Tracking Anomalies in Vehicle Movements using Mobile GIS

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1 Tracking Anomalies in Vehicle Movements using Mobile GIS M.Saravanan Ericsson Research India Ericsson India Global Services Pvt.Ltd. Chennai, India Abstract--- Detecting fraud activities and anomalies in vehicle movements are the two major concerns for the vehicle fleet industry. Some of these which incur heavy losses to the organization include fuel theft, loading and unloading related frauds, route variations, inefficient fuel maintenance and frequent breakdowns. The proposed system monitors certain vehicle parameters and provides solution to these issues with the help of sensors and related components. The anomalies and fraud activities detected using sensors present in the vehicle are continuously updated to the server with vehicle ID, location information and other relevant details. The system communicates with the server only on the occurrence of an activity which reduces the operating cost and corresponding alerts are also given to the monitoring agents. The proposed automatic mobile phone-based model uses a new android application to monitor and visualize the locations of various activities that avoids the need for 24x7 monitoring. Keywords Anomaly detection; GSM Network; Embedded System; SMS Gateway; Android Application I. INTRODUCTION Detecting the fraud activities in the vehicle movements have been a challenging task in recent years. Inefficient driving leading to loss of fuel and frequent breakdowns due to abnormal engine temperatures are other concerns considered in this study. Apart from these, the driver fails to cover certain sites for loading payload and also unloading is done in unofficial sites. Despite the technological advancements and the various solutions put forth, newer ways of frauds are still prevailing and need for easier monitoring are demanding better solutions in the information technology era. Our system addresses the present needs and provides an efficient way for detecting fraud activities and overall vehicle monitoring using minimal number of vehicle parameters at a reduced cost. We have considered the various fraud activities and anomalies as events taking place during the vehicle s fleet. Cost reduction is brought about by accomplishing data transfer only in case of an event happening, but the system still ensures proper maintenance and tracking of the vehicles. Fuel thefts are not only occurring at the work sites but also across the various fleet areas of the vehicle, requiring a real time status monitoring of the vehicle with location information. The system is designed to deal with this and keeps the server informed about the fuel level whenever a fuel theft is detected. Apart from fuel thefts, inefficient usage of fuel by the drivers is another case of loss of capital for the S.Aishwarya and L.N.Aravindan Dept. of Electronics and Communication Engineering Meenakshi Sundararajan Engineering College Chennai, India organization. A two minute idling will consume the same amount of fuel consumed by the vehicle to ride a mile. Thus idling of vehicles for a longer duration of time is a serious issue and monitoring this will be useful to increase the efficient fuel consumption. A number of organizations have their vehicles assigned to collect loads from various sites and to deliver them at specific sites. In many cases the drivers bypass some site points and also deliver at some unofficial sites. It therefore demands an automatic tracking system to maintain and record the load and unload points for maintaining safety of the goods/payload and also for ensuring loading and unloading at correct sites. In addition to all these, route fixing and monitoring have been a major area of concern for the fleet industry. The system presents a new, simple yet an efficient way for route fixing and route variation tracking using the concept of intermediate landmarks. From the driver s perspective, an alert about the vehicle parameter abnormality before they eventually lead to the breakdown of the vehicle is a real need. The system ensures this by alerting them about the engine temperature anomaly, so that necessary actions can be taken before burnout of the engine takes place. Fig.1 shows the basic working of the proposed system which includes GSM network, centralizer server and monitoring services. Fig. 1. Basic operation of the system 845 P a g e

2 The system contains an embedded system module placed at each vehicle, containing essential sensors which are used to sense the fuel level, payload weight, engine temperature, distance covered. These are continuously processed by the controller placed at the vehicle. The controller on processing sends SMS to the server in case of an event occurrence e.g. fuel theft or loading. All the site points of the above events can be monitored by using a new android application that uses mobile GIS (Geographic Information System) to locate the various points on the Google Maps for visualization. It provides individual vehicle monitoring and also real time monitoring to know the current details of the vehicles plying on different routes. Information about the past incidents that have taken place at the theft positions are also provided by the server to the monitoring agents who are the regular users of the system. Another advantage