Artificial Intelligence (AI) applications are gaining popularity as they
seamlessly integrate into end-user devices, enhancing the quality of life.
In recent years, there has been a growing focus on designing Smart EyeWear (SEW) that can optimize user experiences based on specific usage
domains. However, SEWs face limitations in computational capacity and
battery life. This paper investigates SEW and proposes an algorithm
to minimize energy consumption and 5G connection costs while ensuring high Quality-of-Experience. To achieve this, a management software,
based on Q-learning, offloads some Deep Neural Network (DNN) computations to the user’s smartphone and/or the cloud, leveraging the possibility
to partition the DNNs. Performance evaluation considers variability in 5G
and WiFi bandwidth as well as in the cloud latency. Results indicate execution time violations below 14%, demonstrating that the approach is
promising for efficient resource allocation and user satisfaction
