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

  • Rakesh Ranjan SwainEmail author
  • Pabitra Mohan Khilar
  • Tirtharaj DashEmail author
Original Research

Abstract

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.

Keywords

Wireless sensor network Artificial neural network Failure analysis Computational study Theoretical analysis Adaptive learning Multilayer perceptron 

Notes

Compliance with ethical standards

Ethical standards

The experiments conducted in this research do not harm the environment.

Conflict of interest

The authors declare that they have no conflict of interest. The data sources, if any, are clearly identified in this research article.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology RourkelaOdishaIndia
  2. 2.Department of Computer Science and Information SystemsBirla Institute of Technology and Science Pilani, Goa CampusGoaIndia

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