CFP last date
16 December 2024
Call for Paper
January Edition
IJAIS solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 16 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Optimizing the Energy Consumption of Wireless Sensor Networks

by A.H. Mohamed, K.H. Marzouk
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 2
Year of Publication: 2015
Authors: A.H. Mohamed, K.H. Marzouk
10.5120/ijais2015451465

A.H. Mohamed, K.H. Marzouk . Optimizing the Energy Consumption of Wireless Sensor Networks. International Journal of Applied Information Systems. 10, 2 ( December 2015), 1-5. DOI=10.5120/ijais2015451465

@article{ 10.5120/ijais2015451465,
author = { A.H. Mohamed, K.H. Marzouk },
title = { Optimizing the Energy Consumption of Wireless Sensor Networks },
journal = { International Journal of Applied Information Systems },
issue_date = { December 2015 },
volume = { 10 },
number = { 2 },
month = { December },
year = { 2015 },
issn = { 2249-0868 },
pages = { 1-5 },
numpages = {9},
url = { https://www.ijais.org/archives/volume10/number2/838-2015451465/ },
doi = { 10.5120/ijais2015451465 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:02:09.278887+05:30
%A A.H. Mohamed
%A K.H. Marzouk
%T Optimizing the Energy Consumption of Wireless Sensor Networks
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 10
%N 2
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wireless sensor networks (WSNs) have great attention and applications in many fields in the recent years. One of the main challenges in using the WSNs is their energy consumptions. Although, many methods have been developed to overcome this problem, there are still some limitations facing the WSNs in this manner. In this paper, the proposed system introduces a new system that uses genetic algorithm (GA) for optimizing the node deployment, their locations and dividing the sensor nodes into two modes of operation that can minimize the energy consumption of the WSN. Suggested system has been applied for a simulated WSN used in the radiation discovering sites as a case of study. Its obtained results have proved its success to be applied in the practical sites.

References
  1. K. Y. Bendigeri and J. D. Mallapur, (2014), “Energy Aware Node Placement Algorithm for Wireless Sensor Network, Advance in Electronic and Electric Engineering”, 4(6): 541-548.
  2. G. Devi, R. S. Bal and S. M. Nayak, (2015), “Node Deployment and Coverage in Wireless Sensor Network”, International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(1): 139-145.
  3. J. Yuan, Q. Ling, J. Yan, W. Zhang, and H. Gu, (2011), “A Novel Non-Uniform Node Distribution Strategy For Wireless Sensor Networks,” IEEE Conf. Control and Decision, 3737-3741.
  4. S. Teli1 and S. Ganesan, (2014), "Increasing Node Density to Improve the Network Lifetime in Wireless Network", International Journal of Innovative Research in Computer and Communication Engineering, 2(4):51-55 .
  5. N. Jafari ,et. al, (2011), “Reduce Energy Consumption and Increase the Lifetime of Heterogeneous Wireless Sensor Networks: Evolutionary Approach”, 2(5):112-117.
  6. Z. Cheng, M. Perillo and W. B. Heinzelman, (2008), ”General Network Lifetime and Cost Models for Evaluating Sensor Network Deployment Strategies,” IEEE Transactions on Mobile Computing, 7(4): 484-497.
  7. H. Zhang and C. Liu , (2012), “A Review on Node Deployment of Wireless Sensor Network “, International Journal of Computer Science Issues (IJCSI), 9(6), no. 3:378-383 .
  8. M. C. Akewar and N. V. Thakur, (2012), “A Study of Wireless Mobile Sensor Network Deployment”, IRACST – International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(4):533-541.
  9. Mayur C. Akewar, Nileshsingh V. Thakur, (2012), " A Study of Wireless Mobile Sensor Network Deployment" IRACST – International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(4):533-541.
  10. R. Nallusamy, (2010), " Energy Efficient Dynamic Shortest Path Routing in Wireless Ad Hoc Sensor Networks using Genetic Algorithm", International Conference on Wireless Communication and Sensor Computing (ICWCSC 2010), 1-5.
  11. M. Gen and R. Cheng, “Genetic Algorithm and Engineering Optimization”, John Wiley and Sons, New York, (2000), 50-75.
  12. M. Mahalakshmi, P. Kalaivani, E.K. Nesamalar, (2013), "A Review on Genetic Algorithm and its Applications, International Journal of Computing Algorithm, 2:415-423.
  13. A. Ziarati (2010), "A Multilevel Evolutionary Algorithm for Optimizating Numerical Functions", IJICE, 2:419-430.
  14. T.Ch. Kang, (2005), "On the Mean Convergence Time of Multi-Parent Genetic Algorithms Without Selection ", Advances in Artificial Life, 403-412.
  15. R. N. Enam, M. Imam, and R. I. Qureshi, (2012), “Energy Consumption in Random Cluster Head Selection Phase of WSN,” IACSIT press, Singapore, 30: 38–44.
  16. H. Chihfan and L. Ningyan, (2006), “Randomly Duty-cycled Wireless Sensor Networks: Dynamics of Coverage”, IEEE Transactions on Wireless Communications, 5:3182-3192.
  17. F. Akyildiz et. al (2007), “Wireless sensor networks – A survey”, Computer Networks, 38(4):201-204.
  18. Xu, Kenan, et al. (2005), ”Optimal Wireless Sensor Networks (WSNs) Deployment: Minimum Cost with Lifetime Constraint”, IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, (WiMob’2005), 3: 454 – 461.
  19. W. Yiyue, L. Hongmei, H. Hengyang, (2012), "Wireless Sensor Network Deployment Using an Optimized Artificial Fish Swarm Algorithm," International Conference on Computer Science and Electronics Engineering (ICCSEE), Hangzhou, 90-94.
  20. P. Zhou, X. Cui and S. Wang, (2009), “Virtual Force Based Wireless Sensor Network Coverage Enhancing Algorithm”, Journal of System Simulation, 9:1416-1422.
  21. M. Romoozi, M. Vahidipour, S. Maghsoodi, (2010), "Genetic Algorithm for Energy Efficient and Coverage-Preserved Positioning in Wireless Sensor Networks", International Conference on Intelligent Computing and Cognitive Informatics (ICICCI), Kuala Lumpur, 22-25.
  22. Z. Sun, H. Li, H. Chen and W. Wei, (2014), “Optimization Coverage of Wireless Sensor Networks Based on Energy Saving”, International Journal of Future Generation Communication and Networking, 7(4): 35-48.
  23. R. Kaur and Mrs. Sonia, "Efficient Energy Consumption in Wireless Sensor Networks Using Optimization Technique", International Journal of Engineering Science & Advanced Technology 2(5):1290 – 1294.
  24. Y. Yonrim and K. Y. Hyuk, (2013), “An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks”, IEEE Transactions on Cybernetics, 10:1473-1483.
  25. K. J. Rabindra, (2013), “Energy Aware Node Placement in Wireless Sensor Network ACO” Journal of Theoretical and Applied Information Technology, 53(2):291-297.
  26. S. Hussain, A. W. Matin, O. Islam, (2007), "Genetic Algorithm for Hierarchical Wireless Sensor Networks", Journal of Networks, 2(5):87-97.
  27. S. Sengupta, S. Das, M. D. Nasir, B. K. Panigrahi, (2013), "Multi-Objectives Node Deployment in WSNs: in Search of an Optimal Trade-OFF Among Coverage, Lifetime, Energy Consumption, and Connectivity", Engineering Applications of Artificial Intelligence, 26: 405-416.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Networks (WSNs) Genetic Algorithm Optimization Systems and Energy Consumption.

Powered by PhDFocusTM