The implementation of indoor localization based on an experimental ...

Peer-to-Peer Netw. Appl. DOI 10.1007/s12083-015-0372-9

The implementation of indoor localization based on an experimental study of RSSI using a wireless sensor network Cheriet Mohammed El Amine 1 & Ouslim Mohamed 1 & Belaidi Boualam 2

Received: 30 September 2014 / Accepted: 18 May 2015 # Springer Science+Business Media New York 2015

Abstract In this paper, we present the implementation of a new indoor localization system. We studied the behavior of the Received Signal Strength Indication (RSSI) for different configurations depending on the initial energy level of the sensors used. The choice of the best XBee configuration for each sensor is obtained after studying the standard deviation of the RSSI. Thus, we performed an indoor localization application using three algorithms based on the RSSI fingerprinting. Several experiments were conducted on an established test bed made of a certain number of XBee wireless sensors. The obtained results are considered very encouraging as they are suitable to locate a person, inside a building with a precision of 80 cm and an efficiency of 90 %. Keywords Indoor localization . RSSI fingerprinting . Wireless sensor network . XBee

1 Introduction The localization of objects has seen renewed interest highly motivated by the development of wireless sensor networks. * Cheriet Mohammed El Amine [email protected] Ouslim Mohamed [email protected] Belaidi Boualam [email protected] 1

LMSE laboratory, University of science and technology USTOMB, Oran, Algeria

2

Electronics department, University of science and technology USTOMB, Oran, Algeria

The present work aims to provide an answer to this problem which can be classified into two main categories [1–3]. The free-range solutions, which are economical in the resources being limited to the operating assumptions regarding the connectivity of nodes in the network. And range-based solutions presenting characteristics based on real-time measurements of a physical phenomenon. Regardless of the RSSI signal as in the case of the application mentioned in [4],or the Time Of Arrival (TOA) [3], to generate a reliable estimation of the distance, or directly of the position of the target. This second alternative is used in this paper. This topic was the focus of several studies showing that the biggest concern of researchers is still the indoor localization. According to [5], 50 % of the developed applications for the indoor localization, indicate a localization accuracy not exceeding 6.2 m. Since, the main limitations are obstacles, the ground effect and the echoes. Thus, the developed solutions for outdoor localization, e.g., GPS, may not be applied due to the absence of the GPS signal, and the behavior of wireless sensor antennas exchange [6]. Among the work conducted for the improvement of the indoor localization accuracy, we can cite the comparative study of MinMax methods, Maximum Likelihood, ROCRSSI and K-Nearest Neighbor, which was performed in [7]. The results obtained show that the MinMax technique gives a better result with a precision of 1.2 m. The author in [8] has obtained an error between 0.28 and 1.57 m by combining the Euclidean distance with the K-Nearest Neighbor algorithm. Another approach based on clustering and virtual tags, has been proposed in [9], and researchers in [10] have used a RSSI signal acquisition based on a filter to reduce the distance estimation error. On the other hand, the results obtained in [11] show that the distance’s estimation based on the RSSI is not suitable for indoor applications.

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2 Related work The distance estimation from the RSSI has been used in various fields such as telecommunications, location and routing. Converting the RSSI to distance requires mathematical models which take into account the electromagnetic wave propagation. The Friss model is the best known one [12, 13], which is summarized by Eq. 1: Pr 1 ¼ Gt  Gr   L Pt




λ 4πd

2 ð1Þ

W here Pr and Pt are, respectively, the transmitted and the received signal power, Gt and Gr are, respectively, the antenna gains of the transmitter and the receiver, L is a coefficient which characterizes the crossed medium, λ is the radio’s wavelength, and d is the distance between the two antennas. Therefore, this model assumes a uniform energy distribution on concentric spheres, and it does not take into account the effects of the ground and obstacles. It is often used for applications in the open space. On the other hand, for communication applications near the ground, the Two Ray Reflection Model [13] was proposed. This model handles the ground effect, the heights of the two radio transmission and reception antennas ht and hr , the distance between the two antennas d and the critical distance Dc , as shown in Eqs 2 and 3: P r Gr  Gt ¼ Pt L

Dc ¼




ht  hr d2

2 ð2Þ

4πht hr λ

ð3Þ

There is also the Shadowing model [12, 14], which is based on a reference measurement of the received power signal, at a known distance d 0 linked to such information by Eq. 4 Pðd 0 Þ ¼ P ðd Þ




d do

β

ð4Þ

Equation 5 illustrates the variation in the received power signal using the logarithmic form. 

