Abstract
This article comes from a solar WSN air monitoring system deployed outdoors. We find that two issues have not been properly resolved: 1, The actual deployment environment of the node has a part time shadow, resulting in a significant reduction in the accuracy of solar prediction algorithms. 2, The length of solar prediction and the topology adjustment period have a great influence on the network lifetime. According to the above, this paper proposes a distributed Solar WSN Adaptive Framework (SWAF), designs a distributed method to distinguish the shadow time of nodes and a dynamic method to select the charging prediction period, which can effectively integrate the existing charging prediction algorithm and energy aware routing algorithm. The experimental results show that SWAF can reduce node mortality, and thus improve network lifetime. Compared with the case of simply using the existing prediction model and the routing algorithm, the SWAF can increase the network lifetime by 5%–27%.
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Acknowledgments
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1800302, in part by the Natural Science Foundation of China under Grant 61702013, in part by the Beijing Natural Science Foundation under Grant KZ201810009011, Grant 4202020, and Grant 19L2021, and in part by the Science and Technology Innovation Project of North China University of Technology under Grant 19XN108.
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Hu, Y., Ma, D., Huang, X., Du, X., Xiao, A. (2020). SWAF: A Distributed Solar WSN Adaptive Framework. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12452. Springer, Cham. https://doi.org/10.1007/978-3-030-60245-1_32
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DOI: https://doi.org/10.1007/978-3-030-60245-1_32
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