## 2 Materials and Methods
### Technical Discussions on Related Works
#### 2.1.1 Wireless Sensor Networks and Challenges
Energy-focused research is driven towards WSNs in order to extend network lifetime and node availability. Reliable energy considerations and energy-optimized computing frameworks are deeply analyzed with body sensor networks and IoT systems, respectively [12; 13]. IoT architectures and software-defined networks depend highly on minimal energy consumptions under WSN pitches. [PERSON] et al. [14] provided the necessity of heat, ventilation and air conditioning (HVAC)-based communication schedules for saving the node's energy in wireless local area networks (WLANs). The technical diversity provided in the energy optimization field was mandatory for specific wireless communication technologies. The application of WSNs decides the required level of energy savings during data communication. With this in mind, [PERSON] et al. [15] proposed intelligent energy optimization techniques for cardio sensor networks. In this work, [PERSON] et al. identified the use of sensor data compression possibilities against energy wastage. In the same way, many research works have been conducted to produce green computing application using WSNs [16; 17]. The need for green computing technologies and energy optimization principles are mandatory for the innovative applications of WSNs such as IoT, smart city systems, intelligent farming systems, fault-tolerant energy models, edge computing systems and smart learning systems [18; 19; 20; 21; 22]. The well-defined energy optimization technique must focus on both WMAC policies and routing policies to save the node's energy. Specifically, the energy wastage of every sensor node happens during active listening, passive listening, data transmitting and data receiving activities. Under these unavoidable circumstances, an efficient solution has a huge impact on each sensor node to save energy. In this manner, [PERSON] et al. [23] proposed machine learning (ML) techniques for enabling intelligent routing processes to reduce overhead and energy consumption rates. This work found the benefits and limitations under ML-based routing solutions. The benefits were noted, with this technique providing the optimal routing process with minimal latency. At the same time, the limitations were considered as how to develop lightweight ML algorithms in order to reduce the computation overhead and energy consumption rate. The detailed comparisons are illustrated in Table 2. The increasing demand of sensors and WSNs through military equipment, drone applications, IoT environments and secure sensor environments must be addressed with energy optimization models and load management models [24; 25; 26; 27]. The detailed review about WSNs, deployment issues, MAC policies, channel assignment policies and routing schemes identifies the following research problems and future scopes:  
* Energy-efficient WMAC principles are required for application-specific sensor nodes. * Energy optimization and green computing models are expected for increasing the lifetime and availability of WSNs. * Reactive energy-saving routing protocols and on-demand channel establishment strategies are estimated. * Channel quality determination and reactive scheduling mechanisms are required to build green computing platforms for future WSN architectures. \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Related Articles** & **Strategies** & **Problems Considered** \\ \hline [PERSON] et al. [1] & Energy optimization using disjoint and connected dominating sets & Energy optimization and live connectivity identification Connectivity, lifetime and mobility and lifetime management (Linear programming and serial channel parametric analysis) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: WSNs and recognized challenges. As mentioned above, the basic identifications and expectations of WSNs and their recognized challenges should be the motive for any researcher.