Self-adaptation for Individual Self-aware Computing Systems

Maggio, Martina; Abdelzaher, Tarek; Esterle, Lukas; Giese, Holger; Kephart, Jeffrey O.; Mengshoel, Ole J.; Papadopoulos, Alessandro Vittorio; Robertsson, Anders; Wolter, Katinka

doi:10.1007/978-3-319-47474-8_12

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…Often goal management boils down to solving an optimization problem. It consists in translating goals into utility function policies: a goal is characterized as the desired set of feasible states for which the objective function is optimized subject to a set of constraints [9].…”

Section: The Decision Modulementioning

confidence: 99%

Autonomous Systems – An Architectural Characterization

Sifakis

2019

Lecture Notes in Computer Science

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The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We lack a rigorous common semantic framework for autonomous systems. It is remarkable that the debate about autonomous vehicles focuses almost exclusively on AI and learning techniques while it ignores many other equally important autonomous system design issues.Autonomous systems involve agents and objects coordinated in some common environment so that their collective behavior meets a set of global goals. We propose a general computational model combining a system architecture model and an agent model. The architecture model allows expression of dynamic reconfigurable multi-mode coordination between components. The agent model consists of five interacting modules implementing each one a characteristic function: Perception, Reflection, Goal management, Planning and Self-adaptation. It determines a concept of autonomic complexity accounting for the specific difficulty to build autonomous systems.We emphasize that the main characteristic of autonomous systems is their ability to handle knowledge and adaptively respond to environment changes. We advocate that autonomy should be associated with functionality and not with specific techniques. Machine learning is essential for autonomy although it can meet only a small portion of the needs implied by autonomous system design. We conclude that autonomy is a kind of broad intelligence. Building trustworthy and optimal autonomous systems goes far beyond the AI challenge.

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Section: The Decision Modulementioning

confidence: 99%

Autonomous Systems – An Architectural Characterization

Sifakis

2019

Lecture Notes in Computer Science

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“…S ELF-AWARENESS is the property of a system to evaluate its self-performance in the interaction with the environment and to take corrective measures based on such feedback to maximize the performance [1]- [3]. In such self-aware systems, several parameters of the system and the environment are observed to take the appropriate actions and to meet the requirements, while optimizing the objectives.…”

Section: Introductionmentioning

confidence: 99%

Self-Aware Machine Learning for Multimodal Workload Monitoring during Manual Labor on Edge Wearable Sensors

et al. 2020

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The design of reliable wearable technologies for real-time and long-term monitoring presents a major challenge. Self-awareness is a promising solution that enables the system to monitor itself in interaction with the environment and to manage its resources more efficiently. In this work, we aim to utilize the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring. Specifically, we consider cognitive workload detection during manual labor as a case study to illustrate the impact of our proposed technique in wearable technologies. Our multimodal machine-learning algorithm is able to detect cognitive workload during manual labor with a performance of 81.75%. By adopting the notion of self-awareness, we achieve an improvement of 27.6% in energy consumption, with less than 6% of performance loss.

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“…To ensure extreme energy efficiency, having knowledge about self and environment is necessary for the system. Self-awareness can be applied in seizure detection systems by fulfilling three major properties: reflecting current situation, predicting the possibilities to improve energy reduction and adopting the right strategy in the system [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

Forooghifar

Aminifar

Cammoun³

et al. 2019

Mobile Netw Appl

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Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. In this context, wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints with respect to time and location. In this paper, we propose a self-aware wearable system for real-time detection of epileptic seizures on a long-term basis. First, we propose a multi-parametric machine learning technique to detect seizures by analyzing both cardiac and respiratory responses to seizures, which are obtained using only the ECG signal. Second, in order to enable long-time epilepsy detection, we introduce the notion of self-awareness in our real-time wearable system. We evaluate the performance of our proposed solution based on an epilepsy database of more than 211 hours of recording, provided by the Lausanne University Hospital (CHUV), on the INYU wearable sensor. Our proposed system achieves a sensitivity of 88.66% and a specificity of 85.65% before applying self-awareness. Moreover, by controlling the energy-quality trade-offs using our self-aware energy-management technique, we can tune the battery lifetime of the wearable system to last between 67.55 and 136.91 days while, still outperforming the state-of-the-art techniques for wearable seizure detection, by achieving from 85.54% to 79.33% geometric mean of specificity and sensitivity.

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Self-adaptation for Individual Self-aware Computing Systems

Cited by 10 publications

References 33 publications

Autonomous Systems – An Architectural Characterization

Autonomous Systems – An Architectural Characterization

Self-Aware Machine Learning for Multimodal Workload Monitoring during Manual Labor on Edge Wearable Sensors

A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

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