## 2 Materials and Methods
### Technical Discussions on Related Works
#### 2.1.2 WMAC Strategies with Energy Optimization Techniques  
Wireless communication techniques, medium access principles and channel modelling strategies are common in research aspects. At the same time, finding suitable techniques and proposing novel techniques with reliability are challenging tasks. [PERSON] et al. [28] implemented the variances of MAC protocols such as carrier sense multiple access/collision avoidance (CSMA/CA), the sensor MAC (S-MAC) protocol, weak channel or signal detection policies, the timeout MAC (T-MAC) protocol and other variances. This experimental view of MAC policies extracts the functions of each MAC model including delay-sensitive MAC (DS-MAC), energy-sensitive time division multiple access (TDMA) policies and tree MAC policies. According to the details given in this work, MAC models can be expressed as shown in Table 3. In this case, collision models such as collision detection (CD), collision notification (CN) and weak signal detection (WSD) are experimented. Similarly, reduced TDMA (TDMA-R), schedule exchange protocol (SEP), adaptive neighbor election algorithm (AEA) and neighbor discovery protocol (NDP) are evaluated. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Related Articles** & **Strategies** & **Problems Considered** \\ \hline [PERSON] et al. [7] & Cyber security solutions and challenges using light weight methodologies & Network security and energy-efficient cyber security \\ [PERSON] et al. [8] & Physical layer interception probability model & Physical layer and channel-trapping attacks \\ [PERSON] et al. [9] & Heterogeneous social spider energy optimization and coverage scheme & Energy optimization, network coverage and neighbor identification \\ [PERSON] et al. [10] & Optimized cuckoo search and locality management & Node localization error and neighbor identification \\ [PERSON] et al. [11] & Propagation latency and delay prediction with energy optimization & Acoustic signaling and underwater energy \\ [PERSON] et al. [13] & Nature-inspired algorithms (grey wolf & Energy optimization for IoT systems \\ [PERSON] et al. [14] & HVAC scheduling and energy plans (Wi-Fi) & Energy optimization for Wi-Fi systems \\ [PERSON] et al. [15] & Data compression and channel modelling systems & Energy optimization for body sensor networks (cardio health care systems) \\ [PERSON] et al. [18] & Multi-modal resource allocation and load balancing systems & Energy optimization for agriculture IoT sensors \\ [PERSON] et al. [20] & Smart energy plans for fifth-generation IoT systems & Network lifetime and energy plans \\ [PERSON] et al. [21] & Artificial-IoT systems with energy-efficient scheduling frameworks & Energy optimization and channel timelines \\ [PERSON] et al. [22] & A review on ML-based smart sensor platforms & Current and future challenges in WSNs \\ [PERSON] et al. [23] & A review on routing protocols, energy problems, lifetime and localization & Routing and neighbor monitoring challenges \\ [PERSON] et al. [24] & Low-cost link establishment using cuckoo neural network System & Network lifetime and nonlinear network \\ [PERSON] et al. [25] & Multi-attribute learning and secure sensor modelling system & Wireless security, uncertainty and mobility models \\ [PERSON] et al. [26] & Green energy modelling, future load forecasting and energy balancing system & Load balancing, energy optimization, delay computing and distance management \\ [PERSON] et al. [27] & Edge computing and energy modelling for smart city & Green energy and edge models \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._However, this experiment limits the assumptions in MAC policies only for energy wastage in WSNs. This assumption shall be extended into routing policies with the considerations of various technical glitches of wireless routing protocols. [PERSON] et al. [29] discovered a novel energy-efficient network architecture with cluster head coordination principles and hot-spot analysis procedures.