A survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current status and major challenges

F. Fernando Jurado-Lasso*, Letizia Marchegiani, J. F. Jurado, Adnan M. Abu-Mahfouz, Xenofon Fafoutis

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Wireless Sensor Networks (WSNs), which are enablers of the Internet of Things (IoT) technology, are typically used en-masse in widely physically distributed applications to monitor the dynamic conditions of the environment. They collect raw sensor data that is processed centralised. With the current traditional techniques of state-of-art WSNs programmed for specific tasks, it is hard to react to any dynamic change in the conditions of the environment beyond the scope of the intended task. To solve this problem, a synergy between Software-Defined Networking (SDN) and WSNs has been proposed. This paper aims to present the current status of Software-Defined Wireless Sensor Network (SDWSN) proposals and introduce the readers to the emerging research topic that combines Machine Learning (ML) and SDWSN concepts, also called ML-SDWSNs. ML-SDWSN grants an intelligent, centralised and resource-aware architecture to achieve improved network performance and solve the challenges currently found in the practical implementation of SDWSNs. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSNs, mainly the current stateof-art, ML techniques, and open issues
Original languageEnglish
JournalIEEE Access
Volume10
Pages (from-to)23560-23592
Number of pages33
ISSN2169-3536
DOIs
Publication statusPublished - 2022

Keywords

  • Wireless Sensor Networks (WSNs)
  • Internet of Things (IoT)
  • Machine Learning (ML)
  • Software-Defined Wireless Sensor Networks (SDWSNs)
  • Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs)

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