Artificial Intelligent Multi-Access Edge Computing Servers Management

Fragkos, Georgios; Lebien, Sean; Tsiropoulou, Eirini Eleni

doi:10.1109/access.2020.3025047

Cited by 19 publications

(5 citation statements)

References 27 publications

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“…To further optimize the efficiency and accuracy of FL, the issue of client selection in the client-server FL framework needs to be studied. AI technology has been gradually applied to the autonomous organization and management of Internet of Everything systems [25,26]. As one of the main branches of AI for decision-making, DRL is becoming a promising solution to federated client selection (FCS).…”

Section: Drl-based Federated Client Selection For Iovmentioning

confidence: 99%

Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles

Yang

2022

Electronics

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The integration of artificial intelligence (AI) technology into the Internet of Vehicles (IoV) has provided smart services for intelligent connected vehicles (ICVs). However, due to gradually upgrading to ICVs, an increasing number of external communications interfaces exposes the in-vehicle networks (IVNs) to malicious network intrusion. The malicious intruders can take over the compromised ICVs and mediately intrude into the ICVs connected through IoV. Therefore, it is urgent to develop IVN intrusion detection methods for IoV security protection. In this paper, a ConvLSTM-based IVN intrusion detection method is developed by leveraging the periodicity of the network message ID. For training the ConvLSTM model, a federated learning (FL) framework with client selection is proposed. The fundamental FL framework works in the client-server mode. ICVs are the local clients, and mobile edge computing (MEC) servers connected to base stations (BSs) function as the parameter servers. Based on the framework, a proximal policy optimization (PPO)-based federated client selection (FCS) scheme is further developed to optimize the model accuracy and system overhead of federated ConvLSTM model training. Simulations are conducted by the exploitation of real-world IoV scenario settings and IVN datasets. The results indicate that by exploiting the ConvLSTM, the model size and convergence time are dramatically reduced, and the 95%-beyond detection accuracy is maintained. The results also unveil that the PPO-based FCS scheme outperforms the benchmarks on the convergence rate, model accuracy, and system overhead.

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Section: Drl-based Federated Client Selection For Iovmentioning

confidence: 99%

Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles

Yang

2022

Electronics

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“…These technological steps not only enable more efficient data management (Demir & Szczepanek, 2017) but also offer opportunities for enhancing the overall functionality and utility of these systems and how they are leveraged for data utilization and analysis (Vitolo et al, 2015). The distribution of these systems primarily relies on the effective management of servers (Fragkos et al, 2020), which play a fundamental role in advancing not just technological systems but also significantly contribute to the progression of earth sciences by handling and disseminating substantial data repositories.…”

Section: Introductionmentioning

confidence: 99%

HydroRTC: A Web-Based Data Transfer and Communication Library for Collaborative Data Processing and Sharing in the Hydrological Domain

Erazo Ramirez,

Sermet,

Shahid

et al. 2024

Preprint

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The exponential growth in data generated by satellites, radars, sensors, and analysis and reanalysis from model outputs for the hydrological domain requires efficient real-time data management and distribution mechanisms. This paper introduces HydroRTC, a web-based data transfer and communication library designed to accelerate large-scale data sharing and analysis. Leveraging next-generation web technologies like WebSockets, WebRTC and Node.js, the library enables seamless peer-to-peer sharing, smart data transmission, and large dataset streaming. Three primary scenarios are presented as use cases, demonstrating the potential of HydroRTC as server-to-peer with intelligent data scheduling and large data streaming, peer-to-peer data sharing, and peer-to-server for data exchange. HydroRTC offers a promising solution for collaborative infrastructures in the hydrological and environmental domain, allowing real-time and high-throughput data sharing and transfer for enhancing research efficiency and collaboration capabilities.

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“…Because of the advantages of low latency, mobile energy saving, context awareness, privacy/security enhancement, etc., edge computing has stimulated extensive efforts in academia and industry to develop this technology [2]. The rapid development of MEC technology has paved the way for integrating MEC servers as intelligent entities into IoT and 5G networks [3]. The architecture of the multi-access edge computing network is depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning and Game Theory for Computation Offloading in Dynamic Edge Computing Markets

Li¹,

2021

IEEE Access

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As a promising paradigm, computation offloading technology can offload computing tasks to multi-access edge computing (MEC) servers, which is an appealing choice for resource-constrained end-devices to reduce their computational effort. However, due to limited resources, one crucial research challenge for computation offloading is to design an appropriate offloading policy to determine which tasks should be offloaded in some complex circumstances. In this paper, we study the offloading decision problem in a software-defined networking (SDN) driven MEC environment with multiple users and multiple servers. To ensure that end-users do not abuse the computing resources in the MEC system, we formulate the profit of MEC servers as our optimization objective. We jointly optimize the selection of MEC servers, the size of offloading data, and the price of MEC computing service to maximize the profit of MEC servers. However, considering the dynamic and stochastic of end-users, it is challenging to obtain an optimal policy in such a MEC environment. We apply deep reinforcement learning (DRL) and Game theory to our proposed approach. Specifically, we propose a proximal policy optimization (PPO) reinforcement learning framework to tackle the selection of MEC servers. Secondly, a two-step optimization problem was formulated to determine the size of offloading data and the pricing of computing services. The optimal values of those two were determined by achieving the Nash equilibrium of the strategy game between end-users. Extensive simulation results prove that our proposal has a better performance than existing solutions in convergence time and stability.

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Artificial Intelligent Multi-Access Edge Computing Servers Management

Cited by 19 publications

References 27 publications

Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles

Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles

HydroRTC: A Web-Based Data Transfer and Communication Library for Collaborative Data Processing and Sharing in the Hydrological Domain

Deep Reinforcement Learning and Game Theory for Computation Offloading in Dynamic Edge Computing Markets

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