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Data-Driven Trust Prediction in Mobile Edge Computing-Based IoT Systems
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DOI：10.1109/TSC.2021.3121879.png)
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We propose a data-driven distributed machine learning approach to scalably predict the trustworthiness of homogeneous IoT services in heterogeneous Mobile Edge Computing (MEC)-based IoT systems. The proposed approach formulates training distributed trust prediction models within an MEC-based IoT system as a Network Lasso problem. We then introduce a variant of the Stochastic Alternating Method of Multipliers framework enriched with the ability for feature selection at each MEC layer. To verify the effectiveness of the proposed approach, we carried out a comprehensive evaluation on three real-world datasets adjusted to exhibit the context-dependent trust information accumulated in MEC environments within a given MEC topology. The experimental results affirmed the effectiveness of our approach and its suitability to predict trustworthiness of IoT services in MEC-based IoT systems.
Keyword:
Internet of Things
Predictive models
Computational modeling
Topology
Training
Stress
Data models
Trust
mobile edge computing
machine learning
期刊
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5.8
论文数:
2.1K
被引数:
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