|Title:||Algorithms for wireless sensor network localization based on received signal strength indication|
|Subject:||Wireless sensor networks -- Mathematical models.|
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Land Surveying and Geo-Informatics|
|Pages:||xxii, 177 p. : ill. (some col.) ; 30 cm.|
|Abstract:||A Wireless Sensor Network (WSN) is a large ad hoc network of densely distributed sensor nodes that are equipped with wireless transceivers and receivers. Such networks can be applied for target detection, monitoring and tracking with the location information of remote wireless nodes. Range estimation is essential in many sensor network localization algorithms. Received Signal Strength Indication (RSSI) has been widely used for WSN localization algorithms, but currently, the result of existing RSSI based algorithms cannot achieve acceptable accuracy especially in a dynamic condition. Fingerprinting and Path Loss Model algorithms have their own advantages and disadvantages. In this thesis, four novel algorithms based on RSSI for different scales of areas are introduced, including differential positioning, positioning based on subset base stations, Direct Weighted Model (DWM) and a Self Calibrating Algorithm (SCA). In rural and urban area, differential Positioning method could reduce the positioning error up to 90% by using corrected distances compared with traditional Center of Gravity Model (CGM). In urban area, positioning with Subset Base Stations could obviously enhance the positioning performance by selecting correct base stations, compared with CGM method, both mean error and standard deviation are reduced more than 90%. Double antennas may also be used to reduce the coverage on mobile stations, and to enhance the success ratio for positioning. Experimental real data collected from a IEEE 802.15.4 based wireless sensor network system for local area. In both simulation and real data tests, the new DWM algorithm reduces the relative error the same as or less than the traditional fingerprinting method, and SCA also could be better than Path Loss Models.|
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