Title: Recurrent neural network NARX for distributed fault detection in wireless sensor networks

Authors: Jamila Atiga; Monia Hamdi; Ridha Ejbali; Mourad Zaied

Addresses: RTIM, Research Team in Intelligent Machines, Gabès University, Gabès, 6033, Tunisia ' Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia; RTIM, Research Team in Intelligent Machines, Gabès University, Gabès, 6033, Tunisia ' RTIM, Research Team in Intelligent Machines, Gabès University, Gabès, 6033, Tunisia ' RTIM, Research Team in Intelligent Machines, Gabès University, Gabès, 6033, Tunisia

Abstract: Wireless sensor networks (WSNs) comprise a collection of sensors used to collect data, which allow knowing the state of a zone or a monitored system. Sensor are usually deployed in harsh environments, where failures are common. In this work, we propose a distributed fault detection (DFD) algorithm based on the nonlinear automatic regression recurrent non-linear autoregressive with exogenous inputs (NARX) for failure detection. Defective sensors are identified by comparisons between series of actually measured values and their predicted values. The proposed method, in the first step, divides the network into a set of cliques, forming different areas. In the next step, the damaged cliques are identified using the Gaussian distribution theorem. Finally, the NARX approach is applied only to the damaged cliques to determine the defective nodes. The comparisons of simulation results to other existing algorithms show that the proposed method reaches the best results.

Keywords: WSN; wireless sensor network; fault detection; DFD algorithm; NARX; non-linear autoregressive with exogenous inputs; Gaussian distribution theorem.

DOI: 10.1504/IJSNET.2021.118488

International Journal of Sensor Networks, 2021 Vol.37 No.2, pp.100 - 111

Received: 01 Jan 2021
Accepted: 30 Jan 2021
Published online: 27 Oct 2021 *

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