Abstract
Today’s world wireless sensor networks (WSNs) are gaining more and more importance in any of the domains of industry. Plenty of research and development has happened, and it is going on in the field of WSN. The importance of conservation of energy in WSN in most of its applications will provide the best scope for WSN development. Sufficient literatures are already carried for deployment of nodes leading to conservation of energy. One of the important application areas of WSN is area monitoring where the nodes have to move forward to sense the area and come back to the original positions. This paper provides solution for this problem by the concept of change in the positions of nodes using a simple neural network concept. This concept considers the energy, distance, and sensing of nodes as the basic parameters in moving a single node or all nodes. The main concept in this paper is to propose a deployment strategy that changes the positions of the nodes according to the activation function decision.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nauman Ahad A,N, Junaid Qadir A,B, Nasir Ahsan “Neural networks in wireless networks: Techniques, applications and guidelines”, Elsevier Journal of Network and Computer Applications, http://dx.doi.org/10.1016/j.jnca.2016.04.006 1084-8045/& 2016 Elsevier Ltd.
Haitao Zhang and Cuiping Liu, “A Review on Node Deployment of Wireless Sensor Network”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 3, November 2012
Hanen Ahmadi (1,2), Ridha Bouallegue, “Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector Regression, Neural Network and Naïve Bayes”, 978-1-4799-5344-8/15/$31.00 ©2015 IEEE 1554
Neda Enami1, Reza Askari Moghadam 1, Kourosh Dadashtabar & Mojtaba Hoseini 3, “NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS: A SURVEY”, International Journal of Computer Science & Engineering Survey (IJCSES) Vol. 1, No. 1, August 2010
DAnieal, EDUARDO et. al. “Energy Efficient reprogramming in WSN using constructive neural networks”, International journal of Innovative computing, Information and control vol 8, Nov 11, 2012
Jabal Raval, Bhushan Jagyasi TCS Innovation Labs Mumbai Tata Consultancy Services, India “Distributed Detection in Neural Network based Multihop Wireless Sensor Network”, 978-1-4799-2179-9/14/$31.00 ©2014 IEEE
Wu Ai A, C, Weisheng Chen b, ∗, Jin Xie A, “A zero-gradient-sum algorithm for distributed cooperative learning using a feedforward neural network with random weights”, Elsevier Information Sciences, Available online 7 September 2016 0020-0255/© 2016 Elsevier Inc.
Gursel Serpen*, Jiakai Li, and Linqian Liu, Conference Organized by Missouri University of Science and Technology 2013-Baltimore, MD, AI-WSN: Adaptive and Intelligent Wireless Sensor Network Procedia Computer Science 20 (2013) 406–413, 1877-0509 © 2013 The Authors. Published by Elsevier B.V. doi:10.1016/j.procs.2013.09.294
Duhart Clement∗†, Bertelle Cyrille, “Methodology for Artificial Neural Controllers on Wireless Sensor Network”, 2014 IEEE Conference on Wireless Sensors (ICWiSE), October, 26–28 2014, Subang, Malaysia
Xiaobo Xie, Junqi Guo*, Hongyang Zhang, Tao Jiang, Rongfang Bie, Yunchuan Sun “Neural-Network based Structural Health Monitoring with Wireless Sensor Networks”, 978-1-4673-4714-3/13/$31.00 ©2013 IEEE 163
Chenxian Xiao and Ning Yu Chenxian Xiao and Ning Yu, “A Predictive Localization Algorithm Based on RBF Neural Network for Wireless Sensor Networks, 978-1-4673-7687-7/15/$31.00 ©2015 IEEE
Text book: S.N. Sivanandam, S.N. Deepa, Principles of Soft Computing, 2nd Edition Wiley Publisher
Harish H Kenchannavar, U.P. Kulkarni, “Optimized Overlapping Based Energy-Aware Node Collaboration in Visual Sensor Networks”, International Conference on Wireless Technologies for Humanitarian Relief (ACWR2011) sponsored by ACM SIGCOMM, SIGMM, SIGMOBILE and IEEE held at Amrita University, Amritapuri, Kerala, on 19-21st Dec 2011. doi>10.1145/2185216.2185248, ISBN: 978-1-4503-1011-6. {50% Acceptance Ratio}
Harish H. Kenchannavar, Umakant P. Kulkarni, “Energy Efficient Data Processing in Visual Sensor network”, International Journal of Computer Science and IT Vol. 2, Number 5, October 2010, ISSN: 0975-4660. airccse.org/journal/jcsit/1010ijcsit11.pdf
Gowri. K, Dr. M.K. Chandrasekaran, Kousalya. K, “A Survey on Energy Conservation for Mobile-Sink in WSN”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (6), 2014, 7122–7125
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kulkarni, U.M., Kenchannavar, H.H., Kulkarni, U.P. (2018). Neural Deployment Algorithm for WSN: A Concept. In: Dash, S., Das, S., Panigrahi, B. (eds) International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 632. Springer, Singapore. https://doi.org/10.1007/978-981-10-5520-1_3
Download citation
DOI: https://doi.org/10.1007/978-981-10-5520-1_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5519-5
Online ISBN: 978-981-10-5520-1
eBook Packages: EngineeringEngineering (R0)