###### Abstract  
Fog Computing extends Cloud computing capabilities by providing computational resources closer to end users. Fog Computing has gained considerable popularity in various domains such as drones, autonomous vehicles, and smart cities. In this context, the careful selection of suitable Fog resources and the optimal assignment of services to these resources (the service placement problem (SPP)) is essential. Numerous studies have attempted to tackle this issue. However, to the best of our knowledge, none of the previously proposed works took into consideration the dynamic context awareness and the user preferences for IoT service placement. To deal with this issue, we propose a hybrid recommendation system for service placement that combines two techniques: collaborative filtering and content-based recommendation. By considering user and service context, user preferences, service needs, and resource availability, the proposed recommendation system provides optimal placement suggestions for each IoT service. To assess the efficiency of the proposed system, a validation scenario based on Internet of Drones (IoD) was simulated and tested. The results show that the proposed approach leads to a considerable reduction in waiting time and a substantial improvement in resource utilization and the number of executed services. service placement; fog computing; recommendation system; IoT services +
Footnote †: journal:  
0000-0001-5500-4999-22000  
0000-0001-5500-5500-5500-5500-5500-5500-5500-5500-5500-5500-55000-5500-55000-55000-55000-55000-55000-55000-55000-55  
When applied to the specific needs of IoT, traditional Cloud computing infrastructures, which have historically played a central role in implementing IoT services, face a range of challenges. These challenges include geographical constraints, latency delays, and increasing concerns regarding data confidentiality. This raises questions about the effectiveness of traditional Cloud models in the IoT environment. To address these challenges, Fog Computing (FC) has emerged as an innovative response, offering a decentralized approach that brings data processing closer to the place where it is generated, as close as possible to the connected objects. Although FC presents numerous benefits like enhancing the implementation of IoT applications for time-sensitive tasks by deploying Fog devices close to drones in IoT-Fog networks, it also poses complex challenges when it comes to its application in the IoT context. One of these challenges is determining the strategic placement of IoT services within the distributed and very dynamic environment. The placement of IoT services in the Fog network is critical, as it directly impacts the performance, security, latency, and efficiency of IoT applications. Therefore, it is crucial to determine the optimal placement of IoT services in FC. This helps minimize latency, optimize available resources, and improve application responsiveness. However, numerous challenges must be addressed, including network dynamics, workload variability, and the security of sensitive data exchanged between drones and processing centers. In this work, the main objective is to perform an in-depth analysis of the challenges related to the placement of IoT services in FC. This analysis should consider the heterogeneous nature of drones, the specific requirements of applications, and the resource constraints of the Fog network. It is crucial to understand how these factors interact and how they impact the overall performance of the system. We therefore propose a dynamic services placement approach that considers real-time workload variations. This approach also explores intelligent resource management mechanisms to ensure efficient use of the computing and storage capacities available in the Fog. In summary, we propose an approach that provides practical, informed recommendations for optimizing the performance and latency of IoT applications deployed in FC infrastructure.