- Email: [email protected]
RFID Location Algorithm Based on Target Search and Repeat Calibration
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science (2019) 000–000 Procedia Computer Science 14700 (2019) 453–457
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
2018 International Conference on Identification, Information and Knowledge 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018 in the Internet of Things, IIKI 2018
RFID Location Algorithm Based on Target Search and Repeat RFID Location Algorithm Based on Target Search and Repeat Calibration Calibration a Guangshun Liaa , Chenglong Liaa , Junhua Wua,∗ a,∗, Yanmin Yina Guangshun Li , Chenglong Li , Junhua Wu , Yanmin Yin a
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
a School
Abstract Abstract In the RFID indoor positioning system, how to realize the positional positioning of the tag to be positioned is an important issue In indoor positioning system, how to realize the positional positioning the tag to bemethod positioned is an important issue on the the RFID basis of ensuring the positioning accuracy. In order to solve this problem, of a new search is proposed in this paper. on the basis of ensuringarea thetopositioning In orderbelongs to solveis this problem,the a new search method proposedand in this paper. Firstly, the positioning which the accuracy. tag to be located determined, positioning range isisnarrowed, the center Firstly, theastag be located belongs determined, the positioning rangeinisa narrowed, the the center point ofthe thepositioning positioningarea areato is which selected thetosearch starting point,isand the target search is performed certain stepand along six point of theinpositioning area isarea selected as the search starting the target When search the is performed a certainbetween step along six directions the positioning until the virtual point RSSIpoint, valueand is searched. Euclideanindistance the the signal directions in the area until the virtualthepoint RSSI e, value searched. Whenis the Euclidean distance between the signal strength value ofpositioning the tag to be located satisfies accuracy the is point coordinate output, and then improve the positioning strength of the tag to be located satisfies the accuracy e, the coordinate is output, andon then thealgorithm positioning accuracyvalue by introducing repeat calibration technology. Finally, thepoint influence of search distance theimprove improved is accuracy by adjusting introducing Finally, theshow influence search distance on the improved algorithm is observed by the repeat size of calibration search step.technology. The simulation results that theofminimum positioning error of the new algorithm observed by the adjusting size of search step. The simulation show thatwith the minimum positioning is 0.5m and averagethepositioning accuracy is improved by results 40% compared the original algorithm.error of the new algorithm is 0.5m and the average positioning accuracy is improved by 40% compared with the original algorithm. c 2019 ⃝ 2019 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © c 2019 ⃝ The Authors. Published by Elsevier B.V. This is This is an an open open access access article article under under the the CC CC BY-NC-ND BY-NC-ND license license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility thescientific scientific committee the 2018 International Conference on Identification, Information Peer-review under responsibility ofofthe committee ofof the 2018 International Conference on Identification, Information and Peer-review responsibility the scientific committee of the 2018 International Conference on Identification, Information and Knowledge the Internet of of Things. Knowledge inunder theinInternet of Things. and Knowledge in the Internet of Things. Keywords: radio frequency identification, regional positioning, LANDMARC, target search, repeat calibration; Keywords: radio frequency identification, regional positioning, LANDMARC, target search, repeat calibration;
1. Introduction 1. Introduction With the development of Internet of things and big data[1], researchers at home and abroad have proposed many Withwireless the development oftechnologies, Internet of things big data[1], researchers at for home andBluetooth, abroad have proposed RFID many indoor positioning such and as indoor positioning systems WiFi, infrared,and indoor wireless positioning technologies, such as indoor positioning systems for WiFi, Bluetooth, infrared,and RFID technologies.WiFi technology [2] Local area network (WLAN) composed of wireless access points can be used to technologies.WiFi technology [2]but Local (WLAN)bycomposed of wireless access points can be and usedthe to locate in complex environment, it is area easynetwork to be interfered other signals, thus affecting its accuracy, locate in complex environment, but it is easy to be interfered by other signals, thus affecting its accuracy, and the energy consumption of the locator is high. Bluetooth technology [3] is a short-distance low-power wireless transmisenergy consumption of the locator used is high. technology [3] For is a complex short-distance wireless transmission technology, which is mainly for Bluetooth small-scale positioning. spacelow-power environments, the Bluetooth sion technology, which is mainly used for small-scale positioning. For complex space environments, the Bluetooth positioning system has poor stability and is greatly interfered by noise signals. The infrared technology [4] receives positioning system has poor stability and is greatly interfered by noise signals. The infrared technology [4] receives ∗ ∗
