Wireless Localization Based On Rssi Fingerprint Feature Vector.pdf

Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2015, Article ID 528747, 7 pages http://dx.doi.org/10.1155/2015/528747

Research Article Wireless Localization Based on RSSI Fingerprint Feature Vector Aiguo Zhang,1 Ying Yuan,1 Qunyong Wu,2 Shunzhi Zhu,1 and Jian Deng1 1

College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 351002, China

2

Correspondence should be addressed to Aiguo Zhang; [email protected] Received 1 February 2015; Accepted 14 May 2015 Academic Editor: Dakshnamoorthy Manivannan Copyright © 2015 Aiguo Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. RSSI wireless signal is a reference information that is widely used in indoor positioning. However, due to the wireless multipath influence, the value of the received RSSI will have large fluctuations and cause large distance error when RSSI is fitted to distance. But experimental data showed that, being affected by the combined factors of the environment, the received RSSI feature vector which is formed by lots of RSSI values from different APs is a certain stability. Therefore, the paper proposed RSSI-based fingerprint feature vector algorithm which divides location area into grids, and mobile devices are localized through the similarity matching between the real-time RSSI feature vector and RSSI fingerprint database feature vectors. Test shows that the algorithm can achieve positioning accuracy up to 2–4 meters in a typical indoor environment.

1. Introduction Localization technology based on RSSI (Received Signal Strength Indication) makes use of radio frequency signals to estimate the distance between the transmitting and receiving devices. Then the position of mobile device is obtained by those distances with trilateration technique algorithm [1]. Currently, localization algorithm is divided into two categories, ranging algorithm and nonranging algorithm. Among them, ranging algorithm is implemented in the way of trilateration, triangulation, or maximum likelihood estimation by measuring the distance and angle between receptors and transmitters, such as TOA, TDAO, and AOA. Nonranging localization algorithm is implemented by the connectivity between different devices and does not need the information of distances and angles. Furthermore, the distances based on RSSI can be measured by the signals transmitted from wireless communication devices, and this kind of localization will not increase hardware costs, so it is the common method for localization in wireless sensor networks [2–9]. But in the actual applications, the distance measurement based on RSSI is influenced by reflection, multipath, antenna gain, and so forth, and large positioning error is caused. Therefore, the localization method based on fingerprint

feature vector is put forward in this paper. First, the location area will be divided into grids, and the received RSSI values of all Wi-Fi sources in each grid will be collected. Second, all the received RSSI values will be stored into fingerprint database which can be formed by training sample vectors. Third, the localization results are obtained through the similarity matching between the real-time RSSI value vector of mobile devices and fingerprint database in actual positioning. In this method of localization, neither the known coordinates of WiFi sources are necessary, nor does the RSSI value need to be transferred into distances, so it can reduce the impact of environmental factors.

2. Wireless Localization Design Based on RSSI Fingerprint Feature Vector In the course of wireless localization based on RSSI fingerprint feature vector, the RSSI values received from all of the wireless APs make up the fingerprint feature vectors of the location grids, and the fingerprint database is established. Then the real-time RSSI value vector received can be identified for fingerprint positioning. Its positioning process is divided into two stages, which are the establishment of fingerprint database and real-time positioning, and shown in Figure 1.

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International Journal of Distributed Sensor Networks AP1 AP2 · · ·

APk

Database of RSSI fingerprint feature vector

MU

Assume VRSSI i,j = (VRSSI1, VRSSI2 , . . ., VRSSIk )

VRSSIi,j Collect

2

···

n

1

VRSSI1,1 VRSSI1,2

···

VRSSI1,n

2

VRSSI2,1 VRSSI2,2

···

VRSSI2,n

···

···

···

VRSSIm,n

··· MU (Gi,j )

1

m

···

···

VRSSIm,1 VRSSIm,2

G?,? (RtRSSI 1 , RtRSSI 2 , . . ., RtRSSI k )

Grids matching

Localization results

Real-time localization stage

Establishment of fingerprint database stage

Figure 1: Framework of Wi-Fi localization based on RSSI fingerprint.

