Autonomous Strike UAVs in Support of Homeland Security Missions: Challenges and Preliminary Solutions

Abstract

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we identify the challenges that must be overcome and propose possible technical solutions for them. We also derive analytical expressions for the success probability of an autonomous UAV mission and describe a machine-learning model to train the UAV.

Keywords

• Homeland security
• blockchain
• machine learning technology
• on-board black box
• smart contracts
• UAV