## 1 Introduction
Allocating resources in a fog system, where both cloud and fog computing nodes respond to applications, is challenging [16]. In contrast to cloud computing nodes, fog computing nodes are located throughout the edge network rather than in a central data center [17]. The resource needs of computer nodes shift due to the demands of IoT applications on their processors, storage spaces, and data transfer rates. Load balancing necessitates resource allocation to accommodate the unpredictable resource needs of IoT applications. Load balancing in fog computing is possible thanks to the \"Dynamic Resource Allocation Strategy\" (DRAM) technique. Optimizing the distribution of work across all available compute nodes is essential in cloud and fog computing environments. The four steps of the DRAM approach are:  
1. Segmenting fog processes
2. Identifying nodes with extra storage space
3. Dynamically allocating resources to various parts of the fog web
4. Balancing workloads with global resource allocation  
The DRAM technique effectively distributes the load across different types of computer nodes in cloud and fog settings; however, it was devised without considering critical considerations like energy consumption and processing costs. Because IoT devices do not have direct access to a power source, they are generally powered by batteries. It is frequently brought on by geographic locations that make it impossible to access the electrical network. Finding methods to guarantee minimal energy use by IoT devices is indispensable. Finding and communicating with the next hop is crucial when the network is under heavy traffic. Hence, finding the best route is essential to perform load balancing by selecting the less busy channel when traffic congestion occurs in the network. Figure 1 shows a basic representation of load balancing in fog computing architecture. In IoT systems, load balancing, energy use, and computing cost are three major issues. Therefore, load balancing, reducing operational costs, and power consumption are essential topics and issues. We want to lower the energy consumption of IoT devices. One of the most significant issues is the IoT resource allocation problem (IRAP), and a workable solution will significantly enhance system performance [18]. This paper discusses and proposes a solution to resolve the issues of load balancing, energy use, and computing cost in the form of dynamic energy-efficient load balancing (DEELB). Further, its effectiveness is evaluated using simulated experiments. Our main contributions can be summarized as follows:  
* This paper discusses three massive problems: load balancing, energy use, and computing cost. * Present a particular technique in this work called dynamic energy-efficient load balancing (DEELB) to handle these IoT resource allocation issues. This article continues as follows: Section 2 investigates previous studies and assesses their strengths and weaknesses. The proposed system is described in Section 3. Section 4 compares the DEELB-IoT to the existing system using performance metrics. This section summarizes and illustrates the findings using graphs from previous research. Section 5 concludes with a summary of all proposed and future work. Background  
Users can exchange data and access a variety of resources by using the technology known as cloud computing. Users are only charged for the resources that they actually use. Public data and distributed resources are stored in the cloud, and data storage capacity is growing quickly. As a result, load balancing is the main task in the cloud environment. By avoiding overloads, load balancing assists in the distribution of dynamic workload. Figure 1: Representation of Load Balancing in Fog Computing. The \"cloud computing\" distributed computing approach is centered on granting simple access to virtualization hardware and software infrastructure to a broad range of users. Web services, applications, networking, and distributed computing virtualization are all included [19].