## 2 Literature Review  
Recently, different works have been investigating IoT. To detect the virtual machines (VMs) experiencing overload and to maximize their use, [PERSON] et al. [9] introduced the bat algorithm and its hybridization with heuristic methodologies. In the suggested model, the heuristic techniques (HEFT and PEFT) seeding the bat algorithm parameters (frequency, velocity, loudness, and pulse rate) were used to optimize the time and cost of carrying out the processes. The model was put into practice in CloudSim, and the outcomes are examined in light of cutting-edge ACO and PSO metaheuristics.  
[PERSON] et al. implemented ant colony optimization (ACO) by effectively tweaking the parameters, replacing the first-come-first-serve (FCFS) virtual machine (VM) allocation strategy of Cloud computing in CloudSim [10]. In order to evaluate the effect of the Cloud's reaction times, an IoT-enabled healthcare setting was created that infrequently demands the capacity of the Cloud to process enormous volumes of data. By varying various parameters, including the number of ants, the potency of the pheromone, the number of VMs, the number of hosts, the strength of the computing power of processing elements, the number of users, and the size of the workloads, several experiments were conducted to determine the best VM allocation using ACO. According to experimental findings, the ACO leverages cloud resources more effectively than the default FCFS method.  
For task scheduling in fog computing (TSFC), [PERSON] et al. suggested an energy-aware model based on the marine predator's algorithm (MPA) in order to better meet user-required QoS [11]. They put out the other two variations in addition to the conventional MPA. The first version, known as modified MPA (MMPA), changed the MPA so that it may be more effectively exploited by utilizing the most recent positions rather than the most recent best one. The second one had a better MMPA thanks to a ranking technique that included reinitialization, mutation toward the best, and reinitialization of the remaining half of the population after a certain number of iterations to get rid of local optima. Since the TSFC was regarded as a discrete problem while the MPA was developed to tackle continuous problems, the conventional MPA was transformed into a discrete one using the normalization and scaling phase. Based on several performance indicators, including energy consumption, makespan, flow duration, and carbon dioxide emission rate, the three variants were presented with additional metaheuristic and genetic algorithms.  
To offer an energy-efficient task schedule within reasonable application completion durations, [PERSON] et al. examined workflow scheduling in fog-cloud systems [12]. They proposed a two-phase scheduling approach called energy makespan multi-objective optimization. In order to schedule latency-sensitive tasks on fog resources and computationally complex tasks on cloud resources, the solution first models the problem as a multi-objective optimization problem and calculates a trade-off between accuracy and conflicting objectives while earmarking fog and cloud resources. The deadline-aware stepwise frequency scaling strategy is modified to make use of the spare time between two already scheduled jobs on a single node in order to further cut down on energy consumption.