Channel Optimised Distributed Passive Sensor Networks

Lead Research Organisation: University of Cambridge
Department Name: Engineering

Abstract

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.

Planned Impact

Enabling very low cost wireless sensors to be deployed and operate reliably over wide areas will have significant impacts in a diverse range of areas and applications. Within the project we will demonstate some of this potential in a healthcare setting where low power sensors detecting heartbeat, breathing and temperature could bring intensive care levels of monitoring to patients on other wards, without the encumbrance of wires. Clearly the technology could be equally applied across many other sectors such as structural health monitoring, manufacturing or even retail. With the help of Cambridge Enterprise, opportunities for technology transfer and commercialisation of the developed technologies will be explored. The support we have gained from ARM and PervasID demonstrates the interest which industry is taking in this area.
Beyond passive sensing, the improved power delivery could be used for other low power devices, potentially allowing actuation and computation as well. Further applications include the wireless transfer of much higher powers in other bands for wireless charging.
The IoT has been recognised as a key area where the UK should be highly comptetive, but requires more trained Engineers. The project will provide valuable training for the postdoctoral researcher on serveral areas which relate to the IoT. As a new investigator award, the grant also seeks to establish a leading research group focusing on research into low cost, low power wireless communication and sensing to meet our future needs.
 
Description So far an antenna has been developed which has superior properties for far field wireless energy transfer applications.
Method has been developed to increase the range of wireless power transfer for very low power devices using multiple antennas
Method has been developed to reduce the power required wireless sensors by digitising data at the receiver.
Method of increased communication range to very low power wireless sensor has been developed exploiting pulse width modulation of the sensor data, and algorithms developed for the detection on these signals.
Exploitation Route By enabling sensors to operate by wireless energy transfer rather than by batteries, new sensors will be enabled which were not previously possible. They could be applied to wide range of fields of study.
Sectors Digital/Communication/Information Technologies (including Software),Electronics,Healthcare

 
Description Armstrong Studentship
Amount £75,000 (GBP)
Organisation University of Cambridge 
Sector Academic/University
Country United Kingdom
Start 09/2020 
End 10/2023
 
Description Fast CU
Amount £1,800,000 (GBP)
Organisation Huawei Technologies 
Sector Private
Country China
Start 03/2019 
End 03/2021
 
Description ROME Project
Amount £125,000 (GBP)
Organisation Beijing Institue of Aerospace Control Devices 
Sector Private
Country China
Start 03/2018 
End 04/2021
 
Title Ray Tracing channel model for far field UHF energy harvesting 
Description a 3-dimensional (3D) ray-tracing model is proposed. The model is specifically tailored for RFID applications by taking into account some of its unique characteristics and applications. The proposed model can give accurate power predictions and is suitable for indoor RFID systems operating with low fade margins. The model has been verified by comparison with Method of Moments (MoM) and practical measurements. It is shown to achieve a high accuracy with significantly less computational complexity than MoM. An example application of the model is presented, showing its usefulness in planning, assessing and optimizing RFID systems 
Type Of Material Computer model/algorithm 
Year Produced 2020 
Provided To Others? Yes  
Impact A paper introducing the model has been published at IEEE RFID conference. 
 
Description ARM 
Organisation Arm Limited
Country United Kingdom 
Sector Private 
PI Contribution Preliminary meetings held sharing research plans with IoT team
Collaborator Contribution PhD student to be hosted as an intern, advice offered to applicability of technological solutions.
Impact PhD student to carry out internship jointly with ARM. Possible future collaborations.
Start Year 2019
 
Title SYSTEMS AND METHODS FOR READING RFID TAGS 
Description An RFID system comprises an array of antennas each configured to emit a plurality of beams in different directions. The beams of each pair of adjacent antennas are directed towards one another and overlap. A pair of adjacent antennas transmits simultaneously and the overlapping beams interfere to create an interference pattern. An RFID reader controls the relative phase and/or frequency of the beams to move the interference pattern to read an RFID tag within the moving pattern. As the chance of a RFID tag responding to an emitted beam generally increases with signal strength of the reader beam an area of constructive interference means that RFID tags in that region are more likely to respond to the signal. The system can cover a large proportion of the area below ceiling-mounted antennas, where cover generally means that RFID tags in that area will be successfully read. 
IP Reference WO2020109819 
Protection Patent application published
Year Protection Granted 2020
Licensed Commercial In Confidence
Impact Under development for future product to reduce the cost of RFID system deployment.