Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks

Thomas Pötsch; Lei Pei; Koojana Kuladinithi; Carmelita Goerg

doi:10.1109/ISSNIP.2014.6827658

Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks

Thomas Pötsch, Lei Pei, Koojana Kuladinithi, Carmelita Goerg

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The increasing interest and utilization of Wireless Sensor Networks has increased the requirements of energy saving for battery powered sensor nodes. Even in modern sensor nodes, communication causes the largest part of energy consumption and therefore ways to reduce the amount of data sending are widely concerned. One solution to reduce data transmission is a model-driven data acquisition technique called Derivative-Based Prediction (DBP). Instead of transmitting every measured sample, a sensor node uses algorithms to compute approximated models to represent the measured data. In this work, we developed an algorithm to monitor temperature samples in different environmental scenarios. We also evaluated the algorithm with regard to its efficiency and classified the recorded temperature patterns to enhance the precision. In our tests, the algorithm successfully suppressed up to 99% of data transmissions while the average error of prediction has been kept below 0.1°C.

Original language	English (US)
Title of host publication	IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
Publisher	IEEE Computer Society
ISBN (Print)	9781479928439
DOIs	https://doi.org/10.1109/ISSNIP.2014.6827658
State	Published - 2014
Event	9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore Duration: Apr 21 2014 → Apr 24 2014

Publication series

Name	IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings

Conference

Conference	9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
Country/Territory	Singapore
City	Singapore
Period	4/21/14 → 4/24/14

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems

Access to Document

10.1109/ISSNIP.2014.6827658

Cite this

Pötsch, T., Pei, L., Kuladinithi, K., & Goerg, C. (2014). Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings Article 6827658 (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings). IEEE Computer Society. https://doi.org/10.1109/ISSNIP.2014.6827658

Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks. / Pötsch, Thomas; Pei, Lei; Kuladinithi, Koojana et al.
IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. 6827658 (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Pötsch, T, Pei, L, Kuladinithi, K & Goerg, C 2014, Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks. in IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings., 6827658, IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings, IEEE Computer Society, 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014, Singapore, Singapore, 4/21/14. https://doi.org/10.1109/ISSNIP.2014.6827658

Pötsch T, Pei L, Kuladinithi K, Goerg C. Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society. 2014. 6827658. (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings). doi: 10.1109/ISSNIP.2014.6827658

Pötsch, Thomas ; Pei, Lei ; Kuladinithi, Koojana et al. / Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks. IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings).

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abstract = "The increasing interest and utilization of Wireless Sensor Networks has increased the requirements of energy saving for battery powered sensor nodes. Even in modern sensor nodes, communication causes the largest part of energy consumption and therefore ways to reduce the amount of data sending are widely concerned. One solution to reduce data transmission is a model-driven data acquisition technique called Derivative-Based Prediction (DBP). Instead of transmitting every measured sample, a sensor node uses algorithms to compute approximated models to represent the measured data. In this work, we developed an algorithm to monitor temperature samples in different environmental scenarios. We also evaluated the algorithm with regard to its efficiency and classified the recorded temperature patterns to enhance the precision. In our tests, the algorithm successfully suppressed up to 99% of data transmissions while the average error of prediction has been kept below 0.1°C.",

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Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks

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