Adaptive Fault-Tolerant Strategy for Latency-Aware IoT Application Executing in Edge Computing Environment

Mudassar, Muhammad; Zhai, Yanlong; Liao, Lejian

doi:10.1109/jiot.2022.3144026

Cited by 22 publications

(3 citation statements)

References 29 publications

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“…If there is a record of system breakdown, there will be a crash in the whole process due to a single point of failure, giving access to adversaries to eavesdrop on private data. However, having this fault tolerant system that works out a self-recovery plan from model training error, and loss function due to going offline, makes it resilient [28]. It does this by re-routing and connecting to a nearby edge server (Mobile T/R unit) or a load balancer/fail-over system to return as an online node and restart the model training process for data set upload.…”

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

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“…Mudasser et al [ 63 ] build an adaptive fault-tolerant strategy for latency-aware IoT application. Thus, the focus of the evaluation lays on the end-to-end latency in the presented distributed edge computing application.…”

Section: State Of the Artmentioning

confidence: 99%

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Rosenberger

Selig

Ristić

et al. 2023

Sensors

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With the convergence of information technology (IT) and operational technology (OT) in Industry 4.0, edge computing is increasingly relevant in the context of the Industrial Internet of Things (IIoT). While the use of simulation is already the state of the art in almost every engineering discipline, e.g., dynamic systems, plant engineering, and logistics, it is less common for edge computing. This work discusses different use cases concerning edge computing in IIoT that can profit from the use of OT simulation methods. In addition to enabling machine learning, the focus of this work is on the virtual commissioning of data stream processing systems. To evaluate the proposed approach, an exemplary application of the middleware layer, i.e., a multi-agent reinforcement learning system for intelligent edge resource allocation, is combined with a physical simulation model of an industrial plant. It confirms the feasibility of the proposed use of simulation for virtual commissioning of an industrial edge computing system using Hardware-in-the-Loop. In summary, edge computing in IIoT is highlighted as a new application area for existing simulation methods from the OT perspective. The benefits in IIoT are exemplified by various use cases for the logic or middleware layer using physical simulation of the target environment. The relevance for real-life IIoT systems is confirmed by an experimental evaluation, and limitations are pointed out.

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“…A evoluc ¸ão da Internet permitiu o surgimento e a expansão vertiginosa da Internet das Coisas, onde objetos referenciados como dispositivos IoT interagem com o mundo físico produzindo dados e informac ¸ões sobre seu contexto para serem processadas e armazenadas possivelmente em uma nuvem computacional. Com o aumento significativo do número de dispositivos conectados e o aumento da demanda por processamento em tempo real tem ascendido o interesse por computac ¸ão de borda (em inglês, Edge Computing) e nuvem (em inglês, Cloud Computing) [1], [2], [3]. A eficiência de aplicac ¸ões IoT pressupõe ambientes de execuc ¸ão confiáveis, onde atributos como disponibilidade e confiabilidade são alta prioridade quando relacionadas a qualidade de servic ¸o em soluc ¸ões IoT.…”

Section: Introduc ¸ãOunclassified

Avaliação de modelos para sensores virtuais em sistemas IoT tolerantes a falhas

Loch,

Mattos,

Soares

2023

Anais Estendidos Do XIII Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2023)

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Com a crescente evolução das tecnologias de informação surgem soluções mais eficientes e integradas tal como os sensores virtuais para dispositivos IoT. O sensor virtual é uma técnica que permite estimar dados de sensores físicos em dispositivos IoT de acordo com um contexto da aplicação. Essa técnica permite a redução de custos, de complexidade, além de uma maior flexibilidade e adaptabilidade a diferentes cenários de uso. Adicionalmente, os sensores virtuais também podem garantir tolerância a falhas em sistemas IoT, onde tradicionalmente explora-se a redundância de hardware e software. Diante desse cenário, o objetivo deste trabalho é realizar um estudo sobre modelos de sensores virtuais dedicados a tolerância a falhas em sistemas IoT a fim de minimizar erros causados por falhas no sistema. O trabalho investiga 3 diferentes modelos de sensores virtuais, além de comparar com um modelo da literatura a fim de identificar e corrigir falhas. Os resultados obtidos mostram que os erros introduzidos pelos diferentes modelos de sensores virtuais investigados indicam que o uso de média móvel é mais preciso que o uso de replicação de sensores físicos em alguns cenários.

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Adaptive Fault-Tolerant Strategy for Latency-Aware IoT Application Executing in Edge Computing Environment

Cited by 22 publications

References 29 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

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Avaliação de modelos para sensores virtuais em sistemas IoT tolerantes a falhas

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