Local semantic indexing for resource discovery on overlay network using mobile agents

Article


Singh, M., Cheng, X. and Belavkin, R. 2014. Local semantic indexing for resource discovery on overlay network using mobile agents. International Journal of Computational Intelligence Systems. 7 (3), pp. 432-455. https://doi.org/10.1080/18756891.2013.856257
TypeArticle
TitleLocal semantic indexing for resource discovery on overlay network using mobile agents
AuthorsSingh, M., Cheng, X. and Belavkin, R.
Abstract

One of the most crucial problems in a peer-to-peer system is locating of resources that are shared by various nodes. Various techniques suggested in literature suffer from drawbacks viz. saturation of network, inability to locate multi-keyword based resource or locate resource based on semantics. We present the solution that is more efficient and effective for discovering shared resources on a network that is influenced by content shared by nodes. To reduce the search load on nodes that have uncorrelated content, an efficient migration route is proposed for mobile agent that is based on cosine similarity of content shared by nodes and user query and minimum support. Results show reduction in search load and traffic due to communication, and increase in locating of resources defined by multiple keys using mobile agent that are logically similar to user query. Furthermore, the results indicate that by use of our technique the relevance of search results is higher; that is obtained by minimal traffic generation/communication and hops made by mobile agent.

LanguageEnglish
PublisherAtlantis Press
JournalInternational Journal of Computational Intelligence Systems
ISSN1875-6891
Electronic1875-6883
Publication dates
Online18 Oct 2013
Print01 Jun 2014
Publication process dates
Deposited09 Jul 2018
Accepted27 Sep 2013
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1080/18756891.2013.856257
Scopus EID2-s2.0-84900027466
Web of Science identifierWOS:000336216400003
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Belavkin, R. 2012. Dynamics of information and optimal control of mutation in evolutionary systems. in: Sorokin, A., Murphey, R., Thai, M. and Pardalos, P. (ed.) Dynamics of Information Systems: Mathematical Foundations New York Springer.
A cooperative particle swarm optimizer with statistical variable interdependence learning
Sun, L., Yoshida, S., Cheng, X. and Liang, Y. 2012. A cooperative particle swarm optimizer with statistical variable interdependence learning. Information Sciences. 186 (1), pp. 20-39. https://doi.org/10.1016/j.ins.2011.09.033
On evolution of an information dynamic system and its generating operator
Belavkin, R. 2012. On evolution of an information dynamic system and its generating operator. Optimization Letters. 6 (5), pp. 827-840. https://doi.org/10.1007/s11590-011-0325-z
Resource discovery using mobile agents [book section]
Singh, M., Cheng, X. and He, X. 2009. Resource discovery using mobile agents [book section]. in: Tao, D., Xu, D. and Li, X. (ed.) Semantic Mining Technologies for Multimedia Databases. New York, USA Information Science Reference. pp. 419-448
Survey of grid resource monitoring and prediction strategies.
Hu, L., Cheng, X. and Che, X. 2010. Survey of grid resource monitoring and prediction strategies. International Journal of Intelligent Information Processing. 1 (2).
New e-Learning system architecture based on knowledge engineering technology
Li, Y., Chen, Z., Huang, R. and Cheng, X. 2009. New e-Learning system architecture based on knowledge engineering technology. Systems, Man and Cybernetics, IEEE International Conference. 2009.
Efficient identity-based broadcast encryption without random oracles.
Hu, L., Liu, Z. and Cheng, X. 2010. Efficient identity-based broadcast encryption without random oracles. Journal of Computers. 5 (3), pp. 331-336.
Solving job shop scheduling problem using genetic algorithm with penalty function
Sun, L., Cheng, X. and Liang, Y. 2010. Solving job shop scheduling problem using genetic algorithm with penalty function. International Journal of Intelligent Information Processing. 1 (2), pp. 65-77.
Theory and practice of optimal mutation rate control in Hamming spaces of DNA sequences
Belavkin, R., Channon, A., Aston, E., Aston, J. and Knight, C. 2011. Theory and practice of optimal mutation rate control in Hamming spaces of DNA sequences. Lenaerts, T., Giacobini, M., Bersini, H., Bourgine, P., Dorigo, M. and Doursat, R. (ed.) ECAL 2011: The 11th European Conference on Artificial Life. Paris, France 08 - 12 Aug 2011 MIT Press. pp. 85-92 https://doi.org/10.7551/978-0-262-29714-1-ch017
Conflict resolution and learning probability matching in a neural cell-assembly architecture
Belavkin, R. and Huyck, C. 2011. Conflict resolution and learning probability matching in a neural cell-assembly architecture. Cognitive Systems Research. 12 (2), pp. 93-101. https://doi.org/10.1016/j.cogsys.2010.08.003
Towards a theory of decision-making without paradoxes.
Belavkin, R. 2006. Towards a theory of decision-making without paradoxes. Fum, D., Missier, F. and Stocco, A. (ed.) Proceedings of the Seventh International Conference on Cognitive Modeling. Trieste, Italy 05 - 08 Apr 2006 Trieste, Italy Edizioni Goliardiche. pp. 38-43
Do neural models scale up to a human brain?
Belavkin, R. 2007. Do neural models scale up to a human brain? International Joint Conference on Neural Networks (IJCNN 2007). Orlando, Florida 12 - 17 Aug 2007 IEEE.
Emergence of rules in cell assemblies of fLIF neurons.
