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An Improved role based trust management system based on Artificial bee Algorithms in wireless Networks,

Publication Type : Journal Article

Publisher : International journal of Applied sciences , Engineering and Technology

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2015

Abstract : The aim of this research study is to propose an improved role based trust management system for wirelesssensor networks. The objective is attained using an interactive artificial bee colony algorithm which is used for pathoptimization. Also the purpose is to guarantee to provide robust and trustworthiness among the clusters formed in the wireless sensor network. The reputation among the sensor nodes is assured using the trust model. One of the main concerns of WSN, that have attracted research scholars, is the property to guarantee a less amount of security in a limited environment. Trust among the communicative nodes is one of the major issues that have to be given importance in wireless sensor network. A number of research works have concentrated on only Trust Management (TM) techniques without considering their roles. Existing trust management schemes do not provide significant reliability in all environments. In order to overcome these issues, Role Trust (RT) framework is presented, to select role for each nodes representing policies in a distributed authorized environment. RT integrates the features of rolebased access control and TM schemes. This feature is particularly applicable for attribute based access control. Role trust management schemes generate rules, identify and assure roles based on the rules generation process and then clusters are identified for each trust model. Along with the trust model, this study focuses on efficient data path withreduced data loss. Hence, this study presents a novel Swarm Intelligence based Role Trust and Reputation Model (SIRTRM) to provide trust and reputation in WSNs. Simulations are carried out in NS2 environment and the results portrays the accuracy, robustness and lightness of the proposed model.

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