Intelligent Bus Alarm using Nokia N95

Transcription

1 FINAL PROJECT OF AE498MPA, DECEMBER 15, Intelligent Bus Alarm using Nokia N95 Han-Ul Yoon 1, Cesar Corzo 1, and Kyle Smith 2 1 Department of Electrical and Computer Engineering 2 Department of Aerospace Engineering University of Illinois at Urbana Champaign {hyoon24, ccorzo2, kjsmith}@uiuc.edu Abstract In this paper, we propose the intelligent bus alarm using Nokia N95 smart phone. The goal of this project is to provide a passive application which monitors the status of particular bus routes and alerts the user when they should leave for the bus. First, we created data which contains each bus schedule from C-U MTD web and MTD booklet. Second, we allowed a user to input a specific bus station with a corresponding stop identification number and time to get the station. Next, if the time is close to user s designated departure time, the phone sent a text message to MTD bus control station for required data retrieving. By information from the data retrieval the phone estimates the states of bus schedule using Bayes filter, which are Delayed, OnTime, and Advanced. Finally, the phone determines departure time and sounds alarm to notify the user to leave. Index Terms Intelligent bus alarm, STOPwatch.TEXTMSG, stopid, Bayes filter, N95 smart phone. I. INTRODUCTION THE cellphone technology has been advanced dramatically for recent 10 years. In early 90 s, the function of cellphone was simply to make/receive a call, and to send/receive text messages, even though it costed a lot. Compared with those aspect, now the cellphone does marvelous things, i.e., taking a picture, play mp3 files, and so on. Remarkably, some of advanced cellphone can access the Internet via wireless connection, also perform position tracking using integrated GPS. The intelligent system has a broad range of applications. Not only in academia, but also our real life we can interact with some intelligent systems. For example, when you search information on the Internet, a search window automatically complete keyword or URL using Bayes learning, then wait a user s decision. With drive navigation system (even though most people agree that it is not intelligent perfectly), we can drive more safely without concerning about missing a designated route. Also, the maintenance reminder system in your car tells you the time to change engine oil or transmission liquid, and shows alert lamp when it detects problems in the part of mechanical system. Generally speaking, now we are living with the intelligent systems. Recently, most of computer users now use a Widget as their personal cyber secretary. Macintosh machine had already provided the program which is similar to the widget, however this vogue became more popular with the advent of Microsoft Window Vista. The strongest aspect of the widget should be visual attractive and easy to install. It is also a light application in the view of system resource. Needless to say, it gives numerous information to users, such as time, weather forecast, stock trade, even a scientific calculator (not just as Window s accessory. it is perfect one like Texas Instrument new calculator). Interestingly, Champaign-Urbana MTD provides a nice widget, named by STOPwatch.WIDGET, which a user can use it with YAHOO widget engine. Figure 1 shows C-U MTD Stopwatch.WIDGET [1]. If a user gives the stopid of bus stop, which is unique identification code assigned to every bus stop, the STOPwatch.WIDGET provides bus number[name] and expected time to get the stop by text and background color. The red presents that a bus will be at the stop within 3 minutes, the yellow shows that a bus will come within 4 10 minutes, and the green means that a bus will take 10 or more minutes to get the designated stop. The shortcomings of widget application are wireless network accessibility and portability. Let s assume that a student carries his labtop for widget service while he is walking around campus. Even though the campus area is covered by WiFi nodes 99 % approximately, most of nodes are protected by NetID and password, or private security WAP codes. One solution might be a program application which contains a method[function] to provide NetID and password automatically. However, if he lose the labtop or someone looks up the code of application, his personal information can be misused unfortunately. For portability, the labtop is still heavy device to carry for a long time even though it is smallest model. In this sense, we propose the intelligent bus alarm application which a user can install on the cellphone which is running on Symbian OS. To overcome the problems that were mentioned above, we use STOPwatch.TEXTMSG, which provides bus information to cellphone user by sending a text message. The Fig. 1. Champaign-Urbana MTD STOPwatch.WIDGET.

