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A network clock model for time awareness in the Internet of things and artificial intelligence applications

  • Soyoung HwangEmail author
Article

Abstract

The Internet has immeasurably changed all aspects of life, from work to social relationships. The Internet of things (IoT) promises to add a new dimension by making possible not only communications with and among objects but also, thereby, the vision of anytime, anywhere, anything communications. The IoT allows sensing or control of objects remotely across network infrastructures. Its application, thus, is very extensive. The principal IoT applications are infrastructure management, smart manufacturing, smart agriculture, energy management, environment monitoring, building and home automation, metropolitan-scale deployments, medicine and health care, and smart transportation. Many IoT applications entail the collection and also forwarding of event data. To realize the IoT’s potential, combining it with artificial intelligence (AI) technologies is necessary. The IoT collects data, which AI processes so as to make sense of it. In order to trigger an action in the IoT and in AI applications, knowledge of the time at which an event occurs can be very useful. Time information, in fact, is an essential infrastructural component of any distributed system. Indeed, in IoT and AI applications, time information and time synchronization are among the most fundamental components. The IoT and AI thus require a scheme for data’s combination with time. This paper proposes a network clock model that enables the sharing, by IoT and AI devices, of a consistent notion of time. A proposed network clock model is implemented and evaluated in an actual test platform of MICAz-compatible sensor nodes operated in TinyOS 2.0 and Arduino Uno (R3) in order to verify its feasibility. The experimental results indicate that, for any application, IoT devices are capable of maintaining standard time and serving a standard timestamp.

Keywords

Clock model Internet of things (IoT) Time awareness Artificial intelligence (AI) System time 

Notes

Acknowledgements

The first draft of this paper was presented at the 12th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2017), Taichung, Taiwan, December 18–20, 2017 [42]. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4009167).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of SoftwareCatholic University of PusanBusanRepublic of Korea

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