Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection

arXiv:2403.05106v1 Announce Type: new
Abstract: Advances in Tiny Machine Learning (TinyML) have bolstered the creation of smart industry solutions, including smart agriculture, healthcare and smart cities. Whilst related research contributes to enabling TinyML solutions on constrained hardware, there is a need to amplify real-world applications by optimising energy consumption in battery-powered systems. The work presented extends and contributes to TinyML research by optimising battery-powered image-based anomaly detection Internet of Things (IoT) systems. Whilst previous work in this area has yielded …

abstract advances agriculture amplify anomaly anomaly detection applications arxiv battery cities cs.lg detection enabling energy hardware healthcare image industry machine machine learning reinforcement reinforcement learning research smart smart cities solutions systems tinyml type world