BAAS: Backscatter as a Sensor for Ultra-Low-Power Context Recognition

Abstract

Context recognition has attracted attention for various daily life applications. Many existing approaches use micro-electromechanical systems (MEMS) sensors which require additional silicon chips to process and transmit the sensor data. The energy consumption of such components is relatively large, requiring maintenance for charging or replacing batteries. In this paper, we propose BAAS: a novel concept using Backscatter As A Sensor. BAAS recognizes contexts using a frequency shift backscatter tag with ultra-low power consumption. The key components of the backscatter tag are an oscillator and a motion switch. The state of the motion switch changes according to the movement of humans or the change of the situation of things. While the motion switch is on, the energy is supplied to the oscillator, and the frequency of the backscattered signal shifts according to the oscillation frequency of the oscillator. Context recognition is achieved by observing the existence and absence of the frequency shift. To demonstrate the feasibility of context recognition using the backscatter tag, we have implemented a prototype and evaluated its performance. Our results show that we can detect the frequency shift by BAAS within 3m, backscattering BLE signal from an exciter implemented by a commodity device.