VII.CONCLUSION In this paper,we propose RF-Dial,a battery-free solution 0.6 06 for 2D human-computer interaction via RFID tag arrays. 0.4 0. We attach a tag array to the surface of a specified object, BE-Dial lines and continuously track the translation and rotation of the 02 02 E-Din AbsLoc tagged object.We implemented a prototype system of RF-Dial, and evaluated its performance in the real environment.The R (a)CDF of translatior ton erro experiments show that RF-Dial achieved an average accuracy Fig.12. Performance evaluation against the absolute localization-based of 0.6cm in the translation tracking,and an average accuracy solution (The number of tags in the tag array is 4.) of 1.9 in the rotation tracking. our solution is able to efficiently tackle the rotation estimation ACKNOWLEDGMENT in both the two regions.The above solutions all used the calibration method,we further evaluated the performance for This work is supported in part by National Natural Science Foundation of China under Grant Nos.61472185.61373129. the calibration method.Fig.11(f)shows the rotation error 61321491,61702257.61502224:JiangSu Natural Science with/without calibration in the heavy multi-path situation.The Foundation under Grant Nos.BK20151390,BK20170648. number of tags is 4,and the rotation is randomly selected from 0~30.It is observed that the calibrated solution can further This work is partially supported by Collaborative Innovation reduce 25%and 26%of the rotation error,respectively,for Center of Novel Software Technology and Industrialization. the linear region and non-linear region. The research of Lei Yang is partially supported by ECS(NO 25222917)and NSFC General Program (NO.61572282).Lei C.Macro Benchmark Xie is the corresponding author. We further compare RF-Dial with the state-of-art absolute REFERENCES localization-based solution (AbsLoc).which is also adopted in [l】"Surface Dial'” Tagball [15].For AbsLoc,we estimated the absolute position https://www.microsoft.com/en-us/surface/accessories/surface-dial. of each tag in the tag array,and then used these estimated [2]J.Wang and D.Katabi,Dude,where's my card?:RFID Positioning absolute positions to further compute the rigid transformation. That Works with Multipath and Non-Line of Sight,in Proc.of ACM SIGCOMM.2013. For performance comparison,we continuously moved the [3]L.Yang.Y.Chen,X.Li,C.Xiao,M.Li,and Y.Liu,Tagoram:Real-time tagged eraser to perform the rigid transformation on the table, Tracking of Mobile RFID Tags to High Precision Using COTS Devices and used RF-Dial and AbsLoc to track the translation and in Proc.of ACM MobiCom,2014. [4]W.Ruan,L.Yao,Q.Sheng,N.Falkner and X.Li,TagTrack:Device-free rotation,respectively.Then,we computed the translation error Localization and Tracking Using Passive RFID Tags,in Proc.of ACM and rotation error for the two solutions. MOBIOUITOUS.2014. RF-Dial achieves much better performance than AbsLoc in [5]L.Shangguan.Z.Yang,A.X.Liu,Z.Zhou and Y.Liu,STPP:Spatial- Temporal Phase Profiling-Based Method for Relative RFID Tag Local- term of the translation error.Fig.12(a)plots the Cumulative ization,in IEEE/ACM Transactions on Networking (ToN),vol.25,no.1, Distribution Function(CDF)of the translation error.For RF- pp.596-609.2017. Dial,we evaluated the translation error in the linear region [6]B.Li,Y.He,W.Liu,L.Wang and H.Wang.LocP:An Efficient Localized Polling Protocol for Large-scale RFID Systems,in Proc.of IEEE ICNP, and non-linear region,respectively.For the linear region,the 2016. average translation error is 0.6cm,and the translation error [7]H.Ding,J.Han,L.Shangguan,W.Xi,Z.Jiang.Z.Yang,Z.Zhou, is controlled in the range of 0~1.2cm for 90%of the test P.Yang and J.Zhao,A Platform for Free-weight Exercise Monitoring with RFIDs,in IEEE Transactions on Mobile Computing,vol.16,no. cases.For the non-linear region,the average translation error 12,pp.3279-3293,Dec12017. is 0.66cm,the translation error is controlled in the range of [8]J.Han,H.Ding,C.Qian,W.Xi,Z.Wang.Z.Jiang,L.Shangguan and 0~1.4cm for 90%of the test cases.As AbsLoc uses the J.Zhao,A Customer Behavior Identification System using Passive Tags in IEEE/ACM Transactions on Networking (ToN),vol.24.no.5.pp.2885- absolute positioning method,which is not sensitive to whether 2898.2016. it is the linear region or nonlinear region,we thus plot the [9]J.Wang.D.Vasisht and D.Katabi.RF-IDraw:Virtual Touch