17,692 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
Mobile Edge Computing Empowers Internet of Things
In this paper, we propose a Mobile Edge Internet of Things (MEIoT)
architecture by leveraging the fiber-wireless access technology, the cloudlet
concept, and the software defined networking framework. The MEIoT architecture
brings computing and storage resources close to Internet of Things (IoT)
devices in order to speed up IoT data sharing and analytics. Specifically, the
IoT devices (belonging to the same user) are associated to a specific proxy
Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes
the IoT data (generated by its IoT devices) in real-time. Moreover, we
introduce the semantic and social IoT technology in the context of MEIoT to
solve the interoperability and inefficient access control problem in the IoT
system. In addition, we propose two dynamic proxy VM migration methods to
minimize the end-to-end delay between proxy VMs and their IoT devices and to
minimize the total on-grid energy consumption of the cloudlets, respectively.
Performance of the proposed methods are validated via extensive simulations
Active architecture for pervasive contextual services
International Workshop on Middleware for Pervasive and Ad-hoc Computing MPAC 2003), ACM/IFIP/USENIX International Middleware Conference (Middleware 2003), Rio de Janeiro, Brazil This work was supported by the FP5 Gloss project IST2000-26070, with partners at Trinity College Dublin and Université Joseph Fourier, and by EPSRC grants GR/M78403/GR/M76225, Supporting Internet Computation in Arbitrary Geographical Locations, and GR/R45154, Bulk Storage of XML Documents.Pervasive services may be defined as services that are available "to any client (anytime, anywhere)". Here we focus on the software and network infrastructure required to support pervasive contextual services operating over a wide area. One of the key requirements is a matching service capable of as-similating and filtering information from various sources and determining matches relevant to those services. We consider some of the challenges in engineering a globally distributed matching service that is scalable, manageable, and able to evolve incrementally as usage patterns, data formats, services, network topologies and deployment technologies change. We outline an approach based on the use of a peer-to-peer architecture to distribute user events and data, and to support the deployment and evolution of the infrastructure itself.Peer reviewe
Active architecture for pervasive contextual services
Pervasive services may be defined as services that are available to any client (anytime, anywhere). Here we focus on the software and network infrastructure required to support pervasive contextual services operating over a wide area. One of the key requirements is a matching service capable of assimilating and filtering information from various sources and determining matches relevant to those services. We consider some of the challenges in engineering a globally distributed matching service that is scalable, manageable, and able to evolve incrementally as usage patterns, data formats, services, network topologies and deployment technologies change. We outline an approach based on the use of a peer-to-peer architecture to distribute user events and data, and to support the deployment and evolution of the infrastructure itself
Shortest Path Computation with No Information Leakage
Shortest path computation is one of the most common queries in location-based
services (LBSs). Although particularly useful, such queries raise serious
privacy concerns. Exposing to a (potentially untrusted) LBS the client's
position and her destination may reveal personal information, such as social
habits, health condition, shopping preferences, lifestyle choices, etc. The
only existing method for privacy-preserving shortest path computation follows
the obfuscation paradigm; it prevents the LBS from inferring the source and
destination of the query with a probability higher than a threshold. This
implies, however, that the LBS still deduces some information (albeit not
exact) about the client's location and her destination. In this paper we aim at
strong privacy, where the adversary learns nothing about the shortest path
query. We achieve this via established private information retrieval
techniques, which we treat as black-box building blocks. Experiments on real,
large-scale road networks assess the practicality of our schemes.Comment: VLDB201
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