of the system is that it eradicates the need for a continuous 24x7 monitoring. The agents are alerted with SMS about the vehicle and the type of event in case of an event happening. In this study we have simulated the scenarios for better understanding of the various prevailing issues. With the knowledge gained from simulation, we have created a real time implementation which will give solution for different issues discussed. Section II of the paper discusses the details of present systems designed to address the fraud detection activities. Section III presents the architecture of the entire system, its modules and components. Section IV covers the various scenarios for which the system is designed and how the system reacts to these scenarios. Section V gives the overview of LabVIEW[10] and how to model the scenarios. Section VI compares the simulated environment and the real time implementation. Section VII presents the overall conclusions of the paper. II. RELATED WORK The problem of vehicle monitoring and fraud detection has had numerous solutions in the past. There have been systems for real time monitoring of the vehicle position using GPS and GSM [1, 2, 4]. These studies have focused on location information transfer with the server for tracking the vehicles. GPRS (General Radio Packet Service) has also been used in some systems for communication with the server [5] [6]. Route tracking and path assignment have featured in some systems for different purposes. The detailed discussion of each above system is provided here with its disadvantages. The studies Le-Tien [1] and Mircea [2] are focused on a system that was designed to track the vehicles and to store the locations covered in a database at a server through information sent in the form of SMS. Gannan[3] designed a system to locate the vehicles in their fleet and raised component GIS secondary development pattern based on MapX. Hui[4] developed a monitoring system based on GPS/GIS/GSM and developed a software of monitoring centre based on VC++. Peng [5] and Liu, Dan [6], focused on the usage of GPRS for communication from the vehicle to the central server. They provided various models which used the TCP /UDP protocols for data transfer. Enomoto[7] developed a system for obtaining information regarding loading and unloading sites on vehicle movements. With significant developments in the mobile communication, systems started to use mobile technology and smart applications to monitor the vehicles. Ashraf Tahat et al [8] developed an Android-Based Universal Vehicle Tracking System, which used android mobiles in the vehicles to monitor the vehicle parameters and Bluetooth for data transfer. The information from the phone can also be sent to a server for maintaining the vehicle parameters. The existing systems have revolutionized the way in which vehicle tracking and monitoring has been done, but there are certain drawbacks with these systems. The systems used for tracking of vehicle position using GPS, GIS and GSM [1, 2, 3, 4], all have a centralized maintenance of information. But the problem with the systems was that they required continuous transferring of information from the vehicle to the server. The continuous data transfer demanded a migration to the GPRS connectivity in many systems [5] [6], which increased the operating cost and loss of data in remote areas. The proposed system takes away the need for this continuous communication with the server. The information regarding the sites, type of event and the associated parameters are all sent to the server via SMS, only when the event occurs. Also the continuous monitoring of the events by the monitoring agents is a tough task and the system eliminates this by using a mobile phone-based approach. In the system [7], details about the sites covered were to be entered by the driver manually. No automatic way of understanding the loading and unloading locations are available. Our proposed system updates the loading and unloading site information to the server. The system [8] uses android application in mobile placed at the vehicle for monitoring various parameters and this can be updated at the server. But the use of android application is enhanced in our system, since it is used by the monitoring agents at remote locations. In addition to monitoring the vehicle parameters, it provides viewing of locations of various fraud activities and route setting feature. The system proposes a complete end-to-end solution for vehicle monitoring and tracking of anomalies using minimal parameters, reduced cost and effort. III. SYSTEM ARCHITECTURE The proposed system mainly consists of three modules namely embedded system module, web server module and android application module. The first module containing various sensors with a microcontroller is placed in each vehicle to detect the various events and to interact with the server. The web server module obtains information from the vehicles, maintains record and sends alert messages to the monitoring agents. The android application is used for remote monitoring of vehicles using Mobile GIS. A. Embedded System module This module consists of the components shown in Fig 2. 1) Microcontroller Microcontroller with two serial ports integrates various sensors and modules with it. Data received from the components of the module are processed by the microcontroller to detect the occurrence of various events and corresponding actions are taken accordingly. 846 P a g e

