## 4 Discussion  
An evaluation of the PECR algorithm is needed to check its competitive results for a WSN-assisted IoT network. Four existing algorithms that have the same simulation parameters and environment are chosen to compare the results. The existing algorithms involved in this evaluation are LEACH [6], EC [13], EECRP [19], and EECA [27]. Then, the simulations are conducted utilizing similar factors and platforms for a fair comparison. The simulation results are analyzed based on three evaluation criteria to compare the throughput of the PECR and the other three algorithms. The first evaluation criterion is the sum of alive nodes that demonstrate the sensor nodes participating in the data transmission effectively. The sum of alive nodes presents the absolute life period of the network which is the main imperative in routing protocols. Figure 6 shows a huge contrast in the general number of alive nodes until the fruition of rounds. LEACH has the worst throughput among all because of a plenty of control messages and the absence of a middle point between the CHs and the BS. After 800 rounds, it does not have adequate alive nodes. EC has better execution when contrasted with LEACH, yet not as viable. In both EC and LEACH, the probability model does not ponder about leftover energy. There are a more noteworthy number of chances that it would choose a low-energy CH. This prompts fast energy depletion, and the nodes are not alive after 1000 rounds. EECRP works in a single-hop mode, which causes the energy exhaustion of the CHs. It does not continue after 1100 rounds. EECA has a more prominent number of rounds when contrasted with the past three calculations. However, it could not continue after 1300 rounds. The PECR algorithm outpaces it, as displayed in Figure 6. The initial node expires almost at the 800 th round. Clustering and the multi-hop optimal way selection guarantee the balanced utilization of energy assets. It outperforms LEACH by 55%, EC by 52%, EECRP by 32%, and EECA by 26%. Figure 7 portrays the second evaluation criterion, which is during 1200 rounds, the quantity of sent data packets is effectively obtained by the BS. LEACH has a data packet delivery ratio close to 3 \(\times\) 105. It does not look at the leftover energy of the functional nodes while choosing the CHs, which causes quick energy exhaustion and fewer rounds. EC does not look at the distance all through the course of the midpoint determination, which means extra energy utilization and less successful transmission. Figure 5: Total network energy usage. EC shows a 3.5 \(\times\) 105 ratio. EECR somehow has better outcomes because of its protective procedure [5] and winds up to 4.5 \(\times\) 105, while EECA winds up to 6.2 \(\times\) 105. Both EECR and EECA are poorer than the PECR as it consumes more energy in a single jump transmission. The PECR shows more effective performance, winding up to 10.5 \(\times\) 105. This is because of the optimal multi-hop transmission mode and energy utilization methodology. The result portrays PECR as having 62.5% preferable performance over LEACH, 58% more than EC, 50% more than EECRP, and 35% more than EECA. The third significant assessment metric is the remaining energy. Right away, all sensor nodes have an equivalent energy level that is 0.5 J, which is fixed in case there were no transmissions. As displayed in Figure 8, LEACH has a quick energy consumption level because of a probability-based model and all nodes become lapsed at just about 600 rounds. EC checks the proficient utilization of energy and the dynamic clusters. However, it does not ensure the position of the CHs. In this way, EC closes at right around 800 or a few more rounds. EECRP shows better execution because of clustering in light of the distance through the energy centroid. However, the CHs have direct correspondence with BS that closes EECRP at very nearly 900 transmission rounds, while EECA closes at just about 1000 transmission rounds. The PECR algorithm wins in terms of the management of energy utilization.