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, we propose a novel methodology for node cardinality estimation in wireless networks such as the IoT and Radio-Frequency IDentification (RFID) systems, which uses the privileged feature distillation (PFD) technique and works using a neural network with a teacher-student model. The teacher is trained using both privileged and regular features, and the student is trained with predictions from the teacher and regular features. We propose node cardinality estimation algorithms based on the PFD technique for homogeneous as well as heterogeneous wireless networks. We show via extensive simulations that the proposed PFD based algorithms for homogeneous as well as heterogeneous networks achieve much lower mean squared errors in the computed node cardinality estimates than state-of-the-art protocols proposed in prior work, while taking the same number of time slots for executing the node cardinality estimation process as the latter protocols.

翻译：物联网（IoT）正成为连接传感器、执行器等资源受限设备及家用电器至互联网的关键技术。本文提出一种适用于物联网与射频识别（RFID）系统等无线网络的节点基数估计新方法，该方法采用特权特征蒸馏（PFD）技术，并基于教师-学生模型的神经网络实现。教师模型利用特权特征与常规特征共同训练，学生模型则通过教师模型的预测结果与常规特征进行训练。我们针对同构与异构无线网络分别提出了基于PFD技术的节点基数估计算法。通过大规模仿真实验证明：与现有先进协议相比，本文提出的基于PFD的同构与异构网络算法在节点基数估计的均方误差上显著降低，且完成节点基数估计过程所需的时间槽数量与现有协议相同。