 Open Access
 Authors : M. Jothi Lakshmi, S. Dhanabal
 Paper ID : IJERTCONV8IS08021
 Volume & Issue : NCICCT – 2020 (Volume 8 – Issue 08)
 Published (First Online): 15052020
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Finger Printing using Applications IoT for Localization Indoor
M. Jothi Lakshmi M.E (CSE
S. Dhanabal
Asp/Head of CSE,
PGP College of Engineering and Technology ,Namakkal.
Abstract– Fingerprinting method is one of the preferred method used for indoor localization using WiFi signals because of its low complexity and its cost effectiveness. This paper proposes an indoor localization algorithm using fingerprinting method that is suitable for an indoor IoT application. The proposed algorithm combines the location estimates from two different approaches, deterministic and probabilistic, to estimate the target location. The proposed algorithm was tested for different conditions: stationary and moving IoT targets, lineof sight and nonlineofsight indoor environments. The results showed the proposed combined algorithm performed better in terms of localization accuracy, precision and robustness than deterministic and probabilistic methods individually and similar past research.
Keywords: IoT, indoor localization, fingerprinting
INTRODUCTION
Indoor localization or indoor positioning is a key enabling technology for IoT applications [1] such as guiding customers or visitors inside a shopping mall or a convention centre, where conventional navigation technologies such as GPS is not available.[2],[3] Even though there are indoor localization solutions that use RFID or BLE beacons with known fixed locations inside a building, it requires additional hardware and installation costs thus making the implementation of these systems costly in terms of time and money. However, using WiFi signals to perform localization makes it a better alternative to the beacon based systems as it does not require the installation of new hardware, thus reducing complexity and cost of the system [4]. In the literature, some research have focused on the study of RF signal propagation in indoor environments while others have developed localization methods that exploit various aspects of RF signal propagation such as propagation time, angle of arrival and received signal strength (RSS) to achieve localization. Methods such as Time of Arrival (TOA) and Time Difference of Arrival (TDOA) use propagation time for localization. These methods require time synchronisation between target device and measuring stations and between measuring stations respectively. Methods that use propagation angle, such as Angle of Arrival (AOA) require measuring stations to have
special antenna arrangements in different orientations. In addition to being complex, these systems suffer reduced performance due to propagation time and angle are directly affected by multipath effect existing in indoor environments. Methods using RSS however provide a better alternative. Fingerprinting method uses RSS measurements and is less complex in implementation that it does not require special hardware or the access point locations. It can be implemented in software reducing costs [4]. Performance of localization algorithms are quantified using its accuracy, precision and robustness. Accuracy is a measure of how much the result has deviated from the expected outcome, while precision is a measure of how consistently the result is within a certain value range. Robustness is how well the algorithm perform under poor Radio Frequency (RF) conditions [5]. Past research have resulted in precisions in the range of 90%, but it is also important to know the error value considered when calculating the precision. For example, in [6] the precision values for different algorithms are shown in Table I.
Table I. Precisions of different algorithms in [9]
Method
Accuracy (m)
Precision (< 2m)
Precision (< 1m)
Deterministic
1.6
90%
9%
Probabilistic
1.87
70%
30%
Combined
1.54
65%
30%
As seen above, the precision is 90% when 2m is considered for deterministic (KNN) method while it drops to 9% when
1m is considered. The same applies for Probabilistic and
Combined methods. By comparison, the work in
proposed algorithm achieved only 50% precision below 2m error but achieved 30% precision when 1m error was considered. In [8], the accuracy and precision of some commercial indoor localisation solutions are compared. Table II illustrates some solutions using WiFi RSS for localization.
Table II. Commercial products and their performance [8]
Solution
Algorithm
Accuracy
Precision
Microsoft RADAR
KNN
35m
50% within 2.5m
90% within 5.9m
Horus
probabilistic
2m
90% within 2.1m
DIT
MLP
3m
90% within 5.12m
MultiLoc
SMP
2.7m
50% within 2.7m
Above table shows that the accuracy and precision of some commercial systems are relatively low. Some solutions, EIRIS and Ubisense, provided accuracy below 1m, but their robustness was lower [8]. After considering different existing solutions and past research the importance of achieving high accuracy, precision along with robustness was identified. For this purpose, this paper uses fingerprinting method in the proposed algorithm.
