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.
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Notes
Class to which the pattern belongs e.g. positive, negative
For a binary class problem, a \(+\)ve target class can be represented as (1,0), and a −ve target class can be represented as (0,1).
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Appendix: Fundamental details of graph theory with respect to wireless sensor networks
Appendix: Fundamental details of graph theory with respect to wireless sensor networks
A wireless sensor network, G defined as a graph, i.e. \(G=(N, L)\), where N is set of all nodes in G, and L is set of all links in G.
Digraph
In a Digraph each links represented by order pair of nodes \((N_i,N_j)\), link from \(N_i\) to \(N_j\).
Degree of a node
Degree of a node in a nondirected graph defined deg(N), number of links incident with the node.
Adjacency
In a graph two nodes are said to be adjacent if there exists a link in between the two nodes.
In-degree and out-degree of a node
In-degree of node (\(deg^+(N)\)) is number of links incident to the node. Out-degree of node (\(deg^-(N)\)) is number of links incident from the node.
Connected network
Network is said to be connected if there exists a path between every pair of the node. The network which is not connected will 2 or more connected components.
Cut node
Let G be a connected network if there exists a node \(n\in G\) such that \(G-n\) results on a disconnected network. Then n is called as a cut node of G.
Cut link
Let G be a connected network if there exists a link \(e\in G\) such that \(G-e\) results on a disconnected network. Then e is called as a cut link of G.
Cut set
Let \(G=(N,L)\) be a connected network. A subset \({L}'\) of L is called a cut set of G, if removal of \({L}'\) from G makes the network disconnected and removal of no proper subset of \({L}'\) from G, makes G disconnected.
Node connectivity
Let G be a connected graph, the minimum number of nodes in G, whose removal makes G disconnected or isolated is called node connectivity of G.
Link connectivity
Let G be a connected graph, the minimum number of links in G, which removal makes G disconnected is called as link connectivity of G.
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Swain, R.R., Khilar, P.M. & Dash, T. Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes. J Ambient Intell Human Comput 10, 593–610 (2019). https://doi.org/10.1007/s12652-018-0709-3
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DOI: https://doi.org/10.1007/s12652-018-0709-3