# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 6 Optimized Resource Allocation Algorithm
Figure 4: Average latency of handling IoT data in each considered network, (**a**) first category of applications, (**b**) second category of applications, and (**c**) third category of applications. Figure 5 indicates each system average percentage of resource utilization in each simulation case. Resource utilization includes processing and storage resources. The performance of utilizing resources over the network was measured for the three considered systems with the three categories of workload utilized. For simple computing, i.e., the first category of the workloads, the utilization efficiency of the second and third systems was almost the same, since these services require fewer computing capabilities, and even if there is no professional management scheme, the system can perform well. However, for complex tasks (see Figure 5c), the proposed model outperformed the other two systems due to the proposed resource allocation strategy. The proposed network with the considered offloading scheme achieves the highest utilization efficiency, since resources are arranged in two levels and managed using an efficient offloading scheme. Figure 5: Resource utilization efficiency, (**a**) first category of applications, (**b**) second category of applications, and (**c**) third category of applications. ## 8 Discussion

The results obtained were statistically analyzed to compare the three systems' performance levels. Furthermore, this section introduces the percentage improvement in the considered performance metrics for the proposed model compared to traditional IoT networks. We used the GraphPad Prism (10.4.1) platform to statistically analyze the results of the three systems. The three systems were compared using the one-way ANOVA test. Figure 6 shows a comparison of the energy efficiency of the three systems. The proposed fog/MEC-IoT consumes less energy than those of other systems. It achieves a 19% enhancement in energy conservation. Also, the developed model reduces network congestion by an average of 92% compared to the traditional IoT networks. Figure 7 presents the average value of the percentage of traffic passed through the core network of each system. Moreover, the developed fog/MEC-IoT network utilizes the resources more efficiently than fog-based IoT and traditional IoT networks. Figure 8 provides the mean of resource utilization efficiency for each considered system. The proposed fog/MEC-IoT network achieves a 54% improvement in resource utilization compared with the IoT without edge computing nodes. The introduction of edge computing nodes in the proposed model reduces the communication distance, which reduces the overall latency of handling data with the deployed offloading scheme. Figure 9 provides the average latency required to handle data in each considered system. The proposed fog/MEC-IoT reduces the latency by an average of 86% compared with the IoT networks. Figure 6: Statistical analysis of energy consumption of the three systems. Figure 7: Statistical analysis of network congestion of the three systems. ## 9 Conclusions

Distributed edge computing can be deployed to empower IoT networks by providing resources near sensors on the way to the application server. This work provided a novel IoT network structure based on distributed edge computing technology. The proposed network depends on fog computing and MEC at two levels on the path between end devices and the application server. The proposed network was modeled, and an optimized resource scheduling approach was developed to assist in its performance. The developed network was evaluated using recorded IoT data for real scenarios. The results indicate that the proposed network achieves a higher energy efficiency by an average of 19% and a reduced core network congestion by an average of 92% compared to traditional IoT networks. Furthermore, the proposed fog/MEC-IoT network utilizes resources more efficiently and allows reduced communication latency.