Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network

Abstract

Wireless sensor networks are susceptible to failures of nodes and links due to various physical or computational reasons. Some physical reasons include a very high temperature, a heavy load over a node, and heavy rain. Computational reasons could be a third-party intrusive attack, communication conflicts, or congestion. Automated fault diagnosis has been a well-studied problem in the research community. In this paper, we present an automated fault diagnosis model that can diagnose multiple types of faults in the category of hard faults and soft faults. Our proposed model implements a feed-forward neural network trained with a hybrid metaheuristic algorithm that combines the principles of exploration and exploitation of the search space. The proposed methodology consists of different phases, such as a clustering phase, a fault detection and classification phase, and a decision and diagnosis phase. The implemented methodology can diagnose composite faults, such as hard permanent, soft permanent, intermittent, and transient faults for sensor nodes as well as for links. The proposed implementation can also classify different types of faulty behavior for both sensor nodes and links in the network. We present the obtained theoretical results and computational complexity of the implemented model for this particular study on automated fault diagnosis. The performance of the model is evaluated using simulations and experiments conducted using indoor and outdoor testbeds.