44,800 research outputs found
Fine-grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks
The 5G networks have extensively promoted the growth of mobile users and
novel applications, and with the skyrocketing user requests for a large amount
of popular content, the consequent content delivery services (CDSs) have been
bringing a heavy load to mobile service providers. As a key mission in
intelligent networks management, understanding and predicting the distribution
of CDSs benefits many tasks of modern network services such as resource
provisioning and proactive content caching for content delivery networks.
However, the revolutions in novel ubiquitous network architectures led by
ultra-dense networks (UDNs) make the task extremely challenging. Specifically,
conventional methods face the challenges of insufficient spatio precision,
lacking generalizability, and complex multi-feature dependencies of user
requests, making their effectiveness unreliable in CDSs prediction under 5G
UDNs. In this paper, we propose to adopt a series of encoding and sampling
methods to model CDSs of known and unknown areas at a tailored fine-grained
level. Moreover, we design a spatio-temporal-social multi-feature extraction
framework for CDSs hotspots prediction, in which a novel edge-enhanced graph
convolution block is proposed to encode dynamic CDSs networks based on the
social relationships and the spatio features. Besides, we introduce the
Long-Short Term Memory (LSTM) to further capture the temporal dependency.
Extensive performance evaluations with real-world measurement data collected in
two mobile content applications demonstrate the effectiveness of our proposed
solution, which can improve the prediction area under the curve (AUC) by 40.5%
compared to the state-of-the-art proposals at a spatio granularity of 76m, with
up to 80% of the unknown areas
Pervasive intelligent routing in content centric delay tolerant networks
This paper introduces a Swarm-Intelligence based Routing protocol (SIR) that aims to efficiently route information in content centric Delay Tolerant Networks (CCDTN) also dubbed pocket switched networks. First, this paper formalizes the notion of optimal path in CCDTN and introduces an original and efficient algorithm to process these paths in dynamic graphs. The properties and some invariant features of these optimal paths are analyzed and derived from several real traces. Then, this paper shows how optimal path in CCDTN can be found and used from a fully distributed swarm-intelligence based approach of which the global intelligent behavior (i.e. shortest path discovery and use) emerges from simple peer to peer interactions applied during opportunistic contacts. This leads to the definition of the SIR routing protocol of which the consistency, efficiency and performances are demonstrated from intensive representative simulations
In Vivo Evaluation of the Secure Opportunistic Schemes Middleware using a Delay Tolerant Social Network
Over the past decade, online social networks (OSNs) such as Twitter and
Facebook have thrived and experienced rapid growth to over 1 billion users. A
major evolution would be to leverage the characteristics of OSNs to evaluate
the effectiveness of the many routing schemes developed by the research
community in real-world scenarios. In this paper, we showcase the Secure
Opportunistic Schemes (SOS) middleware which allows different routing schemes
to be easily implemented relieving the burden of security and connection
establishment. The feasibility of creating a delay tolerant social network is
demonstrated by using SOS to power AlleyOop Social, a secure delay tolerant
networking research platform that serves as a real-life mobile social
networking application for iOS devices. SOS and AlleyOop Social allow users to
interact, publish messages, and discover others that share common interests in
an intermittent network using Bluetooth, peer-to-peer WiFi, and infrastructure
WiFi.Comment: 6 pages, 4 figures, accepted in ICDCS 2017. arXiv admin note: text
overlap with arXiv:1702.0565
A template-based sub-optimal content distribution for D2D content sharing networks
We propose Templatized Elastic Assignment (TEA), a light-weight scheme for mobile cooperative caching networks. It consists of two components, (1) one to calculate a sub-optimal distribution of each situation and (2) finegrained ID management by base stations (BSs) to achieve the calculated distribution. The former is modeled from findings that the desirable distribution plotted in a semilog graph forms a downward straight line with which the slope and Yintercept epend on the bias of request and total cache capacity, respectively. The latter is inspired from the identifier (ID)-based scheme, which ties devices and content by a randomly associated ID. TEA achieved the calculated distribution with IDs by using the annotation from base stations (BSs), which is preliminarily calculated by the template in a fine-grained density of devices. Moreover, such fine-grained management secondarily standardizes the cached content among multiple densities and enables the reuse of the content in devices from other BSs. Evaluation results indicate that our scheme reduces (1) 8.3 times more traffic than LFU and achieves almost the same amount of traffic reduction as with the genetic algorithm, (2) 45 hours of computation into a few seconds, and (3) at most 70% of content replacement across multiple BSs
Relieving the Wireless Infrastructure: When Opportunistic Networks Meet Guaranteed Delays
Major wireless operators are nowadays facing network capacity issues in
striving to meet the growing demands of mobile users. At the same time,
3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g.,
Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a
content dissemination framework that harnesses ad hoc communication
opportunities to minimize the load on the wireless infrastructure while
guaranteeing tight delivery delays. It achieves this through a control loop
that collects user-sent acknowledgements to determine if new copies need to be
reinjected into the network through the 3G interface. Push-and-Track includes
multiple strategies to determine how many copies of the content should be
injected, when, and to whom. The short delay-tolerance of common content, such
as news or road traffic updates, make them suitable for such a system. Based on
a realistic large-scale vehicular dataset from the city of Bologna composed of
more than 10,000 vehicles, we demonstrate that Push-and-Track consistently
meets its delivery objectives while reducing the use of the 3G network by over
90%.Comment: Accepted at IEEE WoWMoM 2011 conferenc
Swarm-based Intelligent Routing (SIR) - a new approach for efficient routing in content centric delay tolerant networks
This paper introduces Swarm-based Intelligent Routing (SIR), a swarm intelligence based approach used for routing content in content centric Pocket Switched Networks. We first formalize the notion of optimal path in DTN, then introduce a swarm intelligence based routing protocol adapted to content centric DTN that use a publish/subscribe communication paradigm. The protocol works in a fully decentralized way in which nodes do not have any knowledge about the global topology. Nodes, via opportunistic contacts, update utility functions which synthesizes their spatio-temporal proximity from the content subscribers. This individual behavior applied by each node leads to the collective formation of gradient fields between content subscribers and content providers. Therefore, content routing simply sums up to follow the steepest slope along these gradient fields to reach subscribers who are located at the minima of the field. Via real traces analysis and simulation, we demonstrate the existence and relevance of such gradient field and show routing performance improvements when compared to classical routing protocols previously defined for information routing in DTN
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