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Self-adaptive smart spaces by proactive means–end reasoning

Self-adaptive smart spaces by proactive means–end reasoning The ability of a system to change its behavior at run-time is one of the foundations for engineering intelligent environments. The vision of computing systems that can manage themselves is fascinating, but to date, it presents many intellectual challenges to face. Run-time goal-model artifacts represent a typical approach to communicate requirements to the system and open new directions for dealing with self-adaptation. This paper presents a theoretical framework and a general architecture for engineering self-adaptive smart spaces by breaking out some design-time constraints between goals and tasks. The architecture supports software evolution because goals may be changed during the application lifecycle. The architecture is responsible for configuring its components as the result of a decision-making algorithm working at the knowledge level. The approach is specifically suitable for developing smart space systems, promoting scalability and reusability. The proposed architecture is evaluated through the execution of a set of randomized stress tests. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Reliable Intelligent Environments Springer Journals

Self-adaptive smart spaces by proactive means–end reasoning

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References (20)

Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer International Publishing AG
Subject
Computer Science; Performance and Reliability; Software Engineering/Programming and Operating Systems; Artificial Intelligence (incl. Robotics); Simulation and Modeling; User Interfaces and Human Computer Interaction; Health Informatics
ISSN
2199-4668
eISSN
2199-4676
DOI
10.1007/s40860-017-0047-9
Publisher site
See Article on Publisher Site

Abstract

The ability of a system to change its behavior at run-time is one of the foundations for engineering intelligent environments. The vision of computing systems that can manage themselves is fascinating, but to date, it presents many intellectual challenges to face. Run-time goal-model artifacts represent a typical approach to communicate requirements to the system and open new directions for dealing with self-adaptation. This paper presents a theoretical framework and a general architecture for engineering self-adaptive smart spaces by breaking out some design-time constraints between goals and tasks. The architecture supports software evolution because goals may be changed during the application lifecycle. The architecture is responsible for configuring its components as the result of a decision-making algorithm working at the knowledge level. The approach is specifically suitable for developing smart space systems, promoting scalability and reusability. The proposed architecture is evaluated through the execution of a set of randomized stress tests.

Journal

Journal of Reliable Intelligent Environments – Springer Journals

Published: Jul 31, 2017

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