Challenges and Opportunities in IoT-based Software Defined Wireless Networks (SDWN) and the Current State of ML-SDWSNs

Several challenges, including line of sight, interference, weather, power outages, and so on, may make it difficult to connect an Internet of Things (IoT) sensor and wireless communication between a body area network and a WSN via LAN. The performance limitations in present wireless Internet of Things networks have been demonstrated to be overcome by software-defined networking (SDN), a next-future networking technology. It is one method for advancing the wireless IoT field. Traffic engineering (TE) has long been utilized in traditional network designs to improve communication network performance. On the other hand, research on better versions and their use in SDWN-IoT networks is still underway. This work's study of the corpus of literature focuses on the principal SDWN-IoT network types—Software-Defined Wireless Sensor Network (SDWSN-IoT) and Software-Defined Wireless Mesh Network. The research also examines various contributions and shortcomings, drawing some important conclusions. Finally, a variety of research prospects and challenges for the networks in IoT based Wireless sensor and Wireless Mesh Networks have been addressed. This article aims to introduce readers to the growing research area of ML-SDWSNs, or Machine Learning-Self-Defined Wireless Sensor Networks, which combines the concepts of SDWSN with ML. It also reports on the current progress of SDWSN projects. ML-SDWSN's intelligent, centralized, and resource-aware architecture contributes to improved network performance and the resolution of challenges that emerge in real-world SDWSN applications. The methodical and engineering communities, as well as professional organizations interested in SDWSN, can benefit from this survey's important information and insights, which are largely focused on cutting-edge ML approaches and pressing challenges.