Fog Computing extends storage and computation resources closer to end-devices. In several cases, the Internet of Things (IoT) applications that are time-sensitive require low response time. Thus, reducing the latency in IoT networks is one of the essential tasks. To this end, fog computing is developed with a motive for the data production and consumption to always be within proximity; therefore, the fog nodes must be placed at the edge of the network, which is near the end devices, such that the latency is minimized. The optimal location selection for fog node placement within a network out of a very large number of possibilities, such as minimize latency, is a challenging problem. So, it is a combinatorial optimization problem. Hard combinatorial optimization problems (NP-hard) involve huge discrete search spaces. The fog node placement problem is an NP-hard problem. NP-hard problems are often addressed by using heuristic methods and approximation algorithms. Combinatorial optimization problems can be viewed as searching for the best element of some set of discrete items; therefore in principle, any metaheuristic can be used to solve them. To resolve this, meta-heuristic-based methods is proposed. We apply the Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to design fog node placement algorithms. Genetic Algorithm is observed to give better solutions. Since Genetic Algorithm may get stuck in local optima, Hybrid Genetic Algorithm, and Simulated Annealing (GA-SA), Hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) were compared with GA. By extensive simulations, it is observed that hybrid GA-SA-based for node placement algorithm outperforms other baseline algorithms in terms of response time for the IoT applications.

中文翻译：

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物联网服务基础设施中的低延迟感知雾节点放置

雾计算将存储和计算资源扩展到更接近终端设备。在某些情况下，对时间敏感的物联网 (IoT) 应用程序需要较短的响应时间。因此，减少物联网网络的延迟是基本任务之一。为此，开发雾计算的目的是使数据生产和消费始终在附近；因此，雾节点必须放置在网络边缘，靠近终端设备，以最大限度地减少延迟。从大量可能性（例如最小化延迟）中选择网络内雾节点放置的最佳位置是一个具有挑战性的问题。所以，这是一个组合优化问题。硬组合优化问题（NP-hard）涉及巨大的离散搜索空间。雾节点放置问题是一个 NP-hard 问题。NP-hard 问题通常通过使用启发式方法和近似算法来解决。组合优化问题可以看作是寻找一组离散项的最佳元素；因此原则上，任何元启发式都可以用来解决它们。为了解决这个问题，提出了基于元启发式的方法。我们应用模拟退火 (SA)、遗传算法 (GA) 和粒子群优化 (PSO) 技术来设计雾节点放置算法。观察到遗传算法可以提供更好的解决方案。由于遗传算法可能会陷入局部最优，因此将混合遗传算法和模拟退火 (GA-SA)、混合遗传算法和粒子群优化 (GA-PSO) 与 GA 进行了比较。通过广泛的模拟，