Abstract
With the wireless sensor networks (WSNs) becoming extremely widely used, mobile sensor networks (MSNs) have recently attracted more and more researchers’ attention. Existing routing tree maintenance methods used for query processing are based on static WSNs, most of that are not directly applicable to MSNs due to the unique characteristic of mobility. In particular, sensor nodes are always moving in real world, which seriously affects the stability of the routing tree. Therefore, in this paper, we propose a novel method, named routing tree maintenance based on trajectory prediction in mobile sensor networks (RTTP), to guarantee a long term stability of routing tree. At first, we establish a trajectory prediction model based on extreme learning machine (ELM). And then, we predict sensor node’s trajectory through the proposed ELM based trajectory prediction model. Next, according to the predicted trajectory, an appropriate parent nodes are chose for each non-effective node to prolong the connection time as much as possible, and reduce the instability of the routing tree as a result. Finally, extensive experimental results show that RTTP can effectively improve the stability of routing tree and greatly reduce energy consumption of mobile sensor nodes.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472069 and 61402089; And the Fundamental Research Funds for the Central Universities under Grant Nos. N130404014.
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Xin, J., Li, T., Wang, P., Wang, Z. (2016). Routing Tree Maintenance Based on Trajectory Prediction in Mobile Sensor Networks. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_32
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DOI: https://doi.org/10.1007/978-3-319-28397-5_32
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