2 FINAL PROJECT OF AE498MPA, DECEMBER 15, retrieved information are all bus number[name] and expected arrival time with given stopid. The organization of this paper is as follows. In section 2, the system design is introduced. In detail, a brief discussion of nomenclatures, which are characters and symbols are used in this paper, and target user group is presented in section 2.A and 2.B respectively. In section 2.C, we show data retrieval and the programming flow chart to perform the given tasks. The method to infer the state of specific bus schedule is introduced in section 3. In section 4, project result is presented. Section 5 will be the conclusion of this paper. II. SYSTEM DESIGN Here, we introduce the system design of this project. First, we describe the nomenclature of characters and symbols. Second, we discuss about target user group we are aiming at. Last, we explain how to retrieve data, and the flow of program for the intelligent bus alarm application. A. Normenclatures Table I describes nomenclatures which defines the characters and symbols in this paper. Characters / Symbols stop ID x t y t p(d) p(o) p(a) p(d s) p(o s, n) TABLE I NOMENCLATURES Definitions a unique identification code that is assigned to every bus stop a state of bus schedule at t x t = {Delayed, OnT ime, Advanced} a observation for bus running by msg data at t y t = {slow, normal, fast} a probability of bus schedule will be Delayed p(x t = Delayed) a probability of bus schedule will be OnT ime a probability of bus schedule will be Advanced a probability of bus schedule will be Delayed given y 0 = slow a probability of bus schedule will be OnT ime given y 0 = slow and y 1 = normal Faculties or students who commute school by C-U MTD bus. User s cellphone must have Symbian OS and Python programmable. User whose rate plan provides unlimited data service ex) unlimited text messages, $15 per month. User who easily forget MTD bus schedule, then miss an important event. C. Data Retrieval and Programming Flow Figure 2 shows that how to send a text message to get a bus schedule data, and what information can be retrieved by sending a text message [2]. This process can be done by simply typing in To:, then STOPwatch.TEXTMSG service will send you data as follows: 35890: ILLNNNNGNRNGSD - 24 SCAMP N now DUE. 22 ILLINI S in 02 min. 10 GOLD W in 06 min. 24 SCAMP N in 06 min. as of 1:08:38 PM. Other chgs may apply. As you may notice that, data retrieving can be done by extracting keywords, such as 22, ILLINI, and 02. Here, we may have a problem: when does phone start to send a text message from and what intervals? Therefore, to simply the task, we restrict a user s condition as follows: User can get the desired stop within 30 minutes by normal walking. Time delay between send and receive text message is negligible. It takes less than 5 minutes for user to wrap up the things that he/she is doing. If the user should stop by somewhere before getting the bus stop, user can set up the expected time to get there when he/she set up the alarm. User do NOT miss the bus on his/her purpose. If the user would like to miss the bus, he/she turn off the alarm or p(a s, n, f) a probability of bus schedule will be Advanced given y 0 = slow, y 1 = normal, y 2 = fast B. Target User Group Our application has some constraints, because send/receive text message costs a money (it depends on mobile service provider and rate plan. it costs $0.30 average. Also, the usage of this applicatoin is restricted at the city of Champaing- Urbana. Lastly, user s cellphone runs SymbianOS and programmable in Python. By these facts, we can determine the target user group as follows. Fig. 2. Sending a text message(left) and obtained data(right)

3 FINAL PROJECT OF AE498MPA, DECEMBER 15, set it up again. Those restrictions make the program flow more clear. Figure 3 depicts the total process of intelligent bus alarm. Note that no bus can follow its schedule perfectly; thus we need to estimate the bus schedule as: Will it be possibly [delayed][on time][advanced]? If yes, then what amount of time and what probabilities? The approach to handle these questions will be introduced in the next section. User Input stopid, Expected time to get the stop Timer Check the current time current time bus time < 45? Yes No p(x t y 0,, y t ) = ηp(y t x t ) x t 1 p(x t x t 1 )p(x t 1 y 0,, y t 1 ) At initial state, we set up the probabilities as follows: p(d) = 1 3 p(o) = 1 3 p(a) = 1 3 (1) p(s D) = 0.6 p(s O) = 0.25 p(s A) = 0.1 (2) p(n D) = 0.3 p(n O) = 0.5 p(n A) = 0.3 p(f D) = 0.1 p(f O) = 0.25 p(f A) = 0.6 For the bus is running slow, we can determine the speed of a bus running by the retrieved data from text message. Figure 4 show how we can determine the bus is running slowly. If a bus arrived should at the Illini Union 10:10, however the bus should have been at the Illini Union 10:00, we say the bus is running slowly. We also set up ±3 minutes interval for the normal running. The state transition probability is presented in Fig. 5. If the schedule is OnT ime now, it is reasonable to assume that a bus will able to be Delayed or Advanced with probability 0.2, and still remains OnT ime with probability 0.6. Also, Delayed to Advanced and Advanced to Delayed should occur rarely, we assign 0.1 respectively. Bus Schedule Estimator Start to estimate bus schedule slow normal fast Should user leave now? No 10:10 10:03 10:00 09:57 Bus should have been at the Illini Union Bus is at the Illini Union now Yes Fig. 4. Determine bus running speed y from text message data. Sound Alarm 0.6 Fig. 3. Intelligent bus alarm programming flow diagram. OnTime III. BAYES FILTER AS BUS SCHEDULE ESTIMATOR Bayes filter should be a good approach to estimate a state of bus schedule. The reason is that Bayes filter allows us to derive the posterior probability, p(x t y 0,, y t ) with a state transition probability and prior probability, p(x t 1 y 0,, y t 1 ) by its recursive relationship [3][4]. For discrete state estimation, the well-known Bayes filter equation is Delayed Advanced Fig. 5. The state transition probability among Delayed, OnT ime, and Advanced.

4 FINAL PROJECT OF AE498MPA, DECEMBER 15, Let s assume that we have already received 3-messages from MTD service. Then we can determine the current state of a specific bus schedule as the result of Table II. IV. CONCLUSION In this paper, we proposed that intelligent bus alarm for faculties and students who commute by Champaign-Urbana MTD bus. We collected and arranged data from MTD web and guide booklet. To estimates the state of schedule, we applied Bayes filter under the pre-determined state probability, and showed the result of estimated probabilities of bus schedule with assuming 3-text message observation. To complete the application, we should figure out bugs in Python code, especially data processing with a calender module. Data retrieval from a text message was not easy part, more works should be done to clear it. Also, installing an identified application is really dangerous. It will crash Symbian OS, so you may have no choice but the hard reset of your phone. To display the result on desktop side, we tried to use gnubox, which we had expected to help us for Bluetooth connection. However, we found that gnubox did not work with N95. This information was nowhere, so every student developer or professional developer should not use it with N95. Finally, further works should be done to complete the project. Cesar Corzo is a senior in the department of electrical and computer engineering of University of Illinois at Urbana Champaign. Cesar s research interest are: insect locomotion, robotics, and embedded systems. Kyle Smith is a senior in the department of aerospace engineering of University of Illinois at Urbana Champaign. Kyle is interested with aerodynamics, planning algorithm, application programming in Python.. He is also doing his minor in mathematics department. ACKNOWLEDGMENT We team Smith greatly thank to Prof. Tim Bretl for giving us precious teaching and encouraging us to fight with numerous developing skills of Nokia N95. Also, it was our honor to work as team smith and took the 1st and 2nd place in the first and the second class competition. We will never allow ourselves to forget AE498MPA in Fall 2007 at Urbana-Champaign, sweat and enthusiasm, and outstanding classmates. REFERENCES [1] STOPwatchWIDGET. How to use STOPwatch.WIDGET. [Online]. Available at [2] STOPwatchTEXTMSG. How to use it. [Online] Available at [3] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, [4] H. Choset, K. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavaraki, and S. Thrun, Principle of Robot Motion, MIT press, Han-Ul Yoon is the 2nd year graduate student in the department of electrical and computer engineering of University of Illinois at Urbana Champaign. Han is currently pursuing his Ph.D. degree with Prof. Seth Hutchinson. His research interest are: artificial intelligence, robot motion planning, and machine learning.

5 FINAL PROJECT OF AE498MPA, DECEMBER 15, TABLE II p(x t y 0, y 1, y 2 ) WITH RESPECT TO 3-OBSERVATIONS p(d s, s, s) Delayed p(d n, n, f) p(o s, s, s) p(o n, n, f) p(a s, s, s) p(a n, n, f) Advanced p(d s, s, n) p(d n, f, s) p(o s, s, n) OnTime p(o n, f, s) OnTime p(a s, s, n) p(a n, f, s) p(d s, s, f) p(d n, f, n) p(o s, s, f) OnTime p(o n, f, n) OnTime p(a s, s, f) p(a n, f, n) p(d s, n, s) Delayed p(d n, f, f) p(o s, n, s) p(o n, f, f) p(a s, n, s) p(a n, f, f) Advanced p(d s, n, n) p(d f, s, s) Delayed p(o s, n, n) OnTime p(o f, s, s) p(a s, n, n) p(a f, s, s) p(d s, n, f) p(d f, s, n) p(o s, n, f) OnTime p(o f, s, n) OnTime p(a s, n, f) p(a f, s, n) p(d s, f, s) p(d f, s, f) p(o s, f, s) OnTime p(o f, s, f) OnTime p(a s, f, s) p(a f, s, f) p(d s, f, n) p(d f, n, s) p(o s, f, n) OnTime p(o f, n, s) OnTime p(a s, f, n) p(a f, n, s) p(d s, f, f) p(d f, n, n) p(o s, f, f) p(o f, n, n) OnTime p(a s, f, f) Advanced p(a f, n, n) p(d n, s, s) Delayed p(d f, n, f) p(o n, s, s) p(o f, n, f) p(a n, s, s) p(a f, n, f) Advanced p(d n, s, n) p(d f, f, s) p(o n, s, n) OnTime p(o f, f, s) OnTime p(a n, s, n) p(a f, f, s) p(d n, s, f) p(d f, f, n) p(o n, s, f) OnTime p(o f, f, n) OnTime p(a n, s, f) p(a f, f, n) p(d n, n, s) Delayed p(d f, f, f) p(o n, n, s) p(o f, f, f) p(a n, n, s) p(a f, f, f) Advanced p(d n, n, n) p(o n, n, n) OnTime p(a n, n, n) 0.174