2,712 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
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Network Slicing in Fog Radio Access Networks: Issues and Challenges
Network slicing has been advocated by both academia and industry as a
cost-efficient way to enable operators to provide networks on an as-a-service
basis and meet the wide range of use cases that the fifth generation wireless
network will serve. The existing works on network slicing are mainly targeted
at the partition of the core network, and the prospect of network slicing in
radio access networks should be jointly exploited. To solve this challenge, an
enhanced network slicing in fog radio access networks (F-RANs), termed as
access slicing, is proposed. This article comprehensively presents a novel
architecture and related key techniques for access slicing in F-RANs. The
proposed hierarchical architecture of access slicing consists of centralized
orchestration layer and slice instance layer, which makes the access slicing
adaptively implement in an convenient way. Meanwhile, key techniques and their
corresponding solutions, including the radio and cache resource management, as
well as the social-aware slicing, are presented. Open issues in terms of
standardization developments and field trials are identified
Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds
Network slicing allows network operators to build multiple isolated virtual
networks on a shared physical network to accommodate a wide variety of services
and applications. With network slicing, service providers can provide a
cost-efficient solution towards meeting diverse performance requirements of
deployed applications and services. Despite slicing benefits, End-to-End
orchestration and management of network slices is a challenging and complicated
task. In this chapter, we intend to survey all the relevant aspects of network
slicing, with the focus on networking technologies such as Software-defined
networking (SDN) and Network Function Virtualization (NFV) in 5G, Fog/Edge and
Cloud Computing platforms. To build the required background, this chapter
begins with a brief overview of 5G, Fog/Edge and Cloud computing, and their
interplay. Then we cover the 5G vision for network slicing and extend it to the
Fog and Cloud computing through surveying the state-of-the-art slicing
approaches in these platforms. We conclude the chapter by discussing future
directions, analyzing gaps and trends towards the network slicing realization.Comment: 31 pages, 4 figures, Fog and Edge Computing: Principles and
Paradigms, Wiley Press, New York, USA, 201
An Amateur Drone Surveillance System Based on Cognitive Internet of Things
Drones, also known as mini-unmanned aerial vehicles, have attracted
increasing attention due to their boundless applications in communications,
photography, agriculture, surveillance and numerous public services. However,
the deployment of amateur drones poses various safety, security and privacy
threats. To cope with these challenges, amateur drone surveillance becomes a
very important but largely unexplored topic. In this article, we firstly
present a brief survey to show the state-of-the-art studies on amateur drone
surveillance. Then, we propose a vision, named Dragnet, by tailoring the recent
emerging cognitive internet of things framework for amateur drone surveillance.
Next, we discuss the key enabling techniques for Dragnet in details,
accompanied with the technical challenges and open issues. Furthermore, we
provide an exemplary case study on the detection and classification of
authorized and unauthorized amateur drones, where, for example, an important
event is being held and only authorized drones are allowed to fly over
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
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
The fifth generation (5G) wireless network technology is to be standardized
by 2020, where main goals are to improve capacity, reliability, and energy
efficiency, while reducing latency and massively increasing connection density.
An integral part of 5G is the capability to transmit touch perception type
real-time communication empowered by applicable robotics and haptics equipment
at the network edge. In this regard, we need drastic changes in network
architecture including core and radio access network (RAN) for achieving
end-to-end latency on the order of 1 ms. In this paper, we present a detailed
survey on the emerging technologies to achieve low latency communications
considering three different solution domains: RAN, core network, and caching.
We also present a general overview of 5G cellular networks composed of software
defined network (SDN), network function virtualization (NFV), caching, and
mobile edge computing (MEC) capable of meeting latency and other 5G
requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial
The CTTC 5G end-to-end experimental platform: Integrating heterogeneous wireless/optical networks, distributed cloud, and IoT devices
The Internet of Things (IoT) will facilitate a wide variety of applications
in different domains, such as smart cities, smart grids, industrial automation
(Industry 4.0), smart driving, assistance of the elderly, and home automation.
Billions of heterogeneous smart devices with different application requirements
will be connected to the networks and will generate huge aggregated volumes of
data that will be processed in distributed cloud infrastructures. On the other
hand, there is also a general trend to deploy functions as software (SW)
instances in cloud infrastructures [e.g., network function virtualization (NFV)
or mobile edge computing (MEC)]. Thus, the next generation of mobile networks,
the fifth-generation (5G), will need not only to develop new radio interfaces
or waveforms to cope with the expected traffic growth but also to integrate
heterogeneous networks from end to end (E2E) with distributed cloud resources
to deliver E2E IoT and mobile services. This article presents the E2E 5G
platform that is being developed by the Centre Tecnol\`ogic de
Telecomunicacions de Catalunya (CTTC), the first known platform capable of
reproducing such an ambitious scenario
Internet of Cloud: Security and Privacy issues
The synergy between the cloud and the IoT has emerged largely due to the
cloud having attributes which directly benefit the IoT and enable its continued
growth. IoT adopting Cloud services has brought new security challenges. In
this book chapter, we pursue two main goals: 1) to analyse the different
components of Cloud computing and the IoT and 2) to present security and
privacy problems that these systems face. We thoroughly investigate current
security and privacy preservation solutions that exist in this area, with an
eye on the Industrial Internet of Things, discuss open issues and propose
future directionsComment: 27 pages, 4 figure
Towards Enabling Novel Edge-Enabled Applications
Edge computing has emerged as a distributed computing paradigm to overcome
practical scalability limits of cloud computing. The main principle of edge
computing is to leverage on computational resources outside of the cloud for
performing computations closer to data sources, avoiding unnecessary data
transfers to the cloud and enabling faster responses for clients.
While this paradigm has been successfully employed to improve response times
in some contexts, mostly by having clients perform pre-processing and/or
filtering of data, or by leveraging on distributed caching infrastructures, we
argue that the combination of edge and cloud computing has the potential to
enable novel applications. However, to do so, some significant research
challenges have to be tackled by the computer science community. In this paper,
we discuss different edge resources and their potential use, motivated by
envisioned use cases. We then discuss concrete research challenges that are in
the critical path towards realizing our edge vision. We conclude by proposing a
research agenda to allow the full exploitation of the potential for the
emerging hybrid cloud/edge paradigm
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