## 5 Conclusions  
In summary, our proposed routing protocol, DOS-RL with shaped rewards, offers a dynamic objective selection mechanism that optimizes the energy efficiency of SDWSN-IoT networks. By allowing for adaptive routing objectives based on correlated objectives, our approach significantly enhances the network operation efficiency. The inclusion of additional shaping rewards speeds up the learning process, surpassing the performance of traditional Q-Learning and OSPF algorithms in SDWSN-IoT routing. Leveraging the power of DOS-RL in intelligent routing, our scheme discovers reliable and energy-efficient routing policies, yielding a remarkable performance. One notable benefit of our approach is its ability to incorporate various performance metrics related to Quality of Service (QoS) requirements into the reward function. This inclusion not only accelerates learning but also enables the system to make well-informed routing decisions, reflecting specific QoS needs. The implications of our research are profound. Our proposed routing protocol holds the potential to revolutionize SDWSN-IoT networks by significantly enhancing energy efficiency and the overall network performance. It opens doors to applications that demand efficient routing, reduced energy consumption, and reliable communication. As for future work, we are committed to further investigating the impact of different parameters on the performance of the DOS-RL algorithm. Additionally, we intend to explore alternative methods for speeding up the learning process while striking an acceptable balance among  
Figure 7: Energy efficiency performance. multiple objectives. These efforts aim to continually enhance the efficiency and overall performance of our proposed routing protocol in SDWSN-IoT networks. Conceptualization, D.G. and B.S. ; methodology, D.G., B.S. and B.H.L. ; writing--original draft preparation, D.G., B.S. and K.-C.L. ; writing--review and editing, K.-C.L. and K.-I.K. ; supervision. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government through MSIT under Grant RS-2022-00165225, and in part by the Institute for Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea Government through MSIT [Convergence security core talent training business (Chungnam National University)] under Grant 2022-0-01200. Not Applicable. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The role of machine learning and the internet of things in smart buildings for energy efficiency. _Appl. Sci._**2022**, _12_, 7882. [CrossRef]
* [PERSON] (2021) Suryadevara, N.K. Energy and latency reductions at the fog gateway using a machine learning classifier. _Sustain. Comput. Inform. Syst._**2021**, _31_, 100582. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Reinforcement learning-enabled cross-layer optimization for low-power and lossy networks under heterogeneous traffic patterns. _Sensors_**2020**, _20_, 4158. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] Deep learning for intelligent wireless networks: A comprehensive survey. _IEEE Commun. Surv. Tutor._**2018**, _20_, 2595-2621. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine learning in wireless sensor networks: Algorithms, strategies, and applications. _IEEE Commun. Surv. Tutor._**2014**, _16_, 1996-2018. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine learning for networking: Workflow, advances and opportunities. _IEEE Netw._**2017**, _32_, 92-99. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] A survey of AI-based anomaly detection in IoT and sensor networks. _Sensors_**2023**, _23_, 1352.