Design and Simulation of Early Warning System for Border Areas Based on Mobile Application Framework
Hendra KURNIAWAN1*, Martaleli BETTIZA1, Muhammad YAZIR1 1 Department of Informatics Engineering, Universitas Maritim Raja Ali Haji - Tanjungpinang, Indonesia e-mail: *
[email protected] Indonesia is a large archipelago country and directly borders with some neighboring countries by ocean territorial. Provide accurate information regarding the country borders for population in border areas is an important issue for Indonesia. This paper proposes an early warning system that provides information to user about its location using GPS tracking method in mobile application device. It provides a new system and control capability for monitoring the borders of the country. Using this concept we can easily identify a user crossing the border using the mobile data communication and GPS tracking. Area is divided into three sections, a secure area, warning area and the hazardous area by using country's borders information in GPS coordinates. User will receive a different color for marker display in each area, green, yellow and red for secure area, warning area and hazardous area respectively. The objection of this study was to design and simulate the accuracy of early warning systems for population in border areas. The simulation results show the system running well and provide accurate information for user.
Keywords: early warning system, GPS tracking, mobile application framework
I.
Introduction
The border areas between countries have a very complex problem, the conflict often occurs in that area. The main problem that caused the conflict is ignorance of the people living in the border regions, people did not know about the boundaries that should not be violated. Indonesia is a large archipelago country and has a direct border with the neighboring countries in ocean. The ocean state borders are not clearly delineated between countries so that the need for a system that provides accurate information about a person's position in border area of a country becomes a critical issue for Indonesia. Mobile application is internet applications designed to run on smartphones and other mobile devices. Mobile applications help users by connecting them to internet services more commonly accessed on desktop or notebook computers [1]. Mobile application technology provides a solution for users in obtaining the required information very easily. While opportunities abound, we also have identified three advantages of using mobile application for daily use: speed, volume of information, and advertising. An early warning system can be implemented as a chain of information communication systems and comprises sensors, event detection, decision support, and message broker subsystems. They work together to forecast and signal disturbances that adversely affect the stability of the physical world, providing time for the response system to prepare for the adverse event and to minimize its impact [8]. An early warning system is more than a warning system, which is simply a means by which an alert can be disseminated to the public. In this case, the mobile application will help user to get location data through GPS facility on mobile devices. Utilization of GPS feature in mobile device will assist the user in knowing their current position. An early warning system will provide added value to the GPS data, user are expected to be able to take the right decision in order to avoid violations that often occur in border area. The rest of this paper is organized as follows: Section 2 discusses related work, focusing in particular on design of early warning system in mobile application. Section 3 show the research methodology, where data were obtained and calculate as main process to determine user position related to an area. Section 4 introduces
system design and simulation result, which meets the requirements derived in Section 3. Finally, section 5 concludes our work with a summary.
II.
Related Works
Research on location-aware interaction has been actively pursued for several years. A number of research projects have experimented with the scenario of attaching digital information to real-world locations. By using location aware devices, users can discover and access this information, participate in collaborative mapping activities or engage in location-based social interactions [2] [3]. A Researcher describes the most important features in mobile phones and smartphones are GPS. In addition, the author proposes an algorithm improvement points in GPS location by integrating information from multiple sensors in mobile device [7]. The proposed algorithm is implemented in smartphones and the performance is evaluated on campus. The proposed algorithm has better performance than simply the GPS location information in the GPS interference points and maintain reasonable performance in an open space where the GPS receiver to get accurate results. Authors in [9] propose a document tracking system of enhancing real physical world with an extra layer of information. The project is built around the innovative use of GPS to navigate the office in the office of Faculty at their Campus, in Thailand for case study. The propose model will enable the department to live on general administration officer mobile phone at any time of day. Updated data, location, and amount of official document in office building will provide the raw material for creating layers of graphical and multimedia content which can be accessed and viewed by iPhone using RFID and GPS technology. In order to effectively determine whether a vehicle is turning or not, some authors proposed a method to map arbitrary consecutive GPS heading information to 2 dimensional feature Space [10]. They applied K-means clustering algorithm to divide the feature space into 2 classes: going straight and turning. After that, they used supervised learning algorithm to analyze these labeled data and build a model to recognize vehicle moving state. The experimental results showed that the model built in this way has good generalization. The improved map-matching algorithm also tested on a complex urban road network and the result showed that the new algorithm can significantly improve the performance of the junction match.
