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.

翻译：管理从边缘到云端的数据爆炸式增长需要智能化的监管，例如雾节点的部署，这是评估网络可操作性的关键任务。为确保网络可操作性，部署过程必须在两个主要因素（即连通性和覆盖范围）方面高效执行。网络连通性基于决定网络物理拓扑的雾节点部署，而覆盖范围则决定网络可达性。两者对网络性能有显著影响，并能保障网络的服务质量。确定一种能最小化成本、减少计算与通信开销、并提供高度网络连接覆盖范围的最优雾节点部署方法极为困难。因此，最大化覆盖范围并同时保持网络连通性是一个非平凡问题。本文提出了一种能有效连接雾节点并覆盖所有边缘设备的雾部署算法。首先，我们将雾部署形式化为一个具有大搜索空间的多目标优化问题实例。然后，我们利用海洋捕食者算法（MPA）解决该部署问题，并证明与其他基于种群的算法相比，MPA因其快速收敛和低计算复杂度而特别适用于雾节点部署。最后，我们在不同雾场景配置的生成实例基准上对所提算法进行了评估。实验结果表明，与用于确定最优雾节点部署的现有方法相比，我们的算法能够提供非常理想的结果。