research-article

Data-driven link quality prediction using link features

Authors:

Alberto E. CerpaAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 10, Issue 2

Article No.: 37, Pages 1 - 35

https://doi.org/10.1145/2530535

Published: 31 January 2014 Publication History

Abstract

As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In this article, we propose 4C, a novel link estimator that applies link quality prediction along with link estimation. Our approach is data driven and consists of three steps: data collection, offline modeling, and online prediction. The data collection step involves gathering link quality data, and based on our analysis of the data, we propose a set of guidelines for the amount of data to be collected in our experimental scenarios. The modeling step includes offline prediction model training and selection. We present three prediction models that utilize different machine learning methods, namely, naive Bayes classifier, logistic regression, and artificial neural networks. Our models take a combination of PRR and the physical-layer information, that is, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Link Quality Indicator (LQI) as input, and output the success probability of delivering the next packet. From our analysis and experiments, we find that logistic regression works well among the three models with small computational cost. Finally, the third step involves the implementation of 4C, a receiver-initiated online link quality prediction module that computes the short temporal link quality. We conducted extensive experiments in the Motelab and our local indoor testbeds, as well as an outdoor deployment. Our results with single- and multiple-senders experiments show that with 4C, CTP improves the average cost of delivering a packet by 20% to 30%. In some cases, the improvement is larger than 45%.

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Cited By

Hu H(2024)Impacts of Transmission Power Control on Link Quality Estimation in Wireless Sensor NetworksIEEE Access10.1109/ACCESS.2024.336838112(61388-61400)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368381
Yang KZhao XZou JDu W(2023) ATPP: A Mobile App Prediction System Based on Deep Marked Temporal Point ProcessesACM Transactions on Sensor Networks10.1145/358255519:3(1-24)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3582555
Liu WZhang KXie JXia YMao JXu MHu SHuang D(2023)Eliminating Mapping Error of Link Quality Prediction for Low-Power Wireless NetworksIEEE Sensors Journal10.1109/JSEN.2023.327521923:13(15032-15045)Online publication date: 1-Jul-2023
https://doi.org/10.1109/JSEN.2023.3275219
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Index Terms

Data-driven link quality prediction using link features
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 10, Issue 2

January 2014

609 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/2575808

Issue’s Table of Contents

Copyright © 2014 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 31 January 2014

Accepted: 01 September 2013

Revised: 01 September 2013

Received: 01 February 2012

Published in TOSN Volume 10, Issue 2

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Cited By

Hu H(2024)Impacts of Transmission Power Control on Link Quality Estimation in Wireless Sensor NetworksIEEE Access10.1109/ACCESS.2024.336838112(61388-61400)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368381
Yang KZhao XZou JDu W(2023) ATPP: A Mobile App Prediction System Based on Deep Marked Temporal Point ProcessesACM Transactions on Sensor Networks10.1145/358255519:3(1-24)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3582555
Liu WZhang KXie JXia YMao JXu MHu SHuang D(2023)Eliminating Mapping Error of Link Quality Prediction for Low-Power Wireless NetworksIEEE Sensors Journal10.1109/JSEN.2023.327521923:13(15032-15045)Online publication date: 1-Jul-2023
https://doi.org/10.1109/JSEN.2023.3275219
Xia YLiu WXie JXu MLi HZhang QXu CHu SHuang D(2023)Characterization of Low-Power Wireless Links in UAV-Assisted Wireless-Sensor NetworkIEEE Internet of Things Journal10.1109/JIOT.2022.323357610:7(5823-5842)Online publication date: 1-Apr-2023
https://doi.org/10.1109/JIOT.2022.3233576
Grétarsson BOrfanidis CMarchegiani LFafoutis X(2023)Short-Term Wireless Connectivity Prediction for Connected Agricultural Vehicles2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC57777.2023.10421944(5864-5869)Online publication date: 24-Sep-2023
https://doi.org/10.1109/ITSC57777.2023.10421944
Surendran SRamesh MMontresor AMontag M(2023)Link Characterization and Edge-Centric Predictive Modeling in an Ocean NetworkIEEE Access10.1109/ACCESS.2023.323538711(5031-5046)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3235387
Xu MLiu WXu JXia YMao JXu CHu SHuang D(2022)Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless LinksSensors10.3390/s2203121222:3(1212)Online publication date: 5-Feb-2022
https://doi.org/10.3390/s22031212
Liu LFeng YGao SShu J(2022)Link quality estimation based on over-sampling and weighted random forestComputer Science and Information Systems10.2298/CSIS201218041L19:1(25-45)Online publication date: 2022
https://doi.org/10.2298/CSIS201218041L
Xu MZhang QZhao YWang WXia YLiu WHu SHuang D(2022)Statistical Characteristic of Link Quality Metrics in UAV assisted Low Power Wireless Networks2022 IEEE International Conference on Unmanned Systems (ICUS)10.1109/ICUS55513.2022.9987077(333-339)Online publication date: 28-Oct-2022
https://doi.org/10.1109/ICUS55513.2022.9987077
Ateeq MAfzal MAnjum SKim B(2022)Cognitive quality of service predictions in multi-node wireless sensor networksComputer Communications10.1016/j.comcom.2022.06.042193(155-167)Online publication date: Sep-2022
https://doi.org/10.1016/j.comcom.2022.06.042
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