Embracing collisions: enabling parallel channel estimation with COTS passive backscatter tags

Abstract

There is a growing interest in backscatter-based sensing systems in recent years. RFID techniques can be used due to low cost and structural simplicity. However, collision caused by simultaneous tag responses is one of the key issues in backscatter systems. Existing anti-collision techniques try to enable parallel decoding without sensing based applications in mind, which can not operate on COTS RFID systems. To address the issue, we propose COFFEE, which is the first work to enable parallel channel estimation of COTS passive tags by harnessing the collision. By exploiting the characteristics of low sampling rate and channel diversity of passive tags, we separate the collided data, extract the channels and identify the tags. COFFEE is compatible with current RFID standards which can be applied to all RFID-based sensing systems without any modification on tag side. To evaluate the real world performance of our system, we build a prototype and conduct extensive experiments. The experimental results show that we can achieve up to 7.33x median time resolution gain for the best case and 3.42x median gain on average.