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TESLA: Traffic-Aware Elastic Slotframe Adjustment in TSCH Networks

Cited 32 time in Web of Science Cited 38 time in Scopus
Authors

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

Issue Date
2019-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Access, Vol.7, pp.130468-130483
Abstract
Low-power wireless network for the emerging Internet of Things (IoT) should be reliable enough to satisfy the application requirements, and also energy-efficient for embedded devices to remain battery powered. Synchronized communication methods such as Time Slotted Channel Hopping (TSCH) have shown promising results for these purposes, achieving end-to-end reliability over 99% with low duty-cycles. However, they lack one thing: flexibility to support a wide variety of applications and services with unpredictable traffic load and routing topology due to "fixed" slotframe sizes. To this end, we propose TESLA, a traffic-aware elastic slotframe adjustment scheme for TSCH networks which enables each node to dynamically self-adjust its slotframe size at run time. TESLA aims to minimize its energy consumption without sacrificing reliable packet delivery by utilizing incoming traffic load to estimate channel contention level experienced by each neighbor. We extensively evaluate the effectiveness of TESLA on large-scale 110-node and 79-node testbeds, demonstrating that it achieves up to 70.2% energy saving compared to Orchestra (the de facto TSCH scheduling mechanism) while maintaining 99% reliability.
ISSN
2169-3536
URI
https://hdl.handle.net/10371/199351
DOI
https://doi.org/10.1109/ACCESS.2019.2940457
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Research Area Distributed machine learning, Edge, Mobile AI

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