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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2015

01.06.2015

Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks

verfasst von: Ashish Payal, C. S. Rai, B. V. R. Reddy

Erschienen in: Wireless Personal Communications | Ausgabe 4/2015

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Abstract

Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes.
Vorheriger Artikel Secrecy Performance Analysis of Multihop Transmission Protocols in Cluster Networks
Nächster Artikel An Energy-Efficient Topology Control Mechanism for Wireless Sensor Networks Based on Transmit Power Adjustment

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Metadaten
Titel
Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks
verfasst von
Ashish Payal
C. S. Rai
B. V. R. Reddy
Publikationsdatum
01.06.2015
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2015
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-015-2362-x

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