## 4 Comparative Analysis of Proposed Solution with EADUC and TLPER
Figure 6 shows that the cluster design part is an indispensable phase in considering energy-aware routing. In a network of 500 nodes in the area of 500 m \(\times\) 500 m, about 41 J of energy is consumed just in designing the clustered network while implementing the EADUC algorithm. This consideration is reflected more when comparing the energy consumption of the three competing algorithms during the cluster designing process (Figure 6). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Routing Protocols & EADUC, TLPER, EAR4 MCDA (Proposed) \\ \hline Simulation Area & 500 m \(\times\) 500 m \\ \hline Simulator & NS 2.31 \\ \hline Data Rate & 4 Packets/S \\ \hline TCP/IP Layer & Network Layer \\ \hline Node to Node Distance & Random \\ \hline Node Type & Homogenous \\ \hline No. of Nodes & 500 \\ \hline Propagation Model & Two ray ground \\ \hline Initial Energy of Node & 3 J \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 6: Total energy consumption in cluster design. Figure 7 demonstrates this effect with 29% better performance of the proposed algorithm over TLPER and 50% over EADUC. Intra- and inter-cluster routing makes it possible for this sensed data to reach the destination to end the routing. The energy efficient selection of forwarding nodes plays a vital role in the overall energy-aware routing process, as this selection decision has an effect on path length, node congestion, throughput and consequently energy conservation. In this underlying research article, we have evaluated the selection of FN with respect to energy consumption only. Figure 8 shows the competitive energy consumption factor in forwarding the data to the FN. EAR4 MCDA presents the innovative idea of having a list of selected FNs (three in advance) at each node during the cluster design process, so no extra energy conservation results at this point, while in case of TLPER and EADUC, each node that intends to forward the packet needs to collect decision metric information from its neighbor nodes. From this FNS, the most suitable node having the highest decision metric precedence is selected (vicinity head in the case of TLPER). All this process really squeezes the nodes' energy, which deteriorates the overall performance of the algorithm. This is ultimately reflected in the form of higher energy consumption. In this regard, the performance of EAR4 MCDA is 39% and 80% better than TLPER and EADUC, respectively. On comparing the performance of TLPER and EADUC, we come up with the result of 68% outperformance of the former over the latter. Figure 8: Average energy consumption in forwarding node selection. Figure 7: Performance efficiency of EAR4 MCDA over competing algorithms in total energy consumption. In TLPER, FNS may comprise the next CH, assistant CH and BS. The current node selects any one among them that is within reach and communicates the data to it, while in case of EADUC, the current CH disseminates a query message among its neighbor nodes and collects their distance to the BS and their energies. If more than one node has the same proximity to BS then their residual energy is used to break this tie. The effect of this high energy consumption in FN selection makes the overall communication expensive, since the FN in the path makes up a route for packet dissemination from source to sink. Figure 9 illustrates this point. Expensive FN selection in EADUC results in more costly communication of a data packet from end to end. In the graph, the resulting value bar for EAR4 MCDA is far smaller than that of EADUC due to the aforementioned reason, though the path length and other factors also matter, so to make a fair comparison, these calculations are taken for the same path length. Network size, deployment area and node deployment style are almost the same, so the number of designed clusters must certainly be the same but the different cluster design strategies of these algorithms make the difference.