## 1 Introduction
[PERSON], et al. presented an enhanced bat algorithm for 3D path planning of unmanned combat aerial vehicles (UCAVs), focusing on adaptability and obstacle avoidance [17]. [PERSON], et al. combined genetic algorithms and artificial neural networks for efficient 3D UAV path planning, enhancing adaptability in complex environments [18]. [PERSON], et al. proposed a hybrid algorithm integrating multiple methods for efficient UAV route construction under various constraints [19]. [PERSON], et al. discussed methods for optimizing arrival routes to improve flight efficiency in aerial navigation systems [20]. [PERSON], et al. applied mixed-integer programming for efficient multi-vehicle path planning, addressing coordination and collision avoidance [21]. [PERSON], et al. explored pre-flight planning algorithms for UAV groups, focusing on distributed control systems for coordinated missions [22]. [PERSON], et al. provided a comprehensive review of UAV navigation algorithms, discussing trends, challenges, and advancements [23]. [PERSON], et al. surveyed the diverse civil applications of UAVs, highlighting their roles in agriculture, surveillance, and disaster management [24]. [PERSON], et al. proposed a heuristic evolutionary algorithm tailored for UAV path planning, focusing on route efficiency and adaptability [25]. [PERSON], et al. compared various path planning algorithms for UAVs in threat-prone environments, evaluating their performance and robustness [26]. [PERSON], et al. explored deep reinforcement learning techniques for UAV navigation, utilizing sensor data to enable intelligent decision making [27]. Aircraft usually flies over an air route, which is called a \"route in the air\". Each air route determines the direction and the altitude of an aircraft flight for safe navigation. Normal air routes are published as straight lines connecting air navigation radio facilities (VOR [28] and NDB [29]) to each other. Flight routes are formed by combining several straight lines between the departing and arriving airports. When a large number of UAVs fly over the ground for home delivery, air routes will be necessary for UAVs, like normal aircraft routes. We have established a distributed management system for UAVs inspired by the virtual circuit and datagram methods of packet switching networks. In Japan, smart meter networks are becoming popular, in which radio terminals are installed in each house to collect the electricity consumption via wireless multi-hop networks. As of 2020, smart meters have been installed in approximately 80% of homes in the United States. In the EU, smart meters have been introduced in approximately 72% of the electricity market by 2020, and their use is particularly widespread in Western European countries. For example, in the UK, a plan is underway to install smart meters in approximately 27 million homes between 2016 and 2024. China has already installed hundreds of millions of smart meters. Wireless multi-hop networks have been built to communicate power information from smart meters in many countries. This fact indicates that wireless multi-hop routing between all the houses is possible if a radio terminal is installed in every house. [PERSON]. [PERSON], et al. discussed how wireless networks (including smart meters) enable routing and connectivity [30]. Under these conditions, we have investigated a method to create a route between the source and destination nodes of a parcel delivery on a wireless multi-hop network. The radio terminals installed in each house become the nodes of this network and will be used for navigation radio facilities. The UAVs fly over the nodes on the route as while they were transmitting data packets to UAVs [31]. The control of the packet switching network is applied to the flight route control of UAVs by treating UAVs as packets in the network, wireless devices on the ground as routers, and the connection relationship between wireless devices as links.