Research on Innovating and Applying Evolutionary Algorithms Based Hierarchical Clustering and Multiple Paths Routing for Guaranteed Quality of Service on Service Based Routing
Internet of Things and Cloud Computing
Volume 3, Issue 3, October 2015, Pages: 22-28
Received: May 5, 2015;
Accepted: May 6, 2015;
Published: May 12, 2015
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Nguyen Thanh Long, Software development division III, Informatics Center of Hanoi Telecommunications, Hoan Kiem, Hanoi, Vietnam
Nguyen Duc Thuy, Center for applied research and technology development, Research institute of Posts and telecommunications, Hanoi, VietNam
Pham Huy Hoang, Information technology institute, Ha Noi University of Science Technology, Hanoi, Vietnam
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In Service Based Routing (SBR), data is transmitted from a source node to destination nodes are not depended on destination addresses. Hence, it is comfortable with new advanced technology as cloud computing and also flexible and reliable. Genetic and Queen-Bee algorithms (GA, QB) are artificial intelligence techniques for combinatorial optimization problems solving based on some natural rules. In which GA is a branch of the evolutionary strategies that uses some principles of evolution theory, such as natural selection, mutation and crossover. QB is performed based on GA for energy saving. The R^+ tree is an effective data structure that can be used to organize a hierarchical clustering network with fast establishing, updating, tuning algorithms. The usage of the Greedy algorithm to find cyclic routes or multiple paths on each trunk by multiple criterions to transmit data effectively.
Service, Routing, Multi-Paths, Bandwidth, Energy, MANET, Cluster, Clustering, Cluster Head, Genetic, Queen Bee, Greedy
To cite this article
Nguyen Thanh Long,
Nguyen Duc Thuy,
Pham Huy Hoang,
Research on Innovating and Applying Evolutionary Algorithms Based Hierarchical Clustering and Multiple Paths Routing for Guaranteed Quality of Service on Service Based Routing, Internet of Things and Cloud Computing.
Vol. 3, No. 3,
2015, pp. 22-28.
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VikasSiwach, Dr. Yudhvir Singh, Seema, DheerDhwaj Barak, Assistant Professor, Department of CSE, U.I.E.T, M.D.U. Rohtak -124001 (INDIA), “An Approach to Optimize QOS Routing Protocol Using Genetic Algorithm in MANET”, IJCSMS, Vol. 12, Issue 03, Sept 2012.
Sajid Hussain and Abdul W. Matin, Jodrey School of Computer Science, Acadia University Wolfville, Nova Scotia, Canada, “Hierarchical Cluster-based Routing in Wireless Sensor Networks”.
Kartheek Srungaram, Dr. MHM Krishna Prasad, Department of Information Technology, JNTUK-UCEV, Vizianagaram, A.P, India, “ENHANCED CLUSTER BASED ROUTING PROTOCOL FOR MANETS”.
Cândida Ferreira, Departamento de Ciências Agrárias, Universidade dos Açores, 9701-851 Terra-Chã, Angra do Heroísmo, Portugal, “Gene Expression Programming: A New Adaptive Algorithm for Solving Problems”.
Bibhash Roy, Tripura Institute of Technology, Narsingarh, Tripura, India, “Ant Colony based Routing for Mobile Ad-Hoc Networks towards Improved Quality of Services”.
Nguyen Thanh Long, Nguyen Duc Thuy, Pham Huy Hoang, “Innovating R Tree to Create Summary Filter for Message Forwarding Technique in Service-Based Routing”, Springer, ISBN: 978-3-642-41773-3, LNICST 121, p. 178, 2013.
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