## 5 Conclusions  
In the past two decades, energy optimization techniques are considered as one of the challenges of UIoT networks for long-term data transmission. Hence, this paper reviews the articles since 2010 and describes the various energy optimization issues, and state-of-the-art techniques concerning energy optimization in UIoT networks, and highlights the future solutions for energy optimization issues in UIoT networks. In this review, the fundamentals and motivations concerning energy optimization in UIoT networks are described in Section 1, and based on the discussion made in Section 1.1, the research contributions of this paper are pointed out in Section 1.2. Their descriptions is presented in Sections 2-4. Section 2 provides the taxonomy of energy optimization challenges in UIoT networks, including environmental characteristics, technical challenges, design challenges, and other challenges. Along with that, it provides the analysis of existing techniques developed for battery challenges vs. other UIoT challenges in Section 2.4. Section 3 provides state-of-the-art research on various energy optimization techniques in UIoT networks, including underwater wireless power transfer (U-WPT), auto-recharge systems, battery-free sensing approaches, solar charging systems, and battery swapping approaches, etc. In addition, from Section 3.1 to Section 3.6, the systematic review of numerous energy optimization techniques in UIoT networks is summarized in Table 4 and the results are displayed in Figure 10. In Section 4, the significant solutions concerning energy optimization in UIoT networks are highlighted as the future direction of this paper.  
In the future, it will be necessary to adapt energy optimization or power-saving techniques to UIoT networks and devices, including the following: (1) building a multi-medium-based smart energy consumption model, (2) building an auto-recharge power optimization model, (3) building battery-free sensor nodes/battery-less platforms, (4) building a smartenergy harvesting model utilizing UIoT environment, (5) building a machine learning (ML)-based battery management system, and (6) building a standard security model to reduce energy consumption for UIoT networks. In conclusion, to perform energy-efficient communication in UIoT networks and to solve the power optimization issues in UIoT networks, numerous techniques are discussed in Section 4, among which battery-free sensor nodes, automatic recharging using the water particles, and automatic recharging using sea mammal activities are considered as the best solutions for recharging the battery in UIoT environments. Other techniques such as multi-medium based medium selection mechanisms, standard security models for preventing battery attack, AI-enabled power saving models, and ML-based power-optimization techniques are considered as the best solutions for increasing the battery life of UIoT devices and network lifetime in UIoT environments.  
Funding acquisition, D.J.Y.; Investigation, S.-H.P.; Methodology, D.R.K.M. and E.K.; Project administration, S.-H.P. and S.-Y.S.; Resources, D.R.K.M.; Supervision, S.-H.P.; Visualization, S.-Y.S.; Writing--original draft, D.R.K.M. and E.K.; Writing--review and editing, D.R.K.M., E.K., D.J.Y., S.-Y.S. and S.-H.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Polar Research Institute (KOPRI) grant funded by the Ministry of Oceans and Fisheries (KOPRI project No. *PE22900).  
Not applicable.  
The authors declare no conflict of interest.