III.
Research Methodology
3.1 Data gathering and calculation In this study we used two type of data, first is user position data in GPS coordinate ( , ) second is border position ( , ). Border position coordinate was provided by National Border Management Agency (Badan Pengelola Perbatasan) of Kepulauan Riau Province, Indonesia. The connection of the border position coordinates will generate a polygon-shaped area, as a border area of Indonesian territory. In this study we assume all longitude and latitude positions are converting to coordinates. GPS facility in mobile device will generate user position as real time data in ( , system then calculate value from a user position to a path in polygon-shaped area.
( )
((
) (
))
((
) (
))
) coordinate, and
(1)
Where: : polygon starting point; ( ): polygon ending point; ( ): user position, then Signum function [5] result +1 if x is positive, -1 if x is negative, and 0 if x is equal to 0.
( )
{
(2)
3.2 Area detection In the previous section we have determine user and polygon-shaped coordinates, then we calculate whether the user coordinate is located inside polygon-shaped area or outside polygon-shaped area. To determine the user's position whether is inside polygon area or is outside the polygon area, we will perform calculations to user's position to the entire path on the polygon area. b)
Start
User position (xt, yt) from GPS
a) 6
Calculate sign(x) from user position to polygon-shaped area
C(7,6)
K(4,6) D(3,5)
5
Calculate sgn (x) function
4
J(5,4)
3
2
User location detection : secure, warning or hazardous area
A(1,2)
1
B(9,1)
1
2
3
4
5
6
7
8
Early warning color indicator : green, yellow, red
9 End
Figure 1 (a) Two points J and K with a polygon area ABCD; (b) System flow chart Supposed we have 2 user locations in point J( ) and K( ), and a polygon area ABCD as shown in figure 1.a, we should determine whether J( ) and K( ) are inside the polygon area or outside polygon area. First we need to define ( )( ) if true then, sign(x) > 0, if it’s true, the output is +1. Next we define ( )( ) sign(x) , if one of them false, the output is 0. Where: = coordinate for starting point, = coordinate ending point, = y coordinate of user’s point. Example calculation as follows, we examine J( ) point first, where and Step-1, calculate J( ) point to path DA in polygon area: 5 4 (false), 2 4 (true), sig(x) = ((1 - 3) * (4 - 5) - (5 - 3) * (2 - 5)) < 0 = 8 < 0 (false), then output is 0. Step-2, calculate J( ) point to path AB: 2 4 (true), 1 4 (false), 1 4 (true), sign(x) = ((9 - 1) * (4 - 2) - (5 - 1) * (1 - 2)) < 0 20 < 0 (false), then the output is 0. Step-3, calculate J( ) point to path BC: 1 4 (true), 6 4 (true), sign(x) = ((7 - 9) * (4 - 1) - (5 - 9) * (6 - 1)) > 0 14 > 0 (true), Then the output is +1. Step-4, calculate J( ) point to path CD: 6 4 (false), 5 4 (false), then the output is 0. We do operation OR to all output of the calculation so we get result is +1, it means J( ) point is located inside the polygon area.