3 Fig. 2. Architecture of embedded system 2) Fuel capacity sensor This sensor gives the information about the capacity of fuel in the fuel tank with which fuel theft is detected. It determines the capacity of the fuel by measuring the amount of fuel passing through the flow sensors placed in the fuel tank. 3) Payload weight sensor The weight of the payload is measured using a device called load cell. When a force is applied on the device, it converts the force into electrical signal. The output signal is scaled to determine the applied force. This is used to determine the loading and unloading site points. 4) Odometer Electronic resettable odometers are used to measure the distance covered by the vehicle by counting the voltage spikes or pulses. The voltage pulse is produced when the magnet crosses the pick up during the revolution of the wheel. The distance measured by this device is used to track the route and also to monitor idle time. 5) Temperature sensor The controller monitors the temperature of the engine continuously using temperature sensor fitted in the engine and is used to detect any abnormality in the engine temperature. 6) GPS module The Global Positioning System (GPS) is a spacebased satellite navigation system that provides location and time information in all weather conditions, anywhere on or near the Earth, where there is an unobstructed line of sight to four or more GPS satellites. Fig 3 shows the concept of GPS and how it calculates the location information based on the distance from four or more satellites. The GPS module obtains the location information i.e. the latitude and longitude of the current location of the vehicle, only on the occurrence of the event. The obtained location information is then sent to the server. Fig. 3. Working of GPS 7) GSM module GSM module is used to send the information to the server via SMS. The information received from the sensors is put in different SMS formats to differentiate the various events happening. In case of engine temperature anomaly, an alert is sent to the driver and in all other cases the SMS is sent to the server. B. Web Server Module The Web Server module processes the information received from various vehicles and maintains an individual record for each vehicle. The following are the major components of this module. 8) SMS Gateway A SMS Gateway is used at the server to receive the SMS from the GSM module connected to the server to which the messages are sent from the vehicle. A database connector is used to store the incoming messages to the database. The gateway is also used to send the alert messages to the monitoring agents to indicate that a particular event has occurred and to request them to turn on the Android application to monitor the event. Also information about the route to be followed by the drivers is sent using this. Fig. 4. SMS gateway interaction with server 847 P a g e

4 Fig 4 shows the interaction of SMS Gateway with the java application using MySQL database link. The SMS from the GSM module is stored in the database from which the java application routes it to the desired table in the database. 9) Database The received SMS from the vehicles gets stored in a separate table in the database. The database maintains separate table for each vehicle. It also contains tables to store the different routes with their landmark details, past fuel theft points and a list of official unloading points. 10) Java Application A Java application running at the server side is used to route the incoming messages to the corresponding vehicle tables. It is also used to process the data to comment about the route followed, unloading and fuel theft location. C. Android Application A new android application is created for the system to allow remote mobile phone-based monitoring of the vehicles. This application is provided for the monitoring agents and also regional heads of the organization. The application makes use of the mobile GIS to obtain the location of the vehicle on the Google Maps. Data from the web server is retrieved to display information about the various load points, unload points, fuel theft points and idle time points. Also details about the fuel level, distance covered, payload weight is also displayed on clicking these locations. Route selection for the vehicles is also done using the android application. The regional heads are provided with special login access to assign routes to the vehicles under their control. This ensures dynamic route selection based on the requirement. IV. EVENT DETECTION AND HANDLING Various events such as fuel theft, loading, unloading, high engine temperature and idle time takes place during the fleet of the vehicle. The methods used by the system to approach these scenarios are discussed below. D. Fuel theft As mentioned earlier, the rate of decrease of fuel level is higher when fuel theft occurs. Controller continuously monitors the rate of decrease and whenever this is beyond a threshold value, the theft is detected. Fig 5 shows the sequence of events for fuel theft detection. The information about the location is also compared with the past theft locations. If the site is a new location, a new theft id is created to store the new location. If the theft id already