In the course of wireless localization based on RSSI fingerprint feature vector, first, a lot of Wi-Fi signal devices are arranged, typically wireless APs (Access Point), and the number of APs is in correspondence with the dimensions of vector, here represented by 𝑘. Then the localization area is divided into a number of grids, here represented by 𝐺𝑖,𝑗 (𝑖 < 𝑚, 𝑗 < 𝑛). Finally, the mobile device (MU) can be located via grid matching between real-time RSSI value vector and fingerprint database. In the stage of establishment of fingerprint database, the RSSI values and MAC (Media Access Control) address in each center of all the grids are collected, and they are stored into the database. Due to environmental factors, single RSSI value is not stable. In order to weaken the influence of unstable RSSI values, multiple times of measurements are carried out and averaged in each grid. In real-time localization stage, the real-time location of the grid is calculated by comparing real-time RSSI value vector received with grid vectors in the database. To the end, the method includes the RSSI data collection and establishment of fingerprint database, grid matching and localization for real-time RSSI value vector, and accuracy analysis of the positioning results, and they will be introduced separately in the following.

3. The Data Collection and Organization of RSSI Fingerprint Feature Vector 3.1. Data Collection Points Arrangement for RSSI Fingerprint Feature Vector. As is shown in Figure 2, location area is divided into closely linked grids, and the grids can be arbitrarily sized and shaped. In order to illustrate the idea of positioning, the grids are set to squares in the paper; at the same time, the size of the grid represents the spatial positioning accuracy. All grids are numbered from southwest to northeast orderly, and there are a lot of wireless signal devices in the targeted area, ranked by AP1 , AP2 , AP, . . . , AP𝑘 . In the course of RSSI collection, all the RSSI values from APs in each grid will be collected and the corresponding MAC address will also be recorded.

Wireless AP1

Wireless AP2

North

Gm,01

Gm,02

···

Gm,n

···

···

···

···

G02,01

G02,02

···

G02,n

G01,01

G01,02

···

G01,n

West

Wireless AP···

South

East

Wireless APk

Figure 2: Schematic diagram of grid data collection in localization area.

Typically, the larger vector dimensions of 𝑘, the richer expression of vector features, and the higher difference between different vectors will be acquired. In order to improve the wireless Wi-Fi positioning accuracy, enough wireless APs should be laid in the location area, but it also makes the RSSI data collection capacity increase. For example, a location area is 100 × 80 m, mesh size is 2 × 2 m, and the number of the wireless APs is 20; then the recorded number of RSSI values to be stored is 100 × 80 × 20/(2 × 2) = 40000. For the purpose of management of all the grid vector data effectively, database is suitable for storing and organization of the received RSSI data. 3.2. Data Organization of RSSI Fingerprint Feature Vectors. Large quantity of RSSI fingerprint feature vector needs to be read for localization each time. Therefore, a more efficient

International Journal of Distributed Sensor Networks

Determined grid Gi,j

Query

Return

All RSSI data tables

3

RSSI values responding to Gi,j from all the tables

Get

The number of k RSSI values

Output

A fingerprint feature vector construct

Figure 3: The process of RSSI data query and fingerprint feature vector construct.

Table 1: The structure of RSSI data table. MACid (integer)

C01 (text)

C02 (text)

⋅⋅⋅

Record 01 .. .

⋅⋅⋅ .. .

Record 01 .. .

⋅⋅⋅ .. .

C𝑛 (text) Record 01 .. .

