LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case

TL;DR

This paper introduces a comprehensive LoRaWAN-based dataset for smart campus IoT applications, demonstrating a people counting use case with high accuracy and providing open access for further research.

Contribution

It provides a detailed LoRaWAN dataset for smart campuses and develops a neural network model achieving 95% accuracy in people counting.

Findings

Achieved 95% accuracy in predicting room occupancy

Proposed a k-NN method for missing data imputation

Published an open dataset for IoT research

Abstract

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.