Same calculation as previous, we examine K( ) point, where and Step-1, calculate K( ) point to path DA in polygon area: 5 6 (true), 2 6 (false), 2 6 (true), sign(x) = ((1 - 3) * (6 - 5) - (4 - 3) * (2 - 5)) < 0 1 < 0 (false), then the output is 0. Step-2, calculate K( ) point to path AB: 2 6 (true), 1 6 (false), 1 6 (true), sign(x) = ((9 - 1) * (6 - 2) - (4 - 1) * (1 - 2)) < 0 35 < 0 (false), then the output is 0. Step-3, calculate K( ) point to path BC: 1 6 (true), 6 6 (false), 6 6 (true), sign(x) = ((7 - 9) * (6 - 1) (4 - 9) * (6 - 1)) < 0 15 < 0 (false), then the output is 0. Step-4, calculate K( ) point to path CD: 6 6 (true), 5 6 (false), 5 6 (true), sign(x) = ((3 - 7) * (6 - 6) - (4 - 7) * (5 - 6)) < 0 -3 < 0 (true), then the output is -1. We do operation OR to all output of the calculation so we get result is -1, it means K( ) point is located outside the polygon area. Detail calculation from J( ) and K( ) are showed in table 1 and 2.
Step 1 2 3 4
Table 1. Calculation result for point J( ) related to polygon area axis checking output sign(x) value + false true 8 false true false true 20 false true true 14 true false false Point J(
step 1 2 3 4
IV.
0 0 1 0
) is located inside the polygon area
Table 2. Calculation result for point K( ) related to polygon area axis checking output sign(x) value + true false true 1 false true false true 35 false true false true 15 false true false true -3 true Point K(
result
result 0 0 0 -1
) is located outside the polygon area
Design and Simulation
4.1 System design and simulation In this section we provide a full design of the system form beginning till end of process. Figure 1.b shows system flow chart. At the beginning, we obtained user location from GPS coordinates in mobile device, then calculate the location to every path in polygon area, the calculation result will determine whether the location is inside or outside the polygon area. Three type of area are used in this study, secure, warning and hazardous area as shown in figure 2 a,b,c respectively. Simulation of the proposed system was performed by Fake GPS application [6] in mobile device. We can set user current location even we are not in its location at the same time. When user opens the Fake GPS location app, they will be taken to a map where we can select our fake location; just choose a spot from across the globe and Set location. User can access the Fake GPS location app through the notification panel, where they will see the exact coordinates they are spoofing. we can also add a location via search. If we know the ZIP code of the area we want, enter it in the text field and your GPS pin will be moved there. We can also access our location history so we don't have to search for our favorite locations every time we open the app.
a)
b)
c)
Figure 2 (left to right). (a) user location inside polygon-shaped area indicate as secure location, (b) user location inside polygon-shaped area indicate as warning location, (c) user location outside polygon-shaped area indicate as hazardous location. All border area positions are obtained from National Border Management Agency (Badan Pengelola Perbatasan) of Kepulauan Riau Province, Indonesia.
4.2 Real time testing In order to validate the proposed system, we perform extensive real time testing using user location in gps coordinate from mobile device. We assume the polygon area of Universitas Maritim Raja Ali Haji (UMRAH) as secure area, shown by figure 3a. UMRAH secure area was indicated by dark green polygon color and a user location as shown in a marker also set as secure location green color. Figure 3b. lager area than UMRAH was indicated by light green polygon color and a user location as shown in a marker also set as warning location yellow color. Finally in figure 3c, hazardous marker of user location indicates in red color, where the location is outside border area. All testing performs well without any problem due to location detection and marker position, so that user can react to their current location as early warning system design objection.
V.
Conclusion
In this paper we have proposed a design of early warning system for border area and simulate the system using mobile application framework. Simulation was perform properly with real time testing and not real time testing. All condition of proposed early warning system model has tested and the user can determine the next moves based on the information obtained through the early warning system.
Figure 3. (a) Universitas Maritim Raja Ali Haji (UMRAH) location set as secure area in real time testing, (b) Larger area of UMRAH campus set as warning area, (c) Outside location of larger area is set as hazardous area.
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