No abstract

Research on Wi-Fi sensing over the past decade has been thriving, but not smooth. Three major barriers severely hamper the research, namely, the unknown baseband design and its influence on CSI, inability to access the low-level hardware controls and the lack of a flexible and versatile software toolkit for hardware control. This paper tries to break the above three barriers from the following aspects. First, an in-depth study on the baseband design of QCA9300, the popular CSI-enabled Wi-Fi NIC, is presented. The lessons learned is of great guiding significance for understanding what other commercial off-theshelf NICs. Second, several valuable features of QCA9300 are unlocked for research, such as the arbitrary tuning for both the carrier frequency and baseband sampling rate. By leveraging the unlocked features, we identify three important types of CSI distortion, and pinpoint their origin through extensive evaluations. Last, we develop and release PicoScenes, a powerful, hardwareunified and extensible Wi-Fi sensing system. PicoScenes allows direct access to the unlocked features of QCA9300 and IWL5300, and therefore greatly facilitate the research on Wi-Fi sensing. It also supports the SDR-based Wi-Fi sensing by embedding a 802.11a/g/n/ac/ax software baseband implementation. We release PicoScenes at https://zpj.io/ps.

In view of the current oil collection points are most remote and scattered, the existing mobile communication network are often not covered, it is difficult to realize automatic monitoring for working condition of oil well, this paper presents a oil well working parameters monitoring system based on the Internet of things, which can realize the automatic wireless acquisition and monitoring of the wellhead pressure, temperature and displacement parameters etc. The system consists of a collection of nodes, routing nodes, gateway nodes, local networks and cloud computing platform. Collection nodes are developed on core processor ARM7 and embedded uClinux operating system platform, the working parameters data of the oil well is sent to the local area network and cloud computing platform through wireless network. Monitoring personnel can view the real-time working parameters of oil well in the local network which provides an important support for the protection of oil well safety and accident rescue.