Enabling multiple health security threats detection using mobile edge computing

The purposeful acts of bioterrorism and the emergence of new pathogens call for developing efficient biosurveillance systems that are capable of detecting health security threats and localizing their origins. For this sake, several biosurveillance systems have been developed and deployed in different regions around the world. The objectives of these systems are to efficiently detect threats and effectively manage responses. Although modern biosurveillance systems start to leverage recent advances in internet-of-things and communication technologies to detect natural biological outbreaks, it is not evident whether these systems can be generalized to detect threats related to purposeful biological attacks. As opposed to natural disease outbreaks, biological attacks can be triggered concurrently at different locations and in vital areas that are traversed by a large number of humans. In this paper, we propose an efficient framework to detect multiple concurrent threats and localize their origins based on real-time information and mobile-edge computing support. The proposed framework is organized into a hierarchical architecture. At the micro-level, monitored humans are equipped with wearable sensors that periodically capture the monitored human vital signs. To process the captured information efficiently, the monitored environment is partitioned into cells. Each cell is assigned a mobile-edge computing (MEC) component that is responsible for processing the monitoring information within its cell. The MEC components then send the processed information to a super component for final processing and to detect possible threats. The experimental results show that the proposed framework accurately detects multiple concurrent health security threats and localize their origins.