Record 𝑚

⋅⋅⋅

Record 𝑚

⋅⋅⋅

Record 𝑚

data organization way needs to be used in terms of matching algorithm characteristics on RSSI feature vectors. In order to improve data management flexibility for all of the APs, each piece of wireless AP data is stored in a separate table; that is, the number of RSSI data tables is equal to the number of the wireless APs, and all the tables have the same structure. Thus RSSI data in the table is consistent with the array of RSSI data collection grids, as shown in Table 1. In the table, 𝑚, 𝑛 correspond to the values of the row and column of the grids, respectively. “MACid” is the primary key for each record, and C01, C02, . . . , C𝑛 correspond to the column of RSSI data table; accordingly, Record 01, . . ., and Record 𝑚 correspond to collection records of RSSI value. Names of RSSI data table are identified by the wireless APs’ MAC address; for example, if MAC address is “00: 24: b2: eb: 21: 21,” corresponding RSSI data table will be named “0024b2eb2121.” To do this, make sure that the data table names are unique, and the element values of data table are in correspondence with RSSI values collected in the grids easily. In practical applications, the number of wireless APs may appear as changes with equipment damage, updates, and so forth; to this end, the fingerprint database must be adjusted. Therefore, only the changed RSSI data tables need to be updated, and the modification of the database can be accomplished easily; at the same time, the design of the database has a good flexibility. In the course of localization, the RSSI data from all the APs must be queried and read, and its process from data query to build a fingerprint feature vector is shown in Figure 3. In light of the given row and column of the grid ranked by 𝐺𝑖,𝑗 , the RSSI data of 𝐺𝑖,𝑗 from all the tables are queried, and the corresponding 𝑘-dimensional feature vector of 𝐺𝑖,𝑗 can be built. Thus the 𝑘-dimensional feature vector can be exported for real-time location matching.

4. Grids Matching and Localization of RSSI Fingerprint Feature Vector 4.1. The Localization Algorithm of RSSI Fingerprint Feature Vector. After a given database, the received wireless AP RSSI

value vector will compare with the vectors in the fingerprint database in terms of certain matching algorithm, and the mobile location will be estimated. Among them, the matching algorithm is key to the efficiency and positioning accuracy of localization. The usual matching algorithms are nearest neighbor, 𝐾-nearest neighbor, and neural networks [10]. The method of vector angle is used in this paper. Assuming that the observations of received real-time RSSI vector at the mobile device are 𝑉rt = (RtRSSI1 , RtRSSI2 , . . . , RtRSSI𝑘 ) and the fingerprint database has vector 𝑉𝑖,𝑗 = (RSSI1 , RSSI2 , . . . , RSSI𝑘 ), where 𝑘 represents the number of detected different wireless APs on the measuring point, 𝑘 ∈ [1, 𝐾𝑇 ], 𝐾𝑇 is the total number of RSSI tables in the fingerprint database, and 𝑉𝑖,𝑗 is on behalf of 𝑘-dimensional vector at the row 𝑖 and column 𝑗. Therefore, the localization based on RSSI fingerprint feature vector is transformed to determine the similarity between real time received observation vector 𝑉rt and the fingerprint feature vector 𝑉𝑖,𝑗 . 4.2. Similarity Matching Based on Vector Cosine. There are two ways on determination of vector similarity, that is, the similarity function and distance measurement [11]. Among them, similarity function is more popular in practical applications, and the common similarity functions are as follows: vector cosine method, correlation coefficient, generalized Dice coefficient, and generalized Jaccard coefficient method. Vector cosine function is adopted in this paper. Vector cosine is used to calculate the angle between two vectors. Assume that the cosine of the angle between two vectors 𝑥 and 𝑦 is shown in cos (𝑥, 𝑦) =

∑𝑁 (𝑥, 𝑦) 𝑖=1 𝑥𝑖 ⋅ 𝑦𝑖 . 󵄩󵄩 󵄩󵄩 = 𝑁 ‖𝑥‖ ⋅ 󵄩󵄩𝑦󵄩󵄩 (∑ 𝑥2 ⋅ ∑𝑁 𝑦2 )1/2 𝑖=1 𝑖 𝑖=1 𝑖

(1)

The geometric meaning of vector cosine is characterized by the angular dimension between two vectors in 𝑁-dimensional space. Generally, dimensionless treatment for vector elements is required beforehand, and the vector elements are made to be positive; then the cosine of the angle will be at the range of [0, 1]. And the greater the value of the cosine, the lesser the angle, which shows the larger similarity between two vectors. If the value is 1, the two vectors are identical. In addition, the length of the vector is specified in the formula, which means that the role of some important part has not been amplified in similarity calculating [12]. In practical application, assume that the real-time RSSI vector of mobile devices is 𝑉rt , and RSSI fingerprint database grid vector is 𝑉𝑖,𝑗 , where 𝑖 = 0, 1, . . . , 𝑚, and 𝑗 = 0, 1, . . . , 𝑛, shown in Figure 4. Then the vector cosine between 𝑉rt with

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International Journal of Distributed Sensor Networks

Gi+1,j−1

Pi+1,j

... Gi+1,j

Gi+1,j+1

Pi+1,j+1 Vrt

. . . Gi,j−1

Vi,j Gi,j+1 . . .

