Localization in Wireless Sensor Networks Based on Sugeno Fuzzy Logic
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One of the challenges in wireless sensor networks is to determine the location of sensor nodes based on the known location of other nodes. This paper identifies an intelligent localization method, which is based on range free localization to estimate the location of the unknown nodes. In the proposed method, the anchor nodes are connected to the sensor nodes and then each sensor node receives a signal from the anchor node. The Received Signal Strength Indicator is then calculated by the node. The RSSIs are calculated based on the distance of the sensor node to each anchor node. The RSSIs are, ...
One of the challenges in wireless sensor networks is to determine the location of sensor nodes based on the known location of other nodes. This paper identifies an intelligent localization method, which is based on range free localization to estimate the location of the unknown nodes. In the proposed method, the anchor nodes are connected to the sensor nodes and then each sensor node receives a signal from the anchor node. The Received Signal Strength Indicator is then calculated by the node. The RSSIs are calculated based on the distance of the sensor node to each anchor node. The RSSIs are, then, fed to the Sugeno fuzzy inference system to calculate the weights to be used in the centroid relation. The centroid technique is proposed to estimate the location of the unknown sensor nodes. Both analytical and experimental results are discussed in this paper. The results show that with increasing the membership functions, the error decreases and that is because of the RSSI graph, which better fits the corresponding simulation result.
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