Soil moisture sensing plays an important role in agriculture, especially in greenhouses or vertical farms. However, existing soil moisture sensing systems are either expensive and require batteries or suffer from low accuracy, preventing their real-world applications. This paper introduces SoilTAG, a battery-free, chipless tag-based high accuracy soil moisture sensing system. The key insight is that the tag's resonator can convert changes in soil moisture levels into changes in the tag's frequency response. However, two challenges need to be addressed before applying the system to the real world. First, how to design a resonator whose frequency response is sensitive to even small moisture changes, which is the basis for high-precision moisture sensing. To solve the challenge, we design a special structure (i.e., a defected ground structure) as the tag's resonator and optimize its key parameters to increase the frequency response sensitivity for different soil moisture levels. Second, how to design a robust soil moisture feature that is independent of the tag's location changes, since the frequency response varies by both the tag location and soil moisture. To deal with this challenge, we introduce a relative frequency response feature whose amplitude ratio is only related to soil moisture levels and independent of the tag location changes. Extensive experiments show that SoilTAG achieves$90th$percentile moisture sensing error of$2\%$,$3.64\%$, and$8\%$when the distance between transmitter and tag is 6 m, 10 m, and 13.9 m. Compared to current commodity sensors, SoilTAG saves the cost per sensor by more than 70%.

Backscatter tag based sensing has received a lot of attention recently due to the battery-free, low-cost, and widespread use of backscatter tags, e.g., RFIDs. Despite that, they suffer from an extensive, costly, and time-consuming redesign effort when there are changes in application requirements, such as changes in sensing targets or working frequency bands. This paper introduces a reconfigurable sensing framework, which enables us to easily reconfigure the design parameters of chipless backscatter tags for sensing different targets or working with different frequency bands, without the need for onerous design effort. To realize this vision, we capture the relationship between the application requirements and the sensing tag’s design parameters. This relationship enables us to fast and efficiently reconfigure/change an existing sensing tag design to meet new application requirements. Real-world experiments show that, by using our reconfigurable framework to flexibly redesign a tag’s parameters, the sensing tag achieves more than 92.1% accuracy for sensing four different applications and working on four different frequency bands.

Sweat contains a wealth of physiologically relevant information and has been used to detect underlying diseases or the sub-health state. However, existing sweat sensors suffer from high energy consumption due to the need for energy-hungry components (i.e., ADC and DAC) and active radio front-ends, making them unable to support continuous and long-term monitoring.

Backscatter tag based sensing has received a lot of attention recently due to the battery-free, low-cost and widespread use of backscatter tags, e.g., RFIDs. Despite that, they suffer from an ex-tensive, costly, and time-consuming redesign effort when there are changes in application requirements, such as changes in sensing targets or working frequency bands. This paper introduces a reconfigurable sensing framework, which enables us to easily reconfigure the design parameters of chipless backscatter tags for sensing different targets or working with differ-ent frequency bands, without the need of onerous design effort. To realize this vision, we capture the relationship between the application requirements and the sensing tag's design parameters. This relationship enables us to fast and efficiently reconfigure/change an existing sensing tag design for meeting new application requirements. Real-world experiments show that, by using our reconfig-urable framework to flexibly redesign a tag's parameters, the sensing tag achieves more than 92.1 % accuracy for sensing four different applications and working on four different frequency bands.