Corresponding author. Tel.: +86-135-6333-7653 . Corresponding Tel.: +86-135-6333-7653 . E-mail address:author. [email protected] E-mail address: [email protected] c 2019 The Authors. Published by Elsevier B.V. 1877-0509 ⃝ 1877-0509 © The Authors. Published by B.V. c 2019 1877-0509 ⃝ 2019 Thearticle Authors. Published by Elsevier Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the scientific CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the committee of the 2018 Conference on Identification, Information and Knowledge in Peer-review under responsibility of the scientific committee of theInternational 2018 International Conference on Identification, Information and Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet of Things. Knowledge in the Internet of Things. the Internet of Things. 10.1016/j.procs.2019.01.271
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
454 2
the modulated infrared radiation position of each mobile device through an optical sensor installed indoors.The RFID technology [5][6][7] has become the preferred technology in the field of indoor positioning because of its low cost, non-contact, non-line-of-sight and high positioning accuracy, which is superior to other technologies. The LANDMARC algorithm [8][9][10]studied in this paper is a widely used algorithm in indoor positioning of radio frequency identification. Therefore, the improvement of LANDMARC algorithm has important research significance. 2. Related work 2.1. Search method To improve the accuracy of RFID positioning, the layout of the reference tags is especially critical. The reference tag is arranged in a regular hexagonal structure, and the number of tags required to cover the same area is small, which greatly reduces the positioning cost, and the reduction of the number of tags also reduces the signal interference between the tags and improves the positioning accuracy. The reference tag is arranged in a regular hexagonal structure. Given the coordinates of the reference tag, the center point position of each regular hexagon can be obtained. From the distance formula between two points, the distance from each center point to the reader can be obtained, and then the obtained distance is brought into the formula to obtain the RSSI value of each virtual center point. The next step is to compare the Euclidean distance between the RSSI value read by the reader and the signal strength value of each virtual center point, and select the center point with the smallest Euclidean distance value as the starting point of the tag search. The coordinates of the six vertices of the hexagon ABCDEF are: {(xa , ya ), (xb , yb ), (xc , yc ), (xd , yd ), (xe , ye ), (x f , y f )}, connect AD, BE, CF, and the three lines intersect at point o. Take the hexagonal center point O as the starting point for the search, where: xa + x b (1) x′ = 2 ya + ye y′ = (2) 2 Take (x′ , y′ ) as the center and s as the step size. Search along the positive and negative directions of the three lines AD, CF, and BE respectively to get six points.the distance between each point and each reader can be obtained by the distance formula between two points, The resulting distance is brought into equation to obtain the virtual RSSI value of the point on each reader, expressed as:RS S Iu A′ , RS S Iu B′ , RS S Iu C ′ , RS S Iu D′ , RS S Iu E ′ , RS S Iu F ′ , u denoted as a reader. W is one of the points in A’B’C’D’E’F’,and the distance between the RSSI value of the six points obtained and the positioning tags can be expressed as: � � u ∑ D= (RS S Iu W − RS S Iu R)2 , s ∈ (1, u) (3) s=1
By comparing the above six values, a smaller one is selected as the starting point for the next search. Take the new starting point as the search center, s is the step size, then search along the above search direction, regain six points, and repeat the above process. The precision e is given, and the search is terminated when the Euclidean distance of the RSSI value between the point (x f inal, y f inal) and the tag to be located meets the precision e. That is, the point is the final location. In the process of searching,when the √ accuracy e is satisfied,√the RSSI values between the search points α, β and the tag R to be tested are equal, that is: RS S Iα − RS S IR 2 = RS S Iβ − RS S IR 2 . When this happens in the search process, the midpoints of a and b are selected as the final anchor points. 2.2. Algorithm description Start searching from the center point and initialize various parameters. The next search direction is determined by the magnitude of the Euclidean distance between the search point and the RSSI value of the tag to be located. When the accuracy e is satisfied, the search ends. We give the pseudo-code of the search process in algorithm 1.