P ðd 0 Þ P ðd Þ




¼ −10βlog dB

d d0

The Shadowing model is the closest to reality, because the range area is considered as a surface area whose boundaries vary with time. However, in indoor applications besides the ground effect that influences the behavior of the RSSI, there are also obstacles and the ceiling. Nevertheless, RSSI is a heavily used model in simulations such as in [15, 16], where the generation of RSSI maps and the distance estimation were based on this model. Furthermore, the author in [17] has used the multichannel communication to minimize the error distance estimation using the RSSI. Accordingly, in this paper real measurements performed on the RSSI in an indoor environment are achieved to confirm the influence of obstacles on this metric. It was shown that, the most appropriate technique for indoor localization based on wireless sensor networks, is the use of the fingerprint of the RSSI signal. This technique is based on RSSI maps, the study conducted in [18] indicates that it is more efficient than the ML estimation method. Then, several other studies have been done to improve this method. In [19], they got a localization accuracy of 1.5 m by applying a Bayesian method, and based on the inverse propagation model, a new algorithm was proposed in [20]. An enhancement has been proposed in [21] for the target tracking applications by reducing the amount of data used. In our case, we have applied the technique of the RSSI signal fingerprint in our experimental test bed with the use of three algorithms that we have developed by taking into account the adequate configuration of the radio transmitting power for each wireless sensor, using the standard deviation parameter of the RSSI signal. This technique starts with the creation of databases or the signal received maps in the location space, this phase is called the offline mode. Then, in the online phase, the technique seeks the position corresponding to the received RSSI vector. There are several contributions points in the fingerprint technique in the offline mode. Plenty of research work focused on the filtering and the automation way to harvest the data as it is shown in [22]. In this phase, advanced learning algorithms such as neural networks [23] can be used. Several statistical techniques like correlation [24], and deterministic methods, such as the Nearest Neighbor (NN), K Nearest Neighbors (K-NN) or Weighted K Nearest Neighbors (WKNN) [25], were proposed. These techniques use different ways to measure the similarity between the online collected RSSI and the vectors stored in the database. The most common measurements of similarity defined by [16, 26] as shown in table Table 1, are: –

 þ X dB

ð5Þ

Where β is the attenuation coefficient and X dB is the receive signal strength at 1 m.

– – –

Manhattan distance: which is the sum of absolute distances and known as city block distance. Euclidean distance: examines the square root of differences between the values of pair of vectors. Minkowski distance: it is another version of Euclidean distance. Canberra and Sorensen distance.

Peer-to-Peer Netw. Appl. Table 1

Different distance equations

Distance Manhattan

3 The experimental test platform used

Equation

  ¼ ∑ aik −b jk  n

dM i j

k¼1

Euclidean

dM i j ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n  2 ∑ aik −b jk k¼1

Minkowski

dW i j

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n  m ¼ m ∑ aik −b jk k¼1

Canberra

Sorensen

n ja −b j ik jk d Ci j ¼ ∑ ja jþ b k¼1 ik j jk j

The hardware part of the proposed test platform is made of XBee modules, which are a family of wireless components, developed by Digi. They implement various protocols, including 802.15.4 higher-level ZigBee version, and mesh ad-hoc network protocol. Their application scope covers several fields. These components are of two types: XBee classic and XBee pro, each module type has several versions. The figure Fig. 1 shows the different existing XBee modules. There are four different versions of the XBee and XBee Pro, for more details see [4].

n   n  d Si j ¼ ∑ aik −b jk = ∑ aik −b jk k¼1

k¼1

4 Presentation of the test bed for the indoor localization based on a wireless sensor network The Table 1 defines the equation models of the distances. In Table 1, n is the number of elements in a vector, aik represents the kth element of vector A and b jk represents the kth element of vector B and m is the degree of the root. The simplest and the less complex distance is the distance of Manhattan, as it is illustrated in Table 1. Therefore, three algorithms based on the practical application of the fingerprint technique done in [23, 27] are proposed. The first one uses the measurements average value of the database (distance to the cluster centers), the second algorithm uses the full database (the square of distances to all database elements), and the third minimizes the amount of data by taking into account the movement of the target (distance to the K-NN). These algorithms compute the target position based on the quadratic distance or Manhattan distance [23, 28, 29]. This method was selected as it can be easily integrated into an embedded system due to its simplicity. However, the correlation and learning methods provide better accuracy but at the expense of more intensive computations. Fig. 1 The different versions of XBee modules

