Metaheuristic-based optimal 3D positioning of UAVs forming aerial mesh network to provide emergency communication services

The optimal placement of unmanned aerial vehicles (UAVs) to facilitate post-disaster emergency communication services is a paramount research domain. The novelty of the authors' work is to optimally place available UAVs in 3D space to meet the objectives prominent during such situations. The objectives considered here are target coverage, QoS, energy consumption and two newly characterized objectives, i.e. equal load distribution over UAVs and fault tolerance for improving network connectivity and lifetime. To improve these conflicting objectives altogether, the authors proposed two metaheuristic-based hybrid optimization algorithms, namely, HWWO-HSA, a hybrid of Water Wave Optimization (WWO), Harmony Search (HS) and Simulated Annealing (SA); and HGA-SA, a hybrid of Genetic Algorithm (GA) and SA. Furthermore, to improve network performance, the authors performed their parameter tuning using well-known Taguchi's design of experiment. For maintaining network connectivity, the authors used the concept of graphs for connected components. The proposed hybrids are compared with their originals, i.e. GA, HS, SA and WWO over different scaled test scenarios. The non-parametric Kruskal-Wallis test and Dunn's post hoc test suggest that hybrids perform better than the originals. HGA-SA significantly outperforms in small-scale scenarios while HWWO-HSA significantly outperforms in medium-scale and large-scale scenarios. However, being hybrid the computational time of the hybrids is greater than the originals.

Automated summary

In this study, two hybrid optimization algorithms were proposed to optimize the deployment of unmanned aerial vehicles for post-disaster emergency communication services. Experimental results showed that the hybrids outperformed the original algorithms in different scaled test scenarios, but with increased computational time.