An Experimental Study on the Behavior of Received Signal Strength in Indoor Environment

Kifayat Ullah*, Igor Vitorio Custodio*, Nadir Shah**, Edson dos Santos Moreira*

* ICMC, University of São Paulo, São Carlos Campus, Brazil **Department of CS, COMSATS Institute of IT, Wah Campus, Wah Cantt, Pakistan

{kifayat, igorvc}@icmc.usp.br, nadir@ciitwah.edu.pk, edson@icmc.usp.br

Abstract— Due to widespread availability of WiFi networks in buildings, indoor location based systems become a reality. To provide indoor location based services (LBS), finding the current location of a human, a computer, a mobile device or equipment such as a small UAV (like Quadcopter) is of great interest. The most prominent method for this purpose is the received signal strength (RSS)-based location from WiFi Access Points (APs) inside a building. Considerable amount of research is carried out in estimating current location and providing different services based on different wireless technologies. On the other hand, little attention has been paid to study and analyze the behavior of the received signal strength itself. It is challenging because the intensity of signal can change very frequently due to environmental features like topology, temperature, interaction with objects etc. In this paper, we study the behavior of WiFi signals in an indoor environment for RSS based localization by analyzing signals from three different access points by using triangulation technique. Our method is based on fingerprinting method. The experimental results reveal that the behavior of signal changes very frequently. The results lead us to the conclusion that understanding signals behavior is important before estimating current location and providing different LBS.

Keywords—Location Based Services (LBS), Received Signal Strength (RSS), Fingerprinting, WiFi Signal Behavior, Indoor localization, Indoor Positioning System

I. INTRODUCTION A positioning system enables an object to determine its

position, and use its position information for location based services (LBS), such as navigating, tracking or monitoring [1]. In [2] an indoor position system is defined as: A system that can determine the position of an object in a physical space such as in a hospital, a gymnasium or in a school.

Global Positioning System (GPS) is the major technology for outdoor positioning, tracking and navigation. Initially GPS devices were not able to receive signals from the satellites inside buildings. Nowadays the devices become much more powerful and are capable of receiving signals inside buildings, even in the presence of obstacles. Currently these GPS devices are not considered to be a good solution for indoor localization for two reasons. Firstly, the GPS devices require a great amount of time for locating the satellites in order to start functioning. Secondly, the accuracy of GPS technology is not very much precise.

Locating objects (people, computers, PDAs, Quadcopters, etc.) inside buildings is necessary for providing different location based services. For this reason, the interest in the development of indoor positioning systems was started and many systems have been developed using different technologies. Similarly different techniques like Time of Arrival (ToA), Time Difference of Arrival (TDoA), Angle of Arrival (AoA) and Received Signal Strength (RSS) have been used for LBS. Among these techniques the RSS seems to be an attractive approach for indoor localization because it will use the existing WiFi infrastructure and will presents a tremendous cost savings over deploying localization-specific hardware [3]. Similarly, its mature nature and high availability in many places (homes, educational institutions, restaurants, bus stations, airports, organization, super markets, etc.) will make it the right choice for providing indoor LBS.

One of the most common methods, which are used for indoor localization, is the empirical method. This method consists of two phases; namely offline and online phase. In the offline phase a RSS fingerprint database is developed by collecting samples from Access Points (APs). In the online phase the position of the target object is estimated by using location estimation algorithms. These estimation algorithms compare the current position value with each and every value stored in the fingerprint database and select the closest match by using a mathematical calculation [1]. The main advantage of this method is its high accuracy. The drawback is that it is highly dependent on the locations of the APs. Once the location of an AP has changed the offline phase needs to be reconstructed, which is a very time consuming task [4].

In the past, a great amount of work has been done on estimating the locations of objects using different techniques and technologies [3], [5-11]. The main focus of these studies is to improve the accuracy and granularity of indoor localization systems, whereas less attention has been given to understand the behavior of the signals and to analyze it. We believe that the accuracy of such systems would be increase, if the behavior of the RSS is studied and analyzed before providing LBS. In this paper, we experimentally studied and understand the behavior of the WiFi signals in an indoor environment.

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DOI 10.1109/FIT.2013.54

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The rest of the paper is organized as follows: We present the experimental setup for our testbed and the algorithm for samples collection in Section II. In Section III, we discuss the results of our experiments. Finally, in Section IV, the conclusions of our work and some future directions are given.

II. EXPERIMENTAL ENVIRONMENT

A. Testbed In order to conduct the experiments, we select first floor of

Computer and Electronic Department, University of Sao Paulo (USP), Sao Carlos Campus- II. The layout of the floor is presented in Fig. 1 below. We performed experiments in an open hall, having a total area of 8x14 meters. We used three APs (802.11n D-link). The Aps were placed at three different locations.