We have implemented a dedicated hardware test bed for the purpose of the indoor localization application based on a wireless sensor network inside our laboratory. In this specific area, we have installed wireless sensors based on XBees. These modules allow us to collect data, such as temperature from five locations within the laboratory, five sensors named respectively 1, 2, 3, 4 and 5, together with a harvest sink connected to the supervision PC. This PC helps to centralize the collected experimental measures and to visualize the target position. The figure Fig. 2 shows the position of these wireless sensors within the localization space. The XBee components are not only used to communicate using radio waves according to the 802.15.4 standard, but they can also be used to measure a physical phenomenon, without adding a specific extra microcontroller. Due to the fact that the XBee module includes analog and digital pins, which can be easily configured as inputs or outputs based on the packaging information which is sufficient enough to configure the XBee parameters using AT commands [30].

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Fig. 2 The location of the wireless sensors within the localization space

The figure Fig. 3 represents the API frame sent by the XBee. It is composed of several fields, among which the data of the module (MY etc.) as well as the information related to the various pins needed for the purpose of localization. It is the last part of the frame that represents the value of RSSI. Because the target sensor intercepts the frames of each node, and it saves useful information, like the node‘s ID and the signal strength RSSI. Finally, it sends a message with this information to the harvest sink.

Fig. 3 The API frame form [30]

The LM35 temperature sensor is connected to the D03 pin of each XBee, which is configured using the following parameters: the PAN address=common address, MY=specific to each wireless sensor, the API mode, the D03 pin=analogue input, the frame destination DL=the address of the target, and the sending frequency IR=100 ms of the API frame. The figure Fig. 4 represents the possible routes of the API frames corresponding to each wireless sensor carrying useful temperature information and the RSSI. This data

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Fig. 4 Temperature acquisition and localization system based on a wireless sensor network

is periodically transmitted to a harvest sink via two hops (sensors ->Target ->harvest sinks), and it is stored in a database in the supervision PC. The architecture of this platform allows us to measure a physical phenomena such as temperature, and to study the behavior of the radio signal strength in different laboratory places, due mainly to the mobility of the target. We used this test bed to construct the RSSI maps for the five wireless sensors according to the different power levels. The XBEE used has five configurable energy levels which can

Fig. 5 The RSSI acquisition points of the five wireless sensors

be set using an AT command BATPL^. In this study, we conducted a series of experiments in which we have chosen one zone for the localization in the laboratory, and we performed data acquisition of about one hundred RSSI within 72 positions. The distance between two neighbor acquisition points is 80 cm. We repeated this test for each energy level given by the XBee module. The figure Fig. 5 represents the localization zone, as well as data collection points of the RSSI corresponding to the five wireless sensors.

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Fig. 6 The RSSI maps of wireless sensor No. 1 according to the radio transmission power

5 Construction of RSSI maps To study the RSSI’s behavior for each wireless sensor, we have drawn a RSSI map for each radio module using the median value in every harvest point. Furthermore, we used false colors to highlight the zones of the RSSI’s power variation. The XBee sensors N° 1, 2 and 5 are of type XBee Pro, and have the same type of wire antenna (XBee 3), which explains the similarity in the number of RSSI zones for different configurations. The sensor N° 3 has an electronic integrated antenna (XBee 1) with an average sensibility, and the sensor N°4 has a printed antenna (XBee 4) with low sensitivity. The RSSI maps of different wireless sensors show that the RSSI variation does not follow a deterministic mathematical model, neither linear nor logarithmic. The main cause of this fluctuation is the effect of the obstacle echoes. Therefore, the use of the well-known RSSI’s logarithmic equation is not suitable to estimate the distance between two wireless sensors, which complicates the indoor localization task. In our case, we have limited the number of RSSI zones to ten, because when the RSSI exceeds −60 DB, it becomes unstable and the error rate for receiving the API frames increases. According to the figure Fig. 6, we can distinguish nine RSSI areas in the case of the configurations PL=0 and

