844 research outputs found
Socially Trusted Collaborative Edge Computing in Ultra Dense Networks
Small cell base stations (SBSs) endowed with cloud-like computing
capabilities are considered as a key enabler of edge computing (EC), which
provides ultra-low latency and location-awareness for a variety of emerging
mobile applications and the Internet of Things. However, due to the limited
computation resources of an individual SBS, providing computation services of
high quality to its users faces significant challenges when it is overloaded
with an excessive amount of computation workload. In this paper, we propose
collaborative edge computing among SBSs by forming SBS coalitions to share
computation resources with each other, thereby accommodating more computation
workload in the edge system and reducing reliance on the remote cloud. A novel
SBS coalition formation algorithm is developed based on the coalitional game
theory to cope with various new challenges in small-cell-based edge systems,
including the co-provisioning of radio access and computing services,
cooperation incentives, and potential security risks. To address these
challenges, the proposed method (1) allows collaboration at both the user-SBS
association stage and the SBS peer offloading stage by exploiting the ultra
dense deployment of SBSs, (2) develops a payment-based incentive mechanism that
implements proportionally fair utility division to form stable SBS coalitions,
and (3) builds a social trust network for managing security risks among SBSs
due to collaboration. Systematic simulations in practical scenarios are carried
out to evaluate the efficacy and performance of the proposed method, which
shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author
Fog Computing: A Taxonomy, Survey and Future Directions
In recent years, the number of Internet of Things (IoT) devices/sensors has
increased to a great extent. To support the computational demand of real-time
latency-sensitive applications of largely geo-distributed IoT devices/sensors,
a new computing paradigm named "Fog computing" has been introduced. Generally,
Fog computing resides closer to the IoT devices/sensors and extends the
Cloud-based computing, storage and networking facilities. In this chapter, we
comprehensively analyse the challenges in Fogs acting as an intermediate layer
between IoT devices/ sensors and Cloud datacentres and review the current
developments in this field. We present a taxonomy of Fog computing according to
the identified challenges and its key features.We also map the existing works
to the taxonomy in order to identify current research gaps in the area of Fog
computing. Moreover, based on the observations, we propose future directions
for research
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
Mobile Edge Computing
This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists
- …