# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 2 Background and Related Work
To achieve these objectives, IoT networks must enable seamless interaction and data exchange across devices and applications across several networks. This necessitates the adoption of technologies and services that can fulfil the unique needs of various IoT applications. The design of IoT network topologies is particularly crucial, since they must allow for the individual authentication of billions of unique devices across numerous communication protocols and administrative domains, while maintaining security, dependability, and privacy. Reliable, energy-efficient communication links are required to ensure the network overall resilience. The presence of numerous diverse devices in worldwide IoT networks and the intricate interconnections facilitated by collaborative and dispersed methodologies pose considerable difficulties [18; 21]. To tackle this issue, distinctive models and design approaches must be created, possibly drawing inspiration from simulation techniques used for managing complex dynamics of IoT ecosystems on a large scale. To address the latency concerns of IoT applications, the authors of [22] developed a platform that uses a fog computing layer managed by a software-defined network (SDN) to ensure high availability and dependability. Distributed controllers and OpenFlow switches with limited resources are part of the SDN infrastructure. Blockchain technology is employed to guarantee decentralization reliably. In addition, the authors designed a data-offloading mechanism to distribute processing and computing workloads. The presented approach was tested both virtually and in a physical environment. It was shown to be more efficient in terms of latency. However, the work did not consider other performance metrics to ensure reliability and availability. Moreover, the authors have not investigated network scalability. To connect various IoT networks, the authors of [5] presented an approach for connecting IoT with 5G networks. ITU and 3 GPP have declared standards for dense deployment, scalability, and availability, all of which were considered by the presented approach. The suggested architecture used SDN, device-to-device (D2D) communications, and MEC. It was tested in a secure environment under various deployment methods, and the obtained results ensured the soundness of that architecture. The presented IoT/5G blocked tasks were cut by an average of 30 percent compared to other standard IoT networks. More devices and jobs can be added to an IoT network, expanding its availability and scalability compared to preexisting IoT networks. However, the scalability of the presented model was limited to MEC servers, even with a multilevel structure. In the presented work, the authors deployed another edge computing paradigm (i.e., fog), which extends the network scalability. In [11], the authors investigated the feasibility of creating a unique AI-based framework for making traffic predictions across densely-deployed IoT networks. This helps with traffic control and preventing overloaded networks. The created AI technique was a lightweight convolutional-neural-network-based deep learning model designed to be implemented by an edge node with limited computational capabilities (such as a fog node). The model depends on distributed edge computing to facilitate dense deployment and improve network availability, dependability, and energy efficiency. This work was mainly intended to develop an AI model for traffic prediction rather than consider the network structure. The role of edge computing was to implement the presented algorithm; however, other edge roles have not been investigated. In [23], the authors suggested an effective way to install IoT applications on the fog network using autonomic service placement methods. The presented approach uses the whale optimization algorithm (WOA) meta-heuristic technique to keep tabs on IoT services' QoS needs.