الفهرس 
Enhanced weighted centroid localization by fuzzy logic and SOM
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Abstract
Wireless sensor networks (WSNs) have an endless array of potential applications in many critical applications such as robotic land - mine detection, battlefield surveillance, target tracking, environmental monitoring, wildfire detection, and traffic regulation. Location in such applications is very important for decision makers to identify the event source. In some applications as fire detection, it is generally not sufficient to determine if a fire is present, but more importantly, where the fire is present. Two of the famous localization methods are trilateration and centroid methods. Weighted centroid Localization (WCL) algorithm is introduced to minimize the localization error of the pure centroid algorithm. In this thesis fuzzy based Trilateration (FBT) is introduced as an enhanced version of trilateration. In addition, FCS as an enhanced version of the Weighted centroid is also introduced, where fuzzy logic (FL) and self organizing map (SOM) intelligence are utilized. FBT and FCS use fuzzy logic to merge between three important parameters which are received signal strength Indicator (RSSI), link quality Indicator (LQI), and power level (PL) in distance estimation. The usage of the three parameters in calculating the edge of the weighted centroid compensates for the uncertainty in their readings. Moreover FCS uses SOM algorithm in learning to enhance the nodes locations