## 3 Experiments, Comparative Investigations and Results
On successful detection of weak signals, the proposed CSMA/WSD helps to re-tune the data rate and finds handoff possibilities. Thus, the systems reduce the number of collision-based retransmissions and save the sensor node's energy. In the same manner, [PERSON] [30] implemented a memetic algorithm-meta-heuristic suit for improving the ability of MAC-based sleep and wake operations. The proposed memetic algorithm implemented five steps to identify the possible number of active nodes in sensor networks to avoid the failure rate of data transmissions. The optimal selection of an active node on the wireless channel minimizes excessive consumption of energy. According to the model, the active sensor node selection process is illustrated in Figure 4. Notably, the determination of this memetic approach considers the sensor node's coverage quality and connectivity factors, the remaining energy in the sensor node and the duration of sleep-wake periods. Figure 4 shows the modified genetic algorithm-based memetic node selection approach through population vectors and solution vector computations. In this case, population vectors are computed based on successful sensor node identification marks. To an extent, the solution vectors are computed on the basis of available nodes around consecutive neighbors. In this solution vector, each node's neighbors are updated as child entries to create possible active channels. Under this MAC-based memetic approach, coverage costs, connectivity costs and energy costs are identified with maximum quantity to wake up the nodes from sleep mode. In this connection, each sensor node has been identified with possible solution vector entries (covering sleep nodes), coverage crossover points and sleep-wake mode factors. With this in mind, sensor nodes are searched to enable active node communication channels regularly. This work stated that the modified memetic approach reduces channel allocation time and energy consumption rate for channel allotmen. Figure 3: E1-carrier sense and weak channel detection. Comparing E1 and E2, both works minimize the cost of packet retransmission through the proper handling of collision cases and node availability issues, respectively. In the same manner, E3 [37] provided the integrated channel management and routing solutions using energy-optimized underwater sensor networks. Compared to other types of WSNs (radio frequency), underwater WSNs use acoustic communication models. Acoustic signaling models are vulnerable to significant data loss, maximum propagation delay, restricted bandwidth, limited energy provisions and channel distortions. In comparison with E1 and E2, E3 takes critical channel (water medium) characteristics to establish energy optimization solutions. In the implementation phases, E3 found depth modelling procedures for numerous acoustic sensor nodes. In this work, the AUVNet simulator toll was used to observe the benefit of energy-efficient underwater MAC and routing protocols against other techniques such as the focused-beam routing protocol, distance-aware collision avoidance protocol and cluster-based protocol. In this scheme, multi-path clear-to-send (CTS) and request-to-send (RTS) packets are shared among the acoustic sensor nodes. On the basis of acoustic sensor placement, the score of any node was computed with respect to node distances (\(D\)) and energy levels (\(E\)). The score values are calculated based on Equation (1). \[\mathrm{C}=\mathrm{Dr}+\mathrm{Er} \tag{1}\]  
\[\mathrm{Dr}\rightarrow\frac{\mathrm{Distance}\mathrm{between}\mathrm{node} \mathrm{and}\mathrm{sink}}{\mathrm{maximum}\mathrm{distance}}\]  
\[\mathrm{Er}\rightarrow\frac{\mathrm{Available}\mathrm{Energy}\mathrm{in} \mathrm{node}}{\mathrm{maximum}\mathrm{energy}}.\]  
To a certain extent, the appropriate node-level computations are useful for finding multi-path channels to route the packets. In the multi-layer network protocol tuning process, E4 [39] developed quality of service (QoS)-MAC assisted multi-path routing protocols and data priority computation schemes.