Latency and Energy Consumption of Convolutional Neural Network Models from IoT Edge Perspective

Hauschild, Sebastian; Hellbrück, Horst

doi:10.1007/978-3-031-20936-9_31

Cited by 1 publication

(2 citation statements)

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“…Moreover, we also introduce a decentralized trust management system, independent of a centralized authority, which adds resilience against potential security threats. This feature further accentuates the uniqueness of our contribution, as most previous studies [23][24][25] focused on latency reduction in IoT or edge networks, often reliant on centralized control mechanisms.…”

Section: Related Workmentioning

confidence: 74%

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Enhancing Performance and Security in the Metaverse: Latency Reduction Using Trust and Reputation Management

et al. 2023

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In the rapidly evolving landscape of distributed systems, security stands as a significant challenge, especially in the face of network node attacks. Such threats introduce profound complexities into the dynamics of security protocols, trust management, and resource allocation, issues further amplified by the metaverse’s exponential growth. This paper proposes an innovative solution, offering unique technical contributions to address these multi-faceted challenges. We unveil a trust-based resource allocation framework designed to facilitate the secure and efficient sharing of computational resources within the metaverse. This system has the potential to markedly diminish latency, thereby enhancing overall performance. In parallel, we introduce a reputation system that systematically monitors latency across a spectrum of metaverse entities, providing valuable insights for making informed resource allocation decisions. Moreover, we advocate for a decentralized trust management system, specifically designed to withstand potential security breaches without reliance on a centralized authority. This significantly fortifies both system security and user trust. Alongside this, we unveil an inventive proof-of-trust consensus mechanism that fosters trust and collaboration among metaverse entities during resource allocation, thereby cultivating a more secure ecosystem. Our proposed model poses a robust challenge to malicious entities, and it substantially bolsters the security architecture. The simulation results lend substantial credence to the effectiveness of our approach, demonstrating significant improvements in latency reduction, scalability, and the detection of malicious nodes, thereby outperforming existing methodologies.

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Section: Related Workmentioning

confidence: 74%

“…Hauschild and Hellbruck [25] examined the latency and power consumption of convolutional neural network models from the IoT edge perspective. They aimed to minimize the latency and power consumption of these models for edge devices by investigating various optimization techniques.…”

Section: Related Workmentioning

confidence: 99%