Consumer Internet of ings (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. ese functionalities o en come with signi cant privacy and security risks, with notable recent largescale coordinated global a acks disrupting large service providers.us, an important rst step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled ow statistics. In particular, it is challenging for an ISP to e ciently and e ectively track and trace activity from IoT devices deployed by its millions of subscribers-all with sampled network data.In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our ndings indicate that millions of IoT devices are detectable and identi able within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network ow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is e ective for providing network analytics, it also highlights signi cant privacy consequences.