3,854 research outputs found
Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks
Wireless content caching in small cell networks (SCNs) has recently been
considered as an efficient way to reduce the traffic and the energy consumption
of the backhaul in emerging heterogeneous cellular networks (HetNets). In this
paper, we consider a cluster-centric SCN with combined design of cooperative
caching and transmission policy. Small base stations (SBSs) are grouped into
disjoint clusters, in which in-cluster cache space is utilized as an entity. We
propose a combined caching scheme where part of the available cache space is
reserved for caching the most popular content in every SBS, while the remaining
is used for cooperatively caching different partitions of the less popular
content in different SBSs, as a means to increase local content diversity.
Depending on the availability and placement of the requested content,
coordinated multipoint (CoMP) technique with either joint transmission (JT) or
parallel transmission (PT) is used to deliver content to the served user. Using
Poisson point process (PPP) for the SBS location distribution and a hexagonal
grid model for the clusters, we provide analytical results on the successful
content delivery probability of both transmission schemes for a user located at
the cluster center. Our analysis shows an inherent tradeoff between
transmission diversity and content diversity in our combined
caching-transmission design. We also study optimal cache space assignment for
two objective functions: maximization of the cache service performance and the
energy efficiency. Simulation results show that the proposed scheme achieves
performance gain by leveraging cache-level and signal-level cooperation and
adapting to the network environment and user QoS requirements.Comment: 13 pages, 10 figures, submitted for possible journal publicatio
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
Information Centric Networking in the IoT: Experiments with NDN in the Wild
This paper explores the feasibility, advantages, and challenges of an
ICN-based approach in the Internet of Things. We report on the first NDN
experiments in a life-size IoT deployment, spread over tens of rooms on several
floors of a building. Based on the insights gained with these experiments, the
paper analyses the shortcomings of CCN applied to IoT. Several interoperable
CCN enhancements are then proposed and evaluated. We significantly decreased
control traffic (i.e., interest messages) and leverage data path and caching to
match IoT requirements in terms of energy and bandwidth constraints. Our
optimizations increase content availability in case of IoT nodes with
intermittent activity. This paper also provides the first experimental
comparison of CCN with the common IoT standards 6LoWPAN/RPL/UDP.Comment: 10 pages, 10 figures and tables, ACM ICN-2014 conferenc
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
Offloading Content with Self-organizing Mobile Fogs
Mobile users in an urban environment access content on the internet from
different locations. It is challenging for the current service providers to
cope with the increasing content demand from a large number of collocated
mobile users. In-network caching to offload content at nodes closer to users
alleviate the issue, though efficient cache management is required to find out
who should cache what, when and where in an urban environment, given nodes
limited computing, communication and caching resources. To address this, we
first define a novel relation between content popularity and availability in
the network and investigate a node's eligibility to cache content based on its
urban reachability. We then allow nodes to self-organize into mobile fogs to
increase the distributed cache and maximize content availability in a
cost-effective manner. However, to cater rational nodes, we propose a coalition
game for the nodes to offer a maximum "virtual cache" assuming a monetary
reward is paid to them by the service/content provider. Nodes are allowed to
merge into different spatio-temporal coalitions in order to increase the
distributed cache size at the network edge. Results obtained through
simulations using realistic urban mobility trace validate the performance of
our caching system showing a ratio of 60-85% of cache hits compared to the
30-40% obtained by the existing schemes and 10% in case of no coalition
Edge-Caching Wireless Networks: Performance Analysis and Optimization
Edge-caching has received much attention as an efficient technique to reduce
delivery latency and network congestion during peak-traffic times by bringing
data closer to end users. Existing works usually design caching algorithms
separately from physical layer design. In this paper, we analyse edge-caching
wireless networks by taking into account the caching capability when designing
the signal transmission. Particularly, we investigate multi-layer caching where
both base station (BS) and users are capable of storing content data in their
local cache and analyse the performance of edge-caching wireless networks under
two notable uncoded and coded caching strategies. Firstly, we propose a coded
caching strategy that is applied to arbitrary values of cache size. The
required backhaul and access rates are derived as a function of the BS and user
cache size. Secondly, closed-form expressions for the system energy efficiency
(EE) corresponding to the two caching methods are derived. Based on the derived
formulas, the system EE is maximized via precoding vectors design and
optimization while satisfying a predefined user request rate. Thirdly, two
optimization problems are proposed to minimize the content delivery time for
the two caching strategies. Finally, numerical results are presented to verify
the effectiveness of the two caching methods.Comment: to appear in IEEE Trans. Wireless Commu
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