A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges

Jurado-Lasso, FFMarchegiani, LJurado, JFAbu-Mahfouz, Adnan MIFafoutis, X2022-11-062022-11-062022-02Jurado-Lasso, F., Marchegiani, L., Jurado, J., Abu Mahfouz, A.M. & Fafoutis, X. 2022. A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges. IEEE Access, 10. http://hdl.handle.net/10204/12510ML-SDWSNsDOI: 10.1109/ACCESS.2022.3153521http://hdl.handle.net/10204/12510Wireless Sensor Network (WSN), 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 WSN 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 WSN 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 SDWSN, mainly the current state-of-art, ML techniques, and open issues.FulltextenWireless Sensor NetworksWSNsInternet of ThingsIoTMachine learningSoftware-Defined Wireless Sensor NetworksSDWSNsMachine Learning Software-Defined Wireless Sensor NetworksML-SDWSNsA survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challengesArticleJurado-Lasso, F., Marchegiani, L., Jurado, J., Abu Mahfouz, A. M., & Fafoutis, X. (2022). A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges. IEEE Access, 10, http://hdl.handle.net/10204/12510Jurado-Lasso, FF, L Marchegiani, JF Jurado, Adnan MI Abu Mahfouz, and X Fafoutis "A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges." IEEE Access, 10 (2022) http://hdl.handle.net/10204/12510Jurado-Lasso F, Marchegiani L, Jurado J, Abu Mahfouz AM, Fafoutis X. A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges. IEEE Access, 10. 2022; http://hdl.handle.net/10204/12510.TY - Article AU - Jurado-Lasso, FF AU - Marchegiani, L AU - Jurado, JF AU - Abu Mahfouz, Adnan MI AU - Fafoutis, X AB - Wireless Sensor Network (WSN), 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 WSN 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 WSN 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 SDWSN, mainly the current state-of-art, ML techniques, and open issues. DA - 2022-02 DB - ResearchSpace DP - CSIR J1 - IEEE Access, 10 KW - Wireless Sensor Networks KW - WSNs KW - Internet of Things KW - IoT KW - Machine learning KW - Software-Defined Wireless Sensor Networks KW - SDWSNs KW - Machine Learning Software-Defined Wireless Sensor Networks KW - ML-SDWSNs LK - https://researchspace.csir.co.za PY - 2022 SM - ML-SDWSNs T1 - A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges TI - A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges UR - http://hdl.handle.net/10204/12510 ER -26133