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
455 3
Algorithm 1 Algorithm Description 1: Initialization parameters u, h, m, k, s, e 2: The reader reads the RSSI value of the tag to be located and the center point 3: Find the Euclidean distance between the center point and the RSSI value of the tag to be located 4: Choose a smaller Euclidean distance as the starting point for the search 5: Centered on point (X ′ , Y ′ ), s is the step size to search for three straight lines in the positive and negative directions of AD, BE, and CF 6: Use the ”path-loss” model to find the RSSI value of six points, calculate the Euclidean distance between the six points and the tag to be located, and find the minimum distance 7: The minimum distance represents the new starting point, searching in step s1, repeating the above process, and obtaining the distance d 8: if d < e then 9: Output coordinates 10: end if 2.3. Repeat calibration According to the distribution of distance and RSSI value,if there is external environmental interference, the RSSI value received by the reader and the distance show an unstable change. In the experiment, it was also found that the same distance, the RSSI value received by the reader is also different, which may be caused by the internal chip, circuit and environment of the reader and reference tag. Therefore, the introduction of repeat calibration technology [11] makes the new search method more accurate and reliable. In the above search method, when the accuracy e is satisfied, six virtual positions can be obtained, and the six virtual positions can be regarded as six virtual tags Tap1, Tap2, Tap3, Tap4, Tap5, and Tap6, and the obtained points to be located are regarded as Tap A. According to the idea of repeat calibration,in order to obtain the coordinates of the pending positioning tag A, we use R as the set of positioning tag and six virtual tags. Suppose K = 6, then R = {T ap1, T ap2, T ap3, T ap4, T ap5, T ap6, T apA}.LANDMARC algorithm is used to get the coordinates of K virtual tags (x, y). A virtual tag in the set is taken as the tracking tag. According to the coordinates of other K members in the set, its coordinates A are obtained, and the coordinates of each reference tag are calculated. The error can be expressed as: k ∑ ′ ∆x = wi (xi − xi ) (4) i=1
∆y =
k ∑ i=1
′
wi (yi − yi )
(5)
Put (x′ , y′ ) and K reference tags in the set, and repeat the above steps to calculate the coordinates of the pending positioning tag, and repeatedly calibrate the coordinates of the pending positioning tag until it reaches a stable value. The stable coordinates serve as the final positioning position. 3. Simulation Analysis In order to verify the effectiveness of the improved algorithm, this paper uses Matlab simulation software as the experimental platform. The experimental environment is set as 15m ∗ 15m. As shown in Fig.1, 45 reference tags are placed, and the distance between the adjacent reference tags is 2m. The four readers are located at the corner of the deployment area, in coordinates (0,0), (0,15), (15,15), (15,0). 10 location tags were randomly placed in the location area. In the entire positioning region, we randomly generated 10 positioning tags coordinates.Then, according to the log-distance path loss model, corresponding signal intensity vectors were generated for the 10 positioning tags, and
456 4
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
the traditional LANDMARC algorithm and the improved algorithm were used to locate the 10 points. Finally, the positioning errors, average positioning errors and the time consumed by the two algorithms were calculated. The coordinates of the pending positioning tag are (Xi , Yi )(i = 1, 2..., 10), the actual coordinates of the pending positioning tag are (Xtagi , Ytagi )(i = 1, 2..., 10), the error between the actual coordinates and the calculated coordinates 10 √ 1 ∑ ei (i = 1, 2, ..., 10). The result is is ei = (Xtagi − Xi )2 + (Ytagi − Yi )2 (i = 1, 2, ..., 10), the average error is e¯ = 10 i=1
shown in Fig.2. As can be seen from Fig.2, the positioning error of 10 randomly generated positioning tags was reduced, and the positioning accuracy was significantly improved. The positioning error of 10 positioning tags was reduced by 8% to 74% compared with the original LANDMARC algorithm.In order to improve the reliability of the results, 20 positioning tags were randomly generated in the localization area.Experimental results show that the positioning accuracy is improved by 40%.
