SQEE: A Machine Perception Approach to Sensing Quality Evaluation at the Edge by Uncertainty Quantification

Cyber-physical systems are starting to adopt neural network (NN) models for a variety of smart sensing applications. While several efforts seek better NN architectures for system performance improvement, few attempts have been made to study the deployment of these systems in the field. Proper deployment of these systems is critical to achieving ideal performance, but the current practice is largely empirical via trials and errors, lacking a measure of quality. Sensing quality should reflect the impact on the performance of NN models that drive machine perception tasks. However, traditional approaches either evaluate statistical difference that exists objectively, or model the quality subjectively via human perception. In this work, we propose an efficient sensing quality measure requiring limited data samples using smart voice sensing system as an example. We adopt recent techniques in uncertainty evaluation for NN to estimate audio sensing quality. Intuitively, a deployment at better sensing location should lead to less uncertainty in NN predictions. We design SQEE, Sensing Quality Evaluation at the Edge for NN models, which constructs a model ensemble through Monte-Carlo dropout and estimates posterior total uncertainty via average conditional entropy. We collected data from three indoor environments, with a total of 148 transmitting-receiving (t-r) locations experimented and more than 7, 000 examples tested. SQEE achieves the best performance in terms of the top-1 ranking accuracy-whether the measure finds the best spot for deployment, in comparison with other uncertainty strategies. We implemented SQEE on a ReSpeaker to study SQEE's real-world efficacy. Experimental result shows that SQEE can effectively evaluate the data collected from each t-r location pair within 30 seconds and achieve an average top-3 ranking accuracy of over 94%. We further discuss generalization of our framework to other sensing schemes.