research-article

Quantifying the channel quality for interference-aware wireless sensor networks

Authors:

Carlo Alberto Boano,

Thiemo VoigtAuthors Info & Claims

ACM SIGBED Review, Volume 8, Issue 4

Pages 43 - 48

https://doi.org/10.1145/2095256.2095262

Published: 01 December 2011 Publication History

Abstract

Reliability of communications is key to expand application domains for sensor networks. Since Wireless Sensor Networks (WSN) operate in the license-free Industrial Scientific and Medical (ISM) bands and hence share the spectrum with other wireless technologies, addressing interference is an important challenge. In order to minimize its effect, nodes can dynamically adapt radio resources provided information about current spectrum usage is available.

We present a new channel quality metric, based on availability of the channel over time, which meaningfully quantifies spectrum usage. We discuss the optimum scanning time for capturing the channel condition while maintaining energy-efficiency. Using data collected from a number of Wi-Fi networks operating in a library building, we show that our metric has strong correlation with the Packet Reception Rate (PRR). This suggests that quantifying interference in the channel can help in adapting resources for better reliability. We present a discussion of the usage of our metric for various resource allocation and adaptation strategies.

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Cited By

Salomon EBoano CRomer K(2022)Poster: Improving the Reliability of BLE Communications through Packet-level Adaptations on a Per-Channel BasisProceedings of the 2022 INTERNATIONAL CONFERENCE ON EMBEDDED WIRELESS SYSTEMS AND NETWORKS10.5555/3578948.3578976(210-211)Online publication date: 2-Dec-2022
https://dl.acm.org/doi/10.5555/3578948.3578976
Haque ISaha D(2021)SoftIoTJournal of Network and Computer Applications10.1016/j.jnca.2021.103208193:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103208
Nguyen NKim M(2020)Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor NetworksEnergies10.3390/en1323643013:23(6430)Online publication date: 4-Dec-2020
https://doi.org/10.3390/en13236430
Show More Cited By

Index Terms

Quantifying the channel quality for interference-aware wireless sensor networks
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. General and reference
  1. Cross-computing tools and techniques

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Published In

cover image ACM SIGBED Review

ACM SIGBED Review Volume 8, Issue 4

Special Issue on the 10th International Workshop on Real-time Networks (RTN 2011)

December 2011

52 pages

EISSN:1551-3688

DOI:10.1145/2095256

Issue’s Table of Contents

Copyright © 2011 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2011

Published in SIGBED Volume 8, Issue 4

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Cited By

Salomon EBoano CRomer K(2022)Poster: Improving the Reliability of BLE Communications through Packet-level Adaptations on a Per-Channel BasisProceedings of the 2022 INTERNATIONAL CONFERENCE ON EMBEDDED WIRELESS SYSTEMS AND NETWORKS10.5555/3578948.3578976(210-211)Online publication date: 2-Dec-2022
https://dl.acm.org/doi/10.5555/3578948.3578976
Haque ISaha D(2021)SoftIoTJournal of Network and Computer Applications10.1016/j.jnca.2021.103208193:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103208
Nguyen NKim M(2020)Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor NetworksEnergies10.3390/en1323643013:23(6430)Online publication date: 4-Dec-2020
https://doi.org/10.3390/en13236430
Liu WXia YXu MXie JLuo RHuang D(2020)Distributed and Accurate Packet Reception Rate Estimation under Cross-Technology InterferenceGLOBECOM 2020 - 2020 IEEE Global Communications Conference10.1109/GLOBECOM42002.2020.9348010(1-6)Online publication date: Dec-2020
https://doi.org/10.1109/GLOBECOM42002.2020.9348010
Solc TYetgin HGale TMohorcic MFortuna C(2019)Whitelisting in RFDMA NetworksIEEE Access10.1109/ACCESS.2019.29507547(159284-159299)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2950754
Prakash RBalaji Ganesh A(2019)Cognitive Wireless Sensor Network for Elderly Home HealthcareWireless Personal Communications: An International Journal10.1007/s11277-019-06358-2107:4(1815-1822)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s11277-019-06358-2
Nazir UNaqvi ITaj M(2019)Accurate Localization Algorithm in Wireless Sensor Networks in the Presence of Cross Technology InterferenceNeural Information Processing10.1007/978-3-030-36802-9_36(338-346)Online publication date: 5-Dec-2019
https://doi.org/10.1007/978-3-030-36802-9_36
Solc TFortuna C(2018)An Adaptive Channel Quality Metric for Ultra-Narrowband Systems2018 European Conference on Networks and Communications (EuCNC)10.1109/EuCNC.2018.8443208(1-5)Online publication date: Jun-2018
https://doi.org/10.1109/EuCNC.2018.8443208
Dhanapala IMarfievici RPalipana SAgrawal PPesch D(2018)White Space Prediction for Low-Power Wireless Networks: A Data-Driven Approach2018 14th International Conference on Distributed Computing in Sensor Systems (DCOSS)10.1109/DCOSS.2018.00010(9-16)Online publication date: Jun-2018
https://doi.org/10.1109/DCOSS.2018.00010
Boano CDuquennoy SForster AGnawali OJacob RKim HLandsiedel OMarfievici RMottola LPicco GVilajosana XWatteyne TZimmerling M(2018)IoTBench: Towards a Benchmark for Low-Power Wireless Networking2018 IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench)10.1109/CPSBench.2018.00013(36-41)Online publication date: Apr-2018
https://doi.org/10.1109/CPSBench.2018.00013
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