Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes

• Published in: Journal of ambient intelligence and humanized computing Vol. 10; no. 2; pp. 593 - 610
• Main Authors: Swain, Rakesh Ranjan, Khilar, Pabitra Mohan, Dash, Tirtharaj
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Automation; Case studies; Communication channels; Computational Intelligence; Computer simulation; Decision making; Engineering; Failure; Failure detection; False alarms; Machine learning; Neural networks; Nodes; Original Research; Performance evaluation; Robotics and Automation; Sensors; Test stands; User Interfaces and Human Computer Interaction; Wireless sensor networks; Wireless sensor network; Adaptive learning; Computational study; Failure analysis; Multilayer perceptron; Theoretical analysis; Artificial neural network
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-018-0709-3

In the broad research area of wireless sensor networks (WSN), detection of link failure is still in its infancy. In this paper, we propose to use a neural network model for detection of link failure in WSN. The neural network has been allowed to learn and adapt with the help of gradient descent based learning algorithm. We demonstrate the proposed model with regard to the preparation of training data and implementation of the model. This paper also provides a thorough theoretical and analytical investigation of link failures in WSN. The proposed neural network based model has been evaluated carefully with regard to testbed experiments. The simulation-based experiment has been conducted to justify the applicability of the proposed model for dense networks that could contain around 1000 links. We also analyze the theoretical performance of the proposed neural network based algorithm with regard to various performance evaluative measures such as failure detection accuracy, false alarm rate. The simulated experiments, as well as the testbed experiments in indoor and outdoor environments, suggest that the method is capable of link failure detection with higher detection rate and it is consistent. Furthermore, this article also reports a comprehensive case study as an extension of this present research towards automated detection of disjoint and disconnected nodes in a sensor network.