Autonomy Operating System for UAVs: Pilot-in-a-Box

The Autonomy Operating System (AOS) is an open flight software platform with Artificial Intelligence for smart UAVs. It is built to be extendable with new apps, similar to smartphones, to enable an expanding set of missions and capabilities. AOS has as its foundations NASAs core flight executive and core flight software (cFEcFS). Pilot-in-a-Box (PIB) is an expanding collection of interacting AOS apps that provide the knowledge and intelligence onboard a UAV to safely and autonomously fly in the National Air Space, eventually without a remote human ground crew. Longer-term, the goal of PIB is to provide the capability for pilotless air vehicles such as air taxis that will be key for new transportation concepts such as mobility-on-demand. PIB provides the procedural knowledge, situational awareness, and anticipatory planning (thinking ahead of the plane) that comprises pilot competencies. These competencies together with a natural language interface will enable Pilot-in-a-Box to dialogue directly with Air Traffic Management from takeoff through landing. This paper describes the overall AOS architecture, Artificial Intelligence reasoning engines, Pilot-in-a-box competencies, and selected experimental flight tests to date

Dependency-Based Decomposition of Systems Involving Rare Events

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryIBM Ph.D. Fellowshi

Understanding the fault-tolerance properties of large-scale storage systems

Modern storage systems continue to increase in scale and complexity as they attempt to meet the increasing storage needs of our society. Additionally, increased requirements to comply with government regulation and consumer expectations have increased the need to make data more available and reliable for longer periods of time. The design of modern and next-generation storage systems is a difficult task that requires high storage capacity and efficiency while also maintaining the data integrity. The rapid advancement of storage system technologies brings with it a level of uncertainty as to the fitness of new designs and methods for meeting the complex requirements. New technologies, like deduplication, promise improved storage efficiency, but their impact on reliability measures is unclear due to the complex relationships inherent to the systems that employ these technologies. Additionally, as systems scale up, they become subject to faults and errors that previous-generation systems may never have encountered due to the rare nature of these faults. Because of the stiffness of the represented systems, and the complex relationships involved, it can be difficult to analyze these environments correctly and efficiently. In this dissertation, we propose a method to analyze storage system reliability by using component-based models coupled with realistic fault models. We solve these complex systems by identifying fault, fault propagation, and mitigation events; by identifying dependence relationships between state variables, events, and rewards; and by decomposing our model at various points during model solution to improve the efficiency of our solution while maintaining the correctness of our reward measures. In particular, we discuss building scalable component-based models of large-scale systems that employ modern reliability methods, such as RAID, and state-of-the-art storage efficiency methods such as deduplication. We present detailed fault models for these systems, including a novel model for undetected disk errors. To enable efficient solution of these models we propose a method to analyze the dependence relationships that underlie storage systems and propose a way to solve these models by identifying and exploiting these relationships when solving for reliability measures. We apply our methods to real-world systems, detail the consequences for the reliability of deduplication, and suggest and evaluate methods to improve reliability while still maintaining improved storage efficiency

Nonparametric Estimation of the Cumulative Intensity Function for a Nonhomogeneous Service Process

"A nonparametric technique for the estimation of the cumulative intensity function for a nonhomogeneous service process from one or more realizations that may contain idle periods is developed. This technique does not require any arbitrary parameters from the modeler. The estimated cumulative intensity function can be used for the generation of variates for simulation via inversion"--Author abstract

Characterizing Data Dependence Constraints for Dynamic Reliability Using n-Queens Attack Domains

As data centers attempt to cope with the exponential growth of data, new techniques for intelligent, software-defined data centers (SDDC) are being developed to confront the scale and pace of changing resources and requirements. For cost-constrained environments, like those increasingly present in scientific research labs, SDDCs also may provide better reliability and performability with no additional hardware through the use of dynamic syndrome allocation. To do so, the middleware layers of SDDCs must be able to calculate and account for complex dependence relationships to determine an optimal data layout. This challenge is exacerbated by the growth of constraints on the dependence problem when available resources are both large (due to a higher number of syndromes that can be stored) and small (due to the lack of available space for syndrome allocation). We present a quantitative method for characterizing these challenges using an analysis of attack domains for high-dimension variants of the $n$-queens problem that enables performable solutions via the SMT solver Z3. We demonstrate correctness of our technique, and provide experimental evidence of its efficacy; our implementation is publicly available.This article is published as Rozier, Eric W.D., Kristin Y. Rozier, and Ulya Bayram. "Characterizing Data Dependence Constraints for Dynamic Reliability Using n-Queens Attack Domains." Leibniz Transactions on Embedded Systems 4, no. 1 (2017): 05:01-05:26. DOI: 10.4230/LITES-v004-i001-a005. Posted with permission.</p

A Case Study in Safety, Security, and Availability of Wireless-Enabled Aircraft Communication Networks

As the costs of fuel and maintenance increase and regulations on weight and environmental impact tighten, there is an increasing push to transition on-board aircraft networks to wireless, reducing weight, fuel, maintenance time, and pollution. We outline a candidate short-range hybrid wired/wireless network for aircraft on-board communications using the common ZigBee protocol and privacy-preserving search implemented as a secure publish/subscribe system using specially coded meta-data. Formally specifying safety and security properties and modeling the network in NUXMV enables verification and fault analysis via model checking and lays the groundwork for future certification avenues. We report on our experiments building and testing our candidate hybrid network and report on overhead and availability for encrypted and fault-tolerant communications, and propose a system that allows system designers to directly trade fault-tolerance for bandwidth, or vice-versa, in an encrypted privacy-preserving framework.This is a manuscript of a proceeding published as Dureja, Rohit, Eric W. Rozier, and Kristin Y. Rozier. "A case study in safety, security, and availability of wireless-enabled aircraft communication networks." In 17th AIAA Aviation Technology, Integration, and Operations Conference. AIAA 2017-3112. (2017): 3112. DOI: 10.2514/6.2017-3112. Posted with permission.</p