## 2 Materials and Methods
### Technical Discussions on Related Works
#### 2.1.2 WMAC Strategies with Energy Optimization Techniques
In this scheme, cluster heads are selected with the help of the Harris hawk optimization protocol and dynamic clustering protocol. In addition, this technique has been extended with the dynamic routing protocol to reduce the sensor node's energy depletion rate. The implementation section of this work has provided performance metrics such as network energy depletion rate, lifespan of sensor nodes, packet transmission rate, network coverage factor, etc. At any rate, this contribution is limited to node selection procedures rather than channel organizing policies. Energy-efficient MAC models widely apply sleep and wakeup mechanisms to increase the lifespan of nodes in WSNs. The sleep and wakeup protocols work based on reactively initiated transmission schedules for multi-path channels. [PERSON] et al. [30] and [PERSON] et al. [31] proposed energy-efficient sleep and wakeup scheduling techniques in each sensor node to save energy. Particularly, the former work uses memetic techniques for organizing WMAC by determining the qualities of energy consumption rates of sensor node, coverage factor, neighbor connectivity rate and optimal wakeup duration for each channel. Accordingly, this technique ensures the benefit of energy control and liveliness of sensor nodes in WSNs. The latter work analyzes the architectures of ad hoc sensor networks and establishes quorum-assistive sleep and wakeup protocols for organizing WMAC policies. In this case, the quorum properties of each sensor node are validated and slots are activated to minimize energy expenditure. However, both works are limited with WMAC perspectives. The efforts in producing sleep and wake models, duty-sensitive scheduling procedures and adaptive channel utilization with WMAC continue for various types of WSNs [32; 33; 34]. Moreover, the standpoint of energy-optimized WMAC establishment is a required aspect for all researchers. [PERSON] et al. [35] discussed impacts on energy diffusion rate and the network disturbances. According to this model, the network irregularities and traffic turbulences happened due to the excessive diffusion of energy in each node. At the time, the underprivileged deliberations of MAC management rules and irregular load distribution among sensor nodes were creating energy losses in each node. Hence, the entire WSN met communication problems. This work provided the experimental analysis cases for different types of WSNs such as cluster-based environments and random environments around different sizes of geographical regions. Under this testbed, this experiment revealed issues with the lifespan of each sensor node and the downtime of each node. This experiment helped to study the stability of the overall network during continuous energy drops. In the same way, a few other research works tried energy-efficient MAC models in static WSNs [36]. [PERSON] et al. [37] recommended a novel energy-controlled MAC and routing protocol for underwater sensor networks. Generally, underwater sensor networks use acoustic signal propagation models and resource-constrained sensor nodes. Unlike other sensor networks, underwater sensor nodes are not feasible to be charged regularly through any modes (electricity or solar power). The hostile nature of underwater sensor networks requires more efficient energy-saving mechanisms for both MAC protocols and routing protocols. This scheme used network properties such as finite energy limits, a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Collision Models** & **MAC Models** & **Channel Allocation** \\ \hline CSMA/CD & MAC & TDMA \\ CSMA/CA & WMAC & TDMA-R \\ CSMA/CN & S-MAC & SEP \\ CSMA/WSD & DS-MAC & AEA \\  & T-MAC & NDP \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Collision models and MAC models [28]. multi-hop communication session, narrow transmission possibilities, sleep mode executions and a uniform energy utilization factor. The observations of this work revealed the better utilization of channels and routing sessions to improve the quality of energy optimization principles.