Investigation of RBF Kernelized ANFIS for Fault Diagnosis in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSN) are often deployed in human inaccessible environments. Some examples of such environments that are difficult for quick human reach include deep forests, various hazardous industries, hilltops, and sometimes underwater. The occurrence of failures in sensor networks is inevitable due to continuous or instant change in environmental parameters. A failure may lead to faulty readings which in turn may cause economic and physical damages to the environment. In this work, a thorough investigation has been conducted on the application of adaptive neuro-fuzzy inference system (ANFIS) for automated fault diagnosis in WSN. Further, a kernelized version of ANFIS has also been studied for the discussed problem. To avoid the model’s undesired biases toward a specific type of failure, oversampling has been done for multiple version of the ANFIS model. This study would serve as a guideline for the community toward the application of fuzzy inference approaches for fault diagnosis in sensor networks. However, the work focuses on the automated fault diagnosis in open air WSN and has no applicability in underwater sensor network systems.