## 2 Related Work
### Overview of Existing Solutions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Ref** & **Techniques** & **Performance Metrics** & **Domain** & **Evaluation Tool** \\ \hline \multirow{6}{*}{Heuristic} & [19] & GA & Latency, throughput & Smart city & iFogSim \\  & [20] & GA & Latency & Smart city & Testbed \\  & [22] & Load balancing & Latency & General & Simpy \\  & [23] & SCATTER & Cost, EC, RT, latency, and FU & Smart Home & iFogSim \\  & [7] & ODMA & Cost, EC, RT, latency, and FU & Healthcare & iFogSim \\  & [8] & GA & Application delay and network usage & General & iFogSim \\ \hline \multirow{2}{*}{Exact solvers} & [25] & CPLEX solver & Latency, cost & General & Matlab \\  & [27] & ILP solver & Latency, cost & General & iFogSim \\ \hline \multirow{6}{*}{Meta-Heuristic} & [29] & SPP-AOA & \begin{tabular}{c} FU, service cost, EC, \\ delay cost, and \\ throughput \\ \end{tabular} & General & Matlab \\  & [17] & CSA & FU, delay, RT, SLA, EC, and cost & Industry & Matlab \\  & [31] & ICA & FU, service cost, EC, delay, and throughput & General & Matlab \\ \hline \multirow{2}{*}{Evolutionary} & [18] & SPEA-II & FU, service latency, and cost & General & iFogSim \\ \hline \multirow{2}{*}{scheduling} & [21] & \
\begin{tabular}{c} Round robin + \\ Weighted fair queuing \\ Scheduling algorithms \\ \end{tabular} & QOS (SLA) & General & iFogSim + CloudSim \\  & [24] & Scheduling algorithms & Latency & General & Matlab \\ \hline \multirow{2}{*}{Machine learning} & [30] & DRL + A3C-SPP & Latency, cost & General & Matlab \\  & [16] & AFedPPO and CCP FL & Privacy preservation, latency & Vehicular Fog & AirFogSim \\ \hline \multirow{6}{*}{Hybrid} & [15] & ANFIS, CHBA, and &
\begin{tabular}{c} Makespan, delay \\ violation, cost, FU, and \\ EC \\ \end{tabular} & General & Matlab \\  & [28] & MGAPSO + EGAPSO & QOS, service cost, EC, and the service time & Industry & Testbed \\ \cline{1-1}  & HRS & Content + collaborative filtering & FU, delay, and RT & IoD & iFogSim2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of related works. Genetic Algorithm for Constrained Optimization Problems (RCGA-CO). In addition, it presents a task reallocation and retransmission mechanism to address inherent unreliability issues within the CF-IIoT, such as potential Fog node damage and wireless link outages. This mechanism reallocates unfinished subtasks from failed nodes using the RCGA-CO algorithm and retransmits new subtasks to normal nodes to ensure timely task completion. The authors of [27] proposed a strategy for service provisioning and the placement of services in Fog-based computing resources. Their approach aims to optimize the placement of IoT services by prioritizing resources for applications with the earliest deadlines. To achieve this, the services are allocated within the constraints of CPU, RAM, and storage capacities of the respective Fog resources. To describe the interactions between system components and the Fog service placement problem-solving policy, the authors of the paper [7] propose a three-layer conceptual computing framework (Cloud, Fog, and IoT). They also adopt the MADE-k approach, which is commonly used in similar studies, to solve the problem. The study [8] focuses on minimizing application delay and network usage in Fog-Cloud environments. To achieve this, the authors propose a genetic-based service placement algorithm that introduces a penalty-based approach. This approach aims to address both the delay and the reduction in time-consuming Cloud placements. In [28], a hybrid meta-heuristic algorithm was formulated and developed. The algorithm is called MGAPSO and EGAPSO, which are combinations of genetic algorithm (GA) and Particle Swarm Optimization (PSO) and Elitism-based GA and PSO, respectively. To address the complexity of the SPP in FC, an efficient and autonomous scheme named SPP-AOA is proposed in the study of [29]. The solution leverages meta-heuristic approaches and features a shared parallel architecture to navigate the intricate nature of the problem.