Wireless sensor networks (WSNs) are very prone to ongoing security threats due to its resource constraints and unprotected transmission medium. WSN contains hundreds and thousands of resource-constrained and self-organized sensor nodes. These sensor nodes are usually organized in a distributed manner; thus, it permits the creation of an ad hoc network without predefined infrastructure or centralized management. As WSNs are going to get control of real-time applications, where a malicious activity can cause serious damage, the inherent challenge is to fortify the security enforcement in these networks. As a solution, software-defined network (SDN) has come out and has been merged with WSN to form what is known as software-defined wireless sensor network (SDWSN). SDWSN has come into existence, and it legitimizes network operators with more flexibility and control over the network. SDWSN has more tightened the security enforcement based on the global view and centralized control of the network topology. Moreover, machine learning (ML)–based and deep learning (DL)–based network intrusion detection systems (NIDS) have been introduced to the SDN environment to protect the networks against anomaly threats. In this review article, we illustrated the SDN–based security approaches to WSN followed by its architectures, advantages, and possible security threats. Finally, ML/DL–based NIDS integrated with the SDN controller is proposed as a complete solution for the WSN environment to confront the ongoing anomaly threats and to sufficiently protect the network against both known and unknown attacks.
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