Object recognition, localization and private object interaction tracking for maintenance-free IoT

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Object recognition: We prepared and submitted a full paper to the IEEE IoT Journal. We developed RadioRec, a deep learning-based system that recognizes everyday objects based on their interactions with microwave signals in a contact-less manner. RadioRec is a significant upgrade compared to our earlier workshop paper, both in terms of the learning capabilities and in terms of evaluation. RadioRec works by transmitting a microwave signal through the object using a single antenna pair. RadioRec extracts features automatically using an autoencoder, and uses them to train an object classification model. Our evaluation shows that RadioRec can detect and recognize 26 everyday objects of various materials and shapes with an accuracy of over 97%.The paper was rejected. The reviewers requested additional evaluation in a different environment, and also several clarifications in the text. We are now working on addressing the reviewers’ comments and preparing an updated submission to the same journal, as suggested by the reviewers. So far, we have performed additional experiments in another environment, with various object orientations and object positions, and have obtained promising preliminary results.Object localization: We have prepared a preliminary implementation of SpotFi (DOI: 10.1145/2785956.2787487) for the USRP software-defined radio platform to enable the simultaneous estimation of angle of arrival and time of flight on backscatter tags. We are currently working on getting the estimation to work correctly in a controlled environment (for now, without backscatter tags).