## 1 Introduction  
The dramatic and exponential increase in the urban population has created a huge demand for supplying high-quality communication services to its citizens[1]. Simultaneously, recent technological advancements have led to a significant increase in smart electronic devices, such as sensors, actuators, smartphones, and smart appliances, thereby creating new business opportunities for providing high-quality communication services. Effective communication among this vast number of electronic devices is possible through the Internet of Things (IoT), a ubiquitous network of interconnected objects that can interact with the physical world and use existing Internet standards to provide services for information transfers, analytics, and applications [2]. The design and deployment of such sophisticated systems, which entail digitalizing all aspects of our lives, face enormous challenges that require careful consideration, such as networking, security and privacy, intelligent data analysis, and smart sensors [3]. Over the years, traditional wireless sensor networks (WSNs) have evolved into becoming IoT devices capable of providing diverse sensing services in IoT systems [4]. However, wireless IoT devices inherit the characteristics of the traditional WSNs, which inhibit their application in IoT systems due to their energy-constrained behavior. To meet the application-specific requirements of the IoT in real-time, the energy cost for transmission poses a challenge in IoT applications and should be thoroughly considered [5]. IoT-based wireless sensors are deployed in computationally demanding and energy-constrained environments, necessitating the exploration of solutions that enable the prolonged operation of wireless sensor nodes without requiring battery replacements or location changes [6]. Among the various tasks performed by such nodes, data transmission emerges as the most energy-intensive, followed by others such as route computation, idle listening for potential incoming traffic, and data processing [7]. As a result, the development of effective routing protocols that identify optimal routes for a successful data transfer with acceptable energy consumption is critical for enhancing the overall performance of IoT-oriented WSNs. Despite the significant number of existing routing protocols in the literature, it is worth noting that current solutions are relatively limited in their ability to address the energy efficiency challenges of IoT-oriented WSNs. However, the integration of emerging networking technologies, such as Software-Defined Networks (SDN), into wireless sensor networks has shown promising results in the development of effective energy-efficient schemes. By removing energy-consuming tasks such as routing, data processing, and network management from the physical wireless nodes, the SDN has transformed them into data forwarding entities, significantly reducing their energy consumption [8]. The SDN architecture, with its centralized management approach that separates control logic from network devices, eliminates distributed operations and provides a holistic view of the network, better reflecting the actual network conditions. Leveraging its ability to collect comprehensive network information and create a global network view, the SDN architecture has facilitated the introduction of new optimization techniques, such as artificial intelligence and machine learning (ML) algorithms capable of solving complex problems [9]. In this study, we investigate the integration of Reinforcement Learning (RL), a machine learning algorithm, with SDN technology to propose a novel multi-objective energy-efficient routing protocol for IoT-oriented WSNs. The resulting model, named SDWSN-IoT, comprises a multi-layered architecture with multiple controllers working collaboratively to ensure the flexible management and monitoring of network operations. Moreover, we extract network knowledge to facilitate the intelligent routing of collected data.