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Title |
RSS-based Locating in Wireless Sensor Networks using Artificial Neural Network |
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Author |
Safae El Abkari, Abdelilah Jilbab and Jamal El Mhamdi |
| Citation |
Vol. 20 No. 7 pp. 70-76 |
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Abstract |
Wireless Sensor Networks are emerging in various domains. One of the most important and challenging services in demand is locating of network nodes. In this paper, we adopted the methodology of the feed-forward neural network. We used the received signal strength of anchor nodes to locate. We also address the dependency of accuracy on the number of anchors and the network configuration. We then evaluate different training algorithms to obtain the best result using the selected training algorithm. Our proposed model is implemented on ESP8266 module for a real-time evaluation of the model performances. An average error of location of 0.189 meter is achieved using four anchor nodes and a neural network structure of 10-10-3. We can also implement this presented method on any embedded locating system. |
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Keywords |
Locating, Neural Network, Wireless Sensor Network, Received Signal Strength (RSS), Real-time |
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