## 2 Background and Related Work
### SDWSN-IoT  
In recent years, the Internet of Things (IoT) has emerged as one of the most influential technologies of the 21 st century. The IoT encompasses a wide range of devices, from everyday household objects to sophisticated industrial tools, that can exchange data with other devices and systems via the internet [10]. The sharp increase of IoT devices, connected through both wired and wireless networks, has experienced exponential growth and is projected to surpass 22 billion by 2025 [11]. This extensive network holds the potential to optimize the reliability, resilience, and efficiency of IoT systems that are applicable in many areas, including agriculture, transportation, energy, homes, health, industry, and infrastructure [12; 13; 14]. However, the diverse nature of SDWSN-IoT devices still faces a number of challenges, such as security, deployment flexibility, and the absence of efficient energy consumption schemes [15]. To effectively tackle these challenges, the integration of SDWSN-IoT emerges as a pivotal concept, facilitating the establishment of a robust platform for resolving the aforementioned concerns. The SDWSN-IoT framework operates by separating the control logic from the network devices, leaving the sensing devices with only data-forwarding functionality. With the introduction of SDWSN-IoT in networking, tasks that demand a large amount of computing power, such as routing, data processing, and network management, are removed from the physical sensor nodes and handled at the control plane by the SDN controller. As depicted in Figure 1, a typical SDWSN-IoT framework comprises multiple layers, namely the sensing, data, and control layers. Each layer consists of specific devices performing assigned tasks. The lower layer, known as the sensing layer, is composed of deployed IoT wireless sensor objects. These devices are responsible for collecting raw data from the environment and delivering it to the data layer. The data layer comprises devices capable of forwarding the collected data to the IoT cloud. With enhanced computational capacity, memory, and an unlimited power supply, the controller in the control layer assumes the crucial role of managing data flow across the forwarding devices in the data layer. The seamless flow of the substantial amount of collected data relies on the data-forwarding rules, which are installed on the forwarding sensor nodes by the SDN controller [16]. By using protocols such as Sensor OpenFlow [17], the controller efficiently manages the dynamic flow of collected data in real-time by regularly computing optimal routing paths based on the real-time network status. By decoupling the control layer from the data layer, the SDWSN-IoT framework has enabled the programmable control and virtualization of the network functionalities. The SDWSN-IoT paradigm is undeniably a promising and pivotal technology in wireless IoT applications. However, effectively collecting and routing a substantial amount of data through energy-constrained IoT sensor devices remains a significant challenge. Therefore, the development of an energy-efficient routing scheme is crucial for enhancing the overall performance of such networks. The design of such a routing scheme determines the longevity of the network, directly influencing the duration of active data flows across multiple paths. The integration of IoT applications and the SDWSN framework has facilitated the adoption of advanced and resource-intensive intelligent optimization techniques that were previously unattainable [18]. These techniques include resource-demanding intelligent machine-learning algorithms that require considerable energy, computational power, and memory supply to operate effectively [19]. In recent years, the incorporation of machine learning algorithms into the SDWSN has drawn substantial attention from the community, as it introduces the concept of task automation in SDWSN-IoT [20].