2,969 research outputs found
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
Base Station ON-OFF Switching in 5G Wireless Networks: Approaches and Challenges
To achieve the expected 1000x data rates under the exponential growth of
traffic demand, a large number of base stations (BS) or access points (AP) will
be deployed in the fifth generation (5G) wireless systems, to support high data
rate services and to provide seamless coverage. Although such BSs are expected
to be small-scale with lower power, the aggregated energy consumption of all
BSs would be remarkable, resulting in increased environmental and economic
concerns. In existing cellular networks, turning off the under-utilized BSs is
an efficient approach to conserve energy while preserving the quality of
service (QoS) of mobile users. However, in 5G systems with new physical layer
techniques and the highly heterogeneous network architecture, new challenges
arise in the design of BS ON-OFF switching strategies. In this article, we
begin with a discussion on the inherent technical challenges of BS ON-OFF
switching. We then provide a comprehensive review of recent advances on
switching mechanisms in different application scenarios. Finally, we present
open research problems and conclude the paper.Comment: Appear to IEEE Wireless Communications, 201
Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling
Mobile edge computing (MEC) has been considered as a promising technique for
internet of things (IoT). By deploying edge servers at the proximity of
devices, it is expected to provide services and process data at a relatively
low delay by intelligent networking. However, the vast edge servers may face
great challenges in terms of cooperation and resource allocation. Furthermore,
intelligent networking requires online implementation in distributed mode. In
such kinds of systems, the network scheduling can not follow any previously
known rule due to complicated application environment. Then statistical
learning rises up as a promising technique for network scheduling, where edges
dynamically learn environmental elements with cooperations. It is expected such
learning based methods may relieve deficiency of model limitations, which
enhance their practical use in dynamic network scheduling. In this paper, we
investigate the vision and challenges of the intelligent IoT networking with
mobile edge computing. From the systematic viewpoint, some major research
opportunities are enumerated with respect to statistical learning
Resource Management of energy-aware Cognitive Radio Networks and cloud-based Infrastructures
The field of wireless networks has been rapidly developed during the past
decade due to the increasing popularity of the mobile devices. The great demand
for mobility and connectivity makes wireless networking a field whose
continuous technological development is very important as new challenges and
issues are arising. Many scientists and researchers are currently engaged in
developing new approaches and optimization methods in several topics of
wireless networking. This survey paper study works from the following topics:
Cognitive Radio Networks, Interactive Broadcasting, Energy Efficient Networks,
Cloud Computing and Resource Management, Interactive Marketing and
Optimization
Massive MIMO and Millimeter Wave for 5G Wireless HetNet: Potentials and Challenges
There have been active research activities worldwide in developing the
next-generation 5G wireless network. The 5G network is expected to support
significantly large amount of mobile data traffic and huge number of wireless
connections, achieve better cost- and energy-efficiency as well as quality of
service (QoS) in terms of communication delay, reliability and security. To
this end, the 5G wireless network should exploit potential gains in different
network dimensions including super dense and heterogeneous deployment of cells
and massive antenna arrays (i.e., massive multiple input multiple output (MIMO)
technologies) and utilization of higher frequencies, in particular millimeter
wave (mmWave) frequencies. This article discusses potentials and challenges of
the 5G heterogeneous wireless network (HetNet) which incorporates massive MIMO
and mmWave technologies. We will first provide the typical requirements of the
5G wireless network. Then, the significance of massive MIMO and mmWave in
engineering the future 5G HetNet is discussed in detail. Potential challenges
associated with the design of such 5G HetNet are discussed. Finally, we provide
some case studies, which illustrate the potential benefits of the considered
technologies.Comment: IEEE Vehicular Technology Magazine (To appear
When Machine Learning Meets Big Data: A Wireless Communication Perspective
We have witnessed an exponential growth in commercial data services, which
has lead to the 'big data era'. Machine learning, as one of the most promising
artificial intelligence tools of analyzing the deluge of data, has been invoked
in many research areas both in academia and industry. The aim of this article
is twin-fold. Firstly, we briefly review big data analysis and machine
learning, along with their potential applications in next-generation wireless
networks. The second goal is to invoke big data analysis to predict the
requirements of mobile users and to exploit it for improving the performance of
"social network-aware wireless". More particularly, a unified big data aided
machine learning framework is proposed, which consists of feature extraction,
data modeling and prediction/online refinement. The main benefits of the
proposed framework are that by relying on big data which reflects both the
spectral and other challenging requirements of the users, we can refine the
motivation, problem formulations and methodology of powerful machine learning
algorithms in the context of wireless networks. In order to characterize the
efficiency of the proposed framework, a pair of intelligent practical
applications are provided as case studies: 1) To predict the positioning of
drone-mounted areal base stations (BSs) according to the specific tele-traffic
requirements by gleaning valuable data from social networks. 2) To predict the
content caching requirements of BSs according to the users' preferences by
mining data from social networks. Finally, open research opportunities are
identified for motivating future investigations.Comment: This article has been accepted by IEEE Vehicular Technology Magazin
Bi-Directional Mission Offloading for Agile Space-Air-Ground Integrated Networks
Space-air-ground integrated networks (SAGIN) provide great strengths in
extending the capability of ground wireless networks. On the other hand, with
rich spectrum and computing resources, the ground networks can also assist
space-air networks to accomplish resource-intensive or power-hungry missions,
enhancing the capability and sustainability of the space-air networks.
Therefore, bi-directional mission offloading can make full use of the
advantages of SAGIN and benefits both space-air and ground networks. In this
article, we identify the key role of network reconfiguration in coordinating
heterogeneous resources in SAGIN, and study how network function virtualization
(NFV) and service function chaining (SFC) enable agile mission offloading. A
case study validates the performance gain brought by bi-directional mission
offloading. Future research issues are outlooked as the bi-directional mission
offloading framework opens a new trail in releasing the full potentials of
SAGIN.Comment: accepted by IEEE Wireless Communications Magazin
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