IMS Manthan (The Journal of Mgt., Comp. Science & Journalism)

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ANN for Node Localization in Wireless Sensor Network


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1 Guru Gobind Singh Indraprastha University, New Delhi, India
     

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With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to make them more efficient in a cost effective way. An important area which can bring efficiency to WSNs is the localisation process by which the sensor nodes in the network can identify their own location in the overall network. The objective of this paper is to study and test the use of Artificial Neural Networks (ANNs) as a method of localisation in WSNs. This is achieved by using Backpropagation algorithm (BPN) based on multilayer perceptron (MLP) neural networks to carry out the localization process. The network consisting of sensor nodes is initially trained using training algorithms namely Levenberg-Marquardt and Resilient Backpropagation. The network is then tested with a new independent set of data to prove the effectiveness of proposed model. In the paper, other variables like number of anchor nodes, neurons in hidden layers etc. which impact the efficiency of the network in localisation process are also analysed.

Keywords

Wireless Sensor Network, Localization, Backpropagation algorithm, Resilient Backpropagation, Beacon nodes
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  • ANN for Node Localization in Wireless Sensor Network

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Authors

Shikha Bhardwaj
Guru Gobind Singh Indraprastha University, New Delhi, India

Abstract


With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to make them more efficient in a cost effective way. An important area which can bring efficiency to WSNs is the localisation process by which the sensor nodes in the network can identify their own location in the overall network. The objective of this paper is to study and test the use of Artificial Neural Networks (ANNs) as a method of localisation in WSNs. This is achieved by using Backpropagation algorithm (BPN) based on multilayer perceptron (MLP) neural networks to carry out the localization process. The network consisting of sensor nodes is initially trained using training algorithms namely Levenberg-Marquardt and Resilient Backpropagation. The network is then tested with a new independent set of data to prove the effectiveness of proposed model. In the paper, other variables like number of anchor nodes, neurons in hidden layers etc. which impact the efficiency of the network in localisation process are also analysed.

Keywords


Wireless Sensor Network, Localization, Backpropagation algorithm, Resilient Backpropagation, Beacon nodes

References