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Open AccessArticle

Scalability Issues for Remote Sensing Infrastructure: A Case Study

Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada
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Author to whom correspondence should be addressed.
Current address: 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
Sensors 2017, 17(5), 994; https://doi.org/10.3390/s17050994
Received: 2 April 2017 / Revised: 23 April 2017 / Accepted: 25 April 2017 / Published: 29 April 2017
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Canada 2017)
For the past decade, a team of University of Calgary researchers has operated a large “sensor Web” to collect, analyze, and share scientific data from remote measurement instruments across northern Canada. This sensor Web receives real-time data streams from over a thousand Internet-connected sensors, with a particular emphasis on environmental data (e.g., space weather, auroral phenomena, atmospheric imaging). Through research collaborations, we had the opportunity to evaluate the performance and scalability of their remote sensing infrastructure. This article reports the lessons learned from our study, which considered both data collection and data dissemination aspects of their system. On the data collection front, we used benchmarking techniques to identify and fix a performance bottleneck in the system’s memory management for TCP data streams, while also improving system efficiency on multi-core architectures. On the data dissemination front, we used passive and active network traffic measurements to identify and reduce excessive network traffic from the Web robots and JavaScript techniques used for data sharing. While our results are from one specific sensor Web system, the lessons learned may apply to other scientific Web sites with remote sensing infrastructure. View Full-Text
Keywords: remote sensing; sensor web; scientific web site; network traffic measurement; workload characterization; benchmarking; performance remote sensing; sensor web; scientific web site; network traffic measurement; workload characterization; benchmarking; performance
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Liu, Y.; Picard, S.; Williamson, C. Scalability Issues for Remote Sensing Infrastructure: A Case Study. Sensors 2017, 17, 994.

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