N-Body Potential Interaction as a Cost Function in the Elastic Model for SANET Cloud Computing

• Autorzy: Chaczko Z. , Resconi G. , Chiu C. , Aslanzadeh S.
• Konferencja: International Conference on System Engineering - ICSEng (21 ; 16-18.08.2011) ; Las Vegas, USA
• Języki publikacji: EN

Abstrakty

EN

Given a connection graph of entities that send and receive a flow of data controlled by effort and given the parameters, the metric tensor is computed that is in the elastic relational flow to effort. The metric tensor can be represented by the Hessian of the interaction potential. Now the interaction potential or cost function can be among two entities: 3 entities or 'N' entities and can be separated into two main parts. The first part is the repulsion potential the entities move further from the others to obtain minimum cost, the second part is the attraction potential for which the entities move near to others to obtain the minimum cost. For Pauli's model [1], the attraction potential is a functional set of parameters given from the environment (all the elements that have an influence in the module can be the attraction of one entity to another). Now the cost function can be created in a space of macro-variables or macro-states that is less of all possible variables. Any macro-variable collect a set of micro-variables or microstates. Now from the hessian of the macro-variables, the Hessian is computed of the micro-variables in the singular points as stable or unstable only by matrix calculus without any analytical computation - possible when the macro-states are distant among entities. Trivially, the same method can be obtained by a general definition of the macro-variable or macro-states and micro-states or variables. As cloud computing for Sensor-Actor Networks (SANETS) is based on the bonding concept for complex interrelated systems; the bond valence or couple corresponds to the minimum of the interaction potential V and in the SANET cloud as the minimum cost.

Słowa kluczowe

EN

elastic network model; cloud computing; sensor actuator networks; matrix calculus; N-body interactions; cost functions

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2012
• Tom: Vol, 58, No. 1
• Strony: 63--70
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Chaczko Z.

• Faculty of Engineering & IT, University of Technology, Sydney, Australia

autor

Resconi G.

• Dept. of mathematics and Physics Catholic University Brescia, I-25121 Brescia, Italy

autor

Chiu C.

• Faculty of Engineering & IT, University of Technology, Sydney, Australia

autor

Aslanzadeh S.

• Faculty of Engineering & IT, University of Technology, Sydney, Australia

• Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW, Australia

Bibliografia

• [1] W. Pauli and C. G. Jung, The Interpretation of Nature and the Psyche. Random House, 1955.
• [2] I. Akyildiz and I. Kasimoglu, “Wireless Sensor and Actor Networks: Research Challenges,” Ad Hoc Networks, vol. 2, no. 4, pp. 351–367, 2004, Publisher: Elsevier.
• [3] Z. Chaczko and S. Aslanzadeh, “C2EN: Anisotropic Model of Cloud Computing,” in 21st International IEEE Conference on System Engineering, ICSEng 2011, Las Vegas, NV, USA, 16–18 August 2011, pp. 467–473.
• [4] R. Eltarras, M. Eltoweissy, and M. Youssef, “Towards Evolving Sensor Actor NETworks,” in IEEE Conference on Computer Communications Workshops, 2008, pp. 1–6.
• [5] C. Chiu and Z. Chaczko, “Multi-Dimensionality of Laparoscopic Simulations for SANETs,” in 13th International Conference on Computer-Aided Systems Theory, Las Palmas de Gran Canaria, Spain, 6–11 February 2011, pp. 368–370.
• [6] N. Becker, “Sequence Dependent Elasticity of DNA,” 2007, Dissertation for Doctor of Philosophy, Fakultat Mathematik und Naturwissenschaften Technische Universitat Dresden, Deutschland.
• [7] T. L. Lin and G. Song, “Generalized spring tensor models for protein fluctuation dynamics and conformation changes,” BMC Structural Biology, no. 10, 2010, Washington, DC, USA. 1 November.
• [8] G. Resconi and Z. Chaczko, “Fuzzy network of reasoning by SVD and Morphotronic network,” in IEEE International Conference on Fuzzy Systems, Barcelona, Spain, 18–23 July 2010.
• [9] X. L. Nguyen, M. J. Wainwright, and M. I. Jordan, On information divergence measures, surrogate loss functions and decentralized hypothesis testing. Department of EECS and Department of Statistics UC Berkeley, CA, 2005.
• [10] Z. Chaczko, “Anticipatory Biomimetic Middleware,” in AIP Conference Proceedings of CASYS ’09: Ninth International Conference on Computing Anticipatory Systems, Liege, Belgium, 3–8 August 2009, pp. 379–388.