Screen in translation error in the vast region.The average translation the Air Using RF Signals,in Proc.of ACM SIGCOMM,2014. [10]L.Shangguan and K.Jamieson,Leveraging electromagnetic polarization error is 2.8cm,and the translation error is controlled in the in a two-antenna whiteboard in the air,in Proc.of ACM CoNEXT.2016. range of 0~3.7cm for 90%of the test cases. [11]L.Shangguan.Z.Zhou and K.Jamieson.Enabling Gesture-based RF-Dial achieves much better performance than AbsLoc in Interactions with Objects,in Proc.of ACM MobiSys,2017. (12]H.Ding,C.Qian,J.Han,G.Wang,W.Xi,K.Zhao and J.Zhao,RFIPad: term of the rotation error.Fig.12(b)plots the CDF of the Enabling Cost-efficient and Device-free In-air Handwriting using Passive rotation error.For RF-Dial,we evaluated the rotation error in Tags,in Proc.of ICDCS,2017. the linear region and non-linear region,respectively.For the [13]J.Liu,M.Chen,S.Chen,Q.Pan and L.Chen,Tag-Compass:Determin- ing the Spatial Direction of an Object with Small Dimensions,in Proc linear region,the average rotation error is 1.9,the rotation of IEEE INFOCOM.2017. error is controlled in the range of 0~3.9 for 90%of the test [14]T.Wei and X.Zhang,Gyro in the Air:Tracking 3D Orientation of cases.For the non-linear region,the average rotation error is Batteryless Internet-of-Things,in Proc.of ACM MobiCom,2016. [15]Q.Lin,L.Yang,Y.Sun,T.Liu,X.Li,and Y.Liu,Beyond One-dollar 2.8,the rotation error is controlled in the range of 0~5.30 for Mouse:A Battery-free Device for 3D Human-computer Interaction via 90%of the test cases.For AbsLoc,we evaluated the rotation RFID tags,in Proc.of IEEE INFOCOM,2015. error in the vast region,the average rotation error is 12.90,the [16]X.Liu.X.Xie,K.Li.B.Xiao.J.Wu.H.Qi and D.Lu.Fast Tracking the Population of Key Tags in Large-scale Anonymous RFID Systems,in rotation error is controlled in the range of 0~24.6 for 90% IEEE/ACM Transactions on Networking (ToN).vol.25,no.1,pp.278- of the test cases. 291,2017.0 1 2 3 4 5 6 0 0.2 0.4 0.6 0.8 1 Translation error (cm) CDF RF−Dial / linear RF−Dial / non−linear AbsLoc (a) CDF of translation error 0 20 40 60 0 0.2 0.4 0.6 0.8 1 Rotation error (deg) CDF RF−Dial / linear RF−Dial / non−linear AbsLoc (b) CDF of rotation error Fig. 12. Performance evaluation against the absolute localization-based solution (The number of tags in the tag array is 4.) our solution is able to efficiently tackle the rotation estimation in both the two regions. The above solutions all used the calibration method, we further evaluated the performance for the calibration method. Fig.11(f) shows the rotation error with/without calibration in the heavy multi-path situation. The number of tags is 4, and the rotation is randomly selected from 0∼30◦ . It is observed that the calibrated solution can further reduce 25% and 26% of the rotation error, respectively, for the linear region and non-linear region. C. Macro Benchmark We further compare RF-Dial with the state-of-art absolute localization-based solution (AbsLoc), which is also adopted in Tagball [15]. For AbsLoc, we estimated the absolute position of each tag in the tag array, and then used these estimated absolute positions to further compute the rigid transformation. For performance comparison, we continuously moved the tagged eraser to perform the rigid transformation on the table, and used RF-Dial and AbsLoc to track the translation and rotation, respectively. Then, we computed the translation error and rotation error for the two solutions. RF-Dial achieves much better performance than AbsLoc in term of the translation error. Fig.12(a) plots the Cumulative Distribution Function (CDF) of the translation error. For RF￾Dial, we evaluated the translation error in the linear region and non-linear region, respectively. For the linear region, the average translation error is 0.6cm, and the translation error is controlled in the range of 0∼1.2cm for 90% of the test cases. For the non-linear region, the average translation error is 0.66cm, the translation error is controlled in the range of 0∼1.4cm for 90% of the test cases. As AbsLoc uses the absolute positioning method, which is not sensitive to whether it is the linear region or nonlinear region, we thus plot the translation error in the vast region. The average translation error is 2.8cm, and the translation error is controlled in the range of 0∼3.7cm for 90% of the test