Time-Resolved Designs for Narrowband Radio Localization and Vehicular Visual Sensing Compromise

The Internet of Things relies heavily on accurate and reliable sensing for applications. Time-resolved sensing is a paradigm that leverages fine-grained temporal analysis of sensor signals, and the intrinsic relationship between space and time, to extract rich spatial and contextual information. This concept expands the sensing capabilities in IoT applications, but also introduces new challenges in synchronization, processing efficiency, and security. In my research, I explore both the constructive and adversarial aspects of time-resolved designs, highlighting their dual role in improving the capabilities of IoT systems and exposing potential security vulnerabilities. Specifically, I study how time-resolved radio signals can be carefully processed to achieve indoor localization under narrow signal bandwidth constraints. I also reveal how time-resolved camera-based visual sensing can be exploited to affect vehicular perception.