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Details of Grant 

EPSRC Reference: EP/S019405/1
Title: Channel Optimised Distributed Passive Sensor Networks
Principal Investigator: Crisp, Dr M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
ARM Ltd PervasID Ltd
Department: Engineering
Organisation: University of Cambridge
Scheme: New Investigator Award
Starts: 01 January 2019 Ends: 31 December 2020 Value (£): 227,058
EPSRC Research Topic Classifications:
Intelligent Measurement Sys. RF & Microwave Technology
EPSRC Industrial Sector Classifications:
Electronics
Related Grants:
Panel History:
Panel DatePanel NameOutcome
26 Nov 2018 EPSRC ICT Prioritisation Panel November 2018 Announced
Summary on Grant Application Form
The Internet of Things recognises the value of interconnecting vast numbers of physical objects equipped with sensors to enable process automation and new data applications. For a wide range of applications the sensors will be wireless and operate without a battery or internal power supply, reducing the cost and complexity of the sensor. Radiative radio frequency (RF) energy transfer is an attractive method to provide small amounts power to electronic devices over a range of a few meters and is already used in radio frequency identification (RFID) systems which are increasingly replacing barcodes. However, the power available at the sensor or tag is severely limited which has limited the range of sensor tags so far preventing effective wide area operation.

This project seeks to address the limited range of passive wireless sensor tags, by considering for the first time a complete sensor network comprised of multiple sensors and a unified distributed interrogator network. Most RF power delivery systems are limited by reflections which occur in realistic environments. Here use of multiple antennas will allow the wireless channel (reflections) to be characterised and the transmitted signals optimised to mitigate the effects, increasing the RF power available at the sensor tag when it is required to collect a data sample, improving the performance of both digital and analog sensor tags. This will enable both more energy intensive sensor functions and wider operating areas with greater reliability opening up a range of applications where the performance of passive sensor tags has currently insufficient. With a wider operating area, a new problem of large populations of sensor tags simultaneously operating in the interrogation region will also be addressed. An application model will consider which sensors are required to operate with what duty cycle to ensure sufficient data is collected. This can be optimised by considering the mutual information of multiple sensors and also the channel information to select those sensors with the most favourable channels.

Finally, the developed sensor tags and interrogator system will be built into a demonstrator for healthcare applications. This will both guide the development of the application model to cope with multiple different sensors with different power and duty cycle requirements, and also allow the system to be demonstrated to potential future collaborators (both academic and industrial) across various disciplines. We believe the potential impact in healthcare is significant due to the less intrusive nature of wireless sensing, less bulky sensors with no battery and the ability of sensors to be readily disposable at low cost.

Key Findings
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Potential use in non-academic contexts
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