Prior Detection of Explosives to Defeat Tragic Attacks Using Knowledge Based Sensor Networks

In recent years, the concern due to targeted acts of terrorism has grown rapidly. The consequences from a terrorist attack lead to critical economic infrastructure, public safety, and environment. Traditional intelligence gathering methods followed by government agencies and physical security systems at vulnerable facilities are not efficient for long-term implications. In this work, we propose a novel self-control solution using knowledge based decision making system (KBDMS) to detect explosives prior to attacks. The proposed system detects explosive materials using sensors, collects invader images (if any) by surveillance cameras, and forwards the information to a base station system (BSS) and/or a monitoring station to alert the emergency services. Adaptive media access control (A-MAC) protocol is used for the communication between the sensors. RSA (Rivest, Shamir and Adleman) algorithm that has digital signatory and integrity of log messages is used to enhance security. The collected information is analyzed at the monitoring station using face recognition techniques, situation reaction techniques, crime and intelligence analysis techniques, and threat severity estimation. The proposed system is evaluated using an Arduino simulator. Experimental results have shown the promise of this approach to defeat tragic attacks by detecting the explosives in prior.