Rule-based adaptive energy optimization for neural network-based energy-efficient transmission in intelligent systems

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Emmanuel, David and Gamage, Nimesha and Fernando, Anil; (2025) Rule-based adaptive energy optimization for neural network-based energy-efficient transmission in intelligent systems. In: Intelligent Environments 2025. Ambient Intelligence and Smart Environments, 34 . IOS Press, Amsterdam, pp. 51-60. ISBN 9781643686011 (https://doi.org/10.3233/aise250015)

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Abstract

With the increasing demand for real-time video processing in intelligent environments, optimising energy consumption while maintaining video quality remains a challenge. This paper presents a rule-based adaptive energy optimization framework for video compression, integrating dynamic decision-making techniques to regulate computational complexity based on system constraints. The proposed method employs an energy-aware loss function that dynamically adjusts key parameters based on inference conditions, real-time resource availability, and perceptual video quality. The model autonomously balances compression quality and energy efficiency by leveraging a rule-based approach, ensuring optimal trade-offs in resource-constrained devices. Experimental results demonstrate significant improvements in energy-aware video transmission, achieving adaptive complexity modulation with minimal loss in perceptual quality.

ORCID iDs

Emmanuel, David, Gamage, Nimesha and Fernando, Anil ORCID logoORCID: https://orcid.org/0000-0002-2158-2367;
CORE (COnnecting REpositories)