Multi-Objective Path Optimization in Fog Architectures Using the Particle Swarm Optimization Approach

Area of Science:

• Computer Science
• Network Engineering
• Distributed Systems

Background:

• Internet of Things (IoT) systems leverage Wireless Sensor Networks (WSNs) for data collection and fog/edge computing for processing.
• Fog networks comprise heterogeneous, potentially mobile nodes with varying resource constraints (energy, security, computation, latency).
• Key challenges in fog networks include optimal service placement and efficient data routing path determination between end-devices and fog nodes.

Purpose of the Study:

• To propose a novel, lightweight, two-stage method for optimizing data routing paths in fog networks.
• To address the need for rapid, resource-constrained solutions for service placement and path determination.
• To enhance the efficiency and responsiveness of IoT systems within fog computing environments.

Main Methods:

• A two-stage multi-objective path optimization approach is presented.
• Particle Swarm Optimization (PSO) is employed to identify the Pareto Frontier of potential data paths.
• Analytical Hierarchy Process (AHP) is utilized to select the optimal path based on application-specific preferences.

Main Results:

• The proposed method effectively optimizes data routing paths between end-devices and fog nodes.
• It demonstrates compatibility with a broad range of objective functions, allowing for easy expansion.
• The approach yields a set of alternative solutions, enabling selection of secondary or tertiary options if needed.

Conclusions:

• The developed two-stage optimization method provides an effective solution for data routing in resource-constrained fog networks.
• The combination of PSO and AHP offers a flexible and robust approach to path selection.
• This method enhances the performance and adaptability of IoT applications relying on fog computing infrastructure.