IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 3, March 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Energy Efficient Cluster Head Selection In Software Defined Networking Using Improved Particle Swarm Optimization

[Full Text]

 

AUTHOR(S)

S.Suganthi, Dr.D.Usha

 

KEYWORDS

Sensor network, clustering algorithms, energy, cluster head, particle swarm optimization.

 

ABSTRACT

In real world applications internet of things becoming an important role. Most IoT applications are integrated with wireless sensor networks. Usually the wireless sensor networks in IoT require hundreds or thousands of sensors may be deployed and integrated. In this scenario management of networks is the biggest issue. To manage the larger network scenario software defined networking is an added advantage. It gives a promising solution for flexible management of data plane and control plane. Efficient transmission of data with minimum energy is the main goal. So dividing the nodes into multiple clusters and cluster head is needed for manage those clusters. To maximizing the lifetime of the network with minimum energy there is in need of energy efficient cluster head selection. We provide the optimality in cluster head selection by using particle swarm optimization. There are so many researchers are already done this work with PSO but the results are not up to the level. This paper demonstrated the updated PSO algorithm through modified and improved fitness function. The proposed algorithm is experimented in matlab and the results are evaluated to show their supremacy in term of alive nodes, energy expenditure, dead nodes and fitness value.

 

REFERENCES

[1] Chatterjee A., Siarry P. (2016). Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers and Operations Research, 33(3), 859-871.
[2] Chen M.Y., Zhang C.Y., Luo C.Y. (2015). Adaptive evolutionary multi-objective particle swarm optimization algorithm, Control and Decision, 24(12), 1851-1855.
[3] J. Kennedy and R. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2001.
[4] M. Jacqueline and C. Richard, Application of particle swarm to multiobjective optimization, Auburn: Auburn University, 1999.
[5] Alfi and H. Modares, “System identification and control using adaptive particle swarm optimization,” Applied Mathematical Modelling, vol. 35, no. 3, pp. 1210–1221, 2011.
[6] L. Li and Y. Du, “Application of Modified Particle Swarm Optimization in Node Locating of Wireless Sensors Networks”, Computer Applications and Software, vol. 31, no. 4, (2014), pp. 69-72.
[7] Y. Wang and J. Yang, “Localization in wireless sensor network based on improved particle swarm optimization algorithm”, Computer Engineering and Applications, vol. 50, no. 18, (2014), pp. 99-102.
[8] Zungeru, Murtala A, Ang L, Seng KP. Classical and swarm based routing protocols for wireless sensor networks: a survey and comparison. Journal of Network and Computer Applications. 2012 Sep; 5(35):1508–36.
[9] Sundaran K, Ganapathy V. Energy efficient wireless sensor networks using dual cluster head with sleep/active mechanism. Indian Journal of Science and Technology. 2016 Nov; 9(41):1–6.
[10] Joseph PS, Balaj CD. Transmission loss minimization using optimization technique based on PSO. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE). 2013 May – Jun; 6(1):1–5.