# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 5 Conclusions  
Handover management for 6G networks is a critical consideration for ensuring continuous, ultra-reliable connectivity in smart cities, with mobility, high network density, and service continuity being the primary challenges. Some of the handover approaches, such as AI-based decision-making, software-defined networking, and edge computing, were addressed in this paper, which introduced more efficiency, low latency, and better QoS. Unlike previous generations of networks, 6G is founded on an extremely heterogeneous infrastructure with terrestrial, aerial, and satellite networks, and thus, HO management becomes even more difficult to achieve efficiently. While AI-driven handover schemes are beneficial in predictive modeling and adaptive decision-making, they also introduce computational overhead, wastage of energy, and security risks. Some of the significant issues that this research has highlighted are the excessive rates of unnecessary handovers due to ultra-dense network deployment, leading to increased signaling overhead and network load. Security threats such as rogue base stations, man-in-the-middle attacks, and data privacy intrusions also highlight the need for secure authentication techniques. Interoperability of HO protocols in heterogeneous communication environments such as IoT, vehicular communications, and satellite communications also complicates mobility management. Furthermore, while AI and ML-based HO techniques have immense potential solutions, their real-time usage includes cumbersome computational resources, which are not always feasible in real-time systems. Therefore, to overcome such issues, future research must focus on the creation of lightweight, power-efficient AI models to improve HO decision-making with computational efficiency. Hybrid AI-fuzzy logic approaches can enhance decision accuracy with minimal unnecessary handovers and low latency. Moreover, the use of blockchain technology will be capable of providing decentralized, tamper-evident authentication schemes for enhancing security and trust in HO processes. In parallel, emerging large-scale models such as large language models (LLMs) and multi-agent systems hold promises for intelligent mobility management [59]. Their ability to generalize across diverse mobility contexts and support autonomous decision-making in heterogeneous environments opens new directions for adaptive HO policies. Federated or cooperative learning architectures may also allow for privacy-preserving optimization without centralized data reliance. Standardization efforts will also have to take precedence so that interoperability among different network layers, as well as service providers, can occur with cross-domain handovers without degrading services. In all, addressing these critical challenges will be crucial to derive maximum benefits from 6G networks for smart cities and beyond. The development of flexible, secure, and smart HO mechanisms will improve network performance as well as enable next-generation applications including autonomous transportation, industrial automation, and huge-scale IoT ecosystems to be deployed. With such efforts in these areas, researchers and industry stakeholders can be contributors towards guaranteeing a future wireless communication infrastructure capable of keeping up with the changing demands of the integrated digital world. Conceptualization, A.A. and I.S. ; methodology, L.A.; investigation, D.Y. ; resources, A.A.; writing--original draft preparation, A.A.; writing--review and editing, I.S. ; visualization, A.Z. ; supervision, L.A.; project administration, D.Y. All authors have read and agreed to the published version of the manuscript. This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR24992852 \"Intelligent models and methods of Smart City digital ecosystem for sustainable development and the citizens' quality of life improvement\"). Not applicable. No new data were created or analyzed in this study.