# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 3 Proposed Distributed Edge-Based IoT Structure
Fog units are deployed either as standalone entities or are integrated with end devices, and these nodes are characterized by varying levels of mobility, ranging from low to high. In contrast, MEC units are stationary, typically connected directly to IoT gateways or cellular base stations, providing stable and localized computing resources. Integrating edge computing units, i.e., fog and MEC nodes, into the IoT system enhances its computational capabilities, allowing nearby IoT nodes to offload processing tasks. This offloading conserves the energy of the end devices and significantly extends their operational lifespan. Furthermore, deploying edge computing resources enables the implementation of advanced network functionalities, including machine learning (ML) algorithms that require substantial computational power. By processing data closer to the source, edge computing units also facilitate data offloading, effectively reducing the traffic that needs to be transmitted to the core network. This reduction in traffic enhances network efficiency and minimizes latency. The fourth layer is the network layer, which encompasses IoT gateways and access points that serve as the communication backbone of the system. Multiple nodes within this layer can function as network sinks, acting as intermediaries between the end devices and the application server. These gateways can include various devices, such as mobile devices, traditional IoT network gateways, cellular base stations, and specialized hardware kits, all equipped to handle heterogeneous communication interfaces. These interfaces allow the gateways to receive sensed data from end devices, effectively. Gateways within the system are chosen based on their sufficient computing resources and ability to support multiple communication protocols, ensuring reliable connectivity and coverage for IoT end devices. The application layer in the proposed edge-computing-based IoT network is a bridge that links end-user applications with the underlying network infrastructure, handling and processing data provided by IoT devices. This layer evaluates data collected from various IoT sensors and devices, allowing for real-time decision making and control activities. The application layer can minimize latency, improve responsiveness, and increase the efficiency of IoT systems. This is especially important in time-sensitive applications like industrial automation, healthcare, and smart cities, where real-time data processing is critical for efficient operations. Furthermore, the application layer allows for easy connectivity between IoT devices and cloud-based services. It ensures that data are transferred, processed, and analyzed effectively, frequently incorporating data aggregation, filtering, and compression features to maximize network capacity utilization. This layer also facilitates the deployment of edge applications, allowing for localized data analytics and decision making, while decreasing dependency on centralized cloud resources. This decentralization increases the scalability of IoT networks, while improving data privacy and security by reducing the quantity of sensitive information sent to the cloud. Overall, the application layer in an edge-computing-based IoT network is critical for providing intelligent, real-time services, while ensuring the system stability.