# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 6 Optimized Resource Allocation Algorithm
This involves balancing the trade-off between processing power, energy consumption, and network usage to ensure optimal application performance. The emphasis was particularly on maintaining low latency, as many of these applications rely on near-instantaneous responses for functionality and reliability. Additionally, this category helps gauge the system scalability and adaptability when handling fluctuating workloads that are more demanding than light applications but still constrained by edge infrastructure limitations. It also provides insights into how the architecture supports real-world use cases requiring a harmonious integration of computational efficiency and network resource management. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network area & \(10\times 10\) m\({}^{2}\) \\ \(N\) & \(1,2,3,\ldots,10\times 10^{2}\) \\ \(M\) & 10 \\ \(K\) & 5 \\ Packet size & 128 Bytes \\ Processing resources of IoT device & \(\epsilon\) [0.1,0.3] GHz \\ Processing resources of Fog & \(\epsilon\) [0.5,1.0] GHz \\ Processing resources of MEC & \(\epsilon\) [1.5,2.0] GHz \\ Frequency band & 868 MHz \\ Energy dissipated per bit & 50 nJ/bit \\ Radio link & IEEE802.15.4 \\ Bit rate & 250 Kbps \\ Max. workload (fog) & 20 events/s \\ Energy consumption per CPU cycle (IoT device) & 1.2 J/CPU Cycle \\ Energy consumption per CPU cycle (Fog) & 1.15 J/CPU Cycle \\ Energy consumption per CPU cycle (MEC) & 1.0 J/CPU Cycle \\ Max. workload (MEC) & 50 events/s \\ RAM, Storage (IoT) & 16 MB, 0.125 GB \\ RAM, Storage (Fog) & 64 MB, 0.5 GB \\ RAM, Storage (MEC) & 256 MB, 2 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. The third category comprises high-demand applications characterized by massive data generation and intensive real-time processing requirements. Examples include self-driving vehicles, advanced healthcare monitoring systems leveraging AI for diagnostics, and industrial automation integrated with machine learning for predictive analytics and real-time control. These applications represent the upper echelon of IoT workloads, where computational power, data transmission speed, and system reliability are critical to ensuring functionality and safety. This category was specifically introduced to evaluate the proposed system ability to handle extreme workloads while achieving optimal energy efficiency, maintaining stringent reliability standards, and delivering ultra-low latency responses. These performance benchmarks are essential for applications, where even minimal delays or inaccuracies can have significant consequences, as in autonomous driving or critical patient monitoring. By simulating this category, we aimed to assess the system scalability and robustness under peak stress conditions, determining how effectively it can manage resource allocation and prioritize tasks without compromising performance. Additionally, the insights gained from these evaluations offer a deeper understanding of the architecture limitations and potential areas for optimization. This category also highlights the system potential to support emerging technologies and next-generation IoT applications, aligning with the stringent demands of modern high-performance systems in sectors like transportation, healthcare, and smart manufacturing. Various performance metrics were considered for the evaluation process. They include energy consumption, data latency, network congestion, and resource utilization efficiency. The energy consumed by IoT nodes was recorded for each node during the considered scenarios. The average energy consumption was used to compare the three considered systems to indicate which one has the highest energy efficiency and the percentage improvement of energy efficiency.