# Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement
## 3 State-of-the-Art Approaches for MAC-Protocols Optimization
### Machine Learning-Based Approaches  
The literature contains numerous approaches for MAC scheduling based on exploiting the power of the neural network. In the work of [53], a recurrent neural network was proposed to model the channel occupation and predict the best time for transmission. The authors used software-defined radio to monitor WiFi- channel and perform the training. This work can be criticized based on the significant stochastic component of the channel, which is modeled by the neural network inefficiently and lacks convergence. The usage of neural networks for scheduling has been studied in terms of 5G networks for scheduling multi-cell channel accessibility and minimizing user interference.  
In the work of [54], an allocation was carried out for channel resources among multiple small cells to enable scheduling an uplink or downlink for users by each cell in a time-slot. In most NN-based scheduling, neural networks were used to approximate the resource evolution and predict the moment and configuration of transmission for the node. For instance, in the work of [55], frequency and time-slot resources are allocated for sensors based on an NN trained to predict the best channel-slot pair for transmission--i.e., the one that is associated with the minimum resource usage. In [56], cognitive radio technology and machine learning approaches are discussed, emphasizing their roles in increasing the wireless communication network's spectrum and energy efficiency. [PERSON] and [PERSON] [57] proposed a hybrid medium access protocol (MAC) for gathering data in a UAV-based wireless network on overcoming the transmission time constraints between nodes by collecting and processing data from each node and acting accordingly.  
Table 5 presents a summary of machine learning-based approaches that have been proposed for enhancing the performance of the MAC protocol for WSN.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference Paper** & **Approach** & **Type** & **Contribution** & **Results** \\ \hline \multirow{4}{*}{[52]} & Evolutionary Algorithm & & Particle swarm & \\  & & & optimization was & \\  & Evolutionary Algorithm & & integrated with the & \\  & for Scheduling in WSN & & evolution algorithm for the & \\  & & & goal of minimizing & \\  & & & the delay & \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference Paper** & **Approach** & **Type** & **Contribution** & **Results** \\ \hline \multirow{8}{*}{[53]} & & & Makiuchi is a cognitive MAC & \\  & & & protocol that uses a recurrent & \\  & & & neural network to & \\  & & & represent channels & \\  & & & occupied and determine the & \\  & & & precise moment of & \\  & & & transmission opportunity. & \\  & & & The data used for the & \\  & & & training process came from a & \\  & & & real-world experiment that & \\  & & & used Software-Defined & \\  & & & Radio (SDR) to monitor a & \\  & & & Wi-Fi channel & \\ \hline \end{tabular}
\end{table}
Table 5: Machine learning-based approaches.