A Mobile-Edge Computing Bio-Surveillance Framework for Multiple Biological Attacks Detection

Abstract

Hostile biological attacks are becoming increasingly dangerous and life-threatening. Nevertheless, most of these attacks are not detected in a timely manner. Current biosurveillance systems depend on the data gathered from different medical sources such as hospitals and specialized laboratories. Recently, bio-surveillance systems start to utilize recent advances in sensors and IoT technologies to improve their real-time detection of health security threats. Despite the great improvements shown by these systems, there is no evidence on their effectiveness in case of multiple concurrent attacks. Triggering multiple concurrent attacks further complicates the problem as gathered monitoring information tend to obfuscate each other. To address this issue, we propose a multi-agent framework for multiple biological threat detection. The proposed framework utilizes recent advances in wearable sensors to monitor human vital signs. Moreover, intelligent agents are defined in the context of mobile-edge computing platforms to group monitoring information into focused clusters. These clusters are then used to effectively detect multiple concurrent health security threats. The evaluation of the proposed frame-work shows its effectiveness in detecting concurrent biological attacks.