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BiRSM: bio-inspired resource self-management for all IP-networks
Balasubramaniam, Sasitharan,Botvich, Dmitri,Mineraud, Julien,Donnelly, William,Agoulmine, Nazim IEEE 2010 IEEE network Vol.24 No.3
<P>The increased complexity of communication systems has led to new challenges in network management and more specifically, efficient mechanisms to manage communication resources. The vision of autonomic networking aims to overcome these challenges by incorporating self-governance into communication network devices, in order to improve overall efficiency and minimize human intervention. Since biological systems exhibit properties that meet the requirements of self-governance, this article proposes a bio-inspired approach to efficiently manage resources in IP based core networks, called Bio-Inspired Resource Self-Management. The approach aims to provide a holistic solution for ISPs to manage their resources at different timescales as well as automating the interactions with underlying carrier network operators for dynamic resource provisioning. The implemented solution, in a simulator, has shown improved performance compared to traditional approaches.</P>
Privacy-enhanced middleware for location-based sub-community discovery in implicit social groups
Elmisery, A. M.,Rho, S.,Botvich, D. Springer Science + Business Media 2016 The Journal of supercomputing Vol.72 No.1
<P>In our connected world, recommender services have become widely known for their ability to provide expert and personalize information to participants of diverse applications. The excessive growth of social networks, a new kind of services are being embraced which are termed as 'group based recommendation services', where recommender services can be utilized to discover sub-communities within implicit social groups and provide referrals to new participants in order to join various sub-communities of other participants who share similar preferences or interests. Nevertheless, protecting participants' privacy in recommendation services is a quite crucial aspect which might prevent participants from exchanging their own data with these services, which in turn detain the accuracy of the generated referrals. So in order to gain accurate referrals, recommendation services should have the ability to discover previously unknown sub-communities from different social groups in a way to preserve privacy of participants in each group. In this paper, we present a middleware that runs on end-users' mobile phones to sanitize their profiles' data when released for generating referrals, such that computation of referrals continues over the sanitized version of their profiles' data. The proposed middleware is equipped with cryptography protocols to facilitate private discovery of sub-communities from the sanitized version of participants' profiles in a university scenario. Location data are added to participants' profiles to improve the awareness of surrounding sub-communities, so the offered referrals can be filtered based on adjacent locations for participant's location. We performed a number of different experiments to test the efficiency and accuracy of our protocols. We also developed a formal model for the tradeoff between privacy level and accuracy of referrals. As supported by the experiments, the sub-communities were correctly identified with good accuracy and an acceptable privacy level.</P>