Abstract
In opportunistic networks, it is difficult to find the best relay node, which not only makes the message transmission inefficient but also wastes resources in the network. Most of the existing routing algorithms based on node similarity often choose relay nodes from a single point of view such as time or space. In this paper, a new neural network architecture is designed, namely, OSAN. It combined with the self- attention mechanism, the spatial and temporal variation characteristics of nodes in two dimensions are taken into account comprehensively to the maximum. Firstly, we divide the opportunistic networks into opportunity network snapshots according to the time window and input each opportunity network snapshot into the spatial structure self-attention layer. At the same time, to capture the interaction between different snapshots, we use the temporal self-attention mechanism. Thus, the Spatio-temporal characteristics of nodes in different snapshots are extracted. Finally, the similarity between nodes is calculated according to the Spatio-temporal characteristics extracted by nodes, so we propose a Spatio-temporal topology routing algorithm in opportunistic networks based on the self-attention mechanism (STSA). The simulation results show that STSA has advantages in several common performance indexes.
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Acknowledgment
This work was partially supported by the National Natural Science Foundation of China under Grant 62061036, 61841109 and 62077032, Natural Science Foundation of Inner Mongolia under Grand 2019MS06031, in part by the Self-Open Project of Engineering Research Center of Ecological Big Data, Ministry of Education.
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Wu, X., Xu, G., Hao, X., Huang, B., Bai, X. (2022). Spatio-Temporal Topology Routing Algorithm for Opportunistic Network Based on Self-attention Mechanism. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_10
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