Belavkin, R. and Huyck, C. 2008. Emergence of rules in cell assemblies of fLIF neurons. The 18th European Conference on Artificial Intelligence. University of Patras, Greece 21 - 25 Jul 2008
A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies.
Belavkin, R. and Huyck, C. 2009. A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies. 9th International conference on cognitive modelling {ICCM 2009]. University of Manchester 24 - 26 Jul 2009
Utility and value of information in cognitive science, biology and quantum theory.
Belavkin, R. 2010. Utility and value of information in cognitive science, biology and quantum theory. in: Accardi, L., Freudenberg, W. and Ohya, M. (ed.) Quantum bio-informatics III: from quantum information to bio-informatics. London World Scientific.
Resource discovery using mobile agents
Singh, M., Cheng, X. and Belavkin, R. 2010. Resource discovery using mobile agents. Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference. Changchun, Jilin Province 18 - 22 Aug 2010 IEEE. pp. 72 -77 https://doi.org/10.1109/FCST.2010.93
Information trajectory of optimal learning
Belavkin, R. 2010. Information trajectory of optimal learning. in: Hirsch, M., Pardalos, P. and Murphey, R. (ed.) Dynamics of Information Systems: Theory and Applications Springer.
Learning behaviour patterns of classroom and distance students using flexible learning resources.
Dimitrova, M., Belavkin, R., Milankovic-Atkinson, M., Sadler, C. and Murphy, A. 2003. Learning behaviour patterns of classroom and distance students using flexible learning resources. in: Lee, K. and Mitchell, K. (ed.) International conference on computers in education 2003: a conference of the Asia-Pacific chapter of the association for the advancement of computing in education (AACE). Hong Kong ICCE.
Ubiquitous e-learning System for dynamic mini-courseware assembling and delivering to mobile terminals
Li, Y., Guo, H., Gao, G., Huang, R. and Cheng, X. 2009. Ubiquitous e-learning System for dynamic mini-courseware assembling and delivering to mobile terminals. in: Kim, J., Delen, D., Jinsoo, P., Ko, F., Rui, C., Hyung, J., Lee, W. and Kou, G. (ed.) NCM 2009: Fifth International Joint Conference on INC, IMS, and IDC; [proceedings]. IEEE. pp. 1081-1086
Bounds of optimal learning
Belavkin, R. 2009. Bounds of optimal learning. 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning. Sheraton Music City Hotel, Nashville, TN, USA Nashville, TN, USA IEEE.
Formal verification of the merchant registration phase of the SET protocol.
Cheng, X. and Ma, X. 2005. Formal verification of the merchant registration phase of the SET protocol. International Journal of Automation and Computing. 2 (2), pp. 155-162. https://doi.org/10.1007/s11633-005-0155-5
Programming style based program partition
Li, Y., Yang, H., Cheng, X. and Zhu, X. 2005. Programming style based program partition. International Journal of Software Engineering and Knowledge Engineering. 15 (6), pp. 1027-1062. https://doi.org/10.1142/S0218194005002610
An improved model-based method to test circuit faults
Cheng, X., Ouyang, D., Yunfei, J. and Zhang, C. 2005. An improved model-based method to test circuit faults. Theoretical Computer Science. 341 (1-3), pp. 150-161. https://doi.org/10.1016/j.tcs.2005.04.004
Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons.
Huyck, C. and Belavkin, R. 2006. Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons. 7th International Conference on Cognitive Modelling. Trieste, Italy pp. 142-147
The use of entropy for analysis and control of cognitive models
Belavkin, R. and Ritter, F. 2003. The use of entropy for analysis and control of cognitive models. The Fifth International Conference on Cognitive Modelling. Bamberg, Germany 2003 pp. 21-26
Conflict resolution by random estimated costs
Belavkin, R. 2003. Conflict resolution by random estimated costs. 17th European Simulation Multiconference. Nottingham UK pp. 105-110
On relation between emotion and entropy
Belavkin, R. 2004. On relation between emotion and entropy. AISB'04 Symposium on Emotion, Cognition and Affective Computing. Leeds UK pp. 1-8
OPTIMIST: A new conflict resolution algorithm for ACT-R.
Belavkin, R. and Ritter, F. 2004. OPTIMIST: A new conflict resolution algorithm for ACT-R. Sixth International Conference on Cognitive Modelling. Mahwah, NJ pp. 40-45
Entropy and information in models of learning behaviour
Belavkin, R. 2005. Entropy and information in models of learning behaviour. AISB Quarterly. 119, pp. 5-5.
Towards a theory of decision-making with paradoxes.
Belavkin, R. 2006. Towards a theory of decision-making with paradoxes. Proceedings of the Seventh International Conference on Cognitive Modelling. Trieste, Italy 2006 pp. 38-43
The duality of utility and information in optimally learning systems
Belavkin, R. 2008. The duality of utility and information in optimally learning systems. IEEE Systems, Man and Cibernetics Society, UK and Republic of Ireland: 7th conference on cybernetics intelligent systems, 2008.. Middlesex University, London 08 - 09 Sep 2008 London IEEE.
Acting irrationally to improve performance in stochastic worlds
Belavkin, R. 2005. Acting irrationally to improve performance in stochastic worlds. Bramer, M., Coenen, F. and Allen, T. (ed.) 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cambridge, UK 2005 Springer. pp. 305-316 https://doi.org/10.1007/978-1-84628-226-3_23
Topology control of ad hoc wireless networks for energy efficiency
Cheng, M., Cardei, M., Sun, J., Cheng, X., Wang, L., Xu, Y. and Du, D. 2004. Topology control of ad hoc wireless networks for energy efficiency. IEEE Transactions on Computers. 53 (12), pp. 1629-1635. https://doi.org/10.1109/TC.2004.121