Abstract
The development of Internet of Things technology brings new opportunities for the development of edge computing. As an emerging computing model, edge computing makes full use of the equipment resources at the edge of the network and creates a new network computing system at the edge of the network. At the same time, the emergence of edge computing solves the problem of high latency in WAN which cannot be solved for a long time in the field of cloud computing, and brings users with low latency, fast response and good service experience. This article will use the edges computing architecture to construct a multi-layer data collection system. In this system model, sensors upload data to the designated edge nodes for processing, rather than remote cloud computing centers. Data collection and sample training tasks of sensor nodes in different ranges are realized through the design of multi-layer edge nodes. This system reduces the energy consumption of data uploading and the delay in network communication. As a result, it provides a better network experience for the end users. And it tries to solve the problem that the edge node in the edge system cannot satisfy multiple training task requests at the same time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mao, Y., You, C., Zhang, J., et al.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutorials PP(99), 1 (2017)
Wang, S., Tuor, T., Salonidis, T., et al.: When edge meets learning: adaptive control for resource-constrained distributed machine learning. In: IEEE INFOCOM 2018 IEEE Conference on Computer Communications. IEEE, pp. 63–71 (2018)
Teerapittayanon, S., Mcdanel, B., Kung, H.: Distributed deep neural networks over the cloud, the edge and end devices. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), IEEE, pp. 328–339 (2017)
Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the Internet of Things with edge computing. IEEE Netw. 32(1), 96–101 (2018)
Xu, X., Ansari, R., Khokhar, A., Vasilakos, A.V.: Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Trans. Sens. Netw. 11(3), 1–25 (2015). Article 45
Chong, L., Jun, S., Feng, W.: Compressive network coding for approximate sensor data gathering. In: Global Telecommunications Conference, IEEE Press, pp. 1–6 (2011)
Luo, C., Wu, F., Sun, J., Chen, C.W.: Compressive data gathering for large-scale wireless sensor networks. In: Proceedings of MobiCom (2009)
Luo, J., Xiang, L., Rosenberg, C.: Does compressed sensing improve the throughput of wireless sensor networks. In: Proceedings of the IEEE International Conference on Communications (2010)
Zheng, H., Yang, F., Tian, X., Gan, X., Wang, X., Xiao, S.: Data gathering with compressive sensing in wireless sensor networks: a random walk based approach. IEEE Trans. Parallel Distrib. Syst. 26(1), 35–44 (2015)
Wang, L., et al.: CCS-TA: quality-guaranteed online task allocation in compressive crowdsensing. In: UBICOMP 2015, Osaka, Japan, 7–11 September 2015
Kang, K.D., Chen, L., Yi, H., et al.: Real-time information derivation from big sensor data via edge computing. Big Data Cogn. Comput. 1(1), 5 (2017)
Shi, W., Cao, J., Zhang, Q., et al.: Edge computing: vision and challenges. IEEE Internet Thing J. 3(5), 637–646 (2016)
Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., et al.: Estimating smart city sensors data generation. In: 2016 Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net). IEEE, pp. 1–8 (2016)
Sivakumaean, M, Iacopino, P.: The mobile economy 2018. GSMA Intelligence, pp. 1–60 (2018)
Acknowledgments
This research was partly supported by National Natural Science Foundation of China under Grant No. 61672221, and by National Natural Science Foundation of Hunan Province under Grant No. 2020JJ4008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xiang, S., Rong, H., Xu, Z. (2021). Data Gathering System Based on Multi-layer Edge Computing Nodes. In: Jiang, H., Wu, H., Zeng, F. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-030-73429-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-73429-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73428-2
Online ISBN: 978-3-030-73429-9
eBook Packages: Computer ScienceComputer Science (R0)