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An artificial neural network based fault detection and diagnosis for wireless mesh networks

Wireless Mesh Network (WMN) is a wireless communication technology that has many advantageous characteristics such as dynamic self-organization, self-configuration, self-healing, high scalability and reliable services coverage. However, wireless mesh links frequently experience quality variations and undesirable performance or poor connectivity. In this paper we propose a fault detection and diagnosis approach for enhancing performance of WMNs based on Artificial Neural Networks (ANN). The artificial neural network is trained to detect and classify individual or composite faults. We consider node failure, link failure and traffic congestion as the target faults. The approach is implemented in NS3 network simulator and its performance is evaluated considering detection rate, false positive and false negative.