Service-Oriented Reconfiguration in Systems of Systems Assured by Dynamic Modular Safety Cases

Thomas, Carsten; Mirzaei, Elham; Wudka, Björn; Siefke, Lennart; Sommer, Volker

doi:10.1007/978-3-030-86507-8_2

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…For researchers, although there are currently methods to develop the capabilities of reinforcement learning in individual tasks, there is still a lack of understanding and experience in how to construct intelligent systems that integrate these functionalities, relying on expert knowledge for discussions on research problems [50,51]. A key open question is what the best overall organizational method for multiagent systems is: homogeneous nonmodular systems [52]; fixed, static modular systems [53]; dynamically reconfigurable modular systems over time [54]; or other types of systems. In addition to the organizational methods, there are challenges in the transformation and coexistence of alternative knowledge representation approaches, such as symbolic, statistical, neural, and distributed representations [55], because the different representation techniques vary in their expressiveness, computational efficiency, and interpretability.…”

Section: Framework Design Ideasmentioning

confidence: 99%

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Wu,

Zhang,

Miao

et al. 2024

Electronics

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Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks.

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Section: Framework Design Ideasmentioning

confidence: 99%

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Wu,

Zhang,

Miao

et al. 2024

Electronics

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“…Self-adaptation issues in embedded software have been investigated across different areas of software engineering, such as requirements engineering [1][2][3][4], software architecture [5][6][7][8][9][10], middleware architectures [11][12][13][14], component-based development [15][16][17], model-driven development [18][19][20][21] and goal-driven models [22][23][24][25]. The majority of these works have been isolated initiatives.…”

Section: Self-adaptation In Embedded Softwarementioning

confidence: 99%

Engineering Self-Adaptive Capabilities in Wireless Sensors Based on Control Algorithms and Model-Based Design Methodologies

Matamoros¹,

Pruteanu²,

Haber³

2022

Preprint

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The main objective of this work is the design and implementation of self-adaptive capabilities in wireless sensors by applying control engineering and model-based design methodologies. It has been addressed the problem related to the changes in the flow of data packets through the network connection and the excess energy consumption that this causes in these devices. To design the solution, a systemic characterization of the scheduling and execution process of embedded tasks on the device has been carried out. This means defining cause-effect relationships in the system and its modelling theoretically and/or experimentally. In turn, these models facilitate the design of control strategies to improve the dynamic behavior of the system. As a solution, a self-adaptation strategy based on feedforward control algorithm has been designed and developed, which has been applied to improve the dynamic behavior and resource consumption. The developed solution has been satisfactorily evaluated experimentally.

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Exploring Safety Challenges in Dynamic Systems-of-Systems for Flood Management

Patel¹,

Haupt²,

Adler

et al. 2023

2023 18th Annual System of Systems Engineering Conference (SoSe)

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Service-Oriented Reconfiguration in Systems of Systems Assured by Dynamic Modular Safety Cases

Cited by 4 publications

References 35 publications

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Engineering Self-Adaptive Capabilities in Wireless Sensors Based on Control Algorithms and Model-Based Design Methodologies

Exploring Safety Challenges in Dynamic Systems-of-Systems for Flood Management

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