B. Site Survey One of the important considerations in setting up the

testbed is to place the APs in such locations, where signals from each and every AP can be measured at any location inside the testbed. For this purpose we performed a site survey using different tools for two different platforms. For Windows operating system, we performed the site survey with Network Stumbler tool, while for Ubuntu Linux operating system we used IWList tool. These tools provide useful information about the WiFi network; like AP name, MAC address, RSS, Signal to Noise Ratio (SNR) and channel number, etc.

C. Making Grids After performing the site survey and placing the APs in

optimal locations, our next task was to divide the area of the testbed into grids of equal size. We divided the area into two dimensional plans (X-axis and Y-axis). The size of each grid was kept to1 x1 meter (See Fig. 2). The center of each grid was marked. We referred to this central point as a Reference Point (RP). The purpose of this RP was to collect samples during the offline phase. The distance between any two reference points was kept to one meter. The layout of grids and RPs are given in Fig. 2.

Fig. 1. Floor Plan and Experimental Testbed

Fig. 2. Layout of Grids and Reference Points

The inter space among the grids will affect the accuracy and granularity of the position system. On one side, if the spacing is kept too small, then distinguish between two adjacent points will be difficult. On the other side, keeping too much space between them will degrade the accuracy. The recommended spacing among the grids is 1-3 meters [12].

D. Samples Collection The backbone of fingerprinting technique is the collection

of signal strength (SS) samples. We collect SS samples for all three APS at all reference points. We collect samples in four different directions namely: 0°, 90°, 180° and 270°. The collection of samples is a time consuming task which requires lot of human efforts. We use HP Compaq 6530b notebook, with Ubuntu operating system, for this purpose. In order to automate and speed up the process of sample collections, we wrote and implement an algorithm. The algorithm is given at the end of this section.

Instead of collecting a single sample, we collected 20 samples for each AP in all directions at each RP. Hence, a total of 80 samples were collected at each RP for each AP. We consider directions (orientations), in order to study the effects of obstacles (e.g. human body) on the signal behavior. The details of collected samples are given in Table I. The concept of collecting more than one sample at a single RP was introduced in [4].

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TABLE I DETAILS OF SAMPLES COLLECTED

Item Name Details of Collection Total no of grids (x-axis) 8 Total no o of grids (y-axis) 14 Total no of RFs 112 Samples collected at each RF

80 for AP1 (20 for each direction) 80 for AP2 (20 for each direction) 80 for AP3 (20 for each direction)

Total samples collected at each RF 240 (80 for each AP) Total samples collected for all RF 26880

E. Algorithm for Samples Collection We developed an algorithm for samples collection and

implement it using a shell script. The algorithm is given below:

SAMPLES=20 DIRECTIONS=4 X_SIZE= 8 Y_SIZE=14 Function Ask_For_Confirmation (message) { //Press Enter when ready for collecting samples } Function Collect_A_Sample(sample_number, direction, position) { SS, SQ, AP_MAC storeInFile (SS, SQ, APMAC, direction, position) return True if “OK” } for(x=0; x<X_SIZE, x++) { for(y=0; <Y_SIZE, y++) { for(d=0; d<DIRECTIONS, d++) { S=0 While (S<SAMPLES) { RESP=TakeASample(x,y,d,s) If RESP =“ok” S++ } Ask_for_Confirmatinon(“Change direction”) } Ask_for_confirmation(“Move to next Reference Point”) } }

This algorithm works as follow: The notebook was bring to first RP and was placed in the first direction (0°). A total of 20 samples were collected at this RP for each AP. The process is then repeated for the remaining three directions (90°, 180° and 270°). After collecting the samples for each direction, the notebook was moved to the second RF and the process was

repeated for all the remaining RPs in the testbed. The shell script (implementation of algorithm) allows the user to store the collected samples in a text file inside the notebook. The height of the notebook was kept same throughout the sample collection process and all the samples were collected in stationary position.

After completion of this phase, the collected samples were

parsed and stored in a fingerprint database. In our case, we developed two different databases. We called the first one RSS database while the second one is called mean value database. The mean value database stores the mean value of samples for each of the four directions.

III. RESULTS AND DISCUSSIONS In this section, the results obtained from our experiments

are presented and discussed. We used the fingerprint database- developed during the offline phase- for the generation of these results. The first result, given in Fig. 3 shows the behavior of SS values of all the 20 samples, collected at a random RP 5, 5 (i.e. X=5 & Y=5) for each of the three APs. The random direction selected for this sample was 270°. From the given result, it is cleared that the behavior of SS changes for almost each sample collected, at the same RP having same direction. It leads us to the conclusions that even for stationary object; the behavior of SS changes, although the direction of the object is the same. Another observation made from this result is about the SS behavior of AP3. The AP3 was placed at a longer distance from the testbed. The movement of humans in verandas affects the SS of the AP3 at sample number 16 and 18. In this case, the moving objects (human beings) work as an obstacle between the notebook and the AP.

Fig. 3. Behavior of SS(dBm) for all APs at RP (5, 5) with Direction 270°

Since, it is very difficult to study the behavior of signals by considering all the samples collected in the offline phase, we therefore, calculate the mean value of the samples. The results

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presented in Fig. 4(a), 4(b), 4(c) & 4(d) show the behavior of RSS from a single AP (i.e. AP1) for each direction (i.e. 0°, 90°, 180°, 270°) respectively. It can be seen that the strength of the signals is very good for all directions. The reason is the shortest distance between the RPs and the AP. i.e. the AP1 was placed in the same hall, where the experiments were performed(as shown in Fig.1.).

After plotting the mean value graph for AP1, the result shows some similar behavior at adjacent grids. This lead us to another important consideration for building LBS. In order to built LBS, the adjacent grids with similar behavior of RSS needs special care, otherwise the location estimation algorithm will lead to an inaccurate estimated position.

Fig. 4(a). Behavior of AP1 SS(dBm) with Direction 0°

Fig. 4(b). Behavior of AP1 SS(dBm) with Direction 90°

Fig. 4(c). Behavior of AP1 SS(dBm) with Direction 180°

Fig. 4(d). Behavior of AP1 SS(dBm) with Direction 270°

For the final set of results, we calculate the mean value of all the samples for each direction (as done in previous case). After this, we calculate the mean value of the four previously calculated means values (1 mean value for each direction). We called it mean of the means value. By using this, we were actually merging the four mean values into a single mean value (for each RP). This technique would speed up location estimation algorithm, by allowing it to perform only a single comparison. The results of these measurements are mentioned in Fig. 5(a), 5(b) & 5(c) for AP1, Ap2 and Ap3 respectively.

In case of AP1 (located in the same hall), the behavior of

the RSS is smooth, in most of the regions. The reason for this is the short distance between AP and the RPs. However, there

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is a variation in the behavior in the SS, near two corners of the halls, and the possible reason for this affect is the multi-path propagation.

In case of AP2 (located in a separate room, with multiple

obstacles), the results are quite different. Although the distance between AP2 and RPs was very short, but because of the obstacles, the variation in the behavior of signal strength is very irregular. Another interesting observation can be seen at X=2 & 3 and Y=0-13. This variation in RSS is because of the door. The door of the hall is located near X=2. Another reason for this variation is the location of AP2. The AP2 was located in an environment, with multiple obstacles. Hence, an important consideration for the LBS is to place APs in optimal locations (free form obstacles). In the literature the current focus is only on deployment of APs in such locations, which provide maximum coverage area, while the optimal location of their placement is not addressed.

The last graph (Fig. 5 (c)) shows the result of AP3 (located

in a laboratory, far away from the testbed). As expected, the strength of the signal was weak because of the long distance. The effect of the door can also be observed in this case.

Fig. 5(a). Mean-Value Behavior of AP1 SS(dBm)

Fig. 5(b). Mean-Value Behavior of AP2 SS(dBm)

Fig. 5(c). Mean-Value Behavior of AP3 SS(dBm)

IV. CONCLUSIONS AND FUTURE WORK

In this work we performed real world experiments in an indoor environment. We emphasize the significance of understanding and analyzing the behavior of RSS, before implementing LBS in indoor environments. From the experimental results, we concluded that many factors can affect the behavior of the RSS in an indoor environment. Some of these factors are thoughtful deployment of APs, consideration of obstacles (like walls, furniture, and human body), effects of doors, direction of object (notebook), placement of antennas, etc. We believed that by considering these factors before implementation of LBS, the accuracy and granularity of indoor localization systems would be increased.

In the current work, we studied the behavior of the RSS

without using any mathematical modeling. For the future research, we will analyze the different factors, affecting the behavior of RSS, by using some mathematical models. It would also be interesting to see the accuracy of such systems by considering the different factors that influence the behavior of RSS. We will also extend this work to the development of online phase and location estimation techniques.

ACKNOWLEDGMENT

The authors are thankful to The World Academy of Sciences-TWAS, Italy and to the National Counsel of Technological and Scientific Development-CNPq, Brazil for providing financial support under the grant number# 190275/2011-1. The authors would also like to thank the National Science and Technology Institute for Critical Embedded Systems (INCT-SEC, Brazil) for funding this work by means of the following agencies:

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CAPES, CNPq (grant # 573963/2008-8) and FAPESP (grant # 2008/57870-9).

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