## 3 Experiments, Comparative Investigations and Results
In addition, the practical comparison between more recent energy-efficient routing protocols gives diversified solutions to the research community. In this regard, this proposed review article extends the evaluation of E9 [51], E10 [54], E11 [55] and E12 [56] under the considerations of energy optimization and multi-hop routing principles in WSNs. The most recent ideologies on novel energy-optimized routing protocol development and relevant discussions lead to future-generation WSN energy models [91; 92]. In this concern, E9 developed swarm-intelligence-based chimp optimization solutions and hunger game searching principles to find energy-efficient multi-hop routing paths. This work followed the natural habits of chimps to optimize path-finding problems with minimal overhead. Particularly, the first phase of the chimp optimization algorithm initiated the network formation under a hierarchical structure (base station and clusters). The clusters were formed as driving nodes, chaser nodes, barrier nodes and attacker nodes using chaotic cost computations. In the same manner, the second phase of this work provided a hunger search-based path selection approach for initiating an energy-efficient multi-hop routing process in underwater sensor networks. E10 proposed energy classification and channel assessment techniques using a greedy approach for minimizing the overload of the node's energy resources. According to the strategies, each sensor node's energy levels are monitored with adaptive internal buffer management policies on the reception of data packets. Similarly, the routing protocol used for this mechanism found the greedy-based route selection with sufficient node resources to avoid packet losses. In this connection, E11 and E12 considered coverage problems and locality problems, respectively. Particularly, E11 proposed link stability evaluation protocols and grid-level stimulated network models to achieve coverage optimization in WSNs. In this concern, Figure 14 illustrates the functions of link stability evaluation and coverage problem analysis models (holes or inactive nodes). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Iterative Test Cycles** & **E1** & **E2** & **E3** & **E4** & **E5** & **E6** & **E7** & **E8** \\ \hline
5 & 0.278 & 0.36  
Finally, E12 proposed zone-based routing protocols and energy optimization policies throughout the WSNs. In this concern, each sensor node was constructed under various zonal locations with allotted energy resources to be associated with other zonal sensor nodes. The proposed routing protocol running in each sensor node evaluated residual energies to proceed data routing into other zones. Thus, various recent routing protocols were proposed under energy consideration platforms. These works are compared as illustrates below. Figure 15 depicts the performance comparison between E9, E10, E11 and E12 in terms of AECR against changing number of sensor nodes. In this experiment, four different types of energy-efficient routing protocols are compared. E9 has been experimented for underwater sensor networks. Compared to other sensor networks, underwater sensor networks use acoustic sensors with minimal energy resources. In this concern, E9 developed hierarchical energy optimization solutions and chimp optimization policies in order to search active nodes to enable flawless communication. In this case, the network clustering process and node searching processes consume significant energy (1.93 J). On the other hand, protocol implemented in E12 consumes maximum energy as it is related to zonal computational policies. At any rate, E10 and E11 are optimally designed for improving the quality of energy-saving mechanisms via the greedy approach and link stability validation approach. In this comparison, E11 ensures network coverage optimality and link stability concerns to operate successful data transmission from source to destination. In this analysis, E11 provides more stable and optimized channel circumstance to reduce packet drops and retransmissions.