Survey on Mobile Edge-Cloud Computing: A Taxonomy on Computation offloading Approaches

Elgendy, Ibrahim A.; Yadav, Rahul

doi:10.1007/978-3-030-85428-7_6

Cited by 15 publications

(5 citation statements)

References 144 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But we may not be able to disassemble all edge devices, including IoT modules, and change their features. So far, a lot of research has been done on the outsourcing of computing of IoT devices to edge servers, in which its advantages, challenges, architectures, and various approaches have been explained [76], [77]. Further, some IoT devices have limited batteries, and energy costs can be high.…”

Section: A Metaverse Requirements To Be Met By Edge Computingmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

View full text Add to dashboard Cite

In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.

show abstract

Section: A Metaverse Requirements To Be Met By Edge Computingmentioning

confidence: 99%

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Ahsani¹,

Rahimi²,

Letafati³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Computation can be performed near data sources on edge devices. Edge computing has become more important nowadays, as pointed out in a few studies, e.g., [28,80,109,11,29]. The MEC framework primarily relies on edge devices, which have limited resources.…”

Section: B Edge-cloud Computingmentioning

confidence: 99%

An Overview on Generative AI at Scale With Edge–Cloud Computing

Wang,

Xue,

Wei

et al. 2023

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what humans create. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of user requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.

show abstract

“…Consequently, the task offloading concept is first introduced as a potential solution for addressing the limitations of UAVs, where the intensive applications can be remotely processed at a cloud server [ 3 , 4 ]. Despite the advantages of cloud server execution, it is primarily challenged by high-latency and security concerns [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware and Secure Task Offloading for Multi-Tier Edge-Cloud Computing Systems

Alharbi

Aldossary

Almutairi

et al. 2023

Sensors

View full text Add to dashboard Cite

Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be addressed. Therefore, in this paper, to bypass the limited bandwidth and address the potential security threats challenge, a new compression, security, and energy-aware task offloading framework is proposed for the ECC system environment. Specifically, we first introduce an efficient layer of compression to smartly reduce the transmission data over the channel. In addition, to address the security issue, a new layer of security based on an Advanced Encryption Standard (AES) cryptographic technique is presented to protect offloaded and sensitive data from different vulnerabilities. Subsequently, task offloading, data compression, and security are jointly formulated as a mixed integer problem whose objective is to reduce the overall energy of the system under latency constraints. Finally, simulation results reveal that our model is scalable and can cause a significant reduction in energy consumption (i.e., 19%, 18%, 21%, 14.5%, 13.1% and 12%) with respect to other benchmarks (i.e., local, edge, cloud and further benchmark models).

show abstract

Survey on Mobile Edge-Cloud Computing: A Taxonomy on Computation offloading Approaches

Cited by 15 publications

References 144 publications

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

An Overview on Generative AI at Scale With Edge–Cloud Computing

Energy-Aware and Secure Task Offloading for Multi-Tier Edge-Cloud Computing Systems

Contact Info

Product

Resources

About