Fig. 1. Experimental environment arrangement
Fig. 2. Comparison diagram of positioning error
The experimental results obtained in the above simulation indicate that the step length is 0.1m. In order to observe the effect of searching step length on localization, s=0.1m, 0.5m, 1.5m and 2m were taken respectively, and the average error of 10 pending positioning tags was obtained. The experimental results are shown in Fig.3. It can be seen from the experimental results that the positioning error of changing step length has obvious changes. On the whole, when the step length is 0.1m, the positioning error of the pending positioning tag is generally small, and the minimum error is 0.5m. However, as the search step size increases, the average positioning error of the algorithm increases and the positioning accuracy decreases. The step length s = 0.1m was selected to conduct simulation experiments on the search trajectories of two random pending tags. The results are shown in Fig.4. By using repeat calibration technology, the interference of external environment is obviously reduced. Starting from the starting point, the search is approaching the point to be measured slowly according to a certain step length, and there is no deviation from the search direction. The use of repeat calibration technology can effectively control the interference of external environment and improve the positioning accuracy.
Fig. 3. Positioning error for difference length
Fig. 4. Search track of the tag to be tested
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
457 5
4. Conclusion In this paper, based on the existing LANDMARC algorithm, a new method of positioning tag search is proposed. Firstly, by arranging the reference tags into hexagon, this algorithm uses the existing K neighbor localization algorithm to find the region of the positioning. Then, starting from the central point of the positioning region, the minimum value between the virtual point RSSI value and the Euclidean distance of the positioning tag RSSI value was used as the location point. In complex indoor environment, the relationship between RSSI value and distance is constantly changing. The position of the point to be located and the adjacent virtual reference tag coordinates are checked by searching, until the error tends to be stable, and the final position of the point to be located is obtained. The simulation results show that the new algorithm has better positioning efficiency and higher positioning accuracy. In general, the improved algorithm has good practicability and is suitable for indoor positioning. Acknowledgements This work is supported by the National Natural Science Foundation of China(61672321, 61771289), the Shandong provincial Graduate Education Innovation Program(SDYY14052, SDYY15049), Qufu Normal University Science and Technology Project (xkj201525), Shandong Province University Science and Technology Planning Project (J16LN15), the Shandong provincial Specialized Degree Postgraduate Teach Case Library Construction Program, the Shandong provincial Postgraduate Education Quality Curriculum Construction Program. References [1] Y. Sun, H. Song, A. Jara and R. Bie.(2016)“Internet of Things and Big Data Analytics for Smart and Connected Communities”, IEEE, pages 766 - 773. [2] H. Liu, W. Lo and C. Tseng.(2014)“A WiFi-Based Weighted Screening Method for Indoor Positioning Systems”, Wireless Personal Communications An International Journal 79(1):611-627. [3] A. Kalbandhe and C. Patil.(2017)“Indoor Positioning System using Bluetooth Low Energy”, International Conference on Computing, Analytics and Security Trends, pages 451-455. [4] M. Elgargni and A. Al-Habaibeh.(2015)“Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system”,International Conference on Industrial Informatics, pages 782-787. [5] M. Ahmad and A. Mohan.(2013)“Novel Bridge-Loop reader for positioning with HF RFID under sparse tag grid”, Transactions on Industrial Electronics 61(1):555-566. [6] Y. Zhao, K. Liu and Y. Ma.(2017)“Similarity analysis based indoor localization algorithm with backscatter information of passive UHF RFID tags”,Sensors Journal (99):1-1. [7] C. Wang, Z. Shi and F. Wu.(2017)“An RFID indoor positioning system by using particle swarm optimization-based artificial neural network”,International Conference on Audio, Language and Image Processing, pages 738-742. [8] Y. Zhang, L. Tan and H. Jiang.(2012)“The simulation and improved k-nearest neighbors algorithm of LANDMARC for books positioning”,International Conference on Measurement, Information and Control, pages 410-414. [9] X. Liu, M. Wen and G. Qin.(2017)“LANDMARC with improved k-nearest algorithm for RFID location system”,International Conference on Computer and Communications, pages 2569-2572. [10] Y. Zhao, K. Liu and Y. Ma.(2014)“An improved k-NN algorithm for localization in multipath environments”, Eurasip Journal on Wireless Communications & Networking, pages 1-10. [11] X. Jiang, Y. Liu and X. Wang.(2009)“An enhanced approach of indoor location sensing using active RFID”, Wase International Conference on Information Engineering 1(1):169-172.
