2,087 research outputs found
On Content-centric Wireless Delivery Networks
The flux of social media and the convenience of mobile connectivity has
created a mobile data phenomenon that is expected to overwhelm the mobile
cellular networks in the foreseeable future. Despite the advent of 4G/LTE, the
growth rate of wireless data has far exceeded the capacity increase of the
mobile networks. A fundamentally new design paradigm is required to tackle the
ever-growing wireless data challenge.
In this article, we investigate the problem of massive content delivery over
wireless networks and present a systematic view on content-centric network
design and its underlying challenges. Towards this end, we first review some of
the recent advancements in Information Centric Networking (ICN) which provides
the basis on how media contents can be labeled, distributed, and placed across
the networks. We then formulate the content delivery task into a content rate
maximization problem over a share wireless channel, which, contrasting the
conventional wisdom that attempts to increase the bit-rate of a unicast system,
maximizes the content delivery capability with a fixed amount of wireless
resources. This conceptually simple change enables us to exploit the "content
diversity" and the "network diversity" by leveraging the abundant computation
sources (through application-layer encoding, pushing and caching, etc.) within
the existing wireless networks. A network architecture that enables wireless
network crowdsourcing for content delivery is then described, followed by an
exemplary campus wireless network that encompasses the above concepts.Comment: 20 pages, 7 figures,accepted by IEEE Wireless
Communications,Sept.201
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
Content Delivery Latency of Caching Strategies for Information-Centric IoT
In-network caching is a central aspect of Information-Centric Networking
(ICN). It enables the rapid distribution of content across the network,
alleviating strain on content producers and reducing content delivery
latencies. ICN has emerged as a promising candidate for use in the Internet of
Things (IoT). However, IoT devices operate under severe constraints, most
notably limited memory. This means that nodes cannot indiscriminately cache all
content; instead, there is a need for a caching strategy that decides what
content to cache. Furthermore, many applications in the IoT space are
timesensitive; therefore, finding a caching strategy that minimises the latency
between content request and delivery is desirable. In this paper, we evaluate a
number of ICN caching strategies in regards to latency and hop count reduction
using IoT devices in a physical testbed. We find that the topology of the
network, and thus the routing algorithm used to generate forwarding
information, has a significant impact on the performance of a given caching
strategy. To the best of our knowledge, this is the first study that focuses on
latency effects in ICN-IoT caching while using real IoT hardware, and the first
to explicitly discuss the link between routing algorithm, network topology, and
caching effects.Comment: 10 pages, 9 figures, journal pape
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