## 1 Introduction  
UAVs, commonly called drones, have revolutionized the way aerial operations are conducted. Originally developed for military surveillance purposes [1], UAVs have experienced a drastic increase in usage during recent years and have become a valuable tool in various civilian industries [2] (e.g., traffic management, agriculture, surveillance and security, delivery services, and search and rescue, etc.) due to advancements in technology that have made them inexpensive, easy to deploy, and operate in hazardous or inaccessible environments. However, despite the benefits of UAVs, there are some limitations that can affect their operational effectiveness. The current IP architecture is identified as one of these limitations [3]. The current IP architecture was originally designed for communication among fixed devices on the Internet, and it focuses on the location of the devices rather than the data being transmitted. However, in recent years, it has become evident that the current IP architecture is not as well-suited to the demands of the modern Internet [4], which includes heterogeneous communication and a wide variety of highly mobile devices and vehicles such as satellites and UAVs, which have different requirements and characteristics compared to traditional fixed devices. In addition, many modern aircraft, including UAVs, are often employed to gather sensitive data for surveillance, research, and monitoring purposes. Collecting such data requires robust security measures to safeguard against security attacks and ensure their confidentiality and integrity. To this end, several overlay patches have been implemented to address security and mobility issues. Nevertheless, the IP architecture still faces challenges in providing seamless and efficient mobility support for all types of devices and applications. Moreover, overlay patches might not be sufficient for protecting against emerging threats [5]. This hinders the safe and secure use of UAVs for sensitive applications like military operations or privacy-sensitive contexts. In the last decade, the NDN architecture [6] has gained much research attention. It has been proposed as one of the promising future Internet architectures to overcome the current vulnerabilities and cope with the challenges [7] (e.g., addressing, name resolution, security, and privacy) of the IP Internet architecture. NDN is an information-centric networking architecture that changes the communication semantics by focusing on retrieving data by a given unique name rather than location address. Thanks to the built-in security, multi-path forwarding, consumer-driven communication, in-network caching, and name-based routing features, NDN has proven to be a suitable information-centric networking architecture choice for UAV communication [8; 9]. As a result of the built-in features of NDN, the mobility of devices requesting data (consumers) is inherently supported, thanks to its consumer-driven communication feature [10], where consumers only need to reissue requests from their new location without updating their locations or establishing a new connection. In contrast, producer (devices generating data) mobility management remains one of the significant challenges due to NDN's content-centric design, where data retrieval is based on content names rather than the location of devices. Accordingly, when the producer changes their Point of Attachment (PoA), they may become unreachable for a while until the relevant routers in the network update their information to the producers' new location. Furthermore, UAVs, as producers of data, introduce additional challenges for mobility management due to their real-time communication requirements, the ineffective NDN in-network caching feature to support low-latency data, and the UAVs' ability to move quickly and unpredictably, leading to frequent handoffs, which may result in increased latency and packet loss.