Multi-sensor Data Fusion Technique to Detect Radiation Emission in Wireless Sensor Networks

Abstract

Detection of objects emitting radiation is a classical problem widely analyzed by many authors worldwide; however, new computerized data analysis wireless sensor network and systems are emerging daily and require application of optimal sensors data fusion methods to increase their effectiveness even further. Conventional monitoring and other radiation emission detection systems lack optimal resources allocation in harsh and huge environments with shielding materials preventing in-time easy event detection. That is why many security issues are partially omitted in our daily lives and even in industry, including international cargo transportation operations, due to the need for a larger amount of high precision sensors to be deployed on larger areas with different technological standards and their high procurement and exploitation costs. Such conventional sensor systems are already used in many areas including detection of dirty bombs in containers (US Homeland Security initiative - Container security initiative (CSI)), but only in certain areas with relatively low efficiency. In this paper, a theoretical mathematical model is presented and discussed to lower the radiation emission detection time using DAI-DAO data fusion technique in crucial security systems. A known problem of dirty bomb detection in a container terminal is presented as an example to demonstrate the problem area and the effectiveness of the proposed solution.