Gi,j

Pi,j+1

Pi,j Gi−1,j−1

Gi−1,j+1

G .. i−1,j .

Figure 4: Matching diagram of grids vectors.

all 𝑉𝑖,𝑗 can be calculated separately by using the formula of vector cosine, shown in cos (𝑉rt , 𝑉𝑖,𝑗 ) =

∑𝑘𝑙=1 (𝑉rt )𝑙 ⋅ (𝑉𝑖,𝑗 )𝑙 2

2 1/2

(∑𝑘𝑙=1 (𝑉rt )𝑙 ⋅ ∑𝑘𝑙=1 (𝑉𝑖,𝑗 )𝑙 )

.

(2)

Among them, 𝑘 is the number of APs and also for the dimension of a grid vector. As to each real-time mobile localization, the number of 𝑚 × 𝑛 vector angle cosine values will be calculated and takes the grid with maximum cosine value as the positioning result.

5. Wireless Localization Experiment for RSSI Fingerprint Feature Vector Using Java programming language, the Wi-Fi wireless positioning system based on RSSI fingerprint feature vectors has been designed and implemented. Among them, RSSI data collection for mobile devices is developed in Android platform, and the collected RSSI data can be stored into SQLite database in accordance with Section 3.2 in this paper. Then the data in SQLite is dumped into PostgreSQL database. Finally, the data management and application services are developed by the use of Eclipse + Mybatis programming tools, so the cosine between real-time RSSI vector and fingerprint feature vector can be calculated, and also the positioning result corresponding to the largest vector cosine value can be acquired. The following scenario is as an example of a gymnasium. A wireless localization experiment based on RSSI fingerprint feature vector is made by using this developed program, and the RSSI collection and distribution of the fingerprint database in the stadium area are in Figure 5. As shown in Figure 6, the whole localization area is divided into grids by the size of 2 m × 2 m, and it comprises grids by the number of 15 rows × 28 columns; at the same time, the field RSSI vector data in each grid is collected.

Meanwhile, there are 22 kinds of Wi-Fi signals which can be received in any of the grids, including China Telecom and China Mobile, and 20 of them were selected for the experiment. The results of RSSI data distribution and storage of the grid with the MAC address of 00: 24: b2: eb: 21: 21 wireless AP are shown in Figure 6. In this scenario, RSSI value vector of each grid is selected as real-time reference vector individually and used to perform vector cosine matching localization. Meanwhile, in order to check out the positioning results, all kinds of WiFi signals are divided into three groups of 7 + 7 + 6 in Table 2, and different RSSI value deviations are given to the elements of positioned vector, whereby the corresponding different real-time vectors are obtained. In different RSSI deviation range and accuracy, the correct rate of localization by a total of 420 grids is shown in Table 2. As shown in Table 2, (1) when signs of the RSSI value deviations of the three groups are the same, the larger deviation of the RSSI value is set, the less correct rate of positioning accuracy, and the correct rate was significantly reduced by the deviation value at 11 to 16 (or −11 to −16), which is 95.7%–66.2% (or 95.2%–67.8%). (2) when signs of the RSSI value deviations of the three groups are different, the larger deviation of the RSSI value is set, the less correct rate of positioning accuracy, and correct rate of positioning reduced quickly at deviation 2–4, that is, 86.9%–28.3%. Thus different signs of the deviations influence positioning accuracy obviously. At the same time, the sign of each deviation is randomly selected, which may differ from the actual value change trend of RSSI; (3) in the case of the same RSSI value deviations, the larger the range of accuracy, the higher the correct rate of localization. Under the conditions of the fingerprint database and the deviations of the real-time RSSI vector elements being within 10, the correct rate of localization is more than 90%, which can meet necessity for the mostly actual application. Meanwhile, with the vector dimension increasing, the correct rate of localization will be improved to some extent. At the same time, the computational complexity and cost have been tested with Lenovo K23, and the results are shown in Table 3. As shown in Table 3, with the increase of the times of localization in scenario of this paper, the computational complexity and time costs are also increased, and the growth of time costs is not obvious with the increase of times of localization.

