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

arXiv: https://arxiv.org/abs/2304.13366 · HTML: https://mlsanity.com/paper/2304.13366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks](https://mlsanity.com/paper/2006.07322/) (2020)
- [A Survey on LoRa Networking: Research Problems, Current Solutions and Open Issues](https://mlsanity.com/paper/1908.10195/) (2019)
- [Traffic Prediction and Fast Uplink for Hidden Markov IoT Models](https://mlsanity.com/paper/2209.05939/) (2022)
- [A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory](https://mlsanity.com/paper/2108.10070/) (2021)
- [Low Power Wide Area Network Analysis: Can LoRa Scale?](https://mlsanity.com/paper/1610.04793/) (2016)
- [Hybrid Coded Replication in LoRa Networks](https://mlsanity.com/paper/2001.08168/) (2020)
- [Beyond 5G Low-Power Wide-Area Networks: A LoRaWAN Suitability Study](https://mlsanity.com/paper/2002.08256/) (2020)

## Built on by (most cited first, 1 of 1)

- [Modeling IoT Traffic Patterns: Insights from a Statistical Analysis of an MTC Dataset](https://mlsanity.com/paper/2409.01932/) (2024)

## Abstract (arXiv)

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.