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The Internet of Things (IoT) is emerging as a critical technology to connectresource-constrained devices such as sensors and actuators as well asappliances to the Internet. In this paper, we propose a novel methodology fornode cardinality estimation in wireless networks such as the IoT andRadio-Frequency IDentification (RFID) systems, which uses the privilegedfeature distillation (PFD) technique and works using a neural network with ateacher-student model. The teacher is trained using both privileged and regularfeatures, and the student is trained with predictions from the teacher andregular features. We propose node cardinality estimation algorithms based onthe PFD technique for homogeneous as well as heterogeneous wireless networks.We show via extensive simulations that the proposed PFD based algorithms forhomogeneous as well as heterogeneous networks achieve much lower mean squarederrors in the computed node cardinality estimates than state-of-the-artprotocols proposed in prior work, while taking the same number of time slotsfor executing the node cardinality estimation process as the latter protocols.

Node Cardinality Estimation in the Internet of Things Using Privileged Feature Distillation