Measuring radiation dosage rates is becoming more and more important in many applications scenarios. Continuously monitoring radiation in contaminated and poorly accessible areas is challenging due to the frequent data collection/transmission combined with long life requirements. We present a self-sustainable wireless sensor node for low power, high precision radiation dosage rate monitoring. We propose an energy-efficient data acquisition algorithm that can reduce the energy per measurement, while guaranteeing minimal loss of precision. The proposed node is designed to work in collaboration with an unmanned aerial vehicle used for two essential mission steps: air-deployment of the wireless sensor nodes at suitable locations, and acquiring data logs via low-power, short-range radio communication in fly-by mode after a wake-up command. The system uses off-the-shelf components for defining the mission, drop-zone and trajectory, for compressing data and managing communication. The node is equipped with a novel low-power nuclear radiation sensor, and has been designed and implemented with self-sustainability in mind as it will be deployed in hazardous, inaccessible areas. To this end, the proposed node uses a combination of complementary techniques: a low-power microcontroller with non-volatile memory, energy harvesting, adaptive power management and a nano-watt wake-up radio. Experimental results demonstrate the precision and the low energy consumption of the radiation sensor, the energy efficiency of the whole solution and the acquisition algorithms. The node consumes only 31μW in sleep mode and 1.7mW in active mode, and has the capability to achieve perpetual monitoring once deployed.

Gomez, A., Magno, M., Lagadec, M.F., Benini, L. (2018). Precise, Energy-Efficient Data Acquisition Architecture for Monitoring Radioactivity using Self-Sustainable Wireless Sensor Nodes. IEEE SENSORS JOURNAL, 18(1), 459-469 [10.1109/JSEN.2017.2716380].

Precise, Energy-Efficient Data Acquisition Architecture for Monitoring Radioactivity using Self-Sustainable Wireless Sensor Nodes

Gomez, Andres;Magno, Michele;Benini, Luca
2018

Abstract

Measuring radiation dosage rates is becoming more and more important in many applications scenarios. Continuously monitoring radiation in contaminated and poorly accessible areas is challenging due to the frequent data collection/transmission combined with long life requirements. We present a self-sustainable wireless sensor node for low power, high precision radiation dosage rate monitoring. We propose an energy-efficient data acquisition algorithm that can reduce the energy per measurement, while guaranteeing minimal loss of precision. The proposed node is designed to work in collaboration with an unmanned aerial vehicle used for two essential mission steps: air-deployment of the wireless sensor nodes at suitable locations, and acquiring data logs via low-power, short-range radio communication in fly-by mode after a wake-up command. The system uses off-the-shelf components for defining the mission, drop-zone and trajectory, for compressing data and managing communication. The node is equipped with a novel low-power nuclear radiation sensor, and has been designed and implemented with self-sustainability in mind as it will be deployed in hazardous, inaccessible areas. To this end, the proposed node uses a combination of complementary techniques: a low-power microcontroller with non-volatile memory, energy harvesting, adaptive power management and a nano-watt wake-up radio. Experimental results demonstrate the precision and the low energy consumption of the radiation sensor, the energy efficiency of the whole solution and the acquisition algorithms. The node consumes only 31μW in sleep mode and 1.7mW in active mode, and has the capability to achieve perpetual monitoring once deployed.
2018
Gomez, A., Magno, M., Lagadec, M.F., Benini, L. (2018). Precise, Energy-Efficient Data Acquisition Architecture for Monitoring Radioactivity using Self-Sustainable Wireless Sensor Nodes. IEEE SENSORS JOURNAL, 18(1), 459-469 [10.1109/JSEN.2017.2716380].
Gomez, Andres; Magno, Michele; Lagadec, Marie Francine; Benini, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/624055
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