# RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receivers

arXiv: https://arxiv.org/abs/2410.11400 · HTML: https://mlsanity.com/paper/2410.11400/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 15 Oct 2024.

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

- [Time Series Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2002.12478/) (2020)
- [MobileOne: An Improved One millisecond Mobile Backbone](https://mlsanity.com/paper/2206.04040/) (2022)
- [FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning](https://mlsanity.com/paper/2203.02014/) (2022)
- [ReWiS: Reliable Wi-Fi Sensing Through Few-Shot Multi-Antenna Multi-Receiver CSI Learning](https://mlsanity.com/paper/2201.00869/) (2022)
- [CRISLoc: Reconstructable CSI Fingerprintingfor Indoor Smartphone Localization](https://mlsanity.com/paper/1910.06895/) (2019)
- [CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network](https://mlsanity.com/paper/2306.01756/) (2023)
- [RSSI-CSI Measurement and Variation Mitigation with Commodity WiFi Device](https://mlsanity.com/paper/2203.12888/) (2022)

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

- [FedAPA: Federated Learning with Adaptive Prototype Aggregation Toward Heterogeneous Wi-Fi CSI-based Crowd Counting](https://mlsanity.com/paper/2511.21048/) (2025)

## Abstract (arXiv)

Passenger counting is crucial for public transport vehicle scheduling and traffic capacity evaluation. However, most existing methods are either costly or with low counting accuracy, leading to the recent use of Wi-Fi signals for this purpose. In this paper, we develop an efficient edge computing-based passenger counting system consists of multiple Wi-Fi receivers and an edge server. It leverages channel state information (CSI) and received signal strength indicator (RSSI) to facilitate the collaboration among multiple receivers. Specifically, we design a novel CSI feature fusion module called Adaptive RSSI-weighted CSI Feature Concatenation, which integrates locally extracted CSI and RSSI features from multiple receivers for information fusion at the edge server. Performance of our proposed system is evaluated using a real-world dataset collected from a double-decker bus in Hong Kong, with up to 20 passengers. The experimental results reveal that our system achieves an average accuracy and F1-score of over 94%, surpassing other cooperative sensing baselines by at least 2.27% in accuracy and 2.34% in F1-score.