## 2 Related Work
### Load Balancing among the Four IoT Network Layers
All IoT tasks are first sent to the Edge broker (EB) in this algorithm. Inside this EB, there is an application receiver and classifier (AC), and its function is to distribute these tasks among Mist computing, Fog computing, and Cloud computing based on the input size, the number of tasks, and the workload of a task in MIPS. In addition to the AC, there is a Mist resource manager in the EB to assist tasks to select the best Mist node. Moreover, there is a Fog broker (FB) in the Fog computing layer, and its function is similar to EB. MATLAB19a was used to validate the proposed algorithm and compare it with three evolutionary algorithms: router, FCFS, and short job first. The results showed that MSRM-IoT outperforms the three algorithms across all measures [8]. The parameters mentioned in MSRM-IoT are mainly based on the computing size only to determine a resource for the IoT tasks, which is not efficient from our point of view. In allocating resources for IoT tasks, it is essential to determine tasks' importance and their timing requirements in real-time processing. [PERSON] et al., 2020 [9] analysed the difference between computing IoT tasks in the traditional Cloud-IoT model, Edge-Cloud-IoT model, and local Edge-Cloud-IoT model. iFogSim was used to evaluate the three different scenarios. The simulation results showed that processing IoT tasks locally is the best with respect to connectivity and energy consumption compared with the first and second models. However, it is important to know that different IoT tasks might vary in different scenarios, and processing tasks locally is not always practical, as local nodes have limited capabilities. Thus, Cloud and Fog assistance is always required to serve in all scenarios. [PERSON] et al. (2022) [10] introduced the Secure-M2 FBalancer model to enhance the security and workload balancing of IoT networks through the integration of supervised learning and a genetic algorithm. The proposed model was implemented within a healthcare management system and comprised four layers: the IoT layer, Mist layer, Fog layer, and Cloud layer. In this model, the IoT layer forwards data to the Mist layer, which is responsible for resource allocation. If the Mist layer cannot process the data, it is offloaded to the Fog layer. Within the Fog layer, a technique is employed to determine the status of servers, distinguishing between overloaded and underloaded servers to enable accurate resource allocation. Furthermore, encrypted data are offloaded to or received from the Cloud layer for secure communication when additional processing or storage is required. The proposed approach was validated using MATLAB, and the results demonstrated superior makespan performance when compared to least association, round-robin, and weighted round-robin approaches. However, within the Secure-M2 FBalancer model, the authors solely considered response time as the primary factor for resource allocation at the Mist layer. From our perspective, this single factor may not be sufficient for precise decision making in server allocation. Therefore, we suggest the inclusion of additional factors to enhance the accuracy of the decision-making process. Furthermore, the criteria used to determine the state of Fog servers, whether overloaded or underloaded, are not explicitly defined in the study. Mist computing can play a vital role in improving the data collection for IoT devices as the processing is in the Edge computing environment. [PERSON] et al., 2017 [PERSON] et al. (2017) developed a new platform called MistGiS for geospatial big data and applied it in two cases: tourism information infrastructure management and a faculty information retrial system. The Raspberry Pi microprocessor was utilized to build the framework. The study results found that Mist computing can assist Cloud and Fog computing in providing better analysis for geospatial big data. This platform serves as a means to only highlight the significance of Mist computing in evaluating other layers, thus lacking novelty in its approach. [PERSON] et al.