Location-aware Agent using Data mining in the ... - CiteSeerX

Location-aware Agent using Data mining in the Distributed Location-based Services Jaewan Lee, Romeo Mark A. Mateo, Bobby D. Gerardo and Sung-Hyun Ko School of Electronic and Information Engineering, Kunsan National University 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea {jwlee, rmmateo, bgerardo}@kunsan.ac.kr

Abstract. Location awareness becomes more interests and topics of research. Adding this function to location-based services provides necessary information to user within the location to be aware of the user’s location. But not all LBS could provide the necessary information that interests the user. In this research we propose a location-aware agent that interacts with the LBS and providing location awareness service by adding the task of data mining. This agent extracts additional information by deploying a mobile agent in the database of LBS and mines the data. To make efficiency on our approach, we present a data mining which has a feature of user profile to extract the relevant information that the user more interests and allows awareness of the location.

1

Introduction

Location-based services are very abundant in our society. The improvement of these services provides us a complex system but aiming a better service to the user. In [1] discussed some challenges with the location based services to meet the computing needs of the services. It gives more details on improving the data representation, indexing, and precomputation. The technology or location system used with these services is discussed in [2] like using GPS, active badges, sensors and others. These issues also help the developer of location-awareness applications to choose a better location system which can be implemented in location-based services. There is also more opportunity with location-based services that we will experience in the near future as we develop more sophisticated devices. Location awareness is an evolution of mobile computing, location sensing and wireless technology discussed in [3]. A mobile device like PDA can become an information service about the location and necessary on the context knowledge of location and other information can be provided. An example of this is that we can configure our PDA for reminders by setting it whenever we are in a specific building or place with a LBS system exists. Before leaving the library, we set the reminder to borrow some books to the library. The reminder activates once the user agent sense that the previous building is out of range or we are already outside the building. Also, many location-aware services can be acquired on using this system.

In this paper, we present a location-aware agent which integrated with data mining approach on a location or LBS. This approach can make more awareness of the information by mining on the additional information which was not provided by the LBS. The location-aware data mining is provided by factors like selecting the interest of the user to improve the performance and efficiency of data mining.

2

Basic Concepts

In the location-based services we discussed a various technologies and techniques to provide the necessary information. The growth of the LBS becomes an interest of mobile users and opportunity for more information services can be developed. Accompanied by this development is the distributed hardware and software which makes a hindrance of providing necessary and relevant services. Researches solve these problems by providing interoperability by middleware and other techniques are introduced. The following subsection will explain some technology and concept that used in our design of the location awareness agent. 2.1

Location-based Services

Location-based services provide information based on the location of the mobile user. Data storage is necessary in LBS to represent the locations in the world, as well as their attributes and relationships, and the resources available. This database is used for interpreting sensor readings, performing spatial queries and inferences, and triggering actions. In geographic information systems (GIS), the database is usually a geospatial database; in many indoor ubiquitous computing systems, the database may be a simple as a drawing file. In any case, location-based services have requirements that challenge traditional data representation systems. For the reason that the location-based services are distributed software and hardware, middleware implementations are proposed in [7] and [11]. In [7] a proposed multi-agent that manages the location management to solve the problem of the distributed location-based services was presented. The middleware used in the system was the CORBA implementation. Also, the Middlewhere [11] uses CORBA that enables the fusion of different location sensing technologies and facilitates the incorporation of additional location technologies on the fly as they become available. The requirements of these middleware are certainly large and complex to integrate but would benefit the service providers and mobile users. 2.2

