This paper describes Acies-OS, a content-centric platform for edge AI twinning and orchestration that allows easy deployment, re-configuration, and control of edge AI services, augmented by a digital twin. The work is motivated by the proliferation of edge AI in a plethora of IoT applications, ranging from home automation to military defense, and the emergence of digital twins that go beyond monitoring and emulation into configuration management and optimization of edge capabilities. While past work focused on either the edge capabilities themselves or the digital twin, this work focuses on their seamless interactions, offering abstractions that enable the digital twin to manage and optimize an increasingly diverse edge AI system. Acies-OS features a structured namespace, a thin client library with flexible pub/sub-based communication, health monitoring support, and a control plane for twin-based value-added analysis and optimization. To illustrate the use of Acies-OS, we implemented a multi-node multi-modality vehicle classification application and used Acies-OS to interface it to a digital twin. We then deployed the system in the field to showcase run-time twin-based optimizations of inference latency, classification accuracy, and robustness to failures in noisy and challenging conditions.

Digital Twins are evolving as a key component in modern systems with diverse applications like remote prognostics, optimizing run-time operation, anomaly detection, and more. The essential elements of a digital twin are a virtual representation, a physical asset, and the transfer of data/information between the two. IoT deployments are generally characterized by resource constraints, making synchronization of digital twins with IoT devices more challenging. There is a pressing need to optimize the bandwidth of the data transferred between the system and the twin, while ensuring that the twin is able to capture selected key aspects of the current operational state accurately. In this paper, we present TwinSync, a framework that can be utilized to construct flexible real-time representations of deployed IoT systems and efficiently synchronize relevant system states with the twin, over a communication bottleneck, within a configurable application-specific notion of error (henceforth referred to as approximate synchronization). Our approach is optimized to achieve data transfers utilizing less bandwidth without compromising the ability of the twin to replicate real-time system states within the specified approximate synchronization semantics. We evaluate the efficacy of TwinSync's synchronization by conducting both a synthetic analysis and a case study based on a real-life application prototype. Our evaluation indicates that using TwinSync can provide the same or greater accuracy (in many cases) while sending significantly fewer bytes than a bandwidth-insensitive synchronization approach. The result is attributed to a more judicial selection of data to transmit over bottlenecks, compared to bandwidth-insensitive approaches.

Recent concepts in defense herald an increasing degree of automation of future military systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge, reducing their network communication footprint, and improving the inference quality of intelligent components in the loop. These requirements pose resource management challenges, calling for operating-system-like constructs that optimize the use of limited computational resources at the tactical edge. This paper describes these challenges and presents IoBT-OS, an operating system for the Internet of Battlefield Things that aims to optimize decision latency, improve decision accuracy, and reduce corresponding resource demands on computational and network components. A simple case-study with initial evaluation results is shown from a target tracking application scenario.

Abstract The process of developing civil aircraft and their related systems includes multiple phases of Preliminary Safety Assessment (PSA). An objective of PSA is to link the classification of failure conditions and effects (produced in the functional hazard analysis phases) to appropriate safety requirements for elements in the aircraft architecture. A complete and correct preliminary safety assessment phase avoids potentially costly revisions to the design late in the design process. Hence, automated ways to support PSA are an important challenge in modern aircraft design. A modern approach to conducting PSAs is via the use of abstract propagation models, that are basically hyper-graphs where arcs model the dependency among components, e.g. how the degradation of one component may lead to the degraded or failed operation of another. Such models are used for computingfailure propagations: the fault of a component may have multiple ramifications within the system, causing the malfunction of several interconnected components. A central aspect of this problem is that of identifying the minimal fault combinations, also referred to asminimal cut sets, that cause overall failures. In this paper we propose an expressive framework to model failure propagation, catering for multiple levels of degradation as well as cyclic and nondeterministic dependencies. We define a formal sequential semantics, and present an efficient SMT-based method for the analysis of failure propagation, able to enumerate cut sets that are minimal with respect to the order between levels of degradation. In contrast with the state of the art, the proposed approach is provably more expressive, and dramatically outperforms other systems when a comparison is possible.