Foresighted tree configuration games in resource constrained distributed stream mining sensors

We consider the problem of optimizing stream mining applications that are constructed as tree topologies of classifiers and deployed on a set of resource constrained and distributed processing nodes (or sensors). The optimization involves selecting appropriate false-alarm detection tradeoffs (operating points) for each classifier to minimize an end-to-end misclassification penalty, while satisfying resource constraints. We design distributed solutions, by defining tree configuration games, where individual classifiers configure themselves to maximize an appropriate local utility. We define the local utility functions and determine the information that needs to be exchanged across classifiers in order to design the distributed solutions. We analytically show that there is a unique pure strategy Nash equilibrium in operating points, which guarantees convergence of the proposed approach. We develop both myopic strategy, where the utility is purely local to the current classifier, and foresighted strategy, where the utility includes impact of classifier’s actions on successive classifiers. We analytically show that actions determined based on foresighted strategies improve the end-to-end performance of the classifier tree, by deriving an associated probability bound. We also investigate the impact of resource constraints on the classifier action selections for each strategy, and the corresponding application performance. We propose a learning-based approach, which enables each classifier to effectively adapt to the dynamic changes of resource constraints. We evaluate the performance of our solutions on an application for sports scene classification. We show that foresighted strategies result in better performance than myopic strategies in both resource unconstrained and resource constrained scenarios, and asymptotically approach the centralized optimal solution. We also show that the proposed distributed solutions outperform the centralized solution based on the Sequential Quadratic Programming on average in resource unconstrained scenarios.     

Video streaming over P2P networks: Challenges and opportunities

A robust real-time video communication service over the Internet in a distributed manner is an important challenge, as it influences not only the current Internet structure but also the future Internet evolution. In this context, Peer-to-Peer (P2P) networks are playing an imperative position for providing efficient video transmission over the Internet. Recently, several P2P video transmission systems have been proposed for live video streaming services or video-on-demand services over the Internet. In this paper, we describe and discuss existing video streaming systems over P2P. Efficient (delay tolerant and intolerant) data sharing mechanisms in P2P and current video coding trends are elaborated in detail. Moreover, video streaming solutions (live and on-demand) over P2P from the perspective of tree-based and mesh-based systems are explained. Finally, the conclusion is drawn with key challenges and open issues related to video streaming over P2P.     

Non-iterative Coordinated Beamforming for Multicell MIMO Heterogeneous Networks

Wireless Personal Communications - This paper presents ultra-wideband channel characteristics in the presence of float glass slab as obstacle of varied thickness (4 mm, 8 mm,...