Energy‐efficient workload allocation in fog‐cloud based services of intelligent transportation systems using a learning classifier system-Reference-Cited by-同舟云学术

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Energy‐efficient workload allocation in fog‐cloud based services of intelligent transportation systems using a learning classifier system

Published:2020-11 Issue:11 Volume:14 Page:1484-1490
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Abbasi Mahdi¹^ORCID,Yaghoobikia Mina¹,Rafiee Milad¹^ORCID,Jolfaei Alireza²,Khosravi Mohammad R.³⁴

Affiliation:

1. Department of Computer Engineering, Engineering FacultyBu‐Ali Sina UniversityHamedanIran

2. Department of ComputingMacquarie UniversitySydneyNSWAustralia

3. Department of Computer EngineeringPersian Gulf UniversityBushehrIran

4. Department of Electrical and Electronic EngineeringShiraz University of TechnologyShirazIran

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2019.0783

Reference25 articles.

1. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

2. Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management

3. AhmedZ.E.SaeedR.A.MukherjeeA.: ‘Challenges and opportunities in vehicular cloud computing’ Cloud Security: Concepts Methodologies Tools and Applications (IGI Global 2019) pp.2168–2185

4. Collaborative mobile edge computing in Ev2x: a solution for low‐cost driver assistance systems;Keivani A.;Wirel. Pers. Commun.,2019

5. MASM: A Multiple-Algorithm Service Model for Energy-Delay Optimization in Edge Artificial Intelligence

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