Arbabi Monfared Mostafa (7 results)

Condition: New.

Condition: New.

Taschenbuch. Condition: Neu. Localization in Wireless Sensor Networks Based on Sugeno Fuzzy Logic | Mostafa Arbabi Monfared (u. a.) | Taschenbuch | 80 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783659443664 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 80 pp. Englisch.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.…

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