cases. RF-Dial achieves much better performance than AbsLoc in term of the rotation error. Fig.12(b) plots the CDF of the rotation error. For RF-Dial, we evaluated the rotation error in the linear region and non-linear region, respectively. For the linear region, the average rotation error is 1.9◦ , the rotation error is controlled in the range of 0∼3.9◦ for 90% of the test cases. For the non-linear region, the average rotation error is 2.8◦ , the rotation error is controlled in the range of 0∼5.3◦ for 90% of the test cases. For AbsLoc, we evaluated the rotation error in the vast region, the average rotation error is 12.9◦ , the rotation error is controlled in the range of 0∼24.6◦ for 90% of the test cases. VII. CONCLUSION In this paper, we propose RF-Dial, a battery-free solution for 2D human-computer interaction via RFID tag arrays. We attach a tag array to the surface of a specified object, and continuously track the translation and rotation of the tagged object. We implemented a prototype system of RF-Dial, and evaluated its performance in the real environment. The experiments show that RF-Dial achieved an average accuracy of 0.6cm in the translation tracking, and an average accuracy of 1.9 ◦ in the rotation tracking. ACKNOWLEDGMENT This work is supported in part by National Natural Science Foundation of China under Grant Nos. 61472185, 61373129, 61321491, 61702257, 61502224; JiangSu Natural Science Foundation under Grant Nos. BK20151390, BK20170648. This work is partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. The research of Lei Yang is partially supported by ECS(NO. 25222917) and NSFC General Program (NO. 61572282). Lei Xie is the corresponding author. REFERENCES [1] ”Surface Dial”, https://www.microsoft.com/en-us/surface/accessories/surface-dial. [2] J. Wang and D.Katabi, Dude, where’s my card?: RFID Positioning That Works with Multipath and Non-Line of Sight, in Proc. of ACM SIGCOMM, 2013. [3] L. Yang, Y. Chen, X. Li, C. Xiao, M. Li, and Y. Liu, Tagoram: Real-time Tracking of Mobile RFID Tags to High Precision Using COTS Devices, in Proc. of ACM MobiCom, 2014. [4] W. Ruan, L. Yao, Q. Sheng, N. Falkner and X. Li, TagTrack: Device-free Localization and Tracking Using Passive RFID Tags, in Proc. of ACM MOBIQUITOUS, 2014. [5] L. Shangguan, Z. Yang, A. X. Liu, Z. Zhou and Y. Liu, STPP: Spatial￾Temporal Phase Profiling-Based Method for Relative RFID Tag Local￾ization, in IEEE/ACM Transactions on Networking (ToN), vol. 25, no. 1, pp. 596-609, 2017. [6] B. Li, Y. He, W. Liu, L. Wang and H. Wang, LocP: An Efficient Localized Polling Protocol for Large-scale RFID Systems, in Proc. of IEEE ICNP, 2016. [7] H. Ding, J. Han, L. Shangguan, W. Xi, Z. Jiang, Z. Yang, Z. Zhou, P. Yang and J. Zhao, A Platform for Free-weight Exercise Monitoring with RFIDs, in IEEE Transactions on Mobile Computing, vol. 16, no. 12, pp. 3279-3293, Dec.1 2017. [8] J. Han, H. Ding, C. Qian, W. Xi, Z. Wang, Z. Jiang, L. Shangguan and J. Zhao, A Customer Behavior Identification System using Passive Tags, in IEEE/ACM Transactions on Networking (ToN), vol. 24, no. 5, pp. 2885- 2898, 2016. [9] J. Wang, D. Vasisht and D. Katabi, RF-IDraw: Virtual Touch Screen in the Air Using RF Signals, in Proc. of ACM SIGCOMM, 2014. [10] L. Shangguan and K. Jamieson, Leveraging electromagnetic polarization in a two-antenna whiteboard in the air, in Proc. of ACM CoNEXT, 2016. [11] L. Shangguan, Z. Zhou and K. Jamieson, Enabling Gesture-based Interactions with Objects, in Proc. of ACM MobiSys, 2017. [12] H. Ding, C. Qian, J. Han, G. Wang, W. Xi, K. Zhao and J. Zhao, RFIPad: Enabling Cost-efficient and Device-free In-air Handwriting using Passive Tags, in Proc. of ICDCS, 2017. [13] J. Liu, M. Chen, S. Chen, Q. Pan and L. Chen, Tag-Compass: Determin￾ing the Spatial Direction of an Object with Small Dimensions, in Proc. of IEEE INFOCOM, 2017. [14] T. Wei and X. Zhang, Gyro in the Air: Tracking 3D Orientation of Batteryless Internet-of-Things, in Proc. of ACM MobiCom, 2016. [15] Q. Lin, L. Yang, Y. Sun, T. Liu, X. Li, and Y. Liu, Beyond One-dollar Mouse: A Battery-free Device for 3D Human-computer Interaction via RFID tags, in Proc. of IEEE INFOCOM, 2015. [16] X. Liu, X. Xie, K. Li, B. Xiao, J. Wu, H. Qi and D. Lu, Fast Tracking the Population of Key Tags in Large-scale Anonymous RFID Systems, in IEEE/ACM Transactions on Networking (ToN), vol. 25, no. 1, pp. 278- 291, 2017