# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 6 Conclusions  
Motivated by the emergence of DC communication practices and their associated challenges in terms of coverage and placement, different swarm intelligence approaches have been investigated for optimal DC placement with different constraints in multiple scenarios. The DC placement problem has been investigated with three different models and a base line has been achieved with previous research conducted in the field. Parameter tuning has been performed across all environments, followed by additional experiments using the optimized parameters. The results and statistical test show that there has been a noticeable improvement over the default algorithm. With the adoption of parameter tuning and re-initialization, all three algorithms have shown to be capable of DC placement, even when optimizing DC placement in a challenging environment. DFO implementation has demonstrated that it is competitive for both default and re-initialized algorithms; any further experimentation would be conducted with DFO as the basis for DC placement. The objective of this research was to provide an overview of the various techniques used for DC placement and to implement meta-heuristic approaches for a comparative study. The diversity of the algorithms was analyzed, leading to the introduction of phased restarts, particularly for PSO. This enhancement has increased the algorithms' ability to explore the solution space, resulting in an overall performance boost. Parameter tuning was carried out to maximize the performance of each algorithm across different scenarios and threshold values. Despite its minimalist design, DFO maintained consistent performance across all tests, while DE, even without phased restarts, proved to be an effective algorithm for DC placement. The inherent diversity in DE may become even more valuable in cases where the need for exploration is not as evident. This paper contributed to DC placement in its implementation of all available urban environments as proposed in [51] and implemented by other researchers as stated in Section 5.1, giving a full overview of all possible real-world scenarios where some researchers have decided to focus on one urban environment. The investigation of algorithmic diversity has also identified a key limitation in the implementation of different algorithms, which has been neglected in recent papers. It is hypothesized that diversity and the principle working of the algorithms will be a more important part of DC placement as more constraints are added to the current implementation. However, this study has also identified several key limitations that must be addressed to fully utilize the potential of DA-RAN in real-world scenarios. The research did not account for energy efficiency, a critical factor for sustained operations, nor was inter-DC interference a priority, which could impact data flow and redundancy, especially in large-scale distributed systems. Moreover, the scenarios tested mainly consisted of static users, leaving out dynamic user behaviors such as frequent connections, disconnections and varying demand. Building on these limitations, future research should address several critical areas to improve DC placement strategies. Adding support for dynamic user behavior into the DC placement models is essential for developing adaptive, real-time solutions that can maintain optimal performance under dynamic load conditions. In addition to dynamic users, inter-DC interference should be explored leading to the creation of a distributed DC network which could improve load balancing, data flow and system redundancy. Addressing energy efficiency is another critical avenue, as reducing power consumption without sacrificing performance is fundamental to the sustainability of drone-based systems. Beyond these immediate challenges, broader research should focus on scalability and the application of the implemented strategies on large-scale real-world problems.