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ORPL-DT: Opportunistic Routing for Diverse Traffic in Multihop IoT Networks

Cited 2 time in Web of Science Cited 8 time in Scopus
Authors

Kang, Dong-Kyu; Kim, Hyung-Sin; Bahk, Saewoong

Issue Date
2017
Publisher
IEEE
Citation
IEEE Global Communications Conference (GLOBECOM), Vol.2018-January, pp.1-6
Abstract
For over a decade, multihop low power and lossy networks (LLNs) have mainly focused on delivering upward traffic from individual nodes to the servers for supporting remote monitoring applications. However, as part of Internet of Things (IoT), LLN applications and traffic patterns are being diversified. In this paper, we address performance issues of LLNs when delivering various traffic patterns. Specifically, we experimentally show that LLNs suffer from unreliable routes when downward traffic is dominant since current routing protocols do not utilize downward traffic for link quality update. To tackle the problem, we design a novel mechanism, named ORPL-DT, that updates link quality information by using both upward and downward traffic, and implement it on top of ORPL (defacto IPv6 opportunistic routing protocol). We evaluate its performance on a 31-node multihop testbed, showing that ORPL-DT improves performance in terms of packet delivery ratio, control overhead, and radio duty-cycle.
ISSN
2334-0983
URI
https://hdl.handle.net/10371/202144
DOI
https://doi.org/10.1109/GLOCOM.2017.8253953
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  • Graduate School of Data Science
Research Area Distributed machine learning, Edge, Mobile AI

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