# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 2 Background and Related Work
To further illustrate the interplay between the primary elements of the IoT and the fog layers during deployment, the authors created an autonomous service placement framework based on a three-tier architecture of the fog ecosystem. Based on the simulation findings, the presented approach improved resource utilization, service acceptance ratio, latency, and energy consumption. [PERSON] et al. [24] considered the energy efficiency (EE) in heterogeneous networks (HetNets) using a comprehensive strategy. The study commenced by constructing a fundamental framework utilizing a two-tier network architecture. This configuration enabled the creation of a customized energy efficiency model that determines the impact of randomly-placed base stations and users. The model enhanced energy efficiency and upheld QoS criteria by optimizing the joint densities of base stations and customers. Expanding upon this paradigm, the study presented a new and innovative dynamically distributed opportunistic sleep approach (D-DOSS), specifically tailored to enhance energy efficiency. This approach involves grouping base stations around the network and modifying their sleep cycles in response to current traffic load criteria. The efficacy of the D-DOSS approach is thoroughly assessed by Monte Carlo simulations in MATLAB, showcasing substantial enhancements in crucial QoS metrics. This research significantly contributes to optimizing energy efficiency in HetNets by addressing network architectural optimization and unique sleep management algorithms. It offers practical ideas for improving energy efficiency in future wireless networks. In [8], the authors examined how a distributed edge computing scheme and light fidelity (LiFi) might coexist in designing a generic framework for IoT networks. The goals are to improve IoT network availability and reliability and decrease communication latency. The suggested architecture employed MEC and fog computing to cater to the requirements. A demonstration of the constructed model viability in dense deployment scenarios was shown. The tests conducted on the LiFi-integrated fog-MEC model demonstrated its effectiveness under a range of situations. This work was developed for certain applications (i.e., indoor applications) where LiFi can be used; however, our proposed model is introduced for indoor and outdoor IoT applications with any interface. [PERSON] et al. [25] considered the smart grid as an IoT use case. They focused on developing a reliable IoT-based green smart grid, integrating traditional power grids with solar energy and incorporating a control unit to manage the interaction between energy generation and consumption. The presented framework utilized intelligent IoT units to optimize energy demand and effectively manage consumption, enhancing the overall efficiency of the grid. By implementing a distributed edge computing paradigm across four levels, the framework ensures efficient data offloading and power management. The results demonstrated that the developed green grid significantly outperforms traditional grids in terms of reliability and energy efficiency. Specifically, the presented green grid achieved a notable reduction in energy consumption, lowering it by an average of 24.3% across the distribution area compared to conventional grids. The novelty of the presented work in this paper comes from the introduction of fog and MEC with a reliable communication model. The work provides a general framework for all IoT applications. This framework has not been considered in the previously introduced literature. Moreover, the presented work considers network scalability, which is a critical issue with the design of IoT networks.