PL=1 for the sensor N° 1, and eight in the case of the remaining configurations that is PL=2, 3, 4. The RSSI maps of sensor N°2 represented in figure Fig. 7, allow to distinguish different RSSI zones. Nine areas for both PL=1 and PL=0, the most economical energy configuration. Seven areas in the average configuration PL=2 and PL=4. Finally, six zones in the configuration PL=3. According to figure Fig. 8, there is significant attenuation in the signal strength RSSI of the sensor No. 3, and a decrease of the number of areas in the RSSI maps depending on the energy configuration. For instance, we have four areas in the case of PL=0, five regions in PL=1, six in PL=2 and PL=4, and seven areas in PL=3. This fast variation of RSSI is due to the bad sensitivity of the XBee antenna used (XBee 1). The figure Fig. 9 represents the RSSI maps of the sensor N° 4. The region that is greater than −60 dB, occupies almost all maps, which implies the bad quality of the printed antenna (XBee 4). There are four RSSI zones in PL=0, six in PL=1 and PL=2, and seven zones in PL=3 and PL=4. The sensor N ° 5 provides good precision, because almost all zones appear in the RSSI maps. The variation of zones is due to the quality of the antenna and its location in the laboratory. From figure Fig. 10, we can note that in the configuration PL=4 all RSSI zones appear, eight in PL=3 and PL=2, and nine zones in PL=1 and PL=0.

Fig. 7 The RSSI maps of wireless sensor No. 2 according to the radio transmission power

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Fig. 8 The RSSI maps of wireless sensor No. 3 according to the radio transmission power

The RSSI has been often used as a metric to estimate the distance between two wireless sensors, because the radio signal strength decreases away from the emission source, in addition, the degree of sensitivity variation depends on the antenna and the transmission power. In theory, if we want to improve the distance estimation using RSSI, it is sufficient to choose the best type of antenna, and to reduce the power supply of the radio transmission, because a good attenuation in the RSSI implies a good estimate of the distance. Unfortunately, in reality, if the power emission is reduced, the RSSI signal becomes unstable. The reason why, we studied the signal strength variation by computing the RSSI standard deviation of each wireless sensor in the case of our test bed, and for each power level i.e., PL varies from 0 to 4. This study allowed us to choose the best configuration of each node for the proposed indoor localization system. In our case, the RSSI in a given position varies at random over time, and all values i ¼ RSSI 1 ; RSSI 1 ; …RSSI N have the same degree of probability. Then, the standard deviation is given by equation 6. rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 N 1 ∑ ðRSSI i −μÞ; where μ ¼ ∑Ni¼1 RSSI i o¼ N i¼1 N

ð6Þ

From figures Figs. 11, 12, 13, 14 and 15, we note that the RSSI standard deviation of each configuration (PL=4, 3, 2, 1, and 0) varies from one position to another, indicating the instability of the RSSI. This is mainly due to the presence of various obstacles, and to the particular architecture of our laboratory e.g., echoes. Thus; this result confirms that the RSSI is not an appropriate metric to adequately estimate a distance. In order to choose the most stable configuration for each node, we calculated the average of the standard deviation for each power level. As indicated in the table Table 2, the best configuration for the sensor N° 1 is PL=2, PL=0 for sensor N° 2, PL=3 for sensor N°3, and PL=4 for sensors N°3 and 4. We also notice that the sensor N°4 has the smallest standard deviation. As a consequence, we can deduce that its antenna is more suitable for indoor applications (Table 2).

6 Applied algorithms for the target localization In this study, the localization field is sampled spatially into 72 equidistant positions. In every position Pi, we have taken 100 samples of the received signal strength (RSSI) from five sensors, installed in specific locations. These measures represent our database, which will allow us to localize the target indoor of our test field.

Fig. 9 The RSSI maps of wireless sensor No. 4 according to the radio transmission power

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Fig. 10 The RSSI maps of wireless sensor No. 5 according to the radio transmission power

The target regularly sends, a vector v ¼ ½rssi1 rssi2…rssi5 containing the RSSI values of the five sensors, and in our database, the vectors corresponding to a position Pi are concatenated in a form of a matrix M i as indicated in equation 7. Thus, the obtained database contains 72 matrices M each of which has a size of 100x5. 2

M i1;1 i 4 M ¼ ⋮ M i100;1

3 M i1;2 ⋯ M i1;5 5 ⋮ ⋯ ⋮ i i M 100;2 ⋯ M 100;5 i ¼ 1…72

ð7Þ

Each position in the localization field is considered as a cluster (class) characterized by a set of 100 vectors v. Therefore, we can consider our localization

Fig. 11 The RSSI standard deviation of sensor N° 1

system as a classification system of the RSSI vectors, received from the target. Thus, to determine the target location, we have applied three classification algorithms based on the absolute distance between the measures and the elements of the 72 clusters of the stored database. The first algorithm uses the median part of the database. The second algorithm uses the entire database. Whereas, in the third algorithm just a part of this database is considered, together with the target movement velocity. 6.1 Algorithm 1: distance to the clusters centers The aim of this algorithm is to minimize the target position processing time, by reducing the size of the database. In the first step, we calculate the centers of clusters by finding the median values of the measures taken for each position and

