Mobile Sensing Enabled Robust Detection of Security Threats in Urban Environments

Mobile sensing enables data collection from large numbers of participants in ways that previously were not possible. In particular, by affixing a sensory device to a mobile device, such as smartphone or vehicle, mobile sensing provides the opportunity to not only collect dynamic information from environments but also detect the environmental hazards. In this paper, we propose a mobile sensing wireless network for surveillance of security threats in urban environments, e.g., environmental pollution sources or nuclear radiation materials. We formulate the security threats detection as a significant cluster detection problem. Tomake our approach robust to unreliable sensing data, we propose an algorithm based on the Mean Shift method to identify the significant clusters and determine the locations of threats. Extensive simulation studies are conducted to evaluate the effectiveness of the proposed detection algorithm.