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science (2019) 000–000 Procedia Computer Science 14700 (2019) 453–457
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
2018 International Conference on Identification, Information and Knowledge 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018 in the Internet of Things, IIKI 2018
RFID Location Algorithm Based on Target Search and Repeat RFID Location Algorithm Based on Target Search and Repeat Calibration Calibration a Guangshun Liaa , Chenglong Liaa , Junhua Wua,∗ a,∗, Yanmin Yina Guangshun Li , Chenglong Li , Junhua Wu , Yanmin Yin a
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
a School
Abstract Abstract In the RFID indoor positioning system, how to realize the positional positioning of the tag to be positioned is an important issue In indoor positioning system, how to realize the positional positioning the tag to bemethod positioned is an important issue on the the RFID basis of ensuring the positioning accuracy. In order to solve this problem, of a new search is proposed in this paper. on the basis of ensuringarea thetopositioning In orderbelongs to solveis this problem,the a new search method proposedand in this paper. Firstly, the positioning which the accuracy. tag to be located determined, positioning range isisnarrowed, the center Firstly, theastag be located belongs determined, the positioning rangeinisa narrowed, the the center point ofthe thepositioning positioningarea areato is which selected thetosearch starting point,isand the target search is performed certain stepand along six point of theinpositioning area isarea selected as the search starting the target When search the is performed a certainbetween step along six directions the positioning until the virtual point RSSIpoint, valueand is searched. Euclideanindistance the the signal directions in the area until the virtualthepoint RSSI e, value searched. Whenis the Euclidean distance between the signal strength value ofpositioning the tag to be located satisfies accuracy the is point coordinate output, and then improve the positioning strength of the tag to be located satisfies the accuracy e, the coordinate is output, andon then thealgorithm positioning accuracyvalue by introducing repeat calibration technology. Finally, thepoint influence of search distance theimprove improved is accuracy by adjusting introducing Finally, theshow influence search distance on the improved algorithm is observed by the repeat size of calibration search step.technology. The simulation results that theofminimum positioning error of the new algorithm observed by the adjusting size of search step. The simulation show thatwith the minimum positioning is 0.5m and averagethepositioning accuracy is improved by results 40% compared the original algorithm.error of the new algorithm is 0.5m and the average positioning accuracy is improved by 40% compared with the original algorithm. c 2019 ⃝ 2019 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © c 2019 ⃝ The Authors. Published by Elsevier B.V. This is This is an an open open access access article article under under the the CC CC BY-NC-ND BY-NC-ND license license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility thescientific scientific committee the 2018 International Conference on Identification, Information Peer-review under responsibility ofofthe committee ofof the 2018 International Conference on Identification, Information and Peer-review responsibility the scientific committee of the 2018 International Conference on Identification, Information and Knowledge the Internet of of Things. Knowledge inunder theinInternet of Things. and Knowledge in the Internet of Things. Keywords: radio frequency identification, regional positioning, LANDMARC, target search, repeat calibration; Keywords: radio frequency identification, regional positioning, LANDMARC, target search, repeat calibration;
1. Introduction 1. Introduction With the development of Internet of things and big data[1], researchers at home and abroad have proposed many Withwireless the development oftechnologies, Internet of things big data[1], researchers at for home andBluetooth, abroad have proposed RFID many indoor positioning such and as indoor positioning systems WiFi, infrared,and indoor wireless positioning technologies, such as indoor positioning systems for WiFi, Bluetooth, infrared,and RFID technologies.WiFi technology [2] Local area network (WLAN) composed of wireless access points can be used to technologies.WiFi technology [2]but Local (WLAN)bycomposed of wireless access points can be and usedthe to locate in complex environment, it is area easynetwork to be interfered other signals, thus affecting its accuracy, locate in complex environment, but it is easy to be interfered by other signals, thus affecting its accuracy, and the energy consumption of the locator is high. Bluetooth technology [3] is a short-distance low-power wireless transmisenergy consumption of the locator used is high. technology [3] For is a complex short-distance wireless transmission technology, which is mainly for Bluetooth small-scale positioning. spacelow-power environments, the Bluetooth sion technology, which is mainly used for small-scale positioning. For complex space environments, the Bluetooth positioning system has poor stability and is greatly interfered by noise signals. The infrared technology [4] receives positioning system has poor stability and is greatly interfered by noise signals. The infrared technology [4] receives ∗ ∗
