210 research outputs found
Bi-directional Digital Twin and Edge Computing in the Metaverse
The Metaverse has emerged to extend our lifestyle beyond physical
limitations. As essential components in the Metaverse, digital twins (DTs) are
the digital replicas of physical items. DTs enable emulation of real-world
scenarios and prediction for energy and resource-efficient operation, resulting
in sustainable applications. End users access the Metaverse using a variety of
devices (e.g., head-mounted devices (HMDs)), mostly lightweight. Multi-access
edge computing (MEC) provides responsive services to the end users, leading to
an immersive Metaverse experience. With the anticipation to represent physical
objects, end users, and edge computing systems as DTs in the Metaverse, the
construction of these DTs and the interplay between them have not been
investigated. In this paper, we discuss the bidirectional reliance between the
DT and the MEC system and investigate the creation of DTs of objects and users
on the MEC servers and DT-assisted edge computing (DTEC). We also study the
interplay between the DTs and DTECs to allocate the resources fairly and
optimally and provide an immersive experience in the Metaverse. Owing to the
dynamic network states (e.g., channel states) and mobility of the users, we
discuss the interplay between local DTECs (on local MEC servers) and the global
DTEC (on cloud server) to cope with the handover among MEC servers and avoid
intermittent Metaverse services
Dense Moving Fog for Intelligent IoT: Key Challenges and Opportunities
As the ratification of 5G New Radio technology is being completed, enabling
network architectures are expected to undertake a matching effort. Conventional
cloud and edge computing paradigms may thus become insufficient in supporting
the increasingly stringent operating requirements of
\emph{intelligent~Internet-of-Things (IoT) devices} that can move unpredictably
and at high speeds. Complementing these, the concept of fog emerges to deploy
cooperative cloud-like functions in the immediate vicinity of various moving
devices, such as connected and autonomous vehicles, on the road and in the air.
Envisioning gradual evolution of these infrastructures toward the increasingly
denser geographical distribution of fog functionality, we in this work put
forward the vision of dense moving fog for intelligent IoT applications. To
this aim, we review the recent powerful enablers, outline the main challenges
and opportunities, and corroborate the performance benefits of collaborative
dense fog operation in a characteristic use case featuring a connected fleet of
autonomous vehicles.Comment: 7 pages, 5 figures, 1 table. The work has been accepted for
publication in IEEE Communications Magazine, 2019. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Live Data Analytics with Collaborative Edge and Cloud Processing in Wireless IoT Network
Recently, big data analytics has received important attention in a variety of application domains
including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the endusers, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks.
In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some
interesting future research directions
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