# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 3 The Proposed Approach
### System Model and Case Study  
The nodes in the sensor networks receive data from their surroundings and send them to FDs through gateways. These data are either processed in FDs or sent to the cloud. A smart surveillance system is designed to coordinate multiple cameras for monitoring a specific area. Video surveillance/object tracking software is a collection of distributed mobile smart cameras.  
The application includes six modules: the motion detector, object detector, object tracker, user interface, and pan, tilt, and zoom control (PTZ). The camera sends the video stream to the motion detector module. This module sends it to the object detector after applying a filter to the video stream. This module identifies the object's position and sends it to the tracker module; then, the appropriate PTZ is calculated and sent to the PTZ  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **Strategy** & **Scheduling Objectives** & **Environment** & **Pos and Cons** \\ \hline SABA [18] & Heuristic & Makespan, security, and budget & Cloud/Real environment & Improves response time. \\ MOHEFT [19] & Heuristic & Makespan and cost & Cloud/Real environment & Trade-off between cost and makespan. \\ EIPR [20] & Heuristic & Deadlines, total execution & Cloud/Real environment & Improves performance. \\ Heuristic [21] & Heuristic & Makespan and execution cost & Cloud-Fog/CloudSim & Cost efficient. No scalability. \\ JIGA [23] & Meta-heuristic & Makespan and load balancing. & Cloud/MATLAB & Energy efficient. \\ RSS-IN [25] & Meta-heuristic & Latency and stability & Fog/MATLAB & Decreases latency. Ignores \\ ACO [28] & Meta-heuristic & Makespan & Cloud/CloudSim & Local optimum problem. \\ CMSACO [29] & Meta-heuristic & Delay, complete time, & Fog/Simulation & Ignores time complexity. \\  & & and energy consumption & & Better performance than basic \\ BLA [5] & Meta-heuristic & Execution time and memory size & Fog/C++ & evolutionary algorithms. \\  & & & Makespan, resource utilization, & Cloud/Simulation & Optimizes energy consumption \\  & & and degree load balance & & and latency. \\ OPSO [42] & Meta-heuristic & Energy consumption and makespan & Cloud/CloudSim & Convergence of standard PSO, \\ DVFS-MODPSO [32] & Hybrid-heuristic & Makespan, cost, and energy & Cloud/Real environment & Optimizes performance. \\  & & & Response time and optimum & Cloud/PySim & Resource efficient. Ignores \\  & & & usage of resources & energy consumption. \\ HSGA [44] & Hybrid-heuristic & Makespan and load balancing & Cloud/Real environment & Ignores time complexity. \\ MMACO [45] & Hybrid-heuristic & Makespan and load balancing & Cloud/CloudSim & Improves performance. \\ HPSGA [46] & Hybrid-heuristic & Makespan and cost & Fog-Cloud/MATLAB & Optimized for deadline-satisfied tasks. \\ OBLFPA [47] & Hybrid-heuristic & Execution time, cost, & Cloud/CloudSim & Improved time complexity. \\  & & and resource utilization & & Improves scheduling error ratio, \\ JNNHSP [48] & Hybrid-heuristic & Service latency & Edge–Cloud/Real & average service latency, \\  & & & & and execution efficiency. \\ HHSA [8] & Hyper-heuristic & Makespan and computation Time & Cloud/CloudSim and Hadoop & Realistic environment. Time efficient. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categorization of task-scheduling strategies.  
control. Finally, a fraction of the video streams containing a traced object is sent to the user's device [52].  
The FD and application properties are explained as follows.