# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 1 Introduction
Furthermore, there is a notable lack of extensive, rigorously validated simulation studies to test various path-planning approaches. Although meta-heuristic algorithms have been implemented for path planning in the literature, there is limited consideration of IoD formation by researchers. Furthermore, when the IoD scenarios are considered, researchers typically employ simple scenarios and sparse environments. Specifically, the drones are assigned tasks in diverse environmental conditions, including dense environments that require collaborative efforts to accomplish their missions. The objective of this analysis is to establish a baseline performance study for solving drone path planning in highly dense environments as well as to evaluate the limitations and strengths of these algorithms. In accomplishing this, we aim to gain insights into the suitability and challenges associated with employing these algorithms in such scenarios. In this work, we model the path planning of the IoD as a single objective optimization problem where the paths of the IoD are constrained to avoid collision with obstacles and with other UAVs in highly dense environments. We design an objective function involving two important objectives, namely path length and energy consumption. The path can be optimized either for minimum path length or minimum energy consumption. Using simulations, we consider energy consumption as an objective to be minimized. Moreover, the performance of different meta-heuristic approaches is evaluated and compared. The aim is to study the efficiency and the behavior of these algorithms under different conditions. The optimization techniques covered in this work include PSO, IPSO, CIPSO [40], GA, and ABC, which are extensively used in several optimization problems. Extensive simulation experiments have been conducted to investigate the performance of these algorithms. The contributions of this work are summarized as follows. We conduct extensive simulations to evaluate and compare the performance of five meta-heuristic approaches: IPSO, CIPSO, PSO, GA, and ABC, to solve the IoD path-planning problem in highly dense environment scenarios. The ANOVA test is applied to provide a statistically robust analysis of the differences among them, ensuring an effective comparison. This helps to gain a deeper understanding of the performance of the respective approach under various conditions and scenarios, and to provide valuable insights for practical implementation. Furthermore, the comparative analysis sheds light on the suitability of each algorithm for different application scenarios, aiding researchers in selecting the most appropriate algorithm for their specific IoD network requirements. Moreover, this study opens an opportunity for future works to design proper algorithms based on the outlined limitations and strengthens each of the considered approaches. The rest of the paper is organized as follows. Section 2 presents the state of the art in the field of UAV path planning. Section 3 defines the energy model for path planning, the IoD path-planning problem, objectives and constraints, and the meta-heuristic algorithms applied in this study. Results and discussion are illustrated in Section 4. Section 5 presents the discussion of weaknesses and strengths of the analyzed algorithms. Finally, conclusions are drawn in Section 6.