loading page

A survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current status and major challenges
  • +2
  • Fabian Fernando Jurado Lasso ,
  • Letizia Marchegiani ,
  • Jesus Fabian Jurado ,
  • Adnan Abu Mahfouz ,
  • Xenofon Fafoutis
Fabian Fernando Jurado Lasso
Technical University of Denmark, Technical University of Denmark, Technical University of Denmark

Corresponding Author:[email protected]

Author Profile
Letizia Marchegiani
Author Profile
Jesus Fabian Jurado
Author Profile
Adnan Abu Mahfouz
Author Profile
Xenofon Fafoutis
Author Profile


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 state-of-art, ML techniques, and open issues.
2022Published in IEEE Access volume 10 on pages 23560-23592. 10.1109/ACCESS.2022.3153521