Development of Plough-able RFID Sensor Network Systems for Precision Agriculture

UoM administered thesis: Phd

  • Authors:
  • Chuan Wang

Abstract

There is a growing interest in employing sub-soil sensing systems to support precision agriculture. This thesis presents the design of an RFID sub-soil sensing system which is based on integrating passive RFID technology and sub-soil sensing technology. The proposed RFID sub-soil system comprises of an above-ground RFID reader and a number of RFID sub-soil sensor nodes. The key feature of the system is that the sensor nodes do not require an on-board battery, as they are capable of harvesting energy from the ElectroMagnetic (EM) field generated by the RFID reader. The sensor nodes then transmit sensor measurements to the reader wirelessly through soil. With the proposed RFID sub-soil system, the high path loss of the sub-soil wireless channel is a significant problem which leads to the challenge for the system to achieve an acceptable Quality of Service (QoS). In this project, the path loss in soil has been characterised through CST simulations. In the simulations, the effect of the soil on the sensor node antenna has also been investigated. This thesis also presents the design and implementation of a programmable RFID reader platform and an embedded RFID sensor node prototype. The RFID reader platform is implemented using a National Instruments (NI) PXI system, and it is configured and controlled by NI LabVIEW software. The sensor node prototype is capable of harvesting RF energy and transmitting sensor measurements from a temperature sensor through backscatter communication. A series of sub-soil experiments have been carried out to evaluate the performance of the RFID sensor node prototype using the PXI-based RFID reader platform. The experimental results are presented and analysed in this thesis. Additionally, this work has explored trade-offs in the system design, and these design trade-offs are summarised and described.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date1 Aug 2016