exists, a new record under the same theft id is created in the fuel theft table, which is used to give the details about all the vehicles which were involved in the theft at a particular location. This information is available in the android application to let the monitoring agents know about the history of thefts that have taken place at a particular location. Fig. 5. Flow chart for fuel theft detection E. Loading sites Fig. 6. Flow chart for load and unload site detection The payload weight determined by the load cells is read by the controller and whenever there is an increase in the payload level by more than a threshold value, the particular location is fixed as a load site. This is really useful in keeping track of the various points from where the vehicle is loading and also about the weight of the load that has been added. This can be used specially in such organizations where the vehicle is required to cover many site points e.g. Garbage trucks running across the city. The information is then used to check whether the vehicle has covered all the loading points. F. Unloading sites Unloading is another major issue related to the payload. 848 P a g e

5 There are cases where only some sites are allocated for unloading, as is the case with the already mentioned example. In such cases we make use of a table containing a list of official unloading sites. Whenever an unloading site is detected, it is compared with all the official unloading sites. If not, then an alert message indicating the unloading site fraud is sent to the monitoring agents. They can then see the details and get the location through mobile GIS. Fig 6 shows the actions that determine the load and unload site points. G. Engine temperature Engine temperature is an essential criterion for proper working of the vehicle. Once the temperature reaches a critical value, an alert message is sent to the driver s mobile from the vehicle GSM module. This can help the driver to take the necessary steps to prevent break down of the vehicle. H. Inefficient Fuel Usage heads, the authority to set the routes for the vehicles at the start of the day. This information is then given to the corresponding driver using SMS. J. Route Variation Detection The system uses a concept of intermediate landmark fixing to detect variations in the route followed by the vehicles. A number of intermediate landmarks are fixed for every route and their latitudes and longitudes are stored in the server database. Fig. 8. Flow chart for landmark detection used for route tracking Fig. 7. Flow chart for idle time detection A major cause for inefficient fuel usage is excessive idling. As said earlier, for every two minutes of a vehicle idling, it uses about the same amount of fuel it takes to go about one mile. Measuring the amount of idle time can help to rate the performance of the driver and also to account for the excess fuel consumed. Fig 7 shows the steps involved in finding the idle time. The idling time is measured by making use of the fact that the odometer reading does not change when idling occurs. Thus, whenever the odometer reading remains same and the engine is turned on, a timer is started and the timer is stopped when the odometer value varies by some significant amount. If the timer is greater than a fixed threshold( this is done to leave out very short idling that is unavoidable in short traffic situations), a SMS containing the start and end time of idling is sent along with the place of occurrence to the server. At the end of the day, computing the overall idling time, the efficient usage of fuel by the driver is rated. I. Route Setting The system allows dynamic route setting for the vehicles. The android application developed provides the regional The number of landmarks can be increased or decreased depending on the accuracy with which the route deviation has to be detected. Fig 8 shows the flow of events for landmark identification. Once the route is selected by the regional head, information about the selected route is given to the driver as well as to the GSM module in the vehicle in order to initialize the distance between the subsequent landmarks. Once the vehicle has covered a distance equal to the distance which the vehicle must have travelled to reach the next landmark, a SMS indicating number of landmarks covered and the current location (latitude and longitude) is sent to the server. The server processes the SMS messages, compares the actual landmark position and received location, and any deviation is identified as a route variation. Thus the system makes use of a simple landmark system to track the route followed by the vehicle and automatic alert messages are sent to the heads in case of any deviations from the assigned path. A part of the actual path to be taken by the vehicle is shown in Fig 9. The path shown contains four landmarks. A geo-fence is constructed around each landmark with its geopoint at the center and a fixed threshold as radius. The location received from the vehicle is checked with the range of geo- 849 P a g e

