Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT

Wang, Qiannan; Mu, Haibing

doi:10.3390/s21165369

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…State-of-the-art fault-tolerance approaches in the cloud use deep learning due to the ability of such models to capture patterns and trends in large amounts of data. On the other hand, the approaches designed for edge systems are dominated by either heuristic based methods [26], [27] or techniques that use powerful cloud devices as management nodes [28], [29]. This is particularly due to the memory limitations at the edge.…”

Section: Introductionmentioning

confidence: 99%

DRAGON: Decentralized Fault Tolerance in Edge Federations

Tuli

Casale

Jennings

2023

IEEE Trans. Netw. Serv. Manage.

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Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices. To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON). Unlike GANs, GONs use a single network to both discriminate input and generate samples, significantly reducing their memory footprint. Leveraging the low memory footprint of GONs, we propose a decentralized faulttolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation. Extensive experiments with real-world edge computing benchmarks on multiple Raspberry-Pi based federated edge configurations show that DRAGON can outperform the baseline methods in fault-detection and Quality of Service (QoS) metrics. Specifically, the proposed method gives higher F1 scores for fault-detection than the best deep learning (DL) method, while consuming lower memory than the heuristic methods. This allows for improvement in energy consumption, response time and service level agreement violations by up to 74, 63 and 82 percent, respectively.

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Section: Introductionmentioning

confidence: 99%

DRAGON: Decentralized Fault Tolerance in Edge Federations

Tuli

Casale

Jennings

2023

IEEE Trans. Netw. Serv. Manage.

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“…Eavesdropping attack is one of the typical network attacks [5,6]. The security of CPSs has received a lot of attention, among which confidentiality is a basic security issue [7]. The data transmitted in channel is easily intercepted by eavesdropper over another channel.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Confidentiality Fusion Estimation Against Eavesdroppers in Cyber-Physical Systems

Xu,

Chen,

Wang

2023

Preprint

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System state plays an important role in cyber-physical systems (CPSs). Ensuring the security of the CPSs is a key issue that can be widely applied. The confidentiality of system state is a fundamental feature of the CPSs security. This paper studies the distributed fusion estimation problem in the presence of eavesdropper, where local sensors send their estimates to a remote fusion center (FC). To prevent eavesdropping, the event triggered scheduling strategy was adopted on each sensor. Some sufficient conditions on the triggers’ threshold were derived to make the eavesdropping expected covariance unbounded while the expected error covariance for the user remains bounded. Moreover, the distributed confidentiality fusion estimation algorithm is provided to achieve perfect expected secrecy. Finally, simulations of different trigger levels for two local systems are employed to show the effectiveness of the proposed methods.

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