research-article

Link Quality Estimation of Cross-Technology Communication: The Case with Physical-Level Emulation

Authors:

Xiaolong Zheng,

Yuan HeAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 18, Issue 1

Article No.: 14, Pages 1 - 20

https://doi.org/10.1145/3482527

Published: 05 October 2021 Publication History

Abstract

Research on cross-technology communication (CTC) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the In-phase and Quadrature (IQ) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the packet reception rate (PRR) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.

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Index Terms

Link Quality Estimation of Cross-Technology Communication: The Case with Physical-Level Emulation
1. Networks
  1. Network protocols

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 18, Issue 1

February 2022

434 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3484935

Editor:
Yunhao Liu
Tsinghua University, China

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Copyright © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 05 October 2021

Accepted: 01 August 2021

Revised: 01 June 2021

Received: 01 October 2020

Published in TOSN Volume 18, Issue 1

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National Key R&D Program of China
R&D Project of Key Core Technology and Generic Technology in Shanxi Province
Smart Xingfu Lindai Project

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Li ZLiu HPeng XJiang W(2024)Inter-Intra Modality Knowledge Learning and Clustering Noise Alleviation for Unsupervised Visible-Infrared Person Re-IdentificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336730436:8(3934-3947)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3367304
Rong LDing YWang MSaddik AHossain M(2024)A Multi-Modal ELMo Model for Image Sentiment Recognition of Consumer DataIEEE Transactions on Consumer Electronics10.1109/TCE.2024.335754370:1(3697-3708)Online publication date: 31-Jan-2024
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