Location-awareness

Location-awareness is a small part of context-awareness. Context-awareness is the ability of the mobile device to be aware of the user’s surroundings physical environment and state. The application and services used this concept can then be developed to exploit the fact that the mobile device can inspect the environment, rather than the

other way round. A context-awareness mobile device may supply applications with information on location, altitude, orientation, temperature, velocity, biometrics, etc. Location-awareness is only a subset of these concept but powerful on bringing the necessary information. Location awareness is a general term used for something that can show that it is aware of your current location services built on the location awareness capabilities of mobile devices and networks are the location-based services. The Mobile Shadow [4] is an example of location based system that uses the concept of location awareness. It shows the architecture of the location system on presenting the services can be access by the user with their user agent. The infrastructure of the system provides supports for proactive location-aware services. 2.3

Data Mining

There are a lot of algorithm that have already introduce to perform efficient data mining to facilitate more on the processing and interpretation of data. Using data mining enhance the learning of the patterns and knowledge on the distributed databases. Also, data mining is used in location management discussed in [10] where the mobility patterns are determined to predict the next location of cell-to-cell. In the HCARD model of [8], proposed an Integrator agent to perform knowledge discovery in the heterogeneous server in the distributed environment. This agent was developed on CORBA for search and extraction of data from heterogeneous servers. Association rules were generated in the study and these can be practically explain for decision purposes. The research also uses the method on minimizing the operation time by clustering prior to pattern discovery.

3

A Motivation Example for Location-aware Data Mining

A location-aware service is an additional service of LBS which can inform a mobile user of the current location. The scenario explains a typical situation of a locationawareness. A student has a PDA and a user agent named “Jones” which resides on his PDA. In each establishment or building has LBS and communicate with the mobile device like the student’s PDA. To be reminded before leaving the library, “Jones” was set to alarm and popping a message that the student must borrow a book before leaving the library building. This is a typical example of a location-awareness where the user is informed of something about the location. There are still information that can is useful but the LBS or the user agent itself cannot see this. Like in an interactive library which is integrated with LBS. Here we say that the user agent is reminded to borrow a specific book but it was informed by the agent of LBS that the book was already borrowed. Probably, the next action of the mobile user will on his way to exit the library. But this user is interested on the other books that he might borrow without knowing. In this case, we can add the data mining to mine the other books that may interest the user. To solve the problem, we may integrate the data mining function in the location-

aware agent. We give a description on some user agent profile’s attribute to be use in our data mining. Student level: The user agent stores the level of the student. This input may be on the level of elementary, high-school, college and graduate student. This allows the user agent have a knowledge of what Interest: This describes the interest of the user. This maybe a profile of Address:

4

Framework of Location-awareness Agent

In this study, the researchers proposed location-based service architecture and developed on using a CORBA implementation. The protocol concerned in our architecture is based in the wireless CORBA implementation discussed in [9]. Using a PDA to deploy the user agent for providing the location-aware services is our main concern. We use the compliance of the wireless CORBA specification to acquire the functions of the interoperability of the heterogeneous system by using the CORBA implementation in LBS that is in [7].

LBS 1

User Agent

LAM

LBS 2

User Agent

Services LAM

LBS 3 Services LAM

Services

Fig. 2. Location-based service architecture for location awareness agent

Figure 2 shows the architectural design of the location-based services for our proposed location-aware agent. There are three different location-based services which communicate with the user agent every time it transfers to location. The location agent manager (LAM) is the agent which resides in the LBS, communicates with the user agent by wireless protocol and provides the services to the mobile user. Also, this

agent communicates with the other LAM that is in the other LBS. The LBS uses the ORB core which provides additional functionality of the heterogeneous system.

4.1 Mobile Agent Middleware for Distributed LBS Mobile agent-based middleware is issues of research for providing an advanced infrastructure that integrates supports protocols, mechanism, and tools to permit communication and communication of mobile elements [12]. In this research, we design the middleware of mobile agent which is integrated with wireless CORBA [9] and a Javabased platform. The infrastructure is a service layered for designing, implementing, and deploying mobile agent based applications. As shown in Figure 3, the middleware is consists of three layers. The mobile agent services layer consists of communication, migration, naming, security, interoperation, persistence and data mining support. We focus on the last component which is the data mining support. This additional service is