## 1 Introduction
Moreover, by processing/analyzing data using an artificial intelligence (AI) algorithm via the satellite-based computing platform and sending only the useful information that has a much smaller data size to the user, the user can obtain the desired information much faster;
* More rapid remote-sensing information dissemination: Edge computing in the LEO network can lead to improved capabilities in near-real-time remote sensing, in which data are processed and transmitted relatively quickly. Some remote-sensing satellites are designed for specific applications, such as disaster monitoring, in which more rapid information dissemination is critical. By leveraging edge computing, it is possible to reduce the time needed for useful information dissemination obtained by satellite remote sensing. The inference process of deep learning (DL) is an important type of algorithm that is run on the LEO satellite-based edge-computing platform. However, the computing power of an LEO satellite is less than that of an edge-computing node in the terrestrial network, so it is necessary to use multiple satellites' computing resource in order to complete massive inference tasks in a short time [8; 9]. For example, when there are many users who are offloading many inference tasks to the access satellite or when a remote-sensing satellite has many images (or video frames) requiring processing, it is necessary for several nearby satellites to compute collaboratively to avoid overloading the access satellite [10]. Therefore, in this paper, we focus on the collaborative inference problem in the LEO edge-computing system and propose a COllaborative INference algorithm in LEO edge computing called COIN-LEO, in which several satellites can collaboratively complete many inference tasks in a finite time. Specifically, the COIN-LEO algorithm first appropriately selects several satellites to participate in the inference process in the 'neighborhood' of the data source satellite. Then, COIN-LEO splits the deep neural network (DNN) model into several parts, in which a part consists of several consecutive layers of the model. After obtaining the model-splitting result, COIN-LEO assigns these model parts to selected satellites, and each satellite accomplishes the inference task that corresponds to the assigned model part. In this way, several satellites collaboratively accomplish the compete inference process in a pipeline fashion, which can improve the amount of accomplished inference tasks within a given time. Our main contributions are summarized as follows:  
* We propose a simple method to select appropriate satellites to participate in the collaborative inference process, which is easy to implement in engineering. The core idea is to take into the entire topology of the LEO network in consideration and select several satellites to build a circle-like topology;
* We propose an efficient deep reinforcement learning (DRL)-based algorithm for DNN model splitting. To support the model-splitting algorithm, we use a neural network (NN) to predict the inference time of a specific submodel deployed on a specific satellite and conduct many experiments to obtain enough training data;
* We conduct the collaborative inference experiments in a testbed that highly realistically simulates the LEO network and evaluates several performance metrics, such as inference throughput and time. The following article is organized as follows. Section 2 provides a comprehensive overview of related works in the literature. The system model is described in Section 3. In Section 4, we explain the details of the COIN-LEO algorithm. Performance evaluation is presented in Section 5. Section 6 concludes this article.