A Universal Method to Combat Multipaths for RFID Sensing

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost.This paper investigates the possibility of measuring ‘multi-path-free’ signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost – it requires no extra device. We implement CPIX and study two major RFID sensing applications: tag localization and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of human behaviour sensing.

[1]  Wei Xi,et al.  Device-free detection of approach and departure behaviors using backscatter communication , 2016, UbiComp.

[2]  Jan Prokopec,et al.  Propagation path loss models for mobile communication , 2011, Proceedings of 21st International Conference Radioelektronika 2011.

[3]  Markus Cremer,et al.  New measurement results for the localization of UHF RFID transponders using an Angle of Arrival (AoA) approach , 2011, 2011 IEEE International Conference on RFID.

[4]  Fadel Adib,et al.  3D Backscatter Localization for Fine-Grained Robotics , 2019, NSDI.

[5]  Wei Xi,et al.  FEMO: A Platform for Free-weight Exercise Monitoring with RFIDs , 2015, SenSys.

[6]  Xin Li,et al.  VERID: towards verifiable IoT data management , 2019, IoTDI.

[7]  Swarun Kumar,et al.  WiSh: Towards a Wireless Shape-aware World using Passive RFIDs , 2018, MobiSys.

[8]  Swarun Kumar,et al.  Towards Wearable Everyday Body-Frame Tracking using Passive RFIDs , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[9]  Wei Xi,et al.  RFIPad: Enabling Cost-Efficient and Device-Free In-air Handwriting Using Passive Tags , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[10]  Chenglin Miao,et al.  Towards Environment Independent Device Free Human Activity Recognition , 2018, MobiCom.

[11]  Fadel Adib,et al.  Multi-Person Localization via RF Body Reflections , 2015, NSDI.

[12]  Syed K. Islam,et al.  Sensors and Low Power Signal Processing , 2009 .

[13]  Min Chen,et al.  Tag-compass: Determining the spatial direction of an object with small dimensions , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[14]  M. Bouet,et al.  RFID tags: Positioning principles and localization techniques , 2008, 2008 1st IFIP Wireless Days.

[15]  Shwetak N. Patel,et al.  ID-Match: A Hybrid Computer Vision and RFID System for Recognizing Individuals in Groups , 2016, CHI Extended Abstracts.

[16]  Chen Qian,et al.  When Tags ‘Read’ Each Other: Enabling Low-cost and Convenient Tag Mutual Identification , 2019, 2019 IEEE 27th International Conference on Network Protocols (ICNP).

[17]  Lei Yang,et al.  Anchor-free backscatter positioning for RFID tags with high accuracy , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[18]  Qiongzheng Lin,et al.  Revisiting Reading Rate with Mobility: Rate-Adaptive Reading of COTS RFID Systems , 2019, IEEE Transactions on Mobile Computing.

[19]  Ye Yu,et al.  Toward Secure and Efficient Communication for the Internet of Things , 2019, IEEE/ACM Transactions on Networking.