222 research outputs found
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Smart cities demand resources for rich immersive sensing, ubiquitous
communications, powerful computing, large storage, and high intelligence
(SCCSI) to support various kinds of applications, such as public safety,
connected and autonomous driving, smart and connected health, and smart living.
At the same time, it is widely recognized that vehicles such as autonomous
cars, equipped with significantly powerful SCCSI capabilities, will become
ubiquitous in future smart cities. By observing the convergence of these two
trends, this article advocates the use of vehicles to build a cost-effective
service network, called the Vehicle as a Service (VaaS) paradigm, where
vehicles empowered with SCCSI capability form a web of mobile servers and
communicators to provide SCCSI services in smart cities. Towards this
direction, we first examine the potential use cases in smart cities and
possible upgrades required for the transition from traditional vehicular ad hoc
networks (VANETs) to VaaS. Then, we will introduce the system architecture of
the VaaS paradigm and discuss how it can provide SCCSI services in future smart
cities, respectively. At last, we identify the open problems of this paradigm
and future research directions, including architectural design, service
provisioning, incentive design, and security & privacy. We expect that this
paper paves the way towards developing a cost-effective and sustainable
approach for building smart cities.Comment: 32 pages, 11 figure
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Artificial Intelligence-Generated Content (AIGC) is an automated method for
generating, manipulating, and modifying valuable and diverse data using AI
algorithms creatively. This survey paper focuses on the deployment of AIGC
applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile
AIGC networks, that provide personalized and customized AIGC services in real
time while maintaining user privacy. We begin by introducing the background and
fundamentals of generative models and the lifecycle of AIGC services at mobile
AIGC networks, which includes data collection, training, finetuning, inference,
and product management. We then discuss the collaborative cloud-edge-mobile
infrastructure and technologies required to support AIGC services and enable
users to access AIGC at mobile edge networks. Furthermore, we explore
AIGCdriven creative applications and use cases for mobile AIGC networks.
Additionally, we discuss the implementation, security, and privacy challenges
of deploying mobile AIGC networks. Finally, we highlight some future research
directions and open issues for the full realization of mobile AIGC networks
Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordCritical infrastructure systems are vital to underpin
the functioning of a society and economy. Due to ever-increasing
number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected,
security and scalability are becoming burning concerns for
critical infrastructures in industry 4.0. The blockchain technology
is essentially a distributed and secure ledger that records all
the transactions into a hierarchically expanding chain of blocks.
Edge computing brings the cloud capabilities closer to the
computation tasks. The convergence of blockchain and edge
computing paradigms can overcome the existing security and
scalability issues. In this paper, we first introduce the IoT/IIoT
critical infrastructure in industry 4.0, and then we briefly present
the blockchain and edge computing paradigms. After that, we
show how the convergence of these two paradigms can enable
secure and scalable critical infrastructures. Then, we provide a
survey on state-of-the-art for security and privacy, and scalability
of IoT/IIoT critical infrastructures. A list of potential research
challenges and open issues in this area is also provided, which
can be used as useful resources to guide future research.Engineering and Physical Sciences Research Council (EPSRC
A Survey of Mobile Edge Computing in the Industrial Internet
With the advent of a new round of the Industrial Revolution, the Industrial
Internet will carry the convergence of heterogeneous network and the dynamic
reconfiguration of industrial equipment. In order to further provide higher
performance of network capabilities, the Industrial Internet has experienced
unprecedented growth while facing enormous challenges from the actual needs of
industrial networks. The typical scenarios in industrial applications, combined
with the technical advantages of mobile edge computing, are described in view
of the low latency, high bandwidth and high reliability demanded by the
Industrial Internet in the new era. The key technologies of mobile edge
computing for the Industrial Internet have been outlined in this treatise,
whose feasibility and importance are demonstrated by typical industrial
applications that have been deployed. As combined with the development trend of
the Industrial Internet, this paper summarizes the existing work and discusses
the future research direction of key technologies of mobile edge computing for
the Industrial Internet.Comment: 2019 The 7th International Conference on Information, Communication
and Network
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
In the vehicular mixed reality (MR) Metaverse, the distance between physical
and virtual entities can be overcome by fusing the physical and virtual
environments with multi-dimensional communications in autonomous driving
systems. Assisted by digital twin (DT) technologies, connected autonomous
vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the
vehicular MR Metaverse via digital simulations for sharing data and making
driving decisions collaboratively. However, large-scale traffic and driving
simulation via realistic data collection and fusion from the physical world for
online prediction and offline training in autonomous driving systems are
difficult and costly. In this paper, we propose an autonomous driving
architecture, where generative AI is leveraged to synthesize unlimited
conditioned traffic and driving data in simulations for improving driving
safety and traffic efficiency. First, we propose a multi-task DT offloading
model for the reliable execution of heterogeneous DT tasks with different
requirements at RSUs. Then, based on the preferences of AV's DTs and collected
realistic data, virtual simulators can synthesize unlimited conditioned driving
and traffic datasets to further improve robustness. Finally, we propose a
multi-task enhanced auction-based mechanism to provide fine-grained incentives
for RSUs in providing resources for autonomous driving. The property analysis
and experimental results demonstrate that the proposed mechanism and
architecture are strategy-proof and effective, respectively
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