Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions

Mahbub, Mobasshir; Shubair, Raed M.

doi:10.1016/j.jnca.2023.103726

Cited by 9 publications

(2 citation statements)

References 303 publications

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“…Federated learning is designed to be a super artificial intelligent technology entrenched in the Industrial Revolution 4.0 and the current Society 5.0, deployed to execute a well-organized machine learning operation within multi-edge nodes and still maintain a secured and private nature of data [26,27]. Different algorithms are used for machine learning in federated learning.…”

Section: Methodology and System Threat Modelmentioning

confidence: 99%

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

Odeh,

Yang,

Nwakanma

et al. 2024

Electronics

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The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

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Section: Methodology and System Threat Modelmentioning

confidence: 99%

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

Odeh,

Yang,

Nwakanma

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Even so, tends show that edge-native solutions are becoming more common, and the framework and tool ecosystem is evolving as well [8] with tools like OpenYurt [9], KubeEdge [10] and EdgeX [11]. Because the area of Edge Computing is still evolving standardisation is becoming an issue, and should be addressed as noted by [12], while [13] found service migration, security and privacy preservation and deployment as the most pressing issues.…”

Section: Current Trendsmentioning

confidence: 99%

Opportunities and Pitfalls of IT Architectures for Edge Computing

Četina,

Pavlič

2024

Frontiers in Artificial Intelligence and Applications

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Networks and data centres are under a lot of stress due to the rapid growth of connected devices and the significant amount of data that they produce. While data centres are struggling with heavy workloads, the cloud-native IT solutions underutilise the increasingly powerful clients. By bringing computational capabilities closer to the edge, the Edge Computing direction offers a way to alleviate the pressure on data centres, reduce network traffic, and opens the way for novel applications that would take full advantage of the benefits it brings. Edge Computing can also supplement existing applications by giving them the resources to run new, more complex operations, or improve existing ones partially. Edge applications are less reliant on the cloud, and provide more stability and customisation to globally distributed systems.

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RCFS: rate and cost fair CPU scheduling strategy in edge nodes

Zhao,

Rao,

et al. 2024

J Supercomput

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The cloud-edge collaborative edge computing paradigm is driving the comprehensive development of the Internet of Things. With the widespread deployment of 5G mobile networks, IoT end devices tend to offload data to more powerful edge nodes for computing, thereby improving the speed. This has led to an explosive growth in the amount of data processed by edge nodes. General CPU scheduling strategies are unsuitable for edge nodes that frequently occur in data-intensive applications. In this study, we focus on application feature awareness and design a strategy that considers data arrival rate and cost-fair CPU scheduling (RCFS) with two components: CPU resource allocation based on process weights and process scheduling based on distributed weighted round-robin. Compared to the Linux default scheduling strategy, CFS, experiments on edge nodes show that the RCFS strategy can effectively improve CPU utilization, decrease the running time of data-intensive applications, and improve the system's data throughput. In the best-case scenario, the RCFS strategy enhanced CPU utilization by up to 98.5%, decreased running time by up to 45.5%, and increased data throughput by up to 58.7%.

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Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions

Cited by 9 publications

References 303 publications

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

Opportunities and Pitfalls of IT Architectures for Edge Computing

RCFS: rate and cost fair CPU scheduling strategy in edge nodes

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