AUTHORS: Sangamithra, Vani
Download as PDF
ABSTRACT: With the high speed technology based communication in todays world, there will be a duplicate copy of the messages. Due to the duplicate copy of the message the energy is wasted in the network. This paper describe the new system which employs multilayer , trim function and encryption concept to avoid the problem in technology based communication. Today’s technology in social networks contain frequently multiple distinct type of connectivity information. The indirect acknowledgement of friend relationship might match a behavioral measures. Which link the user according to their actions and behavior. We can represent this by the multi-layer graph that means a single source which has different way of path. In this multi-layer graph where each edges will be a unique over the same vertices, The edges present in the different layer are typically related but it will be having the different semantics. When more than one source is connecting to another obviously there will be a occurrence of noise. In our work we are analyzed that , when persons are communicating through social network there will be a at least 40% chances of duplicate message where it can occupy a more space. In this paper multifaceted technique is achieved by reducing the noise using multi layer.
KEYWORDS: RAC , Bayesian Model , Multilayer , Trim , Cipher textREFERENCES:
1] A.O Hero and G.Fleury, “Pareto – optimal
methods for gene ranking” VLSI Signal Process.
Vol 38, no 3, pp 259275 2004.
 V.Nicosia, G.Bianconi, V.Latora and M.Barthelemy, “Growing Multiplex Networks” Lett vol 111, pp 058701 , Jul 2013
 B Oselio, A Kulseza and A Hero, “Multiobjective optimization for multi-level networks,”in social computing behavioral-cultural modeling and prediction, set Lecture Notes in computer science, W Kennedy, N Agarwal and S Yang Eds. New York, NY, USA: Springer, 2014, vol 8393, pp 129- 136.
 Q Ho l Song, and E P Xing, “Evolving cluster mixed-membership block model for time varying networks.” In proc, 14th International conf, Artifintell Statist, 2011, pp 342-350.
 X S Yang multiobjective Optimization , New York, NY, USA: Wiley 2010, pp 231-246.
 A Raftery, “Bayesian model selection in social research,” Social Methodol, vol 25, pp 111-164, 1995.
 Y Lin Multi objective Machine Learning, New York, NY, Usa:Springer, 2006,Vol 16
 P W Holland, K B Laskey, and S Leinhardt, “Stochastic block models,” Social Network, Vol 5, No 2, pp 109-137, 1983
 M Ehrgott, “Multi objective optimization,” Ai Mag, Vol 29, no 4, pp 47-57, Winter2008
 Umashankar M L , Dr. Ramakrishna MV “Optimization Techniques in Wireless Sensor Networks: A Survey” IJSRET, ISSN 2278-0882 , July 2017
 Dharani Ganesh et ol , “Optimization Techniques for Wireless Sensor Networks” INFS 612.
Er. Parimender Kaur et ol, “A survey of Energy Optimization Techniques in Wireless Sensor Network” IJARCCE, ISSN:2278-1021.