Abstract:
A method classifies a mobile terminal by a-periodically obtaining locations of the mobile terminal. Location update intervals are determined from update times associated with the locations. Frequency characteristics are generated from the location update intervals, and a probability distribution function is generated from the location update interval. Then, the mobile terminal is classified according to the frequency characteristics and the probability distribution function.

Description:
FIELD OF THE INVENTION 
   This invention relates in general to location aware services, and more particularly to delivering location aware services according to mobility classifications of mobile terminals. 
   BACKGROUND OF THE INVENTION 
   Location aware services adapt and deliver services and multimedia content according to users locations and users profiles. Users of mobile terminals can generally be classified as a pedestrian, highway vehicle driver, and urban vehicle driver, see Catovic et al., “ Geolocation Updating Scheme For Location Aware Services in Wireless Networks ,” Proc. MobiCom&#39;01, 2001. Other classifications are also possible. 
   In the prior art, terminal mobility has been modeled by using two-dimensional Brownian motion with drift. The average speed and speed variation are input parameters for modeling each 2D-Brownian mobility motion as described by Tekinay, “ Modeling and Analysis of Mobile Cellular Networks with Highly Mobile Heterogeneous Traffic Sources ,” Ph.D. dissertation, School of Information Technology and Engineering, George Mason University, Virginia, 1994, Rose et al., “ Location Uncertainty in Mobile Networks. A Theoretical Framework ,” IEEE Communications Magazine, February 1997, Lei et al., “ Probability Criterion Based Location Tracking Approach for Mobility Management of Personal Communications Systems ,” IEEE 0-7803-4198-8/97 and Lei et al., “ Wireless Subscriber Mobility Management Using Adaptive Individual Location Areas for PCS Systems ,” IEEE 0-7803-4788-9/98. 
   It is desired to classify the mobility characteristics of mobile terminals, and to adapt and deliver location aware services according to the mobile terminal characteristics. 
   SUMMARY OF THE INVENTION 
   A method classifies a mobile terminal by a-periodically obtaining locations of the mobile terminal. 
   Location update intervals are determined from update times associated with the locations. 
   Frequency characteristics are generated from the location update intervals, and a probability distribution function is generated from the location update interval. 
   Then, the mobile terminal is classified according to the frequency characteristics and the probability distribution function. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram of the location aware system with mobility profiles according to the invention; 
       FIG. 2  is a block diagram of a location and mobility profile statistics database according to the invention; 
       FIG. 3  is a block diagram of a mobility profile analyzer for detecting the mobility profile of a mobile terminal by using statistical signal processing tools; 
       FIG. 4  is a block diagram of data flow through the profile analyzer of  FIG. 3 ; 
       FIG. 5  is a graph of location update intervals for pedestrian, urban vehicle and highway vehicle drivers; and 
       FIG. 6  is a graph of frequency spectrum characteristics of processed location update intervals for pedestrian, urban vehicle and highway vehicle drivers. 
   

   DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
   The present invention provides a system and method for classifying mobile terminals, and for customizing and delivering location aware services (LAS) according to mobile terminal classifications. Mobile terminals determine their locations, and a service manager polls this location information so that an application service provider can adapt and deliver personalized and general multimedia services according different classes of mobile terminals. 
   System Diagram 
     FIG. 1  shows a location aware system and method  10  according to the invention. The system  10  includes mobile terminals (MT)  120 , e.g., cellular phones and portable computing devices. The MTs  120  are connected to a service manager (server)  130  and an application service provider (ASP)  140  via an access network  100 . The access network can include wired and wireless portions. There can be multiple ASPs, e.g., local businesses in a particular locale. The services, e.g., multimedia content, to be provided can be stored in a service database  160 . Locations  101  of the mobile terminals  120  can be determined by using any location service, e.g., GPS or see U.S. Pat. No. 5,970,414 issued to Bi et al. on Oct. 19, 1999, entitled, “ Method for estimating a mobile-telephone&#39;s location .” Typically, the location service is part of the network  100 . The locations  101  and location update intervals are stored in a location and mobility profile statistics database  200 , see  FIG. 2  for details, which can be part of the network  100 , the service manager  130 , or the application service provider  140 . 
   A profile analyzer  300 , see  FIG. 3 , performs statistical signal processing on a history of location update intervals stored in the database  200  to determine the mobility profiles of the MTs  120 . The mobility profiles allow one to classify different types of mobile terminal users, e.g., pedestrian, bicycler, urban driver, or highway driver. 
   The profile analyzer  300  a-periodically updates the mobility profiles in the database  200 . The service manager  130  polls the location information and the mobility profiles of the MTs  120 , and customizes the services  160  to be delivered according to the mobility profile. For example, a MT used by a pedestrian has limited battery power, unlike a mobile terminal used by a vehicle driver. Therefore, the content and rate adaptation (transcoding) is necessary to minimize energy consumption of the pedestrian&#39;s MT. The service manager  130  informs the application service provider (ASP)  140  of any MTs within the ASP&#39;s  140  proximity, and MTs&#39;  120  mobility profiles. The ASP  140  performs the content adaptation and delivers customized services to the MTs  120 . 
   Location and Mobility Profile Statistics Database 
   As shown in  FIG. 2 , the location and mobility profile statistics database  200  comprises three basic components: a mobility characteristics database  211 , an inter update intervals database  212 , and a location database  213 . 
   The profile analyzer  300  updates the mobility characteristics database  211  via lines  114 - 115 . These updates occur in an a-periodic fashion. If the location update intervals are small, then the profile updates are done more frequently. As the location update intervals get larger, the frequency of the profile updates is also decreased. Dynamically changing the update frequency maintains preciseness in profile detection and decreases the signaling load, e.g., when there is no need to update, do not update. The mobility characteristics database  211  is accessible by the service manager  130 . The frequency at which the updates are performed depends on the speed at which the mobile terminal is moving. 
   Location update intervals  212  are updated whenever a MT requires a location update. The network  100  performs these updates. The network also updates the location database  213  that keeps track of the locations of the MTs  120 . The location database  213  is also accessible by the service manager  130 . 
   Profile Analyzer 
   As shown in  FIGS. 3 and 4 , the profile analyzer  300  takes location update intervals  115  as an input from the statistics database  200 . Serial to parallel (S/P) conversion  351  performs vector operations on the location update interval data  115  for time intervals T 1 , T 2 , . . . , etc. The vector data are then entered into a sample&amp;hold circuit  352 . The output of the sample&amp;hold circuit  352  is up-sampled  353 . 
   A fast Fourier transform (FFT) operation  354  converts the up-sampled data to frequency characteristics of the location update intervals  115 . The frequency characteristics are fed into the frequency analyzer  356 . 
   A histogram  310  is produced from the output of S/P  351  by a histogram analyzer  357 . The histogram approximates a probability distribution function that (pdf) that characterizes the mobility of the terminal. For example, the pdf can generally be chi-squared (non-central) for urban vehicular, and gamma for highway vehicular. The pdf  310  is entered into a classifier  355 . The classifier  355  classifies the mobile terminals into classes  390  according to the pdf  310  and the analyzed frequency characteristics. 
   As shown in  FIG. 5 , a histogram or pdf of location update intervals for a pedestrian  501  is more spread and heavier tailed than the highway  503  and urban vehicle drivers  502 .  FIG. 6  shows that the frequency spectrum has relatively higher energies at relatively low frequencies for a slow moving pedestrian  601 , at middle frequencies for an urban vehicle driver  602 , and at high frequencies for a highway vehicle driver  603 . 
   Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.