Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability, Fog Device Deployment (FDD) must effectively consider two crucial factors: connectivity and coverage. Network connectivity relies on FDD, determining the physical network topology, while coverage determines the accessibility of the Internet of Things (IoT) or edge devices. Both these objectives significantly impact the network performance and guarantee the network's Quality of Service (QoS). However, determining an optimal FDD method that reduces computation and communication overhead, and provides high network connectivity and coverage, is challenging. In this work, we propose an FDD algorithm that effectively connects the Fog devices for internal communication and covers maximum edge devices to entertain the requests. Firstly, FDD is formulated as a multi-objective optimization problem and then, an emerging metaheuristic Jaya Algorithm (JA) is applied to optimize the multi-objective function. The suitability of the JA, for the FDD problem, is substantiated by its rapid convergence and better computational complexity when contrasted with other contemporary population-based algorithms. In conclusion, the performance of the proposed method is assessed across a spectrum of benchmark-generated instances, each reflecting distinct Fog scenarios. The experimental outcomes showcase the proposed method's remarkable promise, especially when compared against state-of-the-art methodologies.

中文翻译：

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使用元启发式算法优化雾设备部署，实现最大网络连接和边缘覆盖

雾计算的出现是为了解决传统云计算的局限性和挑战，特别是在处理实时、异构和延迟敏感的应用程序方面。然而，雾计算设备在网络中的传播带来了各种挑战，特别是在设备连接性和确保足够的覆盖范围以满足用户请求方面。为了维持网络的可操作性，雾设备部署（FDD）必须有效地考虑两个关键因素：连接性和覆盖范围。网络连接依赖于 FDD，决定了物理网络拓扑，而覆盖范围则决定了物联网 (IoT) 或边缘设备的可访问性。这两个目标都会显着影响网络性能并保证网络的服务质量 (QoS)。然而，确定一种能够减少计算和通信开销并提供高网络连接性和覆盖范围的最佳 FDD 方法具有挑战性。在这项工作中，我们提出了一种 FDD 算法，可以有效连接雾设备进行内部通信，并覆盖最大边缘设备来处理请求。首先，FDD被表述为多目标优化问题，然后应用新兴的元启发式Jaya算法（JA）来优化多目标函数。与其他当代基于群体的算法相比，JA 的快速收敛性和更好的计算复杂性证实了 JA 对于 FDD 问题的适用性。总之，所提出方法的性能是在一系列基准生成的实例中进行评估的，每个实例都反映了不同的雾场景。实验结果展示了所提出的方法的非凡前景，特别是与最先进的方法相比。