Open-source tool enables low-cost, granular smart city data collection

Henry Levesque, a doctoral researcher at the University of Cincinnati College of Design, Architecture, Art and Planning, has developed an open-source, low-cost data collection platform designed to deliver hyperlocal urban insights. The system shifts the paradigm of smart city monitoring by enabling granular, community-driven data gathering that bypasses reliance on expensive municipal infrastructure or proprietary enterprise networks. The hardware architecture leverages inexpensive, commercially available components and can be worn on standard headgear or mounted in fixed locations. Each unit simultaneously records high-definition video, geospatial coordinates, and environmental telemetry such as temperature, humidity, light exposure, and air quality. Rather than locking outputs into closed ecosystems, the platform generates timestamped image sequences and sensor logs in open formats, ensuring compatibility with standard analytical software. The modular design allows researchers to swap components, attach solar arrays for extended operation, or strip GPS modules when location tracking is unnecessary. Field validation recently occurred during a four-week master class at the university, where students with minimal programming or hardware backgrounds successfully deployed the system, executed data collection protocols, and processed results. In a representative pilot project, participants utilized the wearable sensors to capture facial expression metrics during career coaching sessions. An accompanying AI-assisted analysis pipeline converted unstructured video feeds into structured datasets, quantifying emotional shifts across seven categorical benchmarks and providing coaches with longitudinal behavioral insights. The project reflects a deliberate move toward democratizing urban research tools. By reducing technical barriers and emphasizing data sovereignty, the platform allows end users to dictate measurement priorities and directly influence planning outcomes. Levesque’s work, which integrates open-source hardware with accessible AI processing, establishes a scalable framework for participatory smart city development, prioritizing localized agency and verifiable data over centralized surveillance. This approach redefines urban intelligence as a community-empowered discipline, setting a new standard for transparent, cost-effective municipal data collection.