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

The Internet of Things (IoT) is emerging as a critical technology to connect resource-constrained devices such as sensors and actuators as well as appliances to the Internet. In this paper, a novel methodology for node cardinality estimation in wireless networks such as the IoT and Radio-Frequency Identification (RFID) systems is proposed, which uses the Privileged Feature Distillation (PFD) technique and works using a neural network with a teacher-student model. This paper is the first to use the powerful PFD technique for node cardinality estimation in wireless networks. The teacher is trained using both privileged and regular features, and the student is trained with predictions from the teacher and regular features. Node cardinality estimation algorithms based on the PFD technique are proposed for homogeneous wireless networks as well as heterogeneous wireless networks with $T \geq 2$ types of nodes. Extensive simulations, using a synthetic dataset as well as a real dataset, are used to show that the proposed PFD based algorithms for homogeneous as well as heterogeneous networks achieve much lower mean squared errors (MSEs) in the computed node cardinality estimates than state-of-the-art protocols proposed in prior work. In particular, our simulation results for the real dataset show that our proposed PFD based technique for homogeneous (respectively, heterogeneous) networks achieves a MSE that is 92.35% (respectively, 94.08%) lower on average than that achieved by the Simple RFID Counting (SRCs) protocol (respectively, T-SRCs protocol) proposed in prior work while taking the same number of time slots to execute.