Research Article | OPEN ACCESS
Fuzzy Based Optimal Clustering Protocol for Maximizing Lifetime in WSN
1S. Jothi and 2M. Chandrasekaran
1Department of Computer Science and Engineering, St. Joseph
Research Journal of Applied Sciences, Engineering and Technology 2014 6:714-725
Received: January 31, 2014 | Accepted: March 30, 2014 | Published: August 15, 2014
Abstract
In Wireless Sensor Networks (WSN), the clustering protocol requires the nodes local information like energy level, distance between to BS and node density, link quality and load while estimating the cluster heads to handle network lifetime. In this study, we propose fuzzy based optimal clustering protocol for maximizing lifetime in WSN. Initially, several provisional cluster heads are elected in a random manner. The nodes other than provisional cluster heads involve in gathering the neighbor nodes local information such as residual energy, distance, node density and network load. The collected information is fuzzified using fuzzy logic technique and appropriate cluster head and size are estimated. Based on uninterrupted operational mechanism of each cluster head, the cluster heads are updated, thereby reducing the frequency of cluster head updation. By simulation results, we show that the proposed technique enhances the network lifetime.
Keywords:
Cluster head updation , clustering, fuzzy logic , network lifetime, residual energy, wireless sensor networks,
References
-
Alim, M., Y. Wu and W. Wang, 2013. A fuzzy based clustering protocol for energy-efficient wireless sensor networks. Proceeding of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE, 2013).
-
Basaran, C., K. Kang and M. Suzer, 2011. Hop-by-hop congestion control and load balancing in wireless sensor networks. Proceeding of IEEE 35th Conference on Local Computer Networks (LCN, 2011), pp: 448-455.
CrossRef -
Bhattasali, T. and R. Chaki, 2011. A survey of recent intrusion detection systems for wireless sensor network. In: Wyld D.C. et al. (Ed.), Proceeding of 4th International Conference on CNSA, 2011. Springer-Verlag, Berlin, Heidelberg, CCIS 196, pp: 268-280.
CrossRef -
Chhabra, G.S. and D. Sharma, 2011. Cluster-tree based data gathering in wireless sensor network. Int. J. Soft Comput. Eng., 1(1): 27-31, ISSN: 2231-2307.
-
Dasgupta, S. and P. Dutta, 2010. An improved leach approach for head selection strategy in a fuzzy-C means induced clustering of a wireless sensor network. Proceeding of IEMCON 2011, pp: 203-208.
-
Gaddour, O., A. Koubˆaa and M. Abid, 2009. SeGCom: A secure group communication mechanism in cluster-tree wireless sensor networks. Proceeding of 1st International Conference on Communications and Networking, pp: 1-7.
CrossRef -
Kavitha, T. and D. Sridharan, 2010. Security vulnerabilities in wireless sensor networks: A survey. J. Inform. Assur. Secur., 5: 031-044.
-
Khan, A., A. Abdullah and N. Hasan, 2011. Maximizing lifetime of homogeneous wireless sensor network through energy efficient clustering method. Int. J. Comput. Sci. Secur., 3(6).
-
Kumar, S., M. Kumar and V. Sheeba, 2011a. Fuzzy logic based energy efficient hierarchical clustering in wireless sensor networks. Int. J. Res. Rev. Wirel. Sensor Network., 1(4), ISSN: 2047-0037.
-
Kumar, S., J.K. Jagadeesh and T. Purusothaman, 2011b. An enhanced scheduling scheme for QoS guarantee using channel state information in WiMAX networks. Eur. J. Sci. Res., 64(2): 285-292.
-
Lotf, J. and S. Ghazani, 2011. Clustering of wireless sensor networks using hybrid algorithm. Aust. J. Basic Appl. Sci., 5(8): 1483-1489.
-
Malathi, L. and R. Gnanamurthy, 2012. A novel cluster based routing protocol with lifetime maximizing clustering algorithm. IJCET, 3(2): 256-264.
-
Mina, X., S. Wei-Rena, J. Chang-Jiang and Z. Ying, 2009. Energy efficient clustering algorithm for maximizing lifetime of wireless sensor networks. AEU-Int. J. Electron. C., 64(4): 289-298.
-
Peng, J., T. Liu, H. Li and B. Guo, 2013. Energy-efficient prediction clustering algorithm for multilevel heterogeneous wireless sensor networks. Int. J. Distrib. Sens. N., 2013(2013): 8, Article ID 678214.
-
Saxena, S., S. Mishra and M. Singh, 2013. Clustering based on node density in heterogeneous under-water sensor network. I. J. Inform. Technol. Comput. Sci., 5(7): 49-55.
CrossRef -
Sharma, T. and B. Kumar, 2012. F-MCHEL: Fuzzy based master cluster head election leach protocol in wireless sensor network. Int. J. Comput. Sci. Telecommun., 3(10).
-
Song, M. and Z. Cheng-Lin, 2011. Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO. J. China Univ., Posts Telecommun., 18(6): 89-97.
CrossRef -
Vidhya, J. and P. Dananjayan, 2010. Lifetime maximisation of multihop WSN using cluster-based cooperative MIMO scheme. Int. J. Comput. Theor. Eng., 2(1): 1793-8201.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|