141,123 research outputs found
Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G
By caching content at network edges close to the users, the content-centric
networking (CCN) has been considered to enforce efficient content retrieval and
distribution in the fifth generation (5G) networks. Due to the volume,
velocity, and variety of data generated by various 5G users, an urgent and
strategic issue is how to elevate the cognitive ability of the CCN to realize
context-awareness, timely response, and traffic offloading for 5G applications.
In this article, we envision that the fundamental work of designing a cognitive
CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to
associatively learn and control the states of edge devices (such as phones,
vehicles, and base stations) and in-network resources (computing, networking,
and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework
for C-CCN in 5G, which can aggregate the idle computing resources of the
neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive
learning tasks. By leveraging artificial intelligence (AI) to jointly
processing sensed environmental data, dealing with the massive content
statistics, and enforcing the mobility control at network edges, the FEL makes
it possible for mobile users to cognitively share their data over the C-CCN in
5G. To validate the feasibility of proposed framework, we design two
FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network
acceleration, 2) enhanced mobility management. Simultaneously, we present the
simulations to show the FEL's efficiency on serving for the mobile users'
delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and -means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial
Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan
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