Remote Sensing and Detection of Oil Spill Dispersions

The research problem was to understand how to develop a secure and reliable wireless sensor network for remote oil spill detection and monitoring, resulting from leaks from unmanned production platforms. The purpose of this quantitative single case study was to test information assurance of remote sensing data and information for offshore oil spills. The double-loop learning model proposed by Argyris and Schon provided the conceptual framework to determine if a paired parallel sensor configuration could provide a means for immediate and reliable oil spill detection. Data were collected from the environmental sensor installation of one corporate sensor network. Cycling across the ABA baseline/test condition served as the independent variable; whereas oil spill sensor data indicating the confirmation of oil spill, proof of information integrity, and other parameter readings served as dependent variables. Streaming sensor data was aggregated before and after the ABA phase shifts and analyzed using a standardized statistical quality control protocol. Statistical analysis included C statistics for serial dependency, F test and z-test for sample means, and Cohen's d test for intervention effect. Statistically significant differences in oil spill signal data were observed and coinciding with the ABA phase shifts. These results supported the conclusion that the accuracy and assurance of sensor data and information can be demonstrated in actual field environments. Based on the findings, an interactive remote sensing network for organization learning in information assurance setting was modeled for proactive use to data driven corporate security practices. Implications for positive social change included the timely warning from remote oil spill sensing that can result in improved safety and protection for the environment, employees, the public, and equipment.