Comparative Performance Analysis of Metaheuristic Algorithms for Module Placement in Fog Computing-Based Healthcare Monitoring

The proliferation of Internet of Things (IoT) devices in healthcare has led to a continuous flow of delay-sensitive data such as electrocardiography (ECG) and glucose measurements. Traditional cloud-based architectures may not be able to process this data within clinically acceptable response times. Fog computing addresses this limitation by extending computation to regions closer to the data source. This presents a complex multi-objective module deployment problem. In this study, four module deployment strategies (First-In, First-Out (FCFS), Edge-Oriented, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)) on a four-layer fog-cloud architecture modeled in iFogSim are compared in terms of latency, energy consumption, makespan, and cost values calculated in the simulation environment. Two different topologies are considered in this study. The first is a homogeneous topology where all fog nodes share the same resources and gateways, and the second is a heterogeneous topology where the processing capacities of the end devices differ. Each algorithm was evaluated under four patient load scenarios using a weighted multi-objective fitness function incorporating latency, power consumption, and network usage in a homogeneous topology. Using another fitness function with some penalty parameters in a heterogeneous topology, each algorithm was evaluated under four patient loads. Experimental results show that GA provides lower latency at medium loads, while PSO exhibits more competitive latency performance at high loads. However, the cost difference between the two algorithms remains negligible in all scenarios. FCFS exhibits the worst overall performance, demonstrating the inadequacy of cloud-based strategies alone for real-time healthcare. All algorithms reach saturation above 30 patients, indicating that fog layer hardware capacity, rather than algorithm selection, becomes the dominant performance bottleneck.