A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges-Reference-Cited by-同舟云学术

A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges

Published:2022 Issue: Volume:10 Page:23560-23592
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jurado-Lasso F. Fernando¹^ORCID,Marchegiani Letizia²^ORCID,Jurado J. F.³,Abu-Mahfouz Adnan M.⁴^ORCID,Fafoutis Xenofon¹^ORCID

Affiliation:

1. Embedded Systems Engineering Section, DTU Compute, Technical University of Denmark, Lyngby, Denmark

2. Department of Electronic Systems, Aalborg University, Aalborg Øst, Denmark

3. Department of Basic Science, Faculty of Engineering and Administration, Universidad Nacional de Colombia Sede Palmira, Palmira, Colombia

4. Council for Scientific and Industrial Research (CSIR), Pretoria, South Africa

Funder

Electronics Components and Systems for European Leadership (ECSEL) Joint Undertaking (JU) through Distributed Artificial Intelligent Systems

DAIS

European Union’s Horizon 2020 Research and Innovation Program and Sweden, Spain, Portugal, Belgium, Germany, Slovenia, Czech Republic, The Netherlands, Denmark, Norway, and Turkey, for JU

Innovationsfonden

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09718334.pdf?arnumber=9718334

Reference178 articles.

1. A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks

2. Zigbee-based intra-car wireless sensor networks: a case study

3. Classification of Quality of Granary using Machine Learning based on Software-Defined Wireless Sensor Network

4. Orchestra

5. Topology Management and TSCH Scheduling for Low-Latency Convergecast in In-Vehicle WSNs

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