# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 6 Optimized Resource Allocation Algorithm
## 7 Results

This section presents an evaluation of the developed edge-computing-based IoT system. The communication network performance is tested under various scenarios and metrics. ### Experimental Setup

The proposed system was evaluated using the CloudSim environment. A two-level edge computing IoT network was considered, and the topology is introduced in Figure 1. The network parameters are presented in Table 2. We considered the offloading model developed in [5]. The offloading is carried out over one of three main layers: end-device, fog, and MEC. Tasks may be handled at the end devices if there are resources available; however, tasks are offloaded in case of a lack of resources. Each device is assumed to deploy a decision engine that calculates the time and energy required for task handling, and then makes the decision of offloading. Four systems were considered for the evaluation process. The first system represents traditional IoT without deploying the edge computing paradigm. The second system has one-level edge computing for IoT, as distributed fog nodes assist IoT sensors on their way to the application server. The third system is the system proposed in [5], in which the authors integrate IoT with 5G networks, addressing dense deployments, scalability, and availability. The architecture incorporates SDN, D2D communication, and MEC. The fourth system is the proposed two-level IoT network with fog and MEC servers deployed in the path between IoT sensors and the application server. A network of heterogeneous devices is distributed over an area of 10 m \(\times\) 10 m. The network was simulated various times for different numbers of deployed sensors. This was intended to test the effect of dense deployment of devices on the performance of the developed fog/MEC-IoT. Each device was assigned a computing task equivalent to the workload of real tasks. In our simulation, we used three unique workload categories, each representing a different type of application often seen in IoT networks. These categories were carefully chosen to represent a wide range of application needs, allowing to assess the proposed system performance under various scenarios. The first group consisted of basic programs characterized by light workloads that demand low-to-medium computing power. These applications are typically found in scenarios where data collection is steady but not resource-intensive, such as environmental monitoring systems, simple IoT sensor networks, or basic telemetry setups. These programs prioritize efficient data gathering and processing, while maintaining a balance between performance and resource utilization. The focus of this category is to assess the system ability to handle tasks with minimal computational complexity, effectively, ensuring operational energy efficiency, reduced latency, and reliable data delivery. This group also serves as a baseline to evaluate the system responsiveness to routine tasks, its capacity to scale under light demand, and its suitability for use in power-sensitive or bandwidth-constrained environments. Furthermore, it lays the foundation for understanding how the system architecture can support more demanding workloads in categories with higher complexity. The second category of workloads was introduced to simulate more complex applications that generate moderate volumes of data, while demanding higher computational power compared to basic tasks. This category encompassed scenarios such as smart home automation systems, industrial monitoring setups, and real-time data analytics applications. These systems require swift data processing to enable features like automated decision making, predictive maintenance, or adaptive control while operating within the constraints of edge computing resources. By focusing on this category, the evaluation aimed to assess the system ability to efficiently allocate computational resources and manage network bandwidth under moderate demands.