# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 1 Introduction  
Recent improvements in small satellite technologies are making the use of small-satellite-based solutions appealing in different use cases, including the internet of thing (IoT). One practical example among others is the departural satellite (D-SAT) project, where a flexible cubesat-based system has been designed and tested to broadcast data generated by sensors spread over a certain coverage area [1, 2]. Concurrently, because 5G cannot achieve seamless global coverage, an important development trend of B5G and 6G is the space-air-ground-sea integrated communication network [3, 4]. The 3 GPP working group has thus started work in this area [5]. Compared with terrestrial communications, a major advantage of satellite communications is wide coverage, which is particularly beneficial to massive machine communication (mMTC) services with ultralarge-scale access equipment (i.e., IoT services) [6]. The third-generation partnership project (3 GPP) working group has studied satellite networks in three orbits: medium earth orbit (MEO), geosynchronous earth orbit (GEO) and low earth orbit (LEO). The LEO satellite network has become important for delay-sensitive services due to its lower delay. However, LEO satellites require dense deployment of space segments due to their high flight speeds and short coverage times to ensure that the groundterminal has satellite coverage continuously [6]. Therefore, with the gradual maturity of small satellite technology, the LEO mega constellation, which is a satellite system composed of multiple satellite orbits and hundreds of small satellites, has become a hot spot for B5G and 6G [6; 7], and the LEO mega constellation for the IoT has become a common goal of industry and academia [8; 9; 10; 11]. Conversely, with the development of IoT in recent years and the explosive growth of the number of IoT access devices, the big data generated by IoT pose a serious challenge to the network architecture based on TCP/IP to efficiently achieve data distribution and routing [12; 13]. Deep learning and IoT can be combined in TCP/IP-based network architectures to efficiently achieve data distribution and routing [14; 15; 16]. However, how to assign IP addresses to IoT access devices is also a tricky problem. Because IPv4 addressing space is full, the IPv6 address space may also fill in the future [12]. In addition, IPv6 addresses are long [12], which makes them less suitable for the access of billions of devices with various constraints (e.g., power consumption, storage, size) [17; 18; 19; 12], including wireless sensors [17; 18; 19]. Therefore, TCP/IP is clearly inadequate for IoT requirements [12]. From a data perspective, business applications care more about data itself rather than the address of the data source. Therefore, in recent years, to fulfil all these needs of the IoT, designing a new future network architecture from the ground up has become an important subject of special attention in industry, and information-centric networking (ICN) is a landmark ideal solution [12; 13], which is particularly valued in B5G and 6G core networks, IoT and other fields [20; 21; 22]. ICN aims to make the current complex network architecture simpler and more versatile. Network elements are no longer servers, routers, terminals, etc. ICN networking, based on the content goal of naming, is the receiving-driven model, and data naming is one of the basic problems of ICN, which has been a research hotspot in recent years [23]. Named-data networking (NDN) [24; 25] is an implementation of ICN that uses hierarchical and human-readable names to transmit content across the network. In particular, the communication paradigm of NDN is based on request and response patterns with simplified pull-based communication through interest and data packets. Name-based data retrieval can eliminate the dependence on static locations and IP addresses [26].