# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 2 Related Work  
To support the transmission of the observation satellite, many works have been conducted [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. These can be categorized into (1) non-collaborative approach (i.e., without the relay satellite) [7; 8; 9; 10; 11; 12]; and (2) collaborative approach (i.e., with the relay satellite) [13; 14; 15; 16; 17]. Note that our work in this paper can be categorized as a collaborative approach.  
[PERSON] et al. [7] firstly introduced a satellite range scheduling problem to schedule the satellite transmissions with the ground stations within the contact time between them. A mixed-integer programming (MIP) problem is formulated to maximize the total number of satellite transmissions with ground stations and solved by a heuristic method. [PERSON] et al. [8] presented a problem for scheduling satellite tasks to maximize the profit by processing the tasks. [PERSON] et al. [9] formulated a multiprocessor task scheduling problem to maximize the number of satellite missions with ground stations and developed a Lagrangian-based heuristic method to solve the problem. [PERSON] et al. [10] provided a MIP model and algorithm for the communication scheduling problem considering single-satellite and multi-ground stations to maximize the total amount of transmitted data. [PERSON] et al. [11] proposed a binary integer linear programming formulation for the satellite task scheduling to maximize the overall mission performance metric. [PERSON] et al. [12] formulated a non-convex optimization problem to maximize the energy efficiency of satellite-terrestrial integrated networks and proposed two beam-forming schemes with low complexity to solve the formulated problem. [PERSON] et al. [18] formulated an optimization problem to maximize the secrecy energy efficiency while maintaining the total transmission power below a certain level, and decomposed the original optimization problem into sub-problems to obtain the near-optimal solution.  
Note that the above-mentioned previous works focused on the direct transmission between observation satellites and ground stations; however, the opportunities for this direct transmission are limited due to the short connection time between observation satellites and ground stations. To alleviate this problem, we can introduce relay satellites that always have connection to ground stations.  
[PERSON] et al. [13] provided a collaborative data download scheme to schedule communications links both between satellites and between satellite and ground stations to maximize the throughput. [PERSON] et al. [14] presented a dynamic programming framework based on the Markov decision process (MDP) to optimize the transmission scheduling by considering the stochastic data arrival characteristics, the inter-satellite contact model, and the energy dynamics model. [PERSON] et al. [15] proposed a two-phase task scheduling method to dynamically adjust the scheduling according to the frequent task variation in relay-satellite-based systems. [PERSON] et al. [16] introduced a priority-based task scheduling algorithm considering the different requirements of emergency and normal tasks. [PERSON] et al. [17] proposed a data breakpoint transmission method which can divide a single task into multiple subtasks and schedule them in multiple contact time periods to maximize the task completion rate.  
Table 1 summarizes the comparison of related collaborative approaches. Even though these works focused on the transmission scheduling or task scheduling with the relay satellites (i.e., how to use the resources of the relay satellites), it was not investigated how to utilize the storage and computational resources of the observation satellites.