Chapter 2 - Uncertainty Quantification in Internet of Battlefield Things

Abstract

Internet of Things (IoT) technologies have made considerable recent advances in commercial applications, prompting new research on their use in military applications. The Internet of Battlefield Things (IoBT) is the military counterpart of IoT, which is capable of leveraging mixed commercial and military technologies. Machine learning and artificial intelligence are the fundamental algorithmic building blocks of IoBT to address the decision-making problems that arise in underlying control, communication, and networking within the IoBT infrastructure in addition to the inevitable part of almost all military-specific applications developed over IoBT. Uncertainty quantification for machine learning and artificial intelligence within IoBT is critical to provide an accurate measure of error over the output in addition to precision of output. Such information on uncertainty quantification enables risk-aware decision making and control for subsequent intelligent systems and/or humans within the IoBT pipeline. This chapter provides an overview of classical and modern statistical-learning theory, and how numerical optimization can be used to solve the corresponding mathematical problems with an emphasis on uncertainty quantification.