LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
This work provides a dataset and methods for IoT applications in smart campuses, but it is incremental as it applies existing techniques to a new domain-specific dataset.
The paper tackles the problem of missing data and prediction in a LoRaWAN-based smart campus IoT system by proposing a k-nearest neighbor method for handling missing transmissions and using LSTM for future readings, achieving 95% accuracy in predicting room occupancy.
IoT has a significant role in the smart campus. This paper presents a detailed description of the Smart Campus dataset based on LoRaWAN. LoRaWAN is an emerging technology that enables serving hundreds of IoT devices. First, we describe the LoRa network that connects the devices to the server. Afterward, we analyze the missing transmissions and propose a k-nearest neighbor solution to handle the missing values. Then, we predict future readings using a long short-term memory (LSTM). Finally, as one example application, we build a deep neural network to predict the number of people inside a room based on the selected sensor's readings. Our results show that our model achieves an accuracy of $95 \: \%$ in predicting the number of people. Moreover, the dataset is openly available and described in detail, which is opportunity for exploration of other features and applications.