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With the increasing demand for geological and weather information, remote sensing satellite networks (RSSNs) play increasingly important roles in monitoring our planet. On account of the significant differences between RSSNs and traditional communication networks, the traditional communication capacity which only focus on the performance of data transmission process cannot well capture the service capability of RSSNs. In order to provide efficient guidelines to the deployment of RSSNs, in this paper we study a new capacity indicator, called information capacity, which takes into account the whole service process of RSSNs, including information acquisition, processing, storage, and transmission. Specifically, we firstly propose the formal definition of information capacity. Then, a new graph model called microscopic time-expanded graph (MTEG) is developed, which characterizes the intertwined impact of the observation, computational, storage and transmission resources on the service process of RSSNs. Base on this graph model, a mathematical framework is developed to compute the information capacity. Owing to the NP-completeness of the formulated problem, we decompose it into a flow optimization problem and an arc scheduling problem of the MTEG model, and then propose a Graph-based Information Capacity Solving (GICS) algorithm to efficiently solve the problem. Finally, simulation results highlight the necessity of study the information capacity of RSSNs.

Exploring the Information Capacity of Remote Sensing Satellite Networks