Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks

Marina Eskola, Tapio Heikkilä

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

4 Citations (Scopus)

Abstract

The performance of wireless sensor networks (WSNs) in industrial applications may be degraded due to harsh radio signal propagation conditions. Mobile heavy machines and large metal and concrete surfaces usually worsen the signal fading effects resulting in deep fades. We have studied the effects of certain industrial environmental disturbances on the quality of the transmitted signals in real-life industrial environments. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the transmission problems related to signal multipath propagation by calculating the probability density function (PDF) of the received signal and comparing it to the simulated theoretical Rician distributions. The results show that tracking the transformations of the PDF of the received signal helps to recognize the temporal changes in radio link environment and in that way can help to choose the right actions in order to overcome the communication problems
Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationInternational Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014
PublisherInstitute of Electrical and Electronic Engineers IEEE
Number of pages7
ISBN (Print)978-147995745-3
DOIs
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
EventInternational Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014 - Monterey, CA, United States
Duration: 6 Jul 201410 Jul 2014

Conference

ConferenceInternational Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014
CountryUnited States
CityMonterey, CA
Period6/07/1410/07/14

Fingerprint

Probability density function
Wireless sensor networks
Radio links
Multipath propagation
Industrial applications
Concretes
Communication
Metals

Keywords

  • industrial measurements
  • radio disturbances
  • Rician density
  • software defined radio
  • wireless sensor networks

Cite this

Eskola, M., & Heikkilä, T. (2014). Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks. In Proceedings: International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014 Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/SPECTS.2014.6880001
Eskola, Marina ; Heikkilä, Tapio. / Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks. Proceedings: International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014. Institute of Electrical and Electronic Engineers IEEE, 2014.
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Eskola, M & Heikkilä, T 2014, Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks. in Proceedings: International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014. Institute of Electrical and Electronic Engineers IEEE, International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014, Monterey, CA, United States, 6/07/14. https://doi.org/10.1109/SPECTS.2014.6880001

Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks. / Eskola, Marina; Heikkilä, Tapio.

Proceedings: International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014. Institute of Electrical and Electronic Engineers IEEE, 2014.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AU - Eskola, Marina

AU - Heikkilä, Tapio

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N2 - The performance of wireless sensor networks (WSNs) in industrial applications may be degraded due to harsh radio signal propagation conditions. Mobile heavy machines and large metal and concrete surfaces usually worsen the signal fading effects resulting in deep fades. We have studied the effects of certain industrial environmental disturbances on the quality of the transmitted signals in real-life industrial environments. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the transmission problems related to signal multipath propagation by calculating the probability density function (PDF) of the received signal and comparing it to the simulated theoretical Rician distributions. The results show that tracking the transformations of the PDF of the received signal helps to recognize the temporal changes in radio link environment and in that way can help to choose the right actions in order to overcome the communication problems

AB - The performance of wireless sensor networks (WSNs) in industrial applications may be degraded due to harsh radio signal propagation conditions. Mobile heavy machines and large metal and concrete surfaces usually worsen the signal fading effects resulting in deep fades. We have studied the effects of certain industrial environmental disturbances on the quality of the transmitted signals in real-life industrial environments. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the transmission problems related to signal multipath propagation by calculating the probability density function (PDF) of the received signal and comparing it to the simulated theoretical Rician distributions. The results show that tracking the transformations of the PDF of the received signal helps to recognize the temporal changes in radio link environment and in that way can help to choose the right actions in order to overcome the communication problems

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Eskola M, Heikkilä T. Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks. In Proceedings: International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2014. Institute of Electrical and Electronic Engineers IEEE. 2014 https://doi.org/10.1109/SPECTS.2014.6880001