# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 1 Introduction  
Recently, UAVs have attracted much research attention due to recent technology advancement. Unlike terrestrial nodes, UAV operation has fewer constraints, which enables a more flexible mobility model. The communication devices integrated with a UAV, including connectivity attributes and network functionality, can effectively establish connections with a variety of urban devices. Notably, UAVs employ a flexible network of nodes whose actions should be meticulously controlled [1; 2; 3; 4; 5]. Nowadays, in different fields, tremendous attention is given to multi-agent systems for complex problem solutions that individuals cannot achieve [6; 7; 8; 9]. To achieve UAV communication, the Internet of Drones (IoD), a swarm of UAVs, offers several benefits and applications. Firstly, it provides faster and more flexible deployment in harsh and complex environments. Secondly, the IoD enables effective coordination among UAVs for common missions. Thirdly, it supports various use cases in wireless networks, including enhancing wireless capacity and coverage at hotspots or temporary outdoor events [10; 11; 12; 13]. Fourthly, the IoD can be utilized in disaster relief activities and public safety scenarios when traditional networks are damaged [3; 14]. Fifthly, they are valuable for data gathering applications, providing real-time topological information and object location [15; 16]. Lastly, the IoD is widely used in environment monitoring, including search and rescue operations and surveillance, thanks to their equipped cameras and sensors. Generating an obstacle-free path for formations of the IoD is the primary and most difficult objective in environments with dense swarms and various constraints [17; 18; 19; 20; 21; 22]. Furthermore, employing deterministic methods to construct paths for IoD formations necessitates significant storage capacity and extended execution time [23; 24; 25; 26]. This is due to the fact that the optimization criteria encompass various factors, such as minimizing path length, avoiding obstacles, completing missions in shorter time duration, and minimizing energy consumption, which need to be jointly optimized [27; 28]. Despite ample UAV applications, onboard energy has fundamentally limited UAV performance and endurance, which is practically finite [29; 30; 31]. As a result, most applications cannot reach their full potential. Furthermore, in the case of a lengthy path, where the drone is required to carry out its mission in a large area, it becomes crucial to minimize the energy consumption of the UAV in order to generate a safe path and successfully reach its destination. To provide a clearer perspective, enhancing the energy efficiency of the UAV enables it to accomplish more tasks within a shorter time frame. Conversely, if the drone lacks an efficient energy mechanism, it would need to undergo more recharge cycles, thereby prolonging the mission duration [32]. Thus, reducing energy consumption during drone missions poses a significant challenge. To address this problem, powerful optimization techniques, such as game theory [33], convex optimization [34], stochastic optimization, and transport theory [35], have been applied. Although several non-linear approaches have shown satisfactory results, they are typically confirmed in narrow and primary habitats, making them impossible to apply in large-scale and complex environments [11; 29]. In the past few years, popular meta-heuristic algorithms have been seen as a powerful global optimization approach for solving UAV trajectory problems. Examples include the genetic algorithm (GA) for mobile robotic path planning [36; 37], particle swarm optimization (PSO) [38], and the artificial bee colony (ABC) [39]. However, existing research in UAV path planning within the IoD often falls short in effectively navigating complex environments, prioritizing energy efficiency, and thoroughly comparing meta-heuristic optimization techniques.