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

Loading next page...
 
/lp/springer_journal/self-adaptive-smart-spaces-by-proactive-means-end-reasoning-iNUBkOaauD
Publisher
Springer International Publishing
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
D.O.I.
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 EnvironmentsSpringer Journals

Published: Jul 31, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off