Efficient Fog Node Placement using Nature-Inspired Metaheuristic for IoT Applications

TL;DR

The paper tackles optimal fog-node placement in IoT systems to maximize edge coverage and fog connectivity, framing it as a two-objective optimization problem. It introduces a Marine Predator Algorithm (MPA) as a continuous-region, multi-objective metaheuristic that balances exploration and exploitation across three stages. Empirical results show that MPA achieves faster convergence and better fitness than baselines (PSO, HHO, SCA), with robust performance across varying fog/edge densities and transmission ranges, and a balanced choice of the weight $\omega$ around 0.5. The work demonstrates practical impact by enabling efficient, scalable fog deployments that improve network QoS and coverage in edge-heavy IoT environments.

Abstract

Managing the explosion of data from the edge to the cloud requires intelligent supervision such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively in terms of two main factors: connectivity and coverage. The network connectivity is based on fog node deployment which determines the physical topology of the network while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network QoS. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage as well as preserving network connectivity is a non-trivial problem. In this paper, we proposed a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. The experimental results demonstrate that our proposed algorithm is capable of providing very promising results when compared to state-of-the-art methods for determining an optimal deployment of fog nodes.

Figures (8)

• Figure 1: IoT-Edge based Fog infrastructure
• Figure 2: Fog connectivity and Iot-Edge nodes coverage
• Figure 3: Fitness Function Study Under Different $\omega$ Values
• Figure 4: Convergence curve of studied algorithms
• Figure 5: Comparison of MPA,PSO, HHO, and SCA