The advances of communication and computer techniques have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and capable of communicating in short distances. A sensor network is composed of a large number of sensor nodes that are densely deployed either inside the phenomenon to be observed or very close to it. Sensor networks open up new opportunities to observe and interact with the physical world around us.
Despite the recent advances in sensor network applications and technology, sensor networks still suffer from the major problems of limited energy. It is because most sensor nodes use battery as their energy srouce and are inconvenient and sometimes difficult to be replaced when the battery run out. Understanding the events, measures, and tasks required by certain applications has the potential to provide efficient communication techniques for the sensor network.
Our focus in this work is on the efficient processing of continuous queries, by which query results have to be generated according to the sampling rate specified by the user for an extended period of time. In this thesis, we will deal with two types of continuous queries. The first type of queries requires data from all sensor nodes; while the other is only interested in the data returned by some selected nodes. To answer these queries, data have to be sent to the base station at some designated rate, which may consume much energy. Previous works have developed two methods to reduce the energy consumption. They both base on the error range which the user can tolerate to determine whether current sensing data should be transmitted. While the first uses simple cache method, the second uses complex multi-dimensional model. However, the proposed methods required the user to specify the error range, which may not be easy to specify. In addition, the sensed data reported by the sensors were assumed to be accurate, which is by no means true in the real world. This thesis is based on Kalman filter to correct and predict sensing data. As a result, the sampling frequency of each sensor is dynamically adjusted, referred to as resampling which systematically determine the data sensing/transferring rate of sensors. We evaluate our proposed methods using empirical data collected from a real sensor network.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0717107-033248 |
| Date | 17 July 2007 |
| Creators | Liu, Pin-yu |
| Contributors | Bing-chiang Jeng, San-yi Huang, Wei-bo Lee, Chia-mei Chen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0717107-033248 |
| Rights | campus_withheld, Copyright information available at source archive |
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