ENHANCED WOA FOR MOBILE ENERGY EFFICIENT AND DELAY AWARE CLUSTERING IN WSN

Ahmed Ali Saihood, Zainab Saihib Taqi

Abstract


The mobility, energy efficiency and reduction of delay in wireless sensor network (WSN) is most challenges that researchers working on it for more optimizations. The wale optimization algorithm is used in this paper after some modification in some steps to balance between the exploitation and exploration, we enhanced WOA for energy efficient and delay aware with respect to mobility of nodes out of clusters. The performance is evaluated by packet delivery ratio, delay, energy consumption, and throughput with considering the mobility of each node to be selected as cluster head. The proposed mechanism is compared with Hybrid FOA-WOA algorithm, we got good results in term of energy consumption, delay and throughput.

Keywords


WOA, WSN, MANET, IOT, hybrid FOA-WOA

Full Text:

PDF

References


⦁ Pathak, S., & Jain, S. (2017). “An optimized stable clustering algorithm for mobile ad hoc networks. EURASIP Journal On Wireless Communications And Networking”, 2017(1). doi:10.1186/s13638-017-0832-4

⦁ Sathiamoorthy, J., & Ramakrishnan, B. (2015). “Energy and delay efficient dynamic cluster formation using hybrid AGA with FACO in EAACK MANETs”, Wireless Networks, 23(2),371-385. doi: 10.1007/s11276-015-1154-2

⦁ Frigui, H., & Krishnapuram, R. (1999). “A robust competitive clustering algorithm with applications in computer vision,. IEEE Transactions Pattern Analysis Mach Intelligent, 21, 450–465. doi:10.1109/34.765656

⦁ Jain, A., & Dubes, R. (1998). “Algorithms for clustering data. Englewood Cliffs, NJ: Prentice-Hall”.

⦁ Faieghi, M. R., & Baeanu, D. (2012). Anovel adaptive controller for two-degree of freedom polar robot with unkown perturbations. Communications in Nonlinear Science, 17(2), 1021–1030. doi:10.1016/j. cnsns.2011.03.043

⦁ Maulik, U., & Mukhopadhyay, A. (2010).”Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data”. Computation Operational Researcher, 37,1369–1380. doi:10.1016/j.cor.2009.02.025

⦁ Mualik, U., & Bandyopadhyay, S. (2000). “Genetic algorithmbased clustering technique. Pattern Recognition”, 33, 1455–1465. doi:10.1016/S0031-3203(99)00137-5

⦁ Tunchan, C. (2012). “A particle swarm optimization approach to clustering. Experiments System Applications”, 39, 1582–1588.

⦁ Karaboga, D., & Ozturk, C. (2011). “A novel clustering approach: Artificial bee colony (ABC) algorithm. Applications Soft Computation”, 11(1), 652–657. doi:10.1016/j.asoc.2009.12.025

⦁ Zhang, C., Ouyang, D., & Ning, J. (2010). “An artificial bee colony approach for clustering. Experiments System Applications”, 37, 4761–4767. doi:10.1016/j.eswa.2009.11.003

⦁ Armando, G., & Farmani, M. R. (2014). “Clustering analysis with combination of artificial bee colony algorithm and k-means technique”, International Journal of Computer Theory and Engineering, 6(2), 141–145. doi:10.7763/IJCTE.2014.V6.852

⦁ Ahmed Ali Saihood.” Proposed Smart Technique for Traffic Light System”, Journal of Thi-Qar University Vol.13 No. 2 June 2018.

⦁ Ahmed Ali Saihood, Rakesh Kumar. “Enhanced Location Based Energy-Efficient Reliable Routing Protocol for Wireless Sensor Networks”, International Journal of Inventive Engineering and Sciences (IJIES), May 2013.




DOI: https://doi.org/10.26483/ijarcs.v10i5.6468

Refbacks



Bookmark and Share


Copyright (c) 2019 International Journal of Advanced Research in Computer Science