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Data management and data analysis te...

University of California, Riverside.

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Data management and data analysis techniques for wireless sensor networks.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data management and data analysis techniques for wireless sensor networks./
作者:	Lin, Song.
面頁冊數:	190 p.
附註:	Adviser: Dimitrios Gunopulos.
Contained By:	Dissertation Abstracts International69-01B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3298277
ISBN:	9780549416906

Data management and data analysis techniques for wireless sensor networks.
Lin, Song.

Data management and data analysis techniques for wireless sensor networks. - 190 p.

Adviser: Dimitrios Gunopulos.

Thesis (Ph.D.)--University of California, Riverside, 2007.

Fast developments in microelectronics and wireless technologies have made feasible the development of wireless sensor networks (WSN) composed of large numbers of small and smart sensors. Though cheap and small, the sensor devices have many resource limitations that introduce new challenges to the data management in sensor networks. Among all these limitations, energy is usually the primary concern when designing a sensor network algorithm. This dissertation presents two efficient approaches, data compression and data sampling, to minimize the energy consumption of the sensor networks. More specifically, it presents and evaluates an efficient data compression technique---the ALVQ (Adaptive Learning Vector Quantization) algorithm to compress the historical information in sensor networks with high accuracy. It shows how the ALVQ algorithm can be extended to compress multi-dimensional information and how the compressed data are transmitted in a sensor network while maximizing the precision. In addition, it proposes an efficient online sampling algorithm, Region Sampling, to retrieve a small fraction of the sensor data from the network while approximate the aggregate queries accurately. It demonstrates how a sensor network can be segmented into partitions of non-overlapping regions and how to use sampling energy cost rate and sampling statistics to compute the optimal sampling plan in different regions.

ISBN: 9780549416906Subjects--Topical Terms:

626642
Computer Science.

Data management and data analysis techniques for wireless sensor networks.
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502 $a Thesis (Ph.D.)--University of California, Riverside, 2007.
520 $a Fast developments in microelectronics and wireless technologies have made feasible the development of wireless sensor networks (WSN) composed of large numbers of small and smart sensors. Though cheap and small, the sensor devices have many resource limitations that introduce new challenges to the data management in sensor networks. Among all these limitations, energy is usually the primary concern when designing a sensor network algorithm. This dissertation presents two efficient approaches, data compression and data sampling, to minimize the energy consumption of the sensor networks. More specifically, it presents and evaluates an efficient data compression technique---the ALVQ (Adaptive Learning Vector Quantization) algorithm to compress the historical information in sensor networks with high accuracy. It shows how the ALVQ algorithm can be extended to compress multi-dimensional information and how the compressed data are transmitted in a sensor network while maximizing the precision. In addition, it proposes an efficient online sampling algorithm, Region Sampling, to retrieve a small fraction of the sensor data from the network while approximate the aggregate queries accurately. It demonstrates how a sensor network can be segmented into partitions of non-overlapping regions and how to use sampling energy cost rate and sampling statistics to compute the optimal sampling plan in different regions.
520 $a In addition to energy-efficient data transmission, rapid developments in flash memories have made possible the ability to store large amounts of data within individual sensors. This dissertation presents two index structures, MicroHash and MicroGF, for efficient retrieval of one dimensional and multi-dimensional sensor data stored in the flash memory of a sensor device. It shows how to exploit the asymmetric read/write and wear characteristics of flash memory in order to offer high performance indexing and searching capabilities. Finally it considers a typical sensor network application, distributed spatio-temporal similarity search , and shows how to combine local computations of lower and upper bounds in order to find the trajectories that are most similar to the query.
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