Corresponding author. Tel.: +86-135-6333-7653 . Corresponding Tel.: +86-135-6333-7653 . E-mail address:author. [email protected] E-mail address: [email protected] c 2019 The Authors. Published by Elsevier B.V. 1877-0509 ⃝ 1877-0509 © The Authors. Published by B.V. c 2019 1877-0509 ⃝ 2019 Thearticle Authors. Published by Elsevier Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the scientific CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the committee of the 2018 Conference on Identification, Information and Knowledge in Peer-review under responsibility of the scientific committee of theInternational 2018 International Conference on Identification, Information and Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet of Things. Knowledge in the Internet of Things. the Internet of Things. 10.1016/j.procs.2019.01.271
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
454 2
the modulated infrared radiation position of each mobile device through an optical sensor installed indoors.The RFID technology [5][6][7] has become the preferred technology in the field of indoor positioning because of its low cost, non-contact, non-line-of-sight and high positioning accuracy, which is superior to other technologies. The LANDMARC algorithm [8][9][10]studied in this paper is a widely used algorithm in indoor positioning of radio frequency identification. Therefore, the improvement of LANDMARC algorithm has important research significance. 2. Related work 2.1. Search method To improve the accuracy of RFID positioning, the layout of the reference tags is especially critical. The reference tag is arranged in a regular hexagonal structure, and the number of tags required to cover the same area is small, which greatly reduces the positioning cost, and the reduction of the number of tags also reduces the signal interference between the tags and improves the positioning accuracy. The reference tag is arranged in a regular hexagonal structure. Given the coordinates of the reference tag, the center point position of each regular hexagon can be obtained. From the distance formula between two points, the distance from each center point to the reader can be obtained, and then the obtained distance is brought into the formula to obtain the RSSI value of each virtual center point. The next step is to compare the Euclidean distance between the RSSI value read by the reader and the signal strength value of each virtual center point, and select the center point with the smallest Euclidean distance value as the starting point of the tag search. The coordinates of the six vertices of the hexagon ABCDEF are: {(xa , ya ), (xb , yb ), (xc , yc ), (xd , yd ), (xe , ye ), (x f , y f )}, connect AD, BE, CF, and the three lines intersect at point o. Take the hexagonal center point O as the starting point for the search, where: xa + x b (1) x′ = 2 ya + ye y′ = (2) 2 Take (x′ , y′ ) as the center and s as the step size. Search along the positive and negative directions of the three lines AD, CF, and BE respectively to get six points.the distance between each point and each reader can be obtained by the distance formula between two points, The resulting distance is brought into equation to obtain the virtual RSSI value of the point on each reader, expressed as:RS S Iu A′ , RS S Iu B′ , RS S Iu C ′ , RS S Iu D′ , RS S Iu E ′ , RS S Iu F ′ , u denoted as a reader. W is one of the points in A’B’C’D’E’F’,and the distance between the RSSI value of the six points obtained and the positioning tags can be expressed as: � � u ∑ D= (RS S Iu W − RS S Iu R)2 , s ∈ (1, u) (3) s=1
By comparing the above six values, a smaller one is selected as the starting point for the next search. Take the new starting point as the search center, s is the step size, then search along the above search direction, regain six points, and repeat the above process. The precision e is given, and the search is terminated when the Euclidean distance of the RSSI value between the point (x f inal, y f inal) and the tag to be located meets the precision e. That is, the point is the final location. In the process of searching,when the √ accuracy e is satisfied,√the RSSI values between the search points α, β and the tag R to be tested are equal, that is: RS S Iα − RS S IR 2 = RS S Iβ − RS S IR 2 . When this happens in the search process, the midpoints of a and b are selected as the final anchor points. 2.2. Algorithm description Start searching from the center point and initialize various parameters. The next search direction is determined by the magnitude of the Euclidean distance between the search point and the RSSI value of the tag to be located. When the accuracy e is satisfied, the search ends. We give the pseudo-code of the search process in algorithm 1.
