## 3 Experiments, Comparative Investigations and Results
As these works are developed to consider multi-layered network functions (MAC and routing issues) with evenly distributed load management policies, they produce optimal AECR between 1.75 J and 1.85 J. At the same time, the AECR of E2, E6 and E7 fall closely under E1. The reason behind this observation is that the specified approaches consider energy optimization as the main problem. In this regard, E2 used heuristic MAC management strategies to effectively organize the data transmission slots (sleep and wake up node selection). In contrast, E6 and E7 focused on balanced load management on routing procedures to reduce the AECR. In this observation, each existing technique initiated various energy optimization solutions regarding MAC principles or routing protocols. At any rate, the successful engagement of MAC and routing protocol principles of E3 assures minimal AECR. At the same time, other techniques experience a slight hike in AECR where the number of sensor nodes is 300. The variations among the techniques are not huge in AECR, yet the range \begin{table}
\begin{tabular}{c l l} \hline \hline
**No.** & **Configuration Parameters** & **Magnitudes** \\ \hline
1 & Initial energy (joules(J)) & 2.56–2.67 (variable) \\
2 & Transmission energy (J) & 1.06–1.16 (variable) \\
3 & Receiving energy (J) & 0.87–0.99 (variable) \\
4 & Number of sensor nodes & 100, 200, 300 (variable) \\
5 & Antenna type & Omnidirectional \\
6 & Channel and propagation & Wireless (air/water), two-ray \\
7 & Throughput level (Kbps) & 200–250 (variable) \\
8 & Node’s coverage ability (meters (m)) & 30, 40, 50, 60 (variable) \\
9 & Routing protocol & AODV and AODV-LS \\
10 & Signaling modes & Electromagnetic and acoustic \\
11 & Mobility ranges (meters/second (m/s)) & 15, 25, 35, 45 (variable) \\
12 & Simulation cycle time & 100 s \\
13 & WMAC & IEEE 802.11-CSMA-CA \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The environment of WSN. between 1.57 J and 1.95 J shows significant impacts in resource-limited sensor nodes and network lifespan reduction (Figure 8). Figure 9 describes the average liveliness (active) rate of sensor nodes around the WSN. As the network population is increased in the prescribed geographical area (1000 m \(\times\) 1000 m) due to the increasing number of nodes, the frequency of a node's activity increases to manage the neighbor discovery process, data transmission, data collection, route updating process and idle listening process. Hence, each sensor node consumes more energy to accomplish the requested tasks and gradually falls at the critical stage of residual energy. At this point, the need for energy optimization techniques is essential to keep the node live to handle the data transmissions in a dense network field. The illustration given in Figure 9 implicates the fall of the average active conditions of sensor nodes in the network. The proven performance of cross-layered techniques (E3, E4 and E5) shows a better liveliness rate from 0.93 to 0.85 as the number of sensor nodes increases from 50 to 300. In this experiment, E3, E4 and E5 diversely manage their MAC principles by considering channel quality metrics and network dynamics. Significantly, E3 managed both timeline-based channel allocation and route consistencies throughout the increasing number of sensor nodes. In the same way, E5 developed the load distribution and aggressive scheduling procedures. Based on these reasons, E3 and E5 compactly maintained the overall node liveliness rate better than other works. In the next level, E4 achieved an even better liveliness rate (0.83) under a highly populated WSN (300 sensor nodes). In this experiment, other techniques found active node selection procedures (E2-0.81 and E6-0.82) and load-optimized energy control procedures, respectively. This kind of practice improves network lifetime and the active state of nodes (liveliness rate).