The week's pick
Random Articles
Reseach Article
Comparative Performance Analysis of AODV Parameter for ZigBee Network using Artificial Neural Network
by
Prativa P. Saraswala,
Jaymin Bhalani,
Sandhya Sharma
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 140 - Number 6 |
| Year of Publication: 2016 |
| Authors: Prativa P. Saraswala, Jaymin Bhalani, Sandhya Sharma |
10.5120/ijca2016909331
|
Prativa P. Saraswala, Jaymin Bhalani, Sandhya Sharma . Comparative Performance Analysis of AODV Parameter for ZigBee Network using Artificial Neural Network. International Journal of Computer Applications. 140, 6 ( April 2016), 20-25. DOI=10.5120/ijca2016909331
@article{
10.5120/ijca2016909331,
author = {
Prativa P. Saraswala,
Jaymin Bhalani,
Sandhya Sharma
},
title = { Comparative Performance Analysis of AODV Parameter for ZigBee Network using Artificial Neural Network },
journal = {
International Journal of Computer Applications
},
issue_date = { April 2016 },
volume = { 140 },
number = { 6 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = {
20-25
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume140/number6/24598-2016909331/
},
doi = { 10.5120/ijca2016909331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:34.406298+05:30
%A Prativa P. Saraswala
%A Jaymin Bhalani
%A Sandhya Sharma
%T Comparative Performance Analysis of AODV Parameter for ZigBee Network using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 6
%P 20-25
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper emphasizes on the signal transmission range of Zigbee network based on IEEE 802.15.4 standard using Simulink-based simulator called TRUE TIME 2.1. Ad hoc On-Demand Distance Vector (AODV) Routing is implemented in TRUE TIME 2.1. Here a comparison is made between the three Artificial Neural Network Architectures such as Feed forward neural network, Cascade forward neural network and Layered Recurrent Neural Network for various training functions like Levenberg-Marquardt back propagation (trainlm), Bayesian regularization back propagation (trainbr) and BFGS quasi-Newton back propagation (trainbfg) for Feed Forward Neural Network.
References
- ZigBee Alliance, Network Layer Specification 1.0, Dec. 2004,[Online].Available:http://standards.ieee.org/getieee802/download/802.15.4-2003.pdf
- Institute of Electrical and Electronic Engineers, Inc., ”IEEE Std. 802.15.4-2003, IEEE Standard for Information Technology, Telecommunications and Information Exchange between Systems – Local and Metropolitan Area Networks – specific Requirements – Part 15.4 : Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR-WPAN).
- Ran Peng, Sun Mao-heng, Zou You-min, “ZigBee Routing Selection Strategy Based on Data Services and Energy-balanced ZigBee routing”, Proceedings of the IEEE Asia-Pacific Conference on Services Computing, 12-15 Dec. 2006
- C. Perkins and E. Royer, “Ad Hoc On-demand Distance Vector (AODV) Routing” , Internet Draft, MANET working group, draft-ietf-manetaodv- 05.txt, March 2000.
- C. E. Perkins, E. M. Royer, and S. R. Das, “Ad hoc on demand distance vector (AODV) routing,” IETF Internet draft, draft-ietf-manetaodv- 13.txt, Feb. 2003.
- YiGong Peng, YingLi Li, ZhongCheng Lu, JinShou Yu, “Method for Saving Energy in Zigbee Network”, 5th International conference on Wireless Communications, Networking and Mobile Computing, 24-26 Sept. 2009.
- Antonio M. Ortiz, Fernando Royo, Teresa Olivares and Luis Orozco–Barbosa, “Intelligent Route Discovery for ZigBee Mesh Networks ”, IEEE International Symposium on World of Wireless, Mobile and Multimedia Networks , 20-24 June 2011.
- Maged Salah EldinSoliman, SherineMohamed Abd El-kader, Hussein SherifEissa, HodaAnis Baraka, “NEW ADAPTIVE ROUTING PROTOCOL FOR MANET”, published in Ubiquitous Computing and Communication Journal Volume 2,Number 3, pp-16-22.