6. Conclusion The errors of fitted distance with RSSI values are large, because they are affected by multipath and other factors in indoor positioning environment. In this paper, without fitting distance, also without position of the known APs, the real-time localization results can be obtained by matching similarity between real-time RSSI feature vector and different fingerprint feature vectors. At the same time, with an actual experiment, good results of localization are achieved.

International Journal of Distributed Sensor Networks

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Table 2: Localization results based on RSSI fingerprint feature.

Number

Real-time RSSI observations (20 deviations with samples training data)

Accuracy range (unit: grid) Column deviation

Times of real-time localization

Correct grids of localization

Correct rate

7 APs

7 APs

6 APs

Row deviation

1

1

1

1

0

0

420

420

2

2

2

2

0

0

420

420

100%

100%

3

4

4

4

0

0

420

420

100%

4

7

7

7

0

0

420

418

99.5%

5

11

11

11

0

0

420

402

95.7%

6

13

13

13

0

0

420

369

87.8%

7

13

13

13

1

1

420

378

90%

8

13

13

13

2

2

420

385

91.7%

9

16

16

16

0

0

420

278

66.2%

10

16

16

16

1

1

420

299

71.2%

11

16

16

16

2

2

420

321

76.4%

12

16

16

16

3

3

420

341

81.2%

13

−1

−1

−1

0

0

420

420

100%

14

−2

−2

−2

0

0

420

420

100%

15

−4

−4

−4

0

0

420

420

100%

16

−7

−7

−7

0

0

420

417

99.3%

17

−11

−11

−11

0

0

420

400

95.2%

18

−13

−13

−13

0

0

420

364

86.7%

19

−13

−13

−13

1

1

420

372

88.6%

20

−13

−13

−13

2

2

420

376

89.5%

21

−16

−16

−16

0

0

420

285

67.8%

22

−16

−16

−16

1

1

420

301

71.7%

23

−16

−16

−16

2

2

420

316

75.2%

24

−16

−16

−16

3

3

420

333

79.3%

25

−1

1

−1

0

0

420

420

100%

26

2

2

−2

0

0

420

365

86.9%

27

−4

4

−4

0

0

420

119

28.3%

28

−4

4

−4

2

2

420

186

44.3%

29

−4

4

−4

4

4

420

265

63.1%

7

7

0

0

420

28

6.7%

30

−7

31

−7

7

7

2

2

420

99

23.6%

32

−7

7

7

4

4

420

190

45.2%

33

11

−11

−11

0

0

420

10

2.4%

34

11

−11

−11

2

2

420

58

13.8%

35

11

−11

−11

4

4

420

126

30.0%

36

13

−13

−13

0

0

420

7

1.7%

37

13

−13

−13

2

2

420

44

10.5%

38

13

−13

−13

4

4

420

99

23.6%

39

16

16

−16

0

0

420

18

4.3%

40

16

16

−16

2

2

420

75

17.8%

41

16

16

−16

4

4

420

143

34.0%

6

International Journal of Distributed Sensor Networks

