WARP: A ICN architecture for social data

Social network companies maintain complete visibility and ownership of the data they store. However users should be able to maintain full control over their content. For this purpose, we propose WARP, an architecture based upon Information-Centric Networking (ICN) designs, which expands the scope of the ICN architecture beyond media distribution, to provide data control in social networks. The benefit of our solution lies in the lightweight nature of the protocol and in its layered design. With WARP, data distribution and access policies are enforced on the user side. Data can still be replicated in an ICN fashion but we introduce control channels, named \textit{thread updates}, which ensures that the access to the data is always updated to the latest control policy. WARP decentralizes the social network but still offers APIs so that social network providers can build products and business models on top of WARP. Social applications run directly on the user's device and store their data on the user's \textit{butler} that takes care of encryption and distribution. Moreover, users can still rely on third parties to have high-availability without renouncing their privacy

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster

Content source selection in Bluetooth networks

Large scale market penetration of electronic devices equipped with Bluetooth technology now gives the ability to share content (such as music or video clips) between members of the public in a decentralised manner. Achieved using opportunistic connections, formed when they are colocated, in environments where Internet connectivity is expensive or unreliable, such as urban buses, train rides and coffee shops. Most people have a high degree of regularity in their movements (such as a daily commute), including repeated contacts with others possessing similar seasonal movement patterns. We argue that this behaviour can be exploited in connection selection, and outline a system for the identification of long-term companions and sources that have previously provided quality content, in order to maximise the successful receipt of content files. We utilise actual traces and existing mobility models to validate our approach, and show how consideration of the colocation history and the quality of previous data transfers leads to more successful sharing of content in realistic scenarios