- Jun Xiao and Xiaojun Liu, “The Research of E-AOMDVjr Routing Algorithm in ZigBee Network”, IEEE conference on Control and Decision Conference (CCDC), pp. 2360 - 2365 23-25 May 2011.
- Zheng Sun, Xiao-guang Zhang, Dianxu Ruan, Hui Li and Xun Pang, “A Routing Protocol based on Flooding and AODV in the ZigBee Network”, International Workshop on Intelligent Systems and Applications, pp. 1 - 4 23-24 May 2009.
- Zhao Hong-tu , Ma Yue-qi , “Improved Routing Algorithm Research for ZigBee Network ”, Proceedings of the Third International Symposium on Computer Science and Computational Technology, pp. 017-020, Jiaozuo, P. R. China, 14-15,August 2010
- M. Ohlin, D. Henriksson, and A. Cervin, "TrueTime 2.1 Reference Manual," Department of Automatic Control, Lund University, Sweden, 2007. [Online] Available: http://www.control.lth.se/truetime.
- Andersson, Martin, Dan Henriksson, Anton Cervin and Karl-Erik arzen (2005). “Simulation of wireless networked control systems”, Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference, Seville, Spain, 2005
- Tamer M.S. Khattab, Mahmoud T. El-Hadidi and Hebat-Allah M. Mourad, “Analysis of Wireless CSMA/CA Network Using Single Station Superposition (SSS)”, published in International Journal of Electronics and Communications, Vol. 56, pp. 71−81, 2002
- Pejman Tahmasebi, Ardeshir Hezarkhani, “Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran”, published in Australian Journal of Basic and Applied Sciences, ISSN 1991-8178, pp. 408-420, 2010
- Yevgeniy, Gershteyn Larisa Perman, “Matlab: ANFIS Toolbox”, presented on 04/22/2003
- Jyh-Shing Roger Jang, “ANFIS : Adaptive-Network-Based Fuzzy Inference System ”, IEEE transactions on systems, man and cybernetics, vol. 23, may-june 1993
- Dr. Bob John, “ANFISnote” [Online]. Available www.scribd.com/doc/134927592/ANFISnote
- The Fuzzy Logic Toolbox for use with MATLAB, J.S.R. Jang and N. Gulley, Natick, MA: The Math Works Inc., 1995
- Yizheng Xu, Graduate Student Member, IEEE, and Jovica V. Milanovic, Fellow, IEEE, “Artificial-Intelligence-Based Methodology for Load Disaggregation at Bulk Supply Point”, published in IEEE transactions on power systems, Vol. 30, MARCH 2015
- G. Chicco, R. Napoli, and F. Piglione, “Comparisons among clustering techniques for electricity customer classification,” published in IEEE transaction. Power Syst., vol. 21, pp. 933–940, May 2006.
- L. Du, D. HE, Y. Yang, J. A. Restrepo, B. Lu, R. G. Harley, and T. G. Habetler, “Self-organizing classification and identification of miscellaneous electric loads,” in Proc. IEEE PES Gen. Meeting, San Diego, CA, USA, pp. 1–6, 2012.
- Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth, “Neural Network Toolbox™ 7 User’s Guide”, 2009
- CIGRE Working Group C4.605, Rep.5, “Modelling and Aggregation of Loads in Flexible Power Networks,” (566), Feb. 2014.
- H. Ku-Long, Y.-Y. Hsu, and Y. Chien-Chuen, “Short term load forecasting using a multilayer neural network with an adaptive learning algorithm,” IEEE Trans. Power Syst., vol. 7, no. 1, pp. 141–149, Feb. 1992.
- Saduf, Mohd Arif Wani, “Comparative Study of Back Propagation Learning Algorithms for Neural Networks”, pp. 1151-1156, Volume 3, Issue 12, December 2013.
- M. Riedmiller, H. Braun, “A direct adaptive method for faster back-propagation learning: the RPROP algorithm, “, Proc. of the International Conference on Neural Networks, pp. 586–591, 1993
Index Terms
Computer Science
Information Sciences
Keywords