Abstract
In this paper, a new genetic algorithm with elite mutation is proposed for optimization problems. The proposed elite mutation scheme (EM) improves traditional genetic algorithms with a better ability to locate and to approach fast to optimal solutions, even in cases of huge data set. The proposed EM is to select elite chromosomes and mutate according to the similarity between elite chromosomes and selected chromosomes. The designed similarity guides effectively the search toward optimal solutions with less generation. The proposed EM is applied to optimize the cruise area of mobile sinks in hierarchical wireless sensor networks (WSNs). Numeric results show that (1) the proposed EM benefits the discovery of optimal solutions in a large solution space; (2) the approach to optimal solutions is more stable and faster; (3) the search guidance derived from the chromosome similarity is critical to the improvements of optimal solution discovery. Besides, the minimization of cruise are been proved to have the advantages of energy-saving, time-saving and reliable data collection in WSNs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abbasi, A.A., Younis, M.: A survey on clustering algorithms for wireless sensor networks. Computer Communication 30, 2826–2841 (2007)
Hong, T.P., Wu, C.H.: An Improved Weighted Clustering Algorithm for Determination of Application Nodes in Heterogeneous Sensor Network. Journal of Information Hiding and Multimedia Signal Processing 2(2), 173–184 (2011)
Lung, C.H., Zhou, C.J.: Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach. Ad Hoc Networks 8(3), 328–344 (2010)
Manisekaran, S.V.: Energy Efficient Hierarchical clustering for sensor networks. In: 2010 International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–11. IEEE Press, India (2010)
Slavik, M.: Analytical model of energy consumption in hierarchical wireless sensor networks. In: 2010 High-Capacity Optical Networks and Enabling Technologies (HONET), pp. 84–90. IEEE Press, Florida (2010)
Francesco, M.D., Das, S.K.: Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey. ACM Transactions on Sensor Networks 8(1), 7:1–7:31 (2011)
Chen, X.H.: Research on hierarchical mobile wireless sensor network architecture with mobile sensor nodes. In: 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI), pp. 2863–2867. IEEE Press, Lanzhou (2010)
Duan, Z.F.: Shortest Path Routing Protocol for Multi-layer Mobile Wireless Sensor Networks. In: 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC), pp. 106–110. IEEE Press, Nanchang (2009)
Chen, C.F.: Mobile Enabled Large Scale Wireless Sensor Networks. In: 8th International Conference Advanced Communication Technology (ICACT), pp. 333–338. IEEE Press, Beijing (2006)
Puthal, D., Sahoo, B., Sharma, S.: Dynamic Model for Efficient Data Collection in Wireless Sensor Networks with Mobile Sink. International Journal of Computer Science and Technology 3(1), 623–628 (2012)
Luo, J., Panchard, J., Piórkowski, M., Grossglauser, M., Hubaux, J.-P.: MobiRoute: Routing Towards a Mobile Sink for Improving Lifetime in Sensor Networks. In: Gibbons, P.B., Abdelzaher, T., Aspnes, J., Rao, R. (eds.) DCOSS 2006. LNCS, vol. 4026, pp. 480–497. Springer, Heidelberg (2006)
Heinzelman, W.B., Murphy, A.L., Carvalho, H.S., Perillo, M.A.: Middleware to Support Sensor Network Applications. IEEE Network 18(1), 6–14 (2004)
Papadimitriou, I., Georgiadis, L.: Energy-aware Routing to Maximize Lifetime in Wireless Sensor Networks with Mobile Sink. In: 13th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 141–151 (2005)
Liang, W.F.: Prolonging Network Lifetime via a Controlled Mobile Sink in Wireless Sensor Networks. In: 2010 IEEE Global Telecommunications Conference (GLOBECOM), pp. 1–6. IEEE Press, Canberra (2010)
Wu, X.B.: Dual-Sink: Using Mobile and Static Sinks for Lifetime Improvement in Wireless Sensor Networks. In: 16th International Conference on Computer Communications and Networks (ICCCN), pp. 1297–1302. IEEE Press, Nanjing (2007)
Horng, M.F., Chen, Y.T., Chu, S.C., Pan, J.S., Liao, B.Y.: An Extensible Particles Swarm Optimization for Energy-Effective Cluster Management of Underwater Sensor Networks. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010, Part I. LNCS(LNAI), vol. 6421, pp. 109–116. Springer, Heidelberg (2010)
Chen, Y.T., Lo, C.C., Shieh, C.S., Horng, M.F., Pan, J.S.: An optimization of adaptive transmission with guarantee connection degree for wireless sensor networks. In: 2011 IEEE International Conference on Granular Computing (GrC), pp. 121–126. IEEE Press (2011)
Guo, P.F.: The enhanced genetic algorithms for the optimization design. In: 3rd International Conference on Biomedical Engineering and Informatics (BMEI), pp. 2990–2994. IEEE Press (2010)
Jiang, W.J.: Hybrid genetic algorithm research and its application in problem optimization. In: 5th World Congress on Intelligent Control and Automation (WICIA), pp. 2122–2126. IEEE Press (2004)
Guo, L.J.: An Improved Routing Protocol in WSN with Hybrid Genetic Algorithm. In: 2nd International Conference on Networks Security Wireless Communications and Trusted Computing (NSWCTC), pp. 289–292. IEEE Press (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Horng, MF. et al. (2012). A Genetic Algorithm with Elite Mutation to Optimize Cruise Area of Mobile Sinks in Hierarchical Wireless Sensor Networks. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34707-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-34707-8_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34706-1
Online ISBN: 978-3-642-34707-8
eBook Packages: Computer ScienceComputer Science (R0)