G1501 G1502 G1503 G1504 G1505 G1506 G1507 G1508 G1509 G1510 G1511 G1512 G1513 G1514 G1515 G1516 G1517 G1518 G1519 G1520 G1521 G1522 G1523 G1524 G1525 G1526 G1527 G1528 G1401 G1402 G1403 G1404 G1405 G1406 G1407 G1408 G1409 G1410 G1411 G1412 G1413 G1414 G1415 G1416 G1417 G1418 G1419 G1420 G1421 G1422 G1423 G1424 G1425 G1426 G1427 G1428 G1301 G1302 G1303 G1304 G1305 G1306 G1307 G1308 G1309 G1310 G1311 G1312 G1313 G1314 G1315 G1316 G1317 G1318 G1319 G1320 G1321 G1322 G1323 G1324 G1325 G1326 G1327 G1328 G1201 G1202 G1203 G1204 G1205 G1206 G1207 G1208 G1209 G1210 G1211 G1212 G1213 G1214 G1215 G1216 G1217 G1218 G1219 G1220 G1221 G1222 G1223 G1224 G1225 G1226 G1227 G1228 G1101 G1102 G1103 G1104 G1105 G1106 G1107 G1108 G1109 G1110 G1111 G1112 G1113 G1114 G1115 G1116 G1117 G1118 G1119 G1120 G1121 G1122 G1123 G1124 G1125 G1126 G1127 G1128 G1001 G1002 G1003 G1004 G1005 G1006 G1007 G1008 G1009 G1010 G1011 G1012 G1013 G1014 G1015 G1016 G1017 G1018 G1019 G1020 G1021 G1022 G1023 G1024 G1025 G1026 G1027 G1028 G0901 G0902 G0903 G0904 G0905 G0906 G0907 G0908 G0909 G0910 G0911 G0912 G0913 G0914 G0915 G0916 G0917 G0918 G0919 G0920 G0921 G0922 G0923 G0924 G0925 G0926 G0927 G0928 G0801 G0802 G0803 G0804 G0805 G0806 G0807 G0808 G0809 G0810 G0811 G0812 G0813 G0814 G0815 G0816 G0817 G0818 G0819 G0820 G0821 G0822 G0823 G0824 G0825 G0826 G0827 G0828 G0701 G0702 G0703 G0704 G0705 G0706 G0707 G0708 G0709 G0710 G0711 G0712 G0713 G0714 G0715 G0716 G0717 G0718 G0719 G0720 G0721 G0722 G0723 G0724 G0725 G0726 G0727 G0728 G0601 G0602 G0603 G0604 G0605 G0606 G0607 G0608 G0609 G0610 G0611 G0612 G0613 G0614 G0615 G0616 G0617 G0618 G0619 G0620 G0621 G0622 G0623 G0624 G0625 G0626 G0627 G0628 G0501 G0502 G0503 G0504 G0505 G0506 G0507 G0508 G0509 G0510 G0511 G0512 G0513 G0514 G0515 G0516 G0517 G0518 G0519 G0520 G0521 G0522 G0523 G0524 G0525 G0526 G0527 G0528 G0401 G0402 G0403 G0404 G0405 G0406 G0407 G0408 G0409 G0410 G0411 G0412 G0413 G0414 G0415 G0416 G0417 G0418 G0419 G0420 G0421 G0422 G0423 G0424 G0425 G0426 G0427 G0428 G0301 G0302 G0303 G0304 G0305 G0306 G0307 G0308 G0309 G0310 G0311 G0312 G0313 G0314 G0315 G0316 G0317 G0318 G0319 G0320 G0321 G0322 G0323 G0324 G0325 G0326 G0327 G0328 G0201 G0202 G0203 G0204 G0205 G0206 G0207 G0208 G0209 G0210 G0211 G0212 G0213 G0214 G0215 G0216 G0217 G0218 G0219 G0220 G0221 G0222 G0223 G0224 G0225 G0226 G0227 G0228 G0101 G0102 G0103 G0104 G0105 G0106 G0107 G0108 G0109 G0110 G0111 G0112 G0113 G0114 G0115 G0116 G0117 G0118 G0119 G0120 G0121 G0122 G0123 G0124 G0125 G0126 G0127 G0128

Figure 5: Distribution diagram of RSSI grids in positioning area.

Figure 6: RSSI data storage in database.

Table 3: Computational complexity and cost of wireless localization. Times of localization 1 420 12600 29400 63000 105000

Computational times of vector cosine values

Time costs (s)

420 176400 5292000 12348000 26460000 44100000

1 2 12 33 43 70

Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment The work was supported by the National Natural Science Foundation of China (61373147 and 41204032).

International Journal of Distributed Sensor Networks

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