PASA

We evaluate the performance of our proposed method using four benchmark datasets: MNIST, CIFAR-10, Traffic-sign Recognition (TSR), and Room-occupancy Detection (ROD). Each dataset is divided into training and test sets, with specific proportions as described below.MNIST: This dataset consists of grayscale images of handwritten digits, with 10 distinct classes. It includes 60,000 training images and 10,000 test images, each formatted as a 28x28 pixel grayscale map.CIFAR-10: Unlike MNIST, CIFAR-10 is a dataset of color images. It contains 50,000 training images and 10,000 test images, divided into 10 classes. Each image is a 32x32 pixel color image with three color channels.Traffic-sign Recognition (TSR): TSR is an application-oriented dataset for identifying traffic signs using IoT devices. It includes 39,209 training images and 12,630 test images, covering 43 different classes. Each image is an RGB color map of 30x30 pixels.Room-occupancy Detection (ROD): ROD is a dataset designed for detecting room occupancy using IoT sensors without cameras. It consists of 5,430 training samples and 2,714 test samples, categorized into two classes. The input data is structured as 6x1x1, representing the sensor data used for detection.