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Fig. 12 The RSSI standard deviation of sensor N° 2

each sensor, the results are placed in the matrix MED as indicated in equation 8. 2

ME D1;1 M ED ¼ 4 ⋮ MED72;1

ME D1;2 ⋮ MED72;2

⋯ ⋯ ⋯

3 ME D1;5 5 ⋮ MED72;5

ð8Þ

Where M EDi; j is the median value of RSSI measures recorded at the position i by the sensor j. Once M ED is determined, we will proceed to an online localization. The target follows the path illustrated in Fig. 16, and

Fig. 13 The RSSI standard deviation of sensor N° 3

the harvest sink receives periodically a new vector v (1x5), which is the mean of the ten last measures. The supervision system, installed in the base station, calculates the absolute distance of v to the elements of the matrix MED, as it is shown in equation 9.  2   V 1 −MED1;1  6 V 1 −MED2;1  D¼6 4   V 1 −MED72;1 

jþ⋯þj jþ⋯þj ⋮ jþ⋯þj

  V 5 −MED1;5  3   V 5 −MED2;5  7 7 5   V 5 −MED72;5 

ð9Þ

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Fig. 14 The RSSI standard deviation of sensor N° 4

Finally, the position of the target corresponds to the index of the lowest distance (equation 10). Position ¼ arg min fDg i

ð10Þ

vector v, the square of its absolute distance to all the points of the 72 matrices of the database as indicated in equation 11. 2 2   v1 −M i1;1  þ ⋯ 6 di ¼ 6 ⋮ 4 ⋮ 2   i v1 −M 100;1  þ ⋯

⋯ ⋯ ⋯

 2 3   þ v5 −M i1;5  7 7 ⋮ 2 5   þv5 −M i100;5 

ð11Þ

6.2 Algorithm 2: square of distance to database elements In this method, we have taken in consideration all the elements of the database. We calculate for every received signal the

Fig. 15 The RSSI standard deviation of sensor N° 5

After calculating the vectors of distances d i ; i ¼ 1…72, we search for the one that contains the lowest value, which will guide us to find the target’s location. As indicated in equation 12.

Peer-to-Peer Netw. Appl. Table 2 The average of RSSI standard deviation in function of the transmited power PL Sensor

Highest

High

Medium

Low

Lowest

Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5

2,83489 3,17560 2,90554 2,69360 2,84701

3,18026 3,02069 2,72611 3,50800 3,65155

2,85068 3,33435 2,94436 3,39556 3,33988

3,07793 3,38973 2,69468 3,10236 3,16111

3,08755 2,99640 2,83151 2,98188 3,34807

Position ¼ arg min fd i g; where i ¼ 1…72 i

ð12Þ

of RSSI measures, we calculate its distance to the data of the actual position and its neighbors for which the indices are contained in the vectors PV a and PV i . As illustrated in equations 13 and 14. 2 2   v −M a1;1  þ 6 1 da ¼ 6 4 ⋮ 2   v1 −M a100;1  þ

⋯

⋯

⋮ ⋯

⋯ ⋯

2 3    þ v5 −M a1;5  7 7 ⋮ 2 5   a þv5 −M 100;5 

2 2 2 3    PV ð jÞ  PV ð jÞ  v1 −M 1;1 a  þ ⋯ ⋯ þv5 −M 1;5 a  6 7 7 dj ¼ 6 ⋮ ⋯ 4 ⋮ 2 2 5 ⋮   PV a ð jÞ  PV a ð jÞ  v1 −M 100;1  þ ⋯ ⋯ þv5 −M 100;5  j ¼ 1::k a

ð13Þ

ð14Þ

6.3 Algorithm 3: distance to k-nearest neighbors In our case, the target to localize is a person, therefore, the distance covered between two acquisitions of vector v, will never exceed the distance between two previously set up positions that is 80 cm. This algorithm is divided into two phases. In the first one, we estimate the actual target position using the algorithm N°2, then during the second phase we reduce the amount of data to use by employing just the measures that correspond to the actual position Pa and the positions in its neighborhood. Each position Pi among the 72 positions has a specific number k i of neighbors, these indices are recorded in a vector PV i ðk i Þ of size (1x k i ). Whenever we receive a new vector v