6 fence of the corresponding landmark and the same is used to detect the route variations. Fig. 9. Map marked with landmarks and geo-fence K. Location Comparison All the location comparisons done at the server side for fuel theft sites, unloading sites and for route variation uses the concept of geo-fencing. A geo-fence is set up around the actual location of unloading site, landmark or a registered theft point. The distance between the received site information and the location in the database is computed using Haversine formula [9]. This formula calculates the great-circle distance (d) between two geo-points i.e., the shortest distance over the earth s surface. This formula has a reduced computational complexity compared to other formulas available that increases the speed of operation at the server side and provides good accuracy that fits this application well. If the two geopoints between which the distance is needed are given in terms of their latitude(φ) and longitude(λ) as p1(φ 1, λ 1 ) and p2(φ 2, λ 2 ), then the shortest distance between them is computed using the haversine formula as follows. V. SIMULATION A simulated environment is created to mimic the actual parameters considered in the system using LabVIEW[10]. It is a system design platform developed by National Instruments which provides easy understanding of the problem, and also visualizing the solution designed with graphical data. This simulation platform is very flexible and helps in refining a system for maximum performance. Using this we have simulated few methods discussed in the previous section to handle the scenarios. Fig 10 shows the block diagram of the simulated system and Fig 11 shows the User Interface (front panel) of the same. In the simulation, the value of the fuel is allowed to decrease at a constant rate indicating normal performance and fuel consumption of the vehicle. Whenever there is extra fuel consumption which is simulated here using a slider, the rate of decrease of the fuel level increases. This is detected by the system as fuel theft and graph shown in the top of Fig 11. It shows the variation of fuel level with time and also to indicate the time at which the fuel theft occurred along with the rate of decrease. For recording the load and unload points, the system uses sliders to get the loading and unloading values. Once a particular level of load is added using the slider, the value is added with the already existing load value. By comparing the present and past values, loading and unloading is differentiated. The present value of the payload and the number of load points are also displayed in the user interface. The graph shown in bottom left corner of Fig. 11 plots the level of payload and the various site location ID giving information about the amount of load added or unloaded at any location. Δφ = φ 2 - φ 1 (1) Δλ= λ 2 - λ 1 (2) a= sin²(δφ/2) + cos(φ 1 ).cos(φ 2 ).sin²(δλ/2) (3) c= 2.atan2( a, (1 a)) (4) d = R.c (5) where d is the computed distance between the two geo-points, R is earth s radius (mean radius = 6,371 km). Considering an example, p1( , ) and p2( , ) as the two geo-points, the distance calculated using the formula is km. The distance computed (d), is used to decide whether the site lies within the geo-fence and the actions are taken depending on it. Fig. 10. Simulation in LabVIEW 850 P a g e

7 Fig. 11. User interface in LabVIEW Temperature anomaly is detected using the same idea as discussed. The threshold is set as 100 degree Celsius since the safe operating temperature varies between 90 and 105 degree Celsius. Temperature anomaly is detected once it goes beyond this threshold value and the same is indicated in the user interface. The graph shown in the bottom right corner of Fig 11 plots the variation of temperature with respect to time which tells us about the periods of anomalous behavior of engine temperature. The scenarios like fuel theft, payload fraud and engine temperatures have been simulated which provides better understanding of the system s approaches. Now we can implement the scenarios on real time environment. VI. SIMULATION VS REAL TIME The LabVIEW simulation developed explained the three considered scenarios and their handling by the proposed system. The LabVIEW model keeps track of the variations in the fuel level, temperature and the added payload level. Whenever a anomaly is identified by the model, it will indicate the same using LED s placed in the front panel of the LabVEW window. It keeps track of the amount of load added at every load point and maintains the load site information. Also the use of graphical data enhances the location mapping of the anomaly in time with respect to the various considered parameters, thus giving indication about the events that lead to the anomalies. In real time, the same tasks are performed by the embedded system module. It continuously monitors the fuel level, temperature, payload level and distance covered. Similar to the simulation model, it identifies the various anomalies using the methods described here. It then sends messages to the server indicating the type, occurrence and various parameters associated with the events. The SMS Gateway is used for the purpose of storing the incoming SMS in a separate database table. A JAVA application running at the server processes the information received from all the vehicles. The stored SMS have different formats and unique ID to indicate the vehicle, which sent the message. Different message types such as m1 for loading, m2 for unloading, m3 for fuel theft, m4 for idle time monitoring, m5 for landmark information and m6 for temperature anomaly are defined for the system, where