Decision Tree Based Key Management for Secure Group Communication

Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive systems.Secured group communication plays a vital role in case of providing the integrity,authenticity,confidentiality,and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the group.In secure group communications,the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service,particularly in the large groups with highly active membership.Hence,the paper is aimed to achieve better cost and time efficiency through an improved DC multicast routing protocol which is used to expose the path between the nodes participating in the group communication.During this process,each node constructs an adaptive Ptolemy decision tree for the purpose of generating the contributory key.Each of the node is comprised of three keys which will be exchanged between the nodes for considering the group key for the purpose of secure and cost-efficient group communication.The rekeying process is performed when a member leaves or adds into the group.The performance metrics of novel approach is measured depending on the important factors such as computational and communicational cost,rekeying process and formation of the group.It is concluded from the study that the technique has reduced the computational and communicational cost of the secure group communication when compared to the other existing methods.

Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.