WiBorder: Precise Wi-Fi based Boundary Sensing via Through-wall Discrimination

Recent research has shown great potential of exploiting Channel State Information (CSI) retrieved from commodity Wi-Fi devices for contactless human sensing in smart homes. Despite much work on Wi-Fi based indoor localization and motion/intrusion detection, no prior solution is capable of detecting a person entering a room with a precise sensing boundary, making room-based services infeasible in the real world. In this paper, we present WiBorder, an innovative technique for accurate determination of Wi-Fi sensing boundary. The key idea is to harness antenna diversity to effectively eliminate random phase shifts while amplifying through-wall amplitude attenuation. By designing a novel sensing metric and correlating it with human's through-wall discrimination, WiBorder is able to precisely determine Wi-Fi sensing boundaries by leveraging walls in our daily environments. To demonstrate the effectiveness of WiBorder, we have developed an intrusion detection system and an area detection system. Extensive results in real-life scenarios show that our intrusion detection system achieves a high detection rate of 99.4% and a low false alarm rate of 0.68%, and the area detection system's accuracy can be as high as 97.03%. To the best of our knowledge, WiBorder is the first work that enables precise sensing boundary determination via through-wall discrimination, which can immediately benefit other Wi-Fi based applications.

CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools.