Smart Grid and Internet of Things
Third EAI International Conference, SGIoT 2019, TaiChung, Taiwan, December 5-6, 2019, Proceedings
(Sprache: Englisch)
This book constitutes the refereed proceedings of the Third EAI International Conference on Smart Grid and Internet of Things, SGIoT 2019, held in TaiChung, Taiwan, in November 2019. The 10 papers presented were carefully reviewed and selected from 22...
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This book constitutes the refereed proceedings of the Third EAI International Conference on Smart Grid and Internet of Things, SGIoT 2019, held in TaiChung, Taiwan, in November 2019. The 10 papers presented were carefully reviewed and selected from 22 submissions and present results on how to achieve more efficient use of resources based largely on the IoT-based machine-to-machine (M2M) interactions of millions of smart meters and sensors in the smart grid specific communication networks such as home area networks, building area networks, and neighborhood area networks. The smart grid also encompasses IoT technologies, which monitor transmission lines, manage substations, integrate renewable energy generation. Through these technologies, the authorities can smartly identify outage problems, and intelligently schedule the power generation and delivery to the customers. Furthermore, the smart grid should teach us a valuable lesson that security must be designed in from the startof any IoT deployment.
Inhaltsverzeichnis zu „Smart Grid and Internet of Things “
Flag-assisted Early Release of RRC Scheme for Power Saving in NB-IoT System.- DTMFTalk: A DTMF-based Realization of IoT Remote Control for Smart Elderly Care.- IoT Insider Attack - Survey.- Energy Management for Zones-based Isolated DC Multi-microgrids.- Mining network security holes based on data flow analysis technology.- Network Design for Internet of Things in Energy Sector.- Power Prediction via Module Temperature for Solar Modules Under Soiling Conditions.- Customized Attack Detection Under Power Industrial Control System.- HomeTalk: A Smart Home Platform.- Deep Learning Based Pest Identification on Mobile Prediction Traffic Flow with Combination Arima and PageRank.Bibliographische Angaben
- 2020, 1st ed. 2020, X, 141 Seiten, 70 farbige Abbildungen, Masse: 15,9 x 23,7 cm, Kartoniert (TB), Englisch
- Herausgegeben: Der-Jiunn Deng, Ai-Chun Pang, Chun-Cheng Lin
- Verlag: Springer, Berlin
- ISBN-10: 3030496090
- ISBN-13: 9783030496098
Sprache:
Englisch
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