Abstract
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.
Highlights
T HE Internet of Things (IoT) is an emerging technology that has caught tremendous attention from the scientific and industry communities and professional organisations due to its diverse benefits: including financial, efficiency, management, etc
This paper presented a comprehensive review of Software-Defined Wireless Sensor Network (SDWSN) research works and Machine Learning (ML) techniques to perform network management and reconfiguration, and policy enforcement
The surveyed scientific articles have demonstrated that SDWSN is an effective solution for improving network performance and management, which would not have been possible with traditional Wireless Sensor Networks (WSNs) architectures
Summary
T HE IoT (as a general IoT ecosystem including middlewares, servers, cloud, edge) is an emerging technology that has caught tremendous attention from the scientific and industry communities and professional organisations due to its diverse benefits: including financial, efficiency, management, etc. WSNs are used in a range of applications that enable integration of the physical world into the computer-based world, resulting in benefits and improvements in remotely managing the physical world, keeping an electronic record of physical variables, early detection of potential threats, predictions, and economical benefits Their low cost and ease of deployment make WSNs attractive in the practical implementation of the IoT. The ML component has, at hand, real-time data including network statistics (Received Signal Strength Indicator (RSSI), Packet Delivery Ratio (PDR), etc.), network resources (sensor nodes remaining energy, applications load, etc.), network topology, etc. This makes the ideal environment to deploy ML algorithms tailored to user and application requirements. Reviewed recent developments of ML techniques applied to WSN with an emphasis on DL
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