Dynamic and Effect-Driven Output Service Selection for IoT Environments Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Dynamic and Effect-Driven Output Service Selection for IoT Environments Using Deep Reinforcement Learning

Published:2023-02-15 Issue:4 Volume:10 Page:3339-3355
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Baek KyeongDeok¹^ORCID,Ko In-Young¹^ORCID

Affiliation:

1. School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

Funder

Ministry of Science and ICT (MSIT), South Korea

Information Technology Research Center (ITRC) Support Program supervised by the Institute of Information and Communications Technology Planning and Evaluation

National Research Foundation of Korea

Korea Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10038283/09908162.pdf?arnumber=9908162

Reference47 articles.

1. New Design Principles for Visual Acuity Letter Charts

2. Service Selection for Composition with QoS Correlations

3. Peripheral vision and pattern recognition: A review

4. Optimizing selection of competing services with probabilistic hierarchical refinement

5. A study on evolutionary computing based web service selection techniques

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