## 2 Related Work
### Overview of Existing Solutions
SPP-AOA adopts the Archimedes Optimization Algorithm (AOA), inspired by Archimedes's Principle in physics, to formulate the SPP as a multi-objective problem. The main objective of SPP-AOA is to orchestrate the placement of autonomous services across distributed Fog domains, focusing on considerations such as resource utilization, service cost, energy consumption, delay cost, and throughput. By analyzing the distribution of resources over time, SPP-AOA optimizes placement to ensure effective resource allocation for handling future requests. Recognizing the limitations of current approaches, ref. [30] employs the Asynchronous Advantage Actor-Critic (A3C) algorithm as an innovative deep reinforcement learning (DRL) solution for the SPP. The proposed strategy focuses on placing IoT services in a way that minimizes cost and latency within deadline and resource constraints. Aligned with these goals, A3C aims to maximize the long-term cumulative reward to enhance QoS. Placement activities occur within local Fog domains, and neighboring Fog domains are utilized when necessary to enhance Fog resource utilization. Additionally, a technique is integrated to extract and distribute resources over time, conserving resources for efficiently managing future requests. In the context of autonomous planning in FC, the work [31] uses the Imperialist Competitive Algorithm (ICA) as a meta-heuristic approach to address the complexity of this problem. During the allocation process, the algorithm prioritizes Fog nodes with ample resources capable of hosting multiple IoT services, taking advantage of resource distribution. The algorithm aims to optimize IoT services to minimize delays, formulating the SPP as a multi-objective problem. In addition, a conceptual framework is employed to articulate the proposed autonomous planning model, delineating the interactions within the Cloud-Fog-IoT ecosystem components. The authors in [15] propose a secure and efficient service placement strategy in FC for IoT applications. They suggest an evolutionary method to optimize the mappingbetween Fog-Cloud nodes and IoT services based on specific service types. The research introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) to classify requests and identify processing layers. Additionally, a refined Honey Badger Algorithm (CHBA) is implemented for scheduling, enhanced by an Opposition-based Learning (OBL) approach to improve convergence rates. The paper [16] presents a hierarchical framework that combines Federated Learning (FL) with deep reinforcement learning (DRL) to create a privacy-preserving offloading strategy for low-altitude Vehicular Fog Computing (VFC) systems. The proposed method introduces decentralized training and execution (DTDE) at the local level within Roadside Unit (RSU) regions, enhancing scalability and privacy. Additionally, an auto-encoder-based contextual FL mechanism is developed to account for diverse operational scenarios and regional context awareness. As demonstrated, several studies have investigated the SPP in FC. However, the SPP remains a complex optimization challenge influenced by factors such as latency, resource utilization, the dynamic nature of systems, and end user mobility. Table 3 shows different methods to solve this problem, such as heuristic and meta-heuristic methods, machine learning, and hybrid approaches. Table 4 reveals that mobility was considered in only three studies, while some research efforts have overlooked the dynamic nature of the system. In addition, while progress has been made in reducing latency, this reduction is still insufficient for applications that require immediate responses, such as online gaming and remote surgery, where even minor delays can critically affect user satisfaction. Indeed, contextual factors like user proximity (neighboring), service usage history, and behavioral patterns are often overlooked. This highlights the significance of considering not only resource capacity but also geographical and behavioral factors of the users in the service placement process.