학술논문

Pedestrian and vehicular tracking based on Wi-Fi sniffing: a real-world case study
Document Type
Conference
Source
2022 61st FITCE International Congress Future Telecommunications: Infrastructure and Sustainability (FITCE) Future Telecommunications: Infrastructure and Sustainability (FITCE), 2022 61st FITCE International Congress. :1-6 Sep, 2022
Subject
Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Signal Processing and Analysis
Training
Neural networks
Urban areas
Machine learning
Real-time systems
Mobile handsets
Telecommunications
Social IoT
Internet of Services
Smart City
Smart Logistics
Machine Learning
Localization and Location-based Services
Language
Abstract
This paper presents an innovative vehicle monitoring system based on Wi-Fi sniffing devices and real-time data processing using machine learning techniques. Our solution involves the construction of a neural network-based multiclass classifier that can classify the incoming Wi-Fi signal from many sources based on the received signal strength. The solution was carried out by training the neural network to predict different output classes corresponding to different vehicular (0–30 Km/h, 30-60 Km/h, 60-90 Km/h, 90-120 Km/h) and several pedestrian speed ranges among 0-15 Km/h.