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Large scale networks of intelligent sensors that can function without any batteries will have enormous implications in applications that range from smart spaces to structural and environmental monitoring. RF tags present an amenable platform for sensor integration as the backscatter communication offers low energy cost of communication. Current RF tags either use extremely low-power sensors or perform tasks of tag localization and identification based on the strength of the backscatter signal. We present a technique for estimation of amplitude and phase of the tag-to-tag channel that can be performed with very limited computational and energy resources. This enables monitoring of the interactions between tagged objects and activities around tags, as well as assessment of a variety of engineering structures. Experimental results demonstrate high resolution in the amplitude and phase channel measurement at a distances ranging from 22 cm to 1.34 m. 

We present a wake-up receiver amenable to integration in a node of RF backscattering tag-to-tag network. A high input impedance of a passive envelope detector (ED) is accomplished by backward bias that improves the passive voltage gain. Two differential outputs are ac-coupled to a baseband amplifier that operates in the subthreshold region. We develop a closed-form model of the passive ED in order to predict the output and ripple voltages and therefor the receiver’s sensitivity. The wakeup receiver is implemented in 180 nm CMOS technology and consumes 2 nW with 0.8 V supply voltage while demodulating 915 MHz amplitude-shift keying (ASK) signal with data rate of 10 kbps. The receiver demonstrates -67.98 dBm sensitivity in resolving ASK modulated signal. 

We propose a sensing system comprising a large network of tiny, battery-less, Radio Frequency (RF)-powered sensors that use backscatter communication. The sensors use an entirely passive technique to 'sense' the parameters of the wireless channel between themselves. Since the material properties influence RF channels, this fine-grain sensing can uncover multiple material properties both at a large scale and fine spatial resolution. In this paper, we study the feasibility of the proposed passive technique for monitoring parameters of material in which the sensors are embedded. We performed a set of experiments where the sensor-to-sensor wireless channel parameters are well-defined using physics-based modeling, and we compared the theoretical and experimentally obtained values. For some material parameters of interest, like humidity or strain, the relationship with the observed wireless channel parameters have to be modeled relying on data-driven approaches. The initial experiments show an observable difference in the sensor-to-sensor channel phase with variation in the applied weights.