# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Hybrid Approaches  
To address the limitations of standalone decision-making techniques, recent studies have proposed hybrid approaches that integrate multiple methodologies for handover optimization. This subsection focuses on works that combine AI with fuzzy logic, AHP, or other decision frameworks to enhance the accuracy, flexibility, and robustness of handover decisions in heterogeneous and ultra-dense network environments. In 2020, [PERSON] et al. [53] conducted a study on the development of handover decision algorithms in hybrid Li-Fi and Wi-Fi networks. Specifically, it attempted to address the issue of frequent handovers in hybrid Li-Fi and Wi-Fi networks, which degrade system throughput due to user mobility and the small coverage area of Li-Fi access points. To solve this issue, the authors proposed two handover decision algorithms: a fuzzy logic (FL)-based handover decision algorithm and a fuzzy rule-based artificial neural network (ANN) handover decision algorithm. The FL-based algorithm uses input parameters such as instantaneous SINR, average SINR, RSS, and user velocity to determine handover decisions. The ANN-based approach aims to reduce computational complexity by training the ANN with a subset of fuzzy rules, allowing it to generalize and make handover decisions efficiently. A simulation-based evaluation was conducted to compare the accuracy and performance of both models. As a result, the findings revealed that the FL-based algorithm achieved 100% successful handover decisions but had high computational complexity. Also, the ANN-based model learned from limited fuzzy rules and provided lower computational complexity, but its handover accuracy was 60%. However, this work did not consider real-world implementation or hardware constraints, and the ANN model requires further optimization to match the accuracy of the FL-based algorithm. In 2021, [PERSON] et al. [54] investigated dynamic Q-learning and fuzzy convolutional neural network (F-CNN) for vertical handover decision-making in the integration of DSRC, mmWave 5G, and LTE in the Internet of Vehicles. The study aimed to enhance vertical handover decision-making in heterogeneous vehicular networks by leveraging machine learning techniques to improve connectivity and optimize network selection. To address these challenges, the researchers proposed dynamic Q-learning for handover decision-making, which adapts to real-time network conditions using an entropy-based threshold, F-CNN for network selection, considering parameters such as signal strength, distance, vehicle density, and LoS, and the Jellyfish optimization algorithm for V2V chain routing, which selects the optimal vehicle-to-vehicle (V2V) path based on channel, vehicle, and performance metrics. For validation, the researchers conducted simulations in OMNET++ and SUMO, using 100 vehicles within a 2500 m \(\times\) 2500 m area, communicating via DSRC, mmWave 5G, and LTE. Performance evaluation showed that handover success probability increased by 10%, unnecessary handovers were reduced by 25%, throughput improvedby 15-20%, and delays and packet loss were significantly minimized. The key findings of the study indicate that the proposed Q-learning and F-CNN model effectively reduces unnecessary handovers and improves network selection, while the Jellyfish optimization algorithm enhances routing efficiency. The new approach outperforms traditional methods like TOPSIS and is more suitable for dynamic IoV environments. However, one limitation of the study is that it does not address security aspects, which should be considered in future work to integrate cybersecurity measures for ensuring secure and reliable vehicular communication. In 2017, [PERSON] et al. (2017) carried out research on the development of a hybrid artificial intelligence-based handover decision algorithm, aiming to improve handover decision-making in wireless mobile communication systems.