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
455 3
Algorithm 1 Algorithm Description 1: Initialization parameters u, h, m, k, s, e 2: The reader reads the RSSI value of the tag to be located and the center point 3: Find the Euclidean distance between the center point and the RSSI value of the tag to be located 4: Choose a smaller Euclidean distance as the starting point for the search 5: Centered on point (X ′ , Y ′ ), s is the step size to search for three straight lines in the positive and negative directions of AD, BE, and CF 6: Use the ”path-loss” model to find the RSSI value of six points, calculate the Euclidean distance between the six points and the tag to be located, and find the minimum distance 7: The minimum distance represents the new starting point, searching in step s1, repeating the above process, and obtaining the distance d 8: if d < e then 9: Output coordinates 10: end if 2.3. Repeat calibration According to the distribution of distance and RSSI value,if there is external environmental interference, the RSSI value received by the reader and the distance show an unstable change. In the experiment, it was also found that the same distance, the RSSI value received by the reader is also different, which may be caused by the internal chip, circuit and environment of the reader and reference tag. Therefore, the introduction of repeat calibration technology [11] makes the new search method more accurate and reliable. In the above search method, when the accuracy e is satisfied, six virtual positions can be obtained, and the six virtual positions can be regarded as six virtual tags Tap1, Tap2, Tap3, Tap4, Tap5, and Tap6, and the obtained points to be located are regarded as Tap A. According to the idea of repeat calibration,in order to obtain the coordinates of the pending positioning tag A, we use R as the set of positioning tag and six virtual tags. Suppose K = 6, then R = {T ap1, T ap2, T ap3, T ap4, T ap5, T ap6, T apA}.LANDMARC algorithm is used to get the coordinates of K virtual tags (x, y). A virtual tag in the set is taken as the tracking tag. According to the coordinates of other K members in the set, its coordinates A are obtained, and the coordinates of each reference tag are calculated. The error can be expressed as: k ∑ ′ ∆x = wi (xi − xi ) (4) i=1
∆y =
k ∑ i=1
′
wi (yi − yi )
(5)
Put (x′ , y′ ) and K reference tags in the set, and repeat the above steps to calculate the coordinates of the pending positioning tag, and repeatedly calibrate the coordinates of the pending positioning tag until it reaches a stable value. The stable coordinates serve as the final positioning position. 3. Simulation Analysis In order to verify the effectiveness of the improved algorithm, this paper uses Matlab simulation software as the experimental platform. The experimental environment is set as 15m ∗ 15m. As shown in Fig.1, 45 reference tags are placed, and the distance between the adjacent reference tags is 2m. The four readers are located at the corner of the deployment area, in coordinates (0,0), (0,15), (15,15), (15,0). 10 location tags were randomly placed in the location area. In the entire positioning region, we randomly generated 10 positioning tags coordinates.Then, according to the log-distance path loss model, corresponding signal intensity vectors were generated for the 10 positioning tags, and
456 4
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
the traditional LANDMARC algorithm and the improved algorithm were used to locate the 10 points. Finally, the positioning errors, average positioning errors and the time consumed by the two algorithms were calculated. The coordinates of the pending positioning tag are (Xi , Yi )(i = 1, 2..., 10), the actual coordinates of the pending positioning tag are (Xtagi , Ytagi )(i = 1, 2..., 10), the error between the actual coordinates and the calculated coordinates 10 √ 1 ∑ ei (i = 1, 2, ..., 10). The result is is ei = (Xtagi − Xi )2 + (Ytagi − Yi )2 (i = 1, 2, ..., 10), the average error is e¯ = 10 i=1
shown in Fig.2. As can be seen from Fig.2, the positioning error of 10 randomly generated positioning tags was reduced, and the positioning accuracy was significantly improved. The positioning error of 10 positioning tags was reduced by 8% to 74% compared with the original LANDMARC algorithm.In order to improve the reliability of the results, 20 positioning tags were randomly generated in the localization area.Experimental results show that the positioning accuracy is improved by 40%.
