Abstract
The data gathering optimization of the large-scale, collaborative and concurrent multi-task in the sensing layer of internet of things is very important, especially in the environments where multiple geographically overlapping wireless sensor networks are deployed. In order to support large-scale, collaborative and concurrent multi-task monitoring, in this paper, we propose a massive sensor sampling data gathering optimization strategy in formed virtual sensor networks to meet various monitoring requirements from different kinds of application deployment and simplify the complexity of dealing with heterogeneous sensor nodes. Then, for the massive sensor sampling data gathering on the virtual sensor networks framework, the CH nodes set and update MinMax hierarchical thresholds to restrict the data transmission. Finally, the simulation results show that proposed strategy achieves more energy savings and increase the sensing layer lifetime of internet of things.
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Song, X., Wang, C., Xu, Z., Zhang, H. (2013). A Massive Sensor Sampling Data Gathering Optimization Strategy for Concurrent Multi-criteria Target Monitoring Application. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_73
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DOI: https://doi.org/10.1007/978-3-642-39068-5_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
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