Fingerprinting method
Fingerprinting method is one of the most used method for localization because of its abovementioned benefits. Fingerprinting method involves storing the RF characteristics, known as fingerprints, of locations of the indoor environment in a database and comparing the fingerprint of the unknown target location with the fingerprints in the database to find an approximated location of the target [2]. As such fingerprinting method is composed of two phases:
Offline Phase
This phase is also called the Data Collection phase during which, the fingerprints of the concerned indoor area are collected and the database is created. The indoor area is divided into an equally spaced grid where the grid points are called reference points (RP), at which the data will be collected. Past studies have showed that multipath effect, reflection, diffraction and scattering cause RSS to randomly vary around a mean value at a location[9]. RSS value is also affected by fading, which consists of two parts, Largescale fading and smallscale fading. Largescale fading is caused by attenuation due to signals being absorbed by various materials and objects in the environment. Largescale fading decides the mean RSS. Smallscale fading is caused by multipath effect. As such, RSS in an indoor environment can be approximated to a Gaussian distribution with a mean and a standard deviation. For a more accurate approximation of mean and standard deviation, large number of samples will need to be collected at each RP. In literature, number of samples collected were as high as 10,000 [7]. After collecting samples the calculated mean and standard deviation will be part of the fingerprint of that RP [9], [10].
Online phase
In the online phase the algorithm takes a sample fingerprint from the unknown target location and
compares it with the fingerprints in the database to classify the RPs that are most likely (or closest) to the target location. There are several known algorithms such as probabilistic, kNearest Neighbour (KNN), neural networks, support vector machine (SVM) etc. This work uses the probabilistic and KNN methods to find two sets of estimated coordinates and finally combine them [8].
In this paper, Section II describes the proposed fingerprinting method in detail. Section III discusses the results and observations of the testing of the algorithm. Finally, Section IV provides the conclusion of this paper.
FINGERPRINTING ALGORITHM
This paper implements the fingerprinting algorithm based on past research [6] making modifications with the aim of improving performance in terms of precision, accuracy and robustness. Two algorithms were designed to perform tasks in each phase of the fingerprinting method.
Data collection algorithm
Figure 1 shows the flowchart of the proposed data collection software used in during the data collection phase. As seen in the flowchart the data collection will be performed for s number of times at a particular RP. In this paper 100 is chosen, as the number of samples, due to practical reasons, but larger values will give a better representation of the RF behaviour at the RP. When collecting data at the RP, firstly the WiFi signals will be scanned to obtain the list of available WiFi access points (Cells) and their information such as signal level, signal quality, modulation and MAC address. The second block in the flowchart represent the process of extracting the required information from the list. The list will include WiFi signals from other buildings, but only those from the required building needs to be filtered. There after MAC address and RSS of each cell will be extracted and the total number of times a MAC address (i.e. access point) was received and the total RSS will be saved. When the measurement is done for all s number of times, the final fingerprint for the RP will be created by calculating the mean and standard deviation of RSS for each MAC address received and it will them be saved to a log file.
Fig. 1. Data collection software flowchart
After data has been collected for all RP, the fingerprint database (FPDB) can be created using the logfiles of each RP. The FPDB consists of three parts named FPDB1, FPDB2 and FPDB3. FPDB1 contains all RP and the list of MAC addresses received at each RP during data collection phase. FPDB2 contains the fingerprints for each RP. Each fingerprint consists of RSS statistics received for all MAC at each RP. The RSS statistics include mean RSS, standard deviation and unique RSS values received during the measurement period and their frequencies. FPDB3 contains coordinates of each RP. The FPDB will be used as an input to the localization algorithm during the online phase, which will be explained next.
Localization algorithm
Localization algorithm is executed during the online phase. The localization algorithm proposed in this paper takes five rapid samples at the beginning to create the sample fingerprint of the unknown target location. This sample fingerprint is said to be of size N, meaning it contains N number of MAC addresses received at the target location and the average RSS of each MAC received during the sampling
the target location using two different methods, deterministic method and probabilistic method. The difference between the two methods is that in deterministic method RPs that are closest to the target location are found while in probabilistic method RPs that are most probable to be the target location are found.
Fig. 2. Localization algorithm
Deterministic method
This method calculates the Euclidian distance between the
sample and each RP in the prematch.
period. This sampling process allows to get a better representation of RSS, reducing the effect of RSS fluctuation
(1)
caused by fading. Then the sample fingerprint is sent through an above average filter, where MAC addresses whose RSS is higher than the average RSS of the sample are selected to create the sample_n fingerprint of size n where n<N. The resulting sample_n MAC address list is then matched against
the FPDB1 in the prematch phase. In the prematch phase RP that contain all the MAC in the sample_n are selected from FPDB1 to create the prematch set of RP. This reduces the number of RP to m<M where M is the number of RP in the test area. The prematch set of RP is then used to calculate
In Eq. 1, and are RSS values of ith MAC in sample_n and corresponding fingerprint in FPDB2 where i=1,2,3n. The Euclidian distance is found for all RP in prematch where j=1,2,3.m. Then from the prematch, Kd number of RPs that have the lowest Euclidean distance are selected. The value of Kd that gives the best performance must be experimentally found beforehand. [6]
,
of being the target location. The conditional probability of the ith RP is found using Bayes rule [11] as shown in Eq. 3.