Fig. 1. Experimental environment arrangement
Fig. 2. Comparison diagram of positioning error
The experimental results obtained in the above simulation indicate that the step length is 0.1m. In order to observe the effect of searching step length on localization, s=0.1m, 0.5m, 1.5m and 2m were taken respectively, and the average error of 10 pending positioning tags was obtained. The experimental results are shown in Fig.3. It can be seen from the experimental results that the positioning error of changing step length has obvious changes. On the whole, when the step length is 0.1m, the positioning error of the pending positioning tag is generally small, and the minimum error is 0.5m. However, as the search step size increases, the average positioning error of the algorithm increases and the positioning accuracy decreases. The step length s = 0.1m was selected to conduct simulation experiments on the search trajectories of two random pending tags. The results are shown in Fig.4. By using repeat calibration technology, the interference of external environment is obviously reduced. Starting from the starting point, the search is approaching the point to be measured slowly according to a certain step length, and there is no deviation from the search direction. The use of repeat calibration technology can effectively control the interference of external environment and improve the positioning accuracy.
Fig. 3. Positioning error for difference length
Fig. 4. Search track of the tag to be tested
Guangshun Li et al. / Procedia Computer Science 147 (2019) 453–457 Guangshun Li, Chenglong Li, Junhua Wu, Yanmin Yin / Procedia Computer Science 00 (2019) 000–000
457 5
4. Conclusion In this paper, based on the existing LANDMARC algorithm, a new method of positioning tag search is proposed. Firstly, by arranging the reference tags into hexagon, this algorithm uses the existing K neighbor localization algorithm to find the region of the positioning. Then, starting from the central point of the positioning region, the minimum value between the virtual point RSSI value and the Euclidean distance of the positioning tag RSSI value was used as the location point. In complex indoor environment, the relationship between RSSI value and distance is constantly changing. The position of the point to be located and the adjacent virtual reference tag coordinates are checked by searching, until the error tends to be stable, and the final position of the point to be located is obtained. The simulation results show that the new algorithm has better positioning efficiency and higher positioning accuracy. In general, the improved algorithm has good practicability and is suitable for indoor positioning. Acknowledgements This work is supported by the National Natural Science Foundation of China(61672321, 61771289), the Shandong provincial Graduate Education Innovation Program(SDYY14052, SDYY15049), Qufu Normal University Science and Technology Project (xkj201525), Shandong Province University Science and Technology Planning Project (J16LN15), the Shandong provincial Specialized Degree Postgraduate Teach Case Library Construction Program, the Shandong provincial Postgraduate Education Quality Curriculum Construction Program. References [1] Y. Sun, H. Song, A. Jara and R. Bie.(2016)“Internet of Things and Big Data Analytics for Smart and Connected Communities”, IEEE, pages 766 - 773. [2] H. Liu, W. Lo and C. Tseng.(2014)“A WiFi-Based Weighted Screening Method for Indoor Positioning Systems”, Wireless Personal Communications An International Journal 79(1):611-627. [3] A. Kalbandhe and C. Patil.(2017)“Indoor Positioning System using Bluetooth Low Energy”, International Conference on Computing, Analytics and Security Trends, pages 451-455. [4] M. Elgargni and A. Al-Habaibeh.(2015)“Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system”,International Conference on Industrial Informatics, pages 782-787. [5] M. Ahmad and A. Mohan.(2013)“Novel Bridge-Loop reader for positioning with HF RFID under sparse tag grid”, Transactions on Industrial Electronics 61(1):555-566. [6] Y. Zhao, K. Liu and Y. Ma.(2017)“Similarity analysis based indoor localization algorithm with backscatter information of passive UHF RFID tags”,Sensors Journal (99):1-1. [7] C. Wang, Z. Shi and F. Wu.(2017)“An RFID indoor positioning system by using particle swarm optimization-based artificial neural network”,International Conference on Audio, Language and Image Processing, pages 738-742. [8] Y. Zhang, L. Tan and H. Jiang.(2012)“The simulation and improved k-nearest neighbors algorithm of LANDMARC for books positioning”,International Conference on Measurement, Information and Control, pages 410-414. [9] X. Liu, M. Wen and G. Qin.(2017)“LANDMARC with improved k-nearest algorithm for RFID location system”,International Conference on Computer and Communications, pages 2569-2572. [10] Y. Zhao, K. Liu and Y. Ma.(2014)“An improved k-NN algorithm for localization in multipath environments”, Eurasip Journal on Wireless Communications & Networking, pages 1-10. [11] X. Jiang, Y. Liu and X. Wang.(2009)“An enhanced approach of indoor location sensing using active RFID”, Wase International Conference on Information Engineering 1(1):169-172.