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OST: On-Demand TSCH Scheduling with Traffic-Awareness

Cited 33 time in Web of Science Cited 34 time in Scopus
Authors

Jeong, Seungbeom; Kim, Hyung-Sin; Paek, Jeongyeup; Bahk, Saewoong

Issue Date
2020
Publisher
IEEE
Citation
IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS, Vol.2020-July, pp.69-78
Abstract
As the emerging Internet of Things (IoT) devices and applications flourish, demand for reliable and energy-efficient low-power wireless network protocols is surging. For this purpose, IEEE 802.15.4 standardized time-slotted channel hopping (TSCH), a promising and viable link-layer solution that has shown outstanding performance achieving over 99% reliability with low duty-cycles. However, it lacks one thing, flexibility. It is not adaptable to a wide variety of applications with varying traffic load and unpredictable routing topology due to its static timeslot scheduling. To this end, we propose OST, an On-demand Scheduling scheme for TSCH with traffic-awareness. In OST, each node dynamically self-adjusts the frequency of timeslots at run time according to time-varying traffic intensity. Moreover, it features on-demand resource allocation to handle bursty/queued packets in a timely manner. By doing so, OST aims to minimize its energy consumption while guaranteeing reliable packet delivery. We evaluate OST on a large-scale 72-node testbed, demonstrating that it achieves improvement of 60% in reliability and 52% in energy-efficiency compared to the state of the art.
ISSN
0743-166X
URI
https://hdl.handle.net/10371/202141
DOI
https://doi.org/10.1109/INFOCOM41043.2020.9155496
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Research Area Distributed machine learning, Edge, Mobile AI

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