Ant Colony Optimization for Low-Rank Factorization with DNN on People Counting IoT using Environmental Sensors

People counting Internet of Things (IoT), which plays a role in counting people indoors based on sensor values, is a vital part of smart buildings because it affects other IoT systems that regulate devices like lighting and air conditioning (AC), impacting efficiency. However, a lightweight solution is needed to perform people counting without threatening personal privacy. This study aims to develop an edge computing-based people counting system using environmental sensors and a Deep Neural Network (DNN) model optimized using the LRF technique. The system is designed to operate in real-time on edge devices with low latency and efficient resource consumption. In general, the system's work process is divided into three main stages, namely (1) data acquisition and pre-processing, (2) model development and optimization, and (3) overall system performance evaluation. The system runs automatically on edge devices and follows a cyclic workflow to detect the number of people continuously. This study also uses ant colony optimization (ACO) for hyperparameter tuning and obtains optimum hyperparameters. Experimental results support the claim that LRF significantly reduces model size while maintaining high prediction accuracy. ACO on hyperparameter tuning obtains the optimum hyperparameters: the number of neurons as many as 128 units, Adam learning rate of 0.005, and batch size of 8. Then DNN + ACO is proven to perform better than DNN without ACO and the state-of-the-art random forest model with accuracy, precision, recall, and F1-score of 0.98, 0.99, 0.94, and 0.97. This is while overcoming the imbalance problem in the dataset with recall for counts 0, 1, 2, and 3, of 1.00, 1.00, 1.00, and 0.78, respectively. Finally, we found that the optimum rank on LRF to reduce the number of parameters in DNN is 32, where at that rank the model size is reduced from 28.6 KB to 26.6 KB without significant accuracy loss.