.
Thereafter Weighted KNearest Neighbour (WKNN) algorithm is used to calculate the intermediate coordinates
(3)
, of the target as in Eq. 2 where 1/ for the ith

RP with the lowest distance. The , values are retrieved from FPDB3.
Where P(RP) is the prior probability of the target being at a given RP. This value depends on various factors such as user speed, user movement patterns but here it is assumed that
Probabilistic method
The main idea behind the probabilistic method is to find the RPs in the prematch set, which have the highest probability
each R P in t he prematch is equally probable making P(RP) =
Line of Sight (LOS) scenario
1/m.  is the likelihood o f t he sample_n (i.e. S)
occurring at the ith RP. Value of  is given by Eq. 4.
   .  (4)
Where i=1,2,m and  are Gau ssian
probabilities of RSS of ith MAC address,  , in the sample_n modelled by Eq. 5.
 (5)
Where x, Âµ and are the RSS of the ith MAC in sample_n, the mean RSS of the MAC in RP and the standard deviation of RSS of the ith MAC. After calculating  for each RP in prematch, the Kp number of RPs with the highest
probabilities will be selected. Using this, the intermediate coordinates of target, , will be found using the following equation: [6]
, ,
(6)
Where the weight w=p and
, , the coordinates of each
Fig. 3. Test area layout for LOS scenario
The test area is located in an open area with five access
jth RP in the prematch set, will be retrieved from FPDB3. After , and , are found the two results will be combined as shown in Eq. 7. to get the final estimated coordinate (X,Y) of the target location.
, ,
points having clear LOS with the entire test area. Figure 3 shows the test area with the approximate location of access points. (Note that the exact locations of access points are irrelevant when using fingerprinting method)Test parameters are shown in Table below.
Parameter
Value
RF propagation
LOS
No of RP
49
Area size
6m x 6m
Origin
RP1
Xaxis direction
RP1RP7
Yaxis direction
RP1RP43
Test location (xt,yt)
(3.5, 3.5)
User speed
N/A
Readings
100
Kd and Kp
K
Parameter
Value
RF propagation
LOS
No of RP
49
Area size
6m x 6m
Origin
RP1
Xaxis direction
RP1RP7
Yaxis direction
RP1RP43
Test location (xt,yt)
(3.5, 3.5)
User speed
N/A
Readings
100
Kd and Kp
K
Table III. Stationary test parameters for LOS scenario
, (7)
Where E1,E2are errors of , and , with respect
to the test location , respectively. E1 and E2 are found as in Eq. 8 and Eq. 9 respectively.
1 (8)
2 (9)
TESTS, RESULTS AND OBSERVATIONS
This section discusses tests performed to measure the
,
Access points A and D have clear LOS with entire test area while B and C have obstructions to parts of the area. Test results are shown in Table IV.
Table IV. Results for different K in LOS scenario
K
Deterministic
Probabilistic
Combined
Error (m)
Pre (%)
Error (m)
Pre (%)
Error (m)
Pre (%)
3
1.5546
9
2.1247
6
0.4757
88
4
1.5613
1
1.4414
24
0.5383
84
5
1.5784
16
1.5851
29
1.1923
46
6
1.3382
19
1.3812
21
0.9635
49
7
1.7811
5
1.5638
34
1.4347
31
The combined method has an improved the accuracy and precision when compared to the individual methods. The maximum precision of 88% for error below 0.9m and lowest error of 0.4757m were observed by the combined method for K=3. The results of the proposed method for K=3 is shown in Fig. 6.
As seen in Fig. 4 the results are mostly clustered within 1m radius of the test location (3.5, 3.5). A moving test was
performed to track the moving target with the following test parameters:
Parameter
Value
RF propagation
LOS
No of RP
49
Area size
6m x 6m
Origin
RP1
Xaxis direction
RP1RP7
Yaxis direction
RP1RP43
Between points
(3.5,0) (3.5,5)
User speed
0.31 m/s
Readings
100
Kd and Kp
3
Parameter
Value
RF propagation
LOS
No of RP
49
Area size
6m x 6m
Origin
RP1
Xaxis direction
RP1RP7
Yaxis direction
RP1RP43
Between points
(3.5,0) (3.5,5)
User speed
0.31 m/s
Readings
100
Kd and Kp
3
Fig. 4. Stationary test K=3 using combined method for LOS scenario Table V. Moving test parameters for LOS scenario
The apparatus was moved in a straight line, back and forth, slowly at a speed of 0.31 m/s for the 100 readings. A low speed was selected to simulate IoT application where a customer walks in a shopping mall. As seen in Fig. 5, the resulting points from the proposed combined method are mostly above 0.5 m from the actual path of the target, but comparatively, the combined method has more points that are closer to the actual path than the other two method.
