We develop intelligent urban systems based on IoT, edge computing,
and artificial intelligence to improve mobility, infrastructure
efficiency, and urban data analytics.
Our work focuses on building smart city platforms capable of
collecting, analyzing, and optimizing urban data in real time.
Smart Parking
Smart parking solutions use IoT sensors and AI-based analytics
to detect parking occupancy in real time and optimize urban mobility.
These systems allow cities, universities, and commercial areas
to monitor parking availability, reduce traffic congestion,
and improve urban infrastructure efficiency.
System Architecture
Parking Sensors
↓
IoT Network (LoRaWAN / NB-IoT)
↓
Urban Data Platform
↓
AI Analytics
↓
Dashboard / Mobile App
Smart Parking Pilot Proposal
Pilot deployment:
- 30 parking spaces
- IoT parking sensors
- LoRaWAN or NB-IoT connectivity
- Real-time analytics dashboard
- 3–6 month pilot project
This pilot will allow testing real-time parking monitoring,
data analytics, and mobility optimization strategies.
Benefits for Cities
- Reduce traffic caused by drivers searching for parking
- Improve urban mobility planning
- Generate real-time parking data
- Optimize urban infrastructure usage
- Support smart city innovation initiatives
Smart Parking Pilot – Urban IoT Project
Urban parking congestion is one of the main causes of traffic inefficiency in modern cities.
Our Smart Parking initiative explores the use of IoT sensors, edge computing, and AI-based analytics to detect parking occupancy in real time and optimize urban mobility.
The system integrates embedded parking sensors with LoRaWAN or NB-IoT communication networks, allowing cities to monitor parking availability and provide real-time information to drivers and traffic management platforms.
The project aims to demonstrate how intelligent parking infrastructure can reduce congestion, lower emissions, and improve the overall efficiency of urban transportation systems.
Pilot Architecture
The proposed architecture integrates multiple technological layers:
- Parking Sensors – embedded or surface IoT sensors detect vehicle presence.
- IoT Communication Network – LoRaWAN or NB-IoT transmits sensor data.
- Edge Gateway – performs local processing and filtering of sensor data.
- Cloud Platform – stores and processes data using AI analytics.
- Urban Dashboard – provides real-time visualization of parking availability.
- Mobile APIs – allow integration with mobility applications and navigation services.
AI-driven Urban Systems
The project is aligned with our research on Context-Aware Adaptive Mobility Systems (CAMS) and Generative Model-Driven Engineering (GenAI for urban systems).
Through model-driven approaches, the system architecture can automatically adapt to different city environments, allowing scalable deployment across urban infrastructures such as parking, traffic management, and smart mobility platforms.
Collaboration Opportunities
We collaborate with municipalities, universities, technology
providers, and urban innovation programs to develop smart
city infrastructure projects and intelligent urban systems.
We are currently seeking collaboration with municipalities, universities, and technology providers interested in developing pilot smart parking projects.
Potential collaboration areas include:
- IoT sensor deployment
- urban data platforms
- mobility analytics
- smart city infrastructure research
- AI-based urban optimization
We welcome collaboration with municipalities, universities, and technology partners interested in smart city innovation.
Contact
Urban AI & Smart Infrastructure Lab
IoT-BD Services
estevan.gomez@iot-bdservices.com