Optimising Low Power Dual Prediction Systems

John Kemp, Elena Gaura, Michael P. Allen, James Brusey

Research output: Contribution to conferencePaper

Abstract

Abstract Low power protocols and data compression techniques can bring the average energy requirements for a WSN node within the capability of energy harvesting techniques. However, peak energy use may still exceed this capability. In this paper, based on our experience implementing a prototype WSN for an aerospace application, we identify two practical approaches that in combination significantly reduce energy use for sensor nodes using the Z-Stack network stack. While these solutions are necessarily platform-specific, they are expected to translate to other wireless node platforms. The solutions presented use non-default platform features in order to solve the issues of i) high power consumption on node startup due to network discovery and joining and ii) high power consumption of application level acknowledgements. The startup power consumption was reduced by 94.7%, while the cost of a sense-process-transmit-acknowledge cycle was reduced by 38.6%. This represents a significant reduction in node power consumption and is an important step in enabling the use of power harvesting.
Original languageEnglish
Pages7-10
DOIs
Publication statusPublished - 2015
EventACM Workshop on Real World Wireless Sensor Networks - Seoul, Korea, Republic of
Duration: 1 Nov 20154 Nov 2015

Workshop

WorkshopACM Workshop on Real World Wireless Sensor Networks
CountryKorea, Republic of
CitySeoul
Period1/11/154/11/15

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    Kemp, J., Gaura, E., Allen, M. P., & Brusey, J. (2015). Optimising Low Power Dual Prediction Systems. 7-10. Paper presented at ACM Workshop on Real World Wireless Sensor Networks, Seoul, Korea, Republic of. https://doi.org/10.1145/2820990.2820993