Fig. 5. Moving test with K=3 using combined method for LOS scenario
Non Line of sight scenario
Fig. 6. Test area layout for nonLOS scenario
–
To test the robustness of the algorithm, it was tested under nonLOS conditions. For this, a room, which is located at the end of a narrow corridor, was selected as the test area. Fig. 6 shows this area with the locations of the nearby access points. The test location was chosen as (2.6, 2) such that it does not have LOS from any of the access points.
Table VI. Stationary test parameters for nonLOS scenario
Parameter
Value
RF propagation
NonLOS
No of RP
16
Area size
5m x 3m
Origin
RP14
Xaxis direction
RP14RP16
Yaxis direction
RP14RP1
Test location (xt,yt)
(2.6, 2)
User speed
N/A
Readings
100
Kd and Kp
K
The test results in Table VII shows that the proposed algorithm performs better in terms of both accuracy and precision over the other two individual methods.
Table VII. Results for different K in nonLOS scenario
K
Distance
Probabilistic
Combined
Error (m)
Pre (%)
Error (m)
Pre (%)
Error (m)
Pre (%)
3
0.6745
73
0.7814
73
0.5362
91
4
0.6916
68
0.9521
29
0.6019
87
5
0.703
91
0.7676
77
0.5678
95
6
0.6282
82
0.6845
99
0.4968
99
7
0.5939
87
0.471
98
0.4025
99
The lowest average error of 0.4025m and highest precision of 99% for error below 0.9m were obtained by the combined method when K=7, and the results are illustrated in Fig. 7.
In Fig. 7, it can be seen how the results of combined method are clustered closer together. This implies that the precision of the combined method is higher as seen in Table VII. A moving test was performed with the test parameters shown in the following Table VIII.
Fig. 7. Stationary test with K=7 using combined method or non LOS scenario
Table VIII. Moving test parameters for nonLOS conditions
Parameter
Value
RF propagation
NonLOS
No of RP
16
Area size
5m x 3m
Origin
RP14
Xaxis direction
RP14RP16
Yaxis direction
RP14RP1
Between points
(0,2.2) (4.5,2.2)
User speed
0.29 m/s
Readings
100
Kd and Kp
7
Parameter
Value
RF propagation
NonLOS
No of RP
16
Area size
5m x 3m
Origin
RP14
Xaxis direction
RP14RP16
Yaxis direction
RP14RP1
Between points
(0,2.2) (4.5,2.2)
User speed
0.29 m/s
Readings
100
Kd and Kp
7
As in previous section, the apparatus was moved in a straight line back and forth for the 100 readings. The results are shown in Fig 8.
Fig. 8. Moving test (K=7) using deterministic method for nonLOS
Unlike in the LOS scenario, in the nonLOS case the algorithm tracks the target more precisely along the actual path. All three methods provide target location within 0.5m of the actual path. However, only the deterministic method tracked the target along the path for more distance than the other two methods whose results were concentrated. Further coordinates were not received when the target was near the wall at location (0, 2.2).
Observations
After the tests, the accuracy and error results can be used to compare the combined methods performance in terms of accuracy and average error with those of deterministic and
abilistic methods.
Fig. 9. Accuracy and Precision comparison of the combined method for LOS and nonLOS scenarios for combined method
As seen in Fig. 9, when K<5, the combined method performs with an average error <0.7m and precision >84% for both LOS and nonLOS conditions. For the same conditions, the deterministic and probabilistic methods have a higher accuracy and lower precision than the combined method as seen in Fig. 10 and Fig. 11 respectively.
For all K under LOS conditions, the performance of the combined method is better than the deterministic and probabilistic methods. However K<5 values provide the best performance for all situations.
Fig. 10. Accuracy comparison of deterministic, probabilistic and combined methods for LOS and nonLOS scenario
Fig. 11. Precision comparison of deterministic, probabilistic and combined methods for LOS and nonLOS scenarios
CONCLUSIONS
In conclusion, for a stationary target device, the proposed combined algorithm achieved a maximum precision of 88% under LOS conditions with K=3 and a maximum precision of 99% was achieved under nonLOS conditions was with K=6
and K=7. Accuracy of the proposed algorithm remained stable around 0.5m for all K under nonLOS conditions, while it degraded with increasing K for LOS conditions. In addition, the proposed fingerprinting algorithm with combined method achieved a 91% precision and accuracy of less than 1m when K=3 and K=4 for both LOS and nonLOS conditions. Therefore, it was concluded that the proposed algorithm can be used for localization under any RF conditions using K<=4 with satisfactory overall performance with high accuracy, precision and robustness. Overall, the combined method performed better than both deterministic and probabilistic methods for all situations. For a moving target, the algorithm, using deterministic method, performed better under nonLOS conditions by tracking it with less deviation from the actual path. However, further research needed to be done to track a mobile target precisely along its path in an indoor environment
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