m1, m2, m3 etc., are strings added in the message for differentiating the various events. The JAVA application uses these message types, reads the vehicle ID, and routes the parameters to the corresponding vehicle tables. This runs continuously so as to read all the new incoming messages and updates the server database with the received details. The JAVA application also uses the concept of geo-fence for comparing the previous site points with the new site points related to fuel theft, unloading and route tracking. The haversine formula [9] is used to calculate the great circle distance given in eqn. (5) between two locations which are needed to be compared. Once the distance is greater than the radius of the geo-fence created, it is detected as a site point which is not present in the database. The alert message is then sent to the concerned persons from the server to intimate about the type of fraud and the relevant parameters. A new android application is developed to enable visualization of the site points and for viewing details about the different parameters of the vehicle at these points. The android application uses Google Maps API for integrating the maps into the application. The application is developed for two types of monitoring. One is a real time monitoring that allows us to see all the vehicles with their last recorded location, thus enabling us to keep track of all the vehicles on the go. The other option provided by the application is individual vehicle monitoring, which allows viewing the load, unload, idle, fuel theft and landmark sites covered. Fig. 12. Android application interaction with server Fig 12 shows how the android application interacts with the server. On receiving the HTTP request sent by the android application, the server executes a PHP script which is used to retrieve the data from the database. The required parameters are read from the database and encoded into JSON (Java Script Object Notation) format. A JSON array with individual JSON objects each representing the various types of events, such as loading, unloading etc. is created. The JSON array is returned to the application in its response to the HTTP request made by the application. The application on parsing through the JSON array uses the information to display the site points covered. The latitude and longitude information retrieved from the server are used to locate the points in the Google Maps and the parameters corresponding to each site is displayed on clicking the particular site. Fig 13 shows the screenshots of a vehicle selected using the individual monitoring option along with the 851 P a g e

8 details of parameters which is displayed on clicking a load site. The different color markers shown are used to represent the different sites and events. The red markers indicate load sites, blue markers indicate idle time, violet markers indicate fuel theft site and orange color markers indicate the unload sites. Fig. 13. Individual vehicle monitoring screen shots Also the feature of route selection is given to specific authorities with a login facility. The authorities can assign a particular route to the vehicle during the beginning of the day. VII. CONCLUSION The system provides both fraud activity detection and overall monitoring of the vehicle, in a fully automated environment. Reduced use of SMS makes the system costeffective. It avoids the continuous data transfer from the vehicles to the server, and increases the rate of processing at the server since all the updates are discrete. Though the updates are discrete, they are enough to monitor the vehicle performance, detect the fraud activities and track the route, thus making the system effective and efficient. Also the need for a continuous monitoring is avoided by sending alert messages about the various events and vehicle ID to the monitoring agents. The system thus presents a complete end to end solution to the problem of tracking fraud activities and anomalies for easy vehicle monitoring with the help of Mobile GIS. The proposed system can be extended to serve for Mega City and city development planning projects. REFERENCES [1] Le-Tien andthuong, Routing and tracking system for mobile vehicles in large area, Electronic Design, Test and Application, 2010, DELTA 10, Fifth IEEE International Symposium, pp [2] Popa andmircea, A solution for tracking a fleet of vehicles,telecommmunications Forum, 2011,pp [3] Yuan andgannan, Research and design of GIS in vehicle monitoring system,internet Computing in Science and Engineering, 2008, pp [4] Hu and Hui, Design and implementation of vehicle monitoring system based on GPS/GSM/GIS, Intelligent Information Technology Application, 2009,pp [5] Chen and Peng, Intelligent vehicle monitoring system based on GPS, GSM and GIS, Information Engineering (ICIE), 2010,pp [6] Liu and Dan, Research and design of a high performance GPS vehicle monitoring system, Future Information Technology and Management Engineering (FITME), 2010, pp [7] Enomoto and Takashi, A freight status management with restriction to record loading/ unloading information by location, vol.1, Intelligent Transportation Systems, 2003, pp [8] Ashraf Tahat, Ahmad Said, FouadJaouni andwaleedqadamani, Android-based universal vehicle diagnostic and tracking system, IEEE 16 th International Symposium, [9] [10] -National Instruments LabVIEW 852 P a g e