The week's pick
Random Articles
Reseach Article
Enhancing Multi-Objective Optimization for Wireless Sensor Networks Coverage using Swarm Bat Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 9 |
| Year of Publication: 2017 |
| Authors: Ibrahim A. Saleh |
10.5120/ijca2017915326
|
Ibrahim A. Saleh . Enhancing Multi-Objective Optimization for Wireless Sensor Networks Coverage using Swarm Bat Algorithm. International Journal of Computer Applications. 175, 9 ( Oct 2017), 27-33. DOI=10.5120/ijca2017915326
Abstract
Coverage area in wireless sensor network (WSN) is very important for network’s performance.it has several critical and challenges that are to be occupied when designing the techniques and algorithms to increase the Network lifetime. Therefore WSN poses problem involve exchange data between multiple conflicting optimization objectives such as coverage preservation. The proposed paper applies new approach to optimize the coverage performance of WSN. The algorithm strategy new multi-objective optimization bat swarm algorithm with adaptive neighborhood processes and turnoff redundant sensor nodes. Any position of mobile sensor nodes represented by bat which is used in hybrid bat algorithm. The algorithm is presented an adaptive neighborhood which can successfully avoid possibility the turnoff redundant sensor. Simulation results show that experimental results can able to improve coverage of WSN, increase the time life of network and low energy consumption
References
- YUAN , Hao , LI Changbing, DU Maokang, 2012. Optimal Distribution of Nodes in Wireless Sensor Network Based on Multi-objective Optimization” Journal of Computational Information Systems 8: 8 (3331–3338).
- X. F. Zou, Y. Chen, M. Z. Liu, et al., 2008. A New Evolutionary Algorithm for Solving Many-objective Optimization Problems [J]. IEEE Transaction on Systems, Man and Cybernetics, Part B, , 38 (5): 1402 – 1412.
- Liao, S.; Zhang, Q. A , 2013. Multi-Utility Framework with Application for Studying Tradeoff between Utility and Lifetime in Wireless Sensor Networks. IEEE Trans. Veh. Technol. , PP, doi: 10.1109 / TVT.. 2372793.
- Jameii Seyed Mahdi& Jameii Seyed Mohsen 2013 . Multi-objective Energy Efficient Optimization Agorithm for Coverage control in Wireless Sensor Networks. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.3,No.4.
- LYUDMILA M, DAVIDRB. 2011. Localization of Mobile Nodes in Wireless Networks with Correlated in Time Measurement Noise . IEEETRANS ACTIONSON MOBILE COMPUTING, ,10(1):44-53.
- Vinothini M, Umamakeswari, 2014. A Reliable data transmission using efficient neighbor coverage routing protocol in wireless sensor network. Indian J Sci Technol.; 7(12):2118–23.
- Singh Vinay Kumar & Sharma Vidushi , 2013. A Multiobjective Coverage and Connectivity Strategy for Improving the Performance of Wireless Sensor Networks, International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 12, ISSN: 2277 128X.
- Fidanova Stefka and Marinov Pencho. 2014. Multi-objective ACO algorithm for WSN layout: performance according to number of ants .Int. J. Metaheuristics, Vol. 3, No. 2.
- R K Jena1 and P K Mahbati ,2012 Node Placement for Wireless Sensor Network Using Multi-objective PSO. International Conference on Computer Technology and Science) IPCSIT vol. 47 IACSIT Press, Singapore DOI: 10.7763/IPCSIT.
- Jena R K, 2014, Artificial Bee Colony Algorithm based Multi-Objective Node Placement for Wireless Sensor Network. I JTCS. Information Journal Technology and Computer Science, , 06, 25-32.
- Sengupta, S.; Das, S.; Nasir, M.; Panigrahi, B.K. Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Eng. Appl. Artif. Intell., 26, 405–416.
- G. Molina, E. Alba, and E.-G. Talbi, 2008 Optimal sensor network lay out using multi-objective metaheuristics .J. Univer. Comput. Sci., vol. 14, no. 15, pp. 2549–2565.
- Gorain Barun, Mandal Partha Sarathi, 2014 Approximation algorithms for sweep coverage in wireless sensor networks. Journal of Parallel and Distributed Computing 74(8) 2699-2707 ,.
- Yang Xin-She , Deb Suash , Fong Simon,” Bat Algorithm is Better Than Intermittent Search Strategy” Journal of Multiple –Valued Logic and Soft Computing.
- Jiang Fei, 2014 . Application of hybrid ant colony algorithm in wireless sensor network coverage. computer modeling & new technologies 18(12A) 161-166.
- Anand D.G., Chandrakanth H.G. & Giriprasad M.N. 2011. Energy Efficient Coverage Problems in Wireless Ad Hoc Sensor Networks ” Advanced Computing. An International Journal ( ACIJ ), Vol.2, No.2, March.
- Taha , Ahmed & Tang Alicia Y.C. 2013. Bat Algorithm for Rough set Attribute Reduction. Journal of Theoretical and Applied Information Technology 10th May. Vol. 51 No.1 2005 - 2013. ISSN: 1992-8645.
- Jr Iztok Fister , Fister Duˇsan, Yang Xin-She , , 2013. A Hybrid Bat Algorithm, ELEKTROTEHNIˇSKI VESTNIK 80(1-2): 1–7.
- Wang Gai-Ge & Chang Bao2015. A Multi-Swarm Bat Algorithm for Global Optimization. IEEE press 978-1-4799-7492-4/15/$31.00,.
- Singh Vinay Kumar & Sharma Vidushi, 2013 .A Multi-objective Coverage and Connectivity Strategy for Improving the Performance of Wireless Sensor Networks. International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 12, DecemberISSN: 2277 128X.
- Al-Mukhtar Mumtaz M. Ali & Hadi Teeb Hussein, 2014. Monitoring System Using Wireless Sensor Network. Journal of Al-Nahrain University Vol.17 (2), June, , pp.219-226.
Index Terms
Keywords