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Faulty node detection in wireless sensor networks using a recurrent neural network
Author(s): Jamila Atiga; Nour Elhouda Mbarki; Ridha Ejbali; Mourad Zaied
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Paper Abstract

The wireless sensor networks (WSN) consist of a set of sensors that are more and more used in surveillance applications on a large scale in different areas: military, Environment, Health ... etc. Despite the minimization and the reduction of the manufacturing costs of the sensors, they can operate in places difficult to access without the possibility of reloading of battery, they generally have limited resources in terms of power of emission, of processing capacity, data storage and energy. These sensors can be used in a hostile environment, such as, for example, on a field of battle, in the presence of fires, floods, earthquakes. In these environments the sensors can fail, even in a normal operation. It is therefore necessary to develop algorithms tolerant and detection of defects of the nodes for the network of sensor without wires, therefore, the faults of the sensor can reduce the quality of the surveillance if they are not detected. The values that are measured by the sensors are used to estimate the state of the monitored area. We used the Non-linear Auto- Regressive with eXogeneous (NARX), the recursive architecture of the neural network, to predict the state of a node of a sensor from the previous values described by the functions of time series. The experimental results have verified that the prediction of the State is enhanced by our proposed model.

Paper Details

Date Published: 13 April 2018
PDF: 6 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962P (13 April 2018); doi: 10.1117/12.2314837
Show Author Affiliations
Jamila Atiga, Univ. of Gabes (Tunisia)
Nour Elhouda Mbarki, Research Groups in Intelligent Machines (Tunisia)
Ridha Ejbali, Univ. of Gabes (Tunisia)
Research Groups in Intelligent Machines (Tunisia)
Mourad Zaied, Research Groups in Intelligent Machines (Tunisia)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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