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Managing the explosion of data from the edge to the cloud requiresintelligent supervision such as fog node deployments, which is an essentialtask to assess network operability. To ensure network operability, thedeployment process must be carried out effectively in terms of two mainfactors: connectivity and coverage. The network connectivity is based on fognode deployment which determines the physical topology of the network while thecoverage determines the network accessibility. Both have a significant impacton network performance and guarantee the network QoS. Determining an optimumfog node deployment method that minimizes cost, reduces computation andcommunication overhead, and provides a high degree of network connectioncoverage is extremely hard. Therefore, maximizing coverage as well aspreserving network connectivity is a non-trivial problem. In this paper, weproposed a fog deployment algorithm that can effectively connect the fog nodesand cover all edge devices. Firstly, we formulate fog deployment as an instanceof multi-objective optimization problems with a large search space. Then, weleverage Marine Predator Algorithm (MPA) to tackle the deployment problem andprove that MPA is well-suited for fog node deployment due to its rapidconvergence and low computational complexity compared to other population-basedalgorithms. Finally, we evaluate the proposed algorithm on a differentbenchmark of generated instances with various fog scenario configurations. Theexperimental results demonstrate that our proposed algorithm is capable ofproviding very promising results when compared to state-of-the-art methods fordetermining an optimal deployment of fog nodes.

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