Wireless LAN-Based CSI Monitoring System for Object Detection

Sensing services for the detection of humans and animals by analyzing the environmental changes of wireless local area network (WLAN) signals have attracted attention in recent years. In object detection using WLAN signals, a widely known technique is the use of time changes in received signal strength indicators that are easily measured between WLAN devices. Utilizing channel response, including power and phase values per subcarrier on multiple input multiple output (MIMO), the orthogonal frequency division multiplexing transmission was researched as channel state information (CSI) to further improve detection accuracy. This paper describes a WLAN-based CSI monitoring system that efficiently acquires the CSI of multiple links in a target area where multiple CSI measuring stations are distributed. In the system, a novel CSI monitoring station captures wireless packets sent within the area and extracts CSI by analyzing the packets on the sounding protocol, specified by IEEE 802.11ac. The paper also describes the system configuration and shows that indoor experimental measurements confirmed the system’s feasibility.

[1]  A. M. Abdullah,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1997 .

[2]  Moustafa Youssef,et al.  WLAN location determination via clustering and probability distributions , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[3]  Hiroshi Saito,et al.  A System for Detection and Tracking of Human Movements Using RSSI Signals , 2018, IEEE Sensors Journal.

[4]  Kegen Yu,et al.  Improved Wi-Fi RSSI Measurement for Indoor Localization , 2017, IEEE Sensors Journal.

[5]  Naoki Honma,et al.  Evaluation of fast human localization and tracking using MIMO radar in multi-path environment , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[6]  Hao Jiang,et al.  CareFi: Sedentary Behavior Monitoring System via Commodity WiFi Infrastructures , 2018, IEEE Transactions on Vehicular Technology.

[7]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[8]  Maurizio Bocca,et al.  Real-Time Intrusion Detection and Tracking in Indoor Environment through Distributed RSSI Processing , 2011, 2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications.

[9]  Fredrik Tufvesson,et al.  5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice , 2017, IEEE Journal on Selected Areas in Communications.

[10]  Der-Jiunn Deng,et al.  IEEE 802.11ax: Next generation wireless local area networks , 2014, 10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness.

[11]  Qingquan Li,et al.  A New Weighted Algorithm Based on the Uneven Spatial Resolution of RSSI for Indoor Localization , 2018, IEEE Access.

[12]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[13]  Evgeny Khorov,et al.  A Tutorial on IEEE 802.11ax High Efficiency WLANs , 2019, IEEE Communications Surveys & Tutorials.

[14]  Tacha Serif,et al.  Improving RSS-Based Indoor Positioning Algorithm via K-Means Clustering , 2011, EW.

[15]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[16]  Milan Z. Bjelica,et al.  A human detection method for residential smart energy systems based on Zigbee RSSI changes , 2012, IEEE transactions on consumer electronics.