517 research outputs found
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
A Transfer Learning Approach for Cache-Enabled Wireless Networks
Locally caching contents at the network edge constitutes one of the most
disruptive approaches in G wireless networks. Reaping the benefits of edge
caching hinges on solving a myriad of challenges such as how, what and when to
strategically cache contents subject to storage constraints, traffic load,
unknown spatio-temporal traffic demands and data sparsity. Motivated by this,
we propose a novel transfer learning-based caching procedure carried out at
each small cell base station. This is done by exploiting the rich contextual
information (i.e., users' content viewing history, social ties, etc.) extracted
from device-to-device (D2D) interactions, referred to as source domain. This
prior information is incorporated in the so-called target domain where the goal
is to optimally cache strategic contents at the small cells as a function of
storage, estimated content popularity, traffic load and backhaul capacity. It
is shown that the proposed approach overcomes the notorious data sparsity and
cold-start problems, yielding significant gains in terms of users'
quality-of-experience (QoE) and backhaul offloading, with gains reaching up to
in a setting consisting of four small cell base stations.Comment: some small fixes in notatio
A Delay-Aware Caching Algorithm for Wireless D2D Caching Networks
Recently, wireless caching techniques have been studied to satisfy lower
delay requirements and offload traffic from peak periods. By storing parts of
the popular files at the mobile users, users can locate some of their requested
files in their own caches or the caches at their neighbors. In the latter case,
when a user receives files from its neighbors, device-to-device (D2D)
communication is enabled. D2D communication underlaid with cellular networks is
also a new paradigm for the upcoming 5G wireless systems. By allowing a pair of
adjacent D2D users to communicate directly, D2D communication can achieve
higher throughput, better energy efficiency and lower traffic delay. In this
work, we propose a very efficient caching algorithm for D2D-enabled cellular
networks to minimize the average transmission delay. Instead of searching over
all possible solutions, our algorithm finds out the best pairs,
which provide the best delay improvement in each loop to form a caching policy
with very low transmission delay and high throughput. This algorithm is also
extended to address a more general scenario, in which the distributions of
fading coefficients and values of system parameters potentially change over
time. Via numerical results, the superiority of the proposed algorithm is
verified by comparing it with a naive algorithm, in which all users simply
cache their favorite files
Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.
A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends
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