, data aggregation/fusion, and decision propagation are some of the processes that deal with huge data issues. A hierarchical data aggregation scheme developed in this thesis is a highly effective and energy efficient means (by reducing communication packets) to deliver decision milestones to the enduser. The sensing devices are also prone to failure due to the inherent characteristics such as construction and deployment. It is thus necessary to devise a fault-tolerant mechanism with a low computation overhead to validate the integrity of the data obtained from the sensors. Moreover, a robust diagnostics and decision making process should aid in monitoring and control of critical parameters to efficiently manage the operational behavior of a deployed sensor network. Specifically, this research will focus on innovative approaches to deal with multi-variable multi-space problem domains (data integrity, energy-efficiency and fault-tolerant framework) in wireless sensor networks. We present three information-based methods for improving the performance (faulttolerance and efficiency) of wireless sensor networks (WSNs). The first is a method for time varying weight adaptation in a mixture model for sensor data aggregation. The second technique applies fuzzy inference methods to solve a multi-criteria decision problem, specifically the efficient management of data collection in a WSN. The third method presented proposes the use of spatially variant weights to reduce the significance of sensor readings taken near the boundary of the sensor range, in order to minimize potential corruption of aggregated data. The solutions proposed in this thesis have practical implementation in developing power-aware software components for designing robust networks of sensing devices.en_US
dc.format.extent1114853 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.subjectwireless sensor networksen_US
dc.subjectdata aggregationen_US
dc.subjectintelligent diagnosticsen_US
dc.subjectDecision makingen_US
dc.subjectFault-Toleranceen_US
dc.subjectFuzzy logicen_US
dc.subject.lcshSensor networks--Automatic control
dc.subject.lcshAd hoc networks (Computer networks)
dc.subject.lcshFault-tolerant computing
dc.titleHierarchical aggregation and intelligent monitoring and control in fault-tolerant wireless sensor networksen_US
dc.typeDissertationen_US
dc.description.degreeDoctor of Computer Engineeringen
dc.description.levelDoctoralen
dc.description.departmentUniversity of New Mexico. Dept. of Electrical and Computer Engineeringen
dc.description.advisorJamshidi, Mo
dc.description.committee-memberAbdallah, Chaouki
dc.description.committee-memberMadni, Asad
dc.description.committee-memberReda-Taha, Mahmoud
dc.description.committee-memberVadiee, Nader
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Multiple sensing devices\ncan be used cooperatively and collaboratively to capture events or monitor space more\neffectively than a single sensing device. The realm of applications envisioned for sensor\nnetworks is diverse including military, aerospace, industrial, commercial, environmental\nand health monitoring. Typical examples include: traffic monitoring of vehicles, crossborder\ninfiltration-detection and assessment, military reconnaissance and surveillance,\ntarget tracking, habitat monitoring and structure monitoring, to name a few.\nMost of the applications envisioned with sensor networks demand highly reliable,\naccurate and fault-tolerant data acquisition process. The integrity of data alone can have\ntremendous effects on the performance of any data acquisition system. Due to the low\nmanufacturing cost, the sensors lend themselves to be deployed in large numbers with a\nhigh spatial distribution. Such a large deployment scheme often generates enormous\namount of data that needs to be efficiently summarized and delivered for analysis and processing. In-network data compression, data aggregation/fusion, and decision\npropagation are some of the processes that deal with huge data issues. A hierarchical data\naggregation scheme developed in this thesis is a highly effective and energy efficient\nmeans (by reducing communication packets) to deliver decision milestones to the enduser.\nThe sensing devices are also prone to failure due to the inherent characteristics such\nas construction and deployment. It is thus necessary to devise a fault-tolerant mechanism\nwith a low computation overhead to validate the integrity of the data obtained from the\nsensors. Moreover, a robust diagnostics and decision making process should aid in\nmonitoring and control of critical parameters to efficiently manage the operational\nbehavior of a deployed sensor network. 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