With the increasing use of the Internet of Things (IoT) in various fields and the need to process and store huge volumes of generated data, Fog computing was introduced to complement Cloud computing services. Fog computing offers basic services at the network for supporting IoT applications with low response time requirements. However, Fogs are distributed, heterogeneous, and their resources are limited, therefore efficient distribution of IoT applications tasks in Fog nodes, in order to meet quality of service (QoS) and quality of experience (QoE) constraints is challenging. In this survey, at first, we have an overview of basic concepts of Fog computing, and then review the application placement problem in Fog computing with focus on Artificial intelligence (AI) techniques. We target three main objectives with considering a characteristics of AI-based methods in Fog application placement problem: (i) categorizing evolutionary algorithms, (ii) categorizing machine learning algorithms, and (iii) categorizing combinatorial algorithms into subcategories includes a combination of machine learning and heuristic, a combination of evolutionary and heuristic, and a combinations of evolutionary and machine learning. Then the security considerations of application placement have been reviewed. Finally, we provide a number of open questions and issues as future works.

随着物联网（IoT）在各个领域的使用日益增加以及处理和存储大量生成数据的需求，雾计算被引入以补充云计算服务。雾计算为网络提供基本服务，以支持响应时间要求低的物联网应用。然而，雾是分布式的，异构的，并且它们的资源是有限的，因此，为了满足服务质量（QoS）和体验质量（QoE）约束，在雾节点中有效分布IoT应用程序任务是具有挑战性的。在本次调查中，首先，我们对Fog计算的基本概念进行了概述，然后重点研究了人工智能（AI）技术，回顾了Fog计算中的应用程序放置问题。我们针对三个主要目标，考虑了Fog应用程序放置问题中基于AI的方法的特征：（i）对进化算法进行分类，（ii）对机器学习算法进行分类，以及（iii）将组合算法分类为包含机器学习的组合的子类别和启发式，进化和启发式的结合，以及进化和机器学习的结合。然后，审查了应用程序放置的安全性注意事项。最后，我们在将来的工作中提供了许多未解决的问题。进化与启发式的结合，以及进化与机器学习的结合。然后，审查了应用程序放置的安全性注意事项。最后，我们在将来的工作中提供了许多未解决的问题。进化与启发式的结合，以及进化与机器学习的结合。然后，审查了应用程序放置的安全性注意事项。最后，我们在将来的工作中提供了许多未解决的问题。