Abstract
Satellite Internet of Things (S-IoT), which combines satellite networks with IoT, is a ubiquitous IoT system under the integrated satellite-terrestrial information network architecture. It has the advantages of wide coverage, multiple-type services, and strong robustness. However, as a result of the dynamic changes of topology structure and node status in S-IoT, the effective forwarding of data packets is challenging. In view of the above problem, an adaptive routing strategy based on improved Q-learning for S-IoT is proposed in this paper. First, the whole S-IoT is regarded as a reinforcement learning environment. In the meantime, satellite nodes and ground nodes in S-IoT are regarded as intelligent agents, respectively. What is more, the next hop node of data packets is determined according to the Q value. Second, in order to optimize the Q value, this paper improves the discount factor based on the status of satellite nodes. Finally, simulation results show that the proposed strategy can achieve efficient routing in the high dynamic environment. Compared with the state-of-the-art strategies, it improves the performance in terms of delivery rate, average delay, and overhead ratio.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61972210, 61873131, 61803212).
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Gong, X., Sun, L., Zhou, J., Wang, J., Xiao, F. (2021). Adaptive Routing Strategy Based on Improved Q-learning for Satellite Internet of Things. In: Wang, G., Chen, B., Li, W., Di Pietro, R., Yan, X., Han, H. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2020. Lecture Notes in Computer Science(), vol 12383. Springer, Cham. https://doi.org/10.1007/978-3-030-68884-4_13
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