Fig. 16 Localization of a target using the algorithm N°3

After calculating d a and different vectors d j , we will seek the one containing the smallest distance, and thus its index is the target’s location. As indicated in equation 15. Position ¼ arg min fd i g; where i ∈ fa; PV a g i

ð15Þ

7 Results and discussion The three proposed algorithms were tested indoor in our test field. First, we have to choose a specific path to be followed

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Fig. 17 The position estimation using the three algorithms respectively

by the target to localize, then we will periodically receive the vector v containing RSSI values corresponding to the five sensors. Finally, the target’s position will be estimated using the three algorithms. The Fig. 17 represents, respectively, the path followed by the target, and the position estimation results after applying the three previously mentioned algorithms. In order to correctly interpret the obtained results and to view the efficiency of the algorithms, we calculated the error of the position estimation as illustrated in the Fig. 18. We can deduce from the previous experiments that the three tested algorithms allow us to localize our target with an accuracy of 80 cm but with different degrees of sensitivity. Algorithm N°1 offers 75 % yield. However, algorithms N°2 and N°3 offer 90 %. We should note the jumps in position estimation of the target in algorithms 1 and 2, which exceed 160 cm. On the other hand, the improvement is seen for algorithm N°3, indeed the localization errors are less than 80 cm. This algorithm is faster and optimal in position estimation, since it uses just a small amount of data compared to algorithm N°2.

Fig. 18 Estimation error of position for the applied algorithms respectively

8 Conclusion In this paper, we presented our XBee wireless sensor based test field, and we explained the experiments to study the behavior of RSSI in the interior (indoor) for different configurations of the power level (PL) of used modules. This study confirms that the RSSI is not valid for estimating the distance between two wireless sensors, but it allows us to construct a database for each sensor. Then, we studied a new parameter the standard deviation, to choose the most stable configurations for our wireless sensors, which allows us to perform localization in our test field. In this study, we found that the XBee 1, integrating an electronic antenna, is well suited for the indoor application of wireless sensor network. Following the selection of suitable configurations for each sensor, we have developed a new mapping RSSI based application, which allowed us to pinpoint a target indoor, using three proposed algorithms. The best accuracy was obtained with algorithm N°3 with an accuracy of 80 cm and a 90 % yield. The advantage of this algorithm is its simplicity so it can be integrated into an embedded system but its disadvantage is

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due to the fact that it uses algorithm N°2 in its first phase, which may lead to a position error of a jump that exceeds 160 cm at first, but over time, it can automatically correct itself.

11.

9 Future work

12.

We are working on improving the indoor localization application, by integrating algorithm N°3 in the embedded system of the target and the database will be stored in a micro SD memory card, which allows the target to be located without going through the base station. The method of creating RSSI maps can be improved by using the developed system in [31] in a robot that does the harvesting of RSSI in different locations automatically, using as reference an inertial station, which facilitates the update of the mapping in the case of adding new constraints (chairs, tables, etc.). The localization accuracy can be improved by the use of more powerful classifiers, as neural networks [32], and by melting RSSI mapping with inertial data and adding an accelerometer and a gyroscope to our target [33, 34]. In addition, we are working on tracking of our target using the Kalman filter in our system of supervision.

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Cheriet Mohammed El Amine was born in 1987, Oran, Algeria. He received the B.S. and M.S. degrees in Industrial Computer Science from University of Science a n d Te c h n o l o g y o f O r a n (USTOMB), in 2008 and 2010 respectively. He is currently researcher member in the Laboratory of Microsystems and Embedded Systems, from USTOMB. His field of interest are intelligent systems, embedded systems and wireless sensor network.

Ouslim Mohamed born in 1961, received the Engineer’s degree in electronics, USTOMB in 1985. He received the master’s degree in computer engineering from the Ohio state university (USA) in 1989 and the Ph.D. degree in electrical and electronic engineering from the University of Nottingham (UK) 1997. He is currently a professor in electronics department at university of science and technology (USTOMB) and a member of Microsystems and Embedded Systems Lab. (LMSE). His research interests include wireless sensor networks, biometry, image processing and embedded systems.

Belaidi Boualam was born in Tefreg village, Bordj Bou Arreridj state, Algeria. He received his B.S from BBA university center and M.S from the University of Jijel in Automation and signal processing in 2009 and 2011 respectively. Currently, he is PhD student at the department of electronics, university of science and technology, Oran, Algeria. His fields of interest are brain computer interfaces, hardware implementation of algorithms, x-by wire and control systems.