MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour

Finalised dependability framework and evaluation results

The ambitious aim of CONNECT is to achieve universal interoperability between heterogeneous Networked Systems by means of on-the-fly synthesis of the CONNECTors through which they communicate. The goal of WP5 within CONNECT is to ensure that the non-functional properties required at each side of the connection going to be established are fulfilled, including dependability, performance, security and trust, or, in one overarching term, CONNECTability. To model such properties, we have introduced the CPMM meta-model which establishes the relevant concepts and their relations, and also includes a Complex Event language to express the behaviour associated with the specified properties. Along the four years of project duration, we have developed approaches for assuring CONNECTability both at synthesis time and at run-time. Within CONNECT architecture, these approaches are supported via the following enablers: the Dependability and Performance analysis Enabler, which is implemented in a modular architecture supporting stochastic verification and state-based analysis. Dependability and performance analysis also relies on approaches for incremental verification to adjust CONNECTor parameters at run-time; the Security Enabler, which implements a Security-by-Contract-with-Trust framework to guarantee the expected security policies and enforce them accordingly to the level of trust; the Trust Manager that implements a model-based approach to mediate between different trust models and ensure interoperable trust management. The enablers have been integrated within the CONNECT architecture, and in particular can interact with the CONNECT event-based monitoring enabler (GLIMPSE Enabler released within WP4) for run-time analysis and verification. To support a Model-driven approach in the interaction with the monitor, we have developed a CPMM editor and a translator from CPMM to the GLIMPSE native language (Drools). In this document that is the final deliverable from WP5 we first present the latest advances in the fourth year concerning CPMM, Dependability&Performance Analysis, Incremental Verification and Security. Then, we make an overall summary of main achievements for the whole project lifecycle. In appendix we also include some relevant articles specifically focussing on CONNECTability that have been prepared in the last period

Dynamic Connector Synthesis: Principles, Methods, Tools and Assessment

CONNECT adopts a revolutionary approach to the seamless networking of digital systems, that is, onthe- fly synthesis of the connectors via which networked systems communicate. Within CONNECT, the role of the WP3 work package is to devise automated and efficient approaches to the synthesis of such emergent connectors, provided the behavioral specification of the components to be connected. Thanks to WP3 scientific and technology development, emergent connectors can be synthesized on the fly as networked systems get discovered, implementing the necessary mediation between networked systems' protocols, from application down to middleware layers. This document being the final report about WP3 achievements, it outlines both: (i) specific contributions over the reporting period, and (ii) overall contributions in the area of automated, on-the-fly protocol mediation, from theory to supporting tool

A framework for robust control of uncertainty in self-adaptive software connectors

Context and motivations. The desired behavior of a system in ubiquitous environments considers not only its correct functionality, but also the satisfaction of its non-functional properties, i.e., its quality of service. Given the heterogeneity and dynamism characterizing the ubiquitous environments and the need for continuous satisfaction of non-functional properties, self-adaptive solutions appear to be an appropriate approach to achieve interoperability. In this work, self-adaptation is adopted to enable software connectors to adapt the interaction protocols run by the connected components to let them communicate in a timely manner and with the required level of quality. However, this self-adaptation should be dependable, reliable and resilient to be adopted in dynamic, unpredictable environments with different sources of uncertainty. The majority of current approaches for the construction of self-adaptive software ignore the uncertainty underlying non-functional requirement verification and adaptation reasoning. Consequently, these approaches jeopardize system reliability and hinder the adoption of self-adaptive software in areas where dependability is of utmost importance. Objective. The main objective of this research is to properly handle the uncertainties in the non-functional requirement verification and the adaptation reasoning part of the self-adaptive feedback control loop of software connectors. This will enable a robust and runtime efficient adaptation in software connectors and make them reliable for usage in uncertain environments. Method. In the context of this thesis, a framework has been developed with the following functionalities: 1) Robust control of uncertainty in runtime requirement verification. The main activity in runtime verification is fine-tuning of the models that are adopted for runtime reasoning. The proposed stochastic approach is able to update the unknown parameters of the models at runtime even in the presence of incomplete and noisy observations. 2) Robust control of uncertainty in adaptation reasoning. A general methodology based on type-2 fuzzy logic has been introduced for the control of adaptation decision-making that adjusts the configuration of component connectors to the appropriate mode. The methodology enables a systematic development of fuzzy logic controllers that can derive the right mode for connectors even in the presence of measurement inaccuracy and adaptation policy conflicts. Results. The proposed model evolution mechanism is empirically evaluated, showing a significant precision of parameter estimation with an acceptable overhead at runtime. In addition, the fuzzy based controller, generated by the methodology, has been shown to be robust against uncertainties in the input data, efficient in terms of runtime overhead even in large-scale knowledge bases and stable in terms of control theory properties. We also demonstrate the applicability of the developed framework in a real-world domain. Thesis statement. We enable reliable and dependable self-adaptations of component connectors in unreliable environments with imperfect monitoring facilities and conflicting user opinions about adaptation policies by developing a framework which comprises: (a) mechanisms for robust model evolution, (b) a method for adaptation reasoning, and (c) tool support that allows an end-to-end application of the developed techniques in real-world domains

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Cognitive Decay And Memory Recall During Long Duration Spaceflight

This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors