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An Indoor Tracking System Based on Bluetooth Technology

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  1  Abstract   —Implementations of tracking systems have become prevalent issues in modern technology due to its advantage of location detection of objects. Objects are usually tracked using trackers based on GPS, GSM, RFID and Bluetooth signal strength implementation. These mechanisms usually require line-of-sight operations, limited coverage and low-level programming language for accessing Bluetooth signal strength. This paper presents an alternative technique for tracking the movement of indoor objects based on Bluetooth communication technology, principles of motion and least square statistical method. Algorithms are designed and implemented using Java.  Index Terms  —Algorithm, API, Bluetooth, J2ME, Tracking System, Wireless Communication I.   I  NTRODUCTION  RACKING systems, systems designed to monitor devices or persons, have become prevalent issues in modern technology. There are many advantages of locating and tracking a person or object, in a variety of contexts such as following the movements of a child around an amusement  park, locating colleagues in an office or tracking the movement of luggage through an airport. The predominant mechanisms for tracking humans involve the use of video surveillance systems. These systems require human operator to monitor the CCTV images at a central location. Loss of concentration usually occurs when fatigue sets in [1]. Vehicles and other objects are usually tracked using trackers whose implementation is based on Global Positioning System (GPS). These systems display the location of a vehicle within a specified time frame. GPS, however, supports outdoor navigation since it requires lines-of-sight operation with at least three satellites [2]. Another technique for implementing tracking system is by using Radio Frequency IDentification (RFID). RFID uses either passive or active tags to track objects [3]. Passive RFID tracking is very common in shops and libraries where tags are attached to products and are checked as they leave the shop by passing through receivers near the doors. Active RFID is popularly used in warehouses and locations like airports where a larger range is needed. RFID tracking uses ultra-low power and there is no need for line-of-sight operation. While RFID tags are very cheap, small Manuscript received on November 23, 2011. The author is with the Computer Science Department, Kumasi Polytechnic, Postal Code 854, Kumasi, Ghana, West Africa (phone: +233-242-124-291; e-mail: Samuelk.opoku@kpoly.edu.gh). and suitable for tracking objects, the sensors are considerably more expensive and require extensive configuration and software installations [3], [4]. RFID signals are easily blocked  by objects and other radio waves [4]. One more method for tracking objects is based on GSM communication technology. The GSM equipment communicates with the GSM network through relay stations. The times at which signal arrive together with the angle of arrival from at least three stations allow location detection through triangulation [5]. The main  problem with GSM is inaccuracy in location determination due to its limited coverage in densely populated area [4]-[6]. With the range of personal devices using Bluetooth, the  possibility arises to locate and track the movements of objects. Bluetooth has become an emerging technology for determining indoor and sometimes outdoor position of a communicating device. Although there is no specific support for positioning service in Bluetooth technology yet the  predominant technology used are signal strength measurement, link quality and bit error rate which rely on the services of the Host Controller Interface [7], [8]. Thus the Received Signal Strength Indicator (RSSI) value of the Bluetooth protocol is used to get a correlation to the distance  between sender and receiver in a network. The RSSI value in  providing the distance between the received signal strength and an optimal received power rank is called the Golden Receiver Power Rank (GRPR) [9]. If the value of RSSI is in GRPR defined by zero, no unique function can be approximated [8], [9]. This paper presents an alternative technique to track the movement of indoor objects based on Bluetooth technology, least square statistical method and  principles of motion. The device being tracked is connected to three Access Points and a Central Monitoring System. PC’s with Bluetooth radio adaptors which run multi-threaded Java  based desktop applications are used to implement the Access Points and the Central Monitoring System. The device being tracked runs a J2ME application. The Bluetooth communication protocol has client-server architecture. The client initiates the connection and the server accepts or receives the connection. Bluetooth Specification [10] consists of Bluetooth protocol stack and profiles. The  protocol stack is a software that has direct access to the Bluetooth device controlling device settings, communication  parameters and power levels for the Bluetooth device. The main implemented layers in the stack are the Host Controller Interface (HCI), Logical Link Controller Adaptation Protocol (L2CAP), Service Discovery Protocol (SDP) and Object Exchange Protocol (OBEX) [8], [10]. Communication  between devices depends on the type of data transferred. An Indoor Tracking System Based on Bluetooth Technology Samuel King Opoku,  Member, IEEE    T Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), December Edition, 2011  2 OBEX protocol supports exchange of such physical data as files and images. L2CAP is used for sending packets between host and client whereas Radio Frequency COMMunication (RFCOMM) supports simple data transfer [10]-[12]. The Bluetooth profiles allow different Bluetooth devices to interoperate. Although RFCOMM is easy to setup yet a one-way communication link usually shuts down before data is transferred [10]. This problem is addressed by allowing  bidirectional transmission before shutting down the link. The range of coverage of Bluetooth depends on the class of the Bluetooth adaptor [12]. This could be class 1 type which covers up to 100 meters, class 2 type covers up to 10 meters and class 3 type usually covers up to 1 meter. Virtually all devices use class 2, with class 3 being adopted for some smaller devices such as Bluetooth telephony headsets. Bluetooth is also limited by excessive power consumption [8] and the power level of the energy source [13]. The figure  below provides the general overview of the Bluetooth architecture. Fig. 1. Overview of Bluetooth Architecture  J2ME is the platform used in order to develop applications for portable devices. J2ME is made up of two configurations. These are Connected Limited Device Configuration (CLDC) and Connected Device Configuration (CDC) [14]. The application for mobile phones is developed using MIDlet with the help of CLDC configuration supported by MIDP profile [15]. The CDC configuration has three profiles. The first type is the Foundation Profile (FP) which is a set of Java API for devices having limited resources that do not need a graphical user interface system [16]. It is usually used in embedded devices. It is specified in JSR 219 [17]. The second type is the Personal Basic Profile (PBP) which is a superset of the foundation profile. This profile supports devices with lightweight graphical user interface requirement [18]. Its specification is described in JSR 217 [19]. The last type is the Personal Profile (PP) which is an extension of the Personal Basic Profile with a graphical user interface toolkit based on AWT [20]. It is intended for such higher devices as PDA’s, smart communications, game consoles and automobile dashboard. Its specification is described in JSR 62 [21]. The Bluetooth API (JSR 82) is integrated with the above-mentioned APIs when Bluetooth is required. The compatibility of J2ME and J2SE enables the development of a  backend server to assist the limited processing power of mobile devices. J2SE is ideally used for GUI applications [22]. The Bluecove API is integrated with J2SE for Bluetooth communication between the backend server and the mobile devices [11], [23]. II.   SYSTEM   ARCHITECTURE The system is made up of a central monitoring system, the device being tracked and Bluetooth Access Points. The Access Points, in this work, are implemented applications on PCs that have Bluetooth radio adaptors connected to them. Access Points only resend the message (or signal) sent by the device  being tracked. The Access Points are placed in the room such that their positions are known in the XY plane. The Central Monitoring system knows the initial location of all the devices  being tracked. When the device moves considerably, the monitoring system sounds alarm displaying the new location, in coordinate form, of the device. Each device being tracked is connected to three Access Points. A Bluetooth-enabled tracker is attached to the device being tracked. The tracker communicates with the Central Monitoring System (CMS) and three selected Access Points. The general overview of the system is illustrated in the figure below: Fig. 2. Overview of System Architecture    3 III.   TRACKING   SYSTEM   ALGORITHMS For simplicity sake, only one device is considered. The device  being tracked is connected to three Access Points(AP) whose  positions are known in the XY plane as shown below: Fig. 3. System Architecture in XY Plane From Fig. 2, AP  ’s are the Bluetooth Access Points, D denotes the device being tracked and S  ’s represent the distance  between AP  ’s and D. All computations are done by the Central Monitoring System.  A.    Device and Access Point Distance Algorithm The distance between an Access Point (AP) and the device  being tracked is based on the principle of motion and least square statistical method. By definition of speed which is the time rate of change of distance which is mathematically expressed as S󰁰󰁥󰁥󰁤(󰁶) =  ()() . It can be written as V =    and then 󰁤S = V󰁤󰁴 . Applying the concept of integration, 󰁤S = V󰁤󰁴 , the resulting equation becomes S = V󰁴 + C  with C  catering for transmission errors. Given indoor physical conditions, a linear regression of S  on T  based on least square method determines parameters V  and C  by respectively finding the gradient and S-intercept. T  is the average time taken by a unique signal send from the device  being tracked to the Access Point to return back to the device  being tracked (that is T =   () ). Thus the above equation finally becomes S   = VT   + C   ∀ 󰁩 = 1,2,󲀦,N  where  N is the total number of samples taken in the experiment such that N ≥ 5  The algorithm used to determine the Signal Speed (V) and the transmission error (C) is described below: 1.   Computer average time (T) for each pair as ( T  ,S  ) ∀ 󰁩 = 1,2,󲀦,N  2.   Find the mean time and mean distance (M T  and M S ) as M   = (∑ T   )/N  and M   = (∑ S   )/N  3.   Determine the deviation for each time and distance as D   = T   − M    and D   = S   − M   ∀ 󰁩 = 1,2,󲀦,N  4.   Calculate the mean of squares of the time deviations as MS   = (∑ D   )/N  5.   Calculate the mean of the products of time and distance deviations as MS   = (∑ (D   󰃗 D  ))/N  6.   Find the Signal Speed (in meter per second) which represents the regression coefficient as V =       7.   Compute the transmission error as C = M   − M   󰃗       Using the distance between the Access Points and the device being tracked alone usually results inaccurate estimations. Signal attenuation affects transmission time. Thus, to minimize the degree of error, the coordinate of the device being tracked is calculated. When there is a significant change is any of the x or y coordinate then the device has moved.  B.    Algorithm for the Coordinates of the Device Being Tracked Given three AP’s in a communication network and the  positions of the AP’s, defined by P   = (󰁸  ,󰁹  )  ∀ 󰁫 ∈ 1,2,3 , it follows that the known distances  S   ∀ 󰁩 ∈ 1,2,3  between AP and a point in the XY plane which corresponds to the  position of the device being tracked represented as D = (󰁸,󰁹)  is given, based on the Euclidean inner product [24], by S   =  (󰁸 −󰁸  )   + (󰁹 − 󰁹  )   ∀ 󰁩 ∈ 1,2,3 . This is also written as S   = (󰁸 − 󰁸  )   + (󰁹 −󰁹  )   ∀ 󰁩 ∈ 1,2,3 . Three equations are formed from S   ∀ 󰁩 ∈ 1,2,3  such that S   = 󰁸   − 2󰁸  󰁸 +󰁸   + 󰁹   − 2󰁹  󰁹+ 󰁹   -------(1) S   = 󰁸   −2󰁸  󰁸+ 󰁸   + 󰁹   − 2󰁹  󰁹+ 󰁹   -------(2) S   = 󰁸   −2󰁸  󰁸+ 󰁸   + 󰁹   − 2󰁹  󰁹+ 󰁹   -------(3) The three equations above represent three circles whose radii are S   ∀ 󰁩 ∈ 1,2,3 as shown in the figure below: Fig. 4. Geometric Representation of System Architecture Solving (1) – (2) and (2) – (3), the above equations reduced to two equations as: 2(󰁸   − 󰁸  )󰁸 +2(󰁹   − 󰁹  )󰁹 = S   − S   − 󰁸   + 󰁸   − 󰁹   + 󰁹   and 2(󰁸   − 󰁸  )󰁸 + 2(󰁹   −󰁹  )󰁹 = S   − S   − 󰁸   + 󰁸   − 󰁹   + 󰁹     4 Finally both equations are written in a compact form (representing the equation of a line) as 󰁡󰁸+ 󰁢󰁹 = 󰁥  --------------(4) 󰁣󰁸 +󰁤󰁹 = 󰁦   ---------------(5) where 󰁡,󰁢,󰁣,󰁤,󰁥 󰁡󰁮󰁤 󰁦   are constants and their values are determined as follow: 󰁡 = 2(󰁸   − 󰁸  ) , 󰁢 = 2(󰁹   − 󰁹  ) , 󰁣 = 2(󰁸   −󰁸  ) , 󰁤 = 2(󰁹   − 󰁹  ) , 󰁥 = S   − S   − 󰁸   +󰁸   − 󰁹   +󰁹   and 󰁦 = S   − S   − 󰁸   + 󰁸   −󰁹   + 󰁹   “ Given a set of data, a line that will be as closely as  possible which approximate each of the data points can be  found such that   =   [24] ”. A  is 󰁮 󰃗 󰁭  matrix with 󰁮  equations and 󰁭  unknowns. Representing equations (4) and (5) in the form  A󰁸 = 󰁢 , it follows that A = 󰁡 󰁢󰁣 󰁤 , 󰁸 =     and 󰁢 =      resulting 󰁡 󰁢󰁣 󰁤   =        --------------(6) “ Suppose that    is  󰃗   matrix with linearly independent columns, then       is an invertible matrix [24].” Since A󰁸 = 󰁢  is equivalent to A  A󰁸 = A  󰁢  and (A  A)   exists as indicated  by [24]  ,  it implies that there is a solution for the following equation 󰁸 = (A  A)   A  󰁢  ------------ (7) Substituting the values, equation (7) becomes: D(󰁸,󰁹) = 󰁸    = 󰁡 󰁢󰁣 󰁤  󰃗 󰁡 󰁢󰁣 󰁤  󰃗 󰁡 󰁢󰁣 󰁤  󰃗    .  This is simplified as  1(󰁡   + 󰁣  )(󰁢   +󰁤  )− (󰁡󰁢+ 󰁣󰁤)  (󰁢   +󰁤  )(󰁡󰁥+󰁣󰁦 ) −(󰁡󰁢+󰁣󰁤)(󰁢󰁥+󰁤󰁦)(󰁡   + 󰁣  )(󰁢󰁥+ 󰁤󰁦 ) −(󰁡󰁢+󰁣󰁤)(󰁡󰁥+ 󰁣󰁦)   Finally the coordinate of the device being tracked is: 󰁸 = (󰁢   + 󰁤  )(󰁡󰁥 + 󰁣󰁦 ) −(󰁡󰁢 + 󰁣󰁤)(󰁢󰁥 + 󰁤󰁦)(󰁡   +󰁣  )(󰁢   + 󰁤  ) −(󰁡󰁢 + 󰁣󰁤)    󰁹 = (󰁡   + 󰁣  )(󰁢󰁥 + 󰁤󰁦 ) − (󰁡󰁢 + 󰁣󰁤)(󰁡󰁥 + 󰁣󰁦)(󰁡   + 󰁣  )(󰁢   + 󰁤  ) − (󰁡󰁢 +󰁣󰁤)    C.    Algorithm for Triggering Device Movement Alarm A fractional change in either x-coordinate or y-coordinate is ignored. This ensures that there is a significant change in  position before the alarm sounds. The flowchart in Fig. 4 shows the algorithm for monitoring the device being tracked to determine significant movement. Fig. 5. Alarm Triggering Process
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