Collaborative Sensing

This research theme focuses on the design and implementation of scalable, interoperable, and autonomous platforms that enable multiple distributed mobile devices, such as smartphones and embedded devices, to collaboratively sense, process, and share sensor data. This is crucial to handle the challenges of opportunistic sensing—where devices opportunistically collect data based on availability and context—and to reduce energy consumption, bandwidth usage, and development complexity. Such frameworks aim to support large-scale community or crowd sensing applications that require dynamic, component-based architectures for rapid deployment across heterogeneous devices.

This research area explores the integration of humans as active sensors—providing subjective observations and contextual knowledge—and citizen science initiatives to complement and improve the spatial and temporal coverage of conventional sensor networks. It examines socio-technical challenges such as data quality, usability, participation models, and trust, and develops frameworks empowering the public to collect, share, and validate environmental data. This theme addresses how people’s contributions can provide near real-time, localized observations that enhance environmental monitoring, public safety, and urban governance.

This body of work investigates computational frameworks, communication protocols, and architectures to fuse data from multiple heterogeneous sensors in a distributed, often peer-to-peer manner. It aims to optimize real-time collaboration among sensor nodes and fusion centers by addressing synchronization, resource constraints, network reliability, and energy consumption. Methodologies include multimodal sensing, wake-up radios, peer selection algorithms, and collective communication leveraging constructive interference. The goal is to produce higher accuracy, reduced latency, and improved reliability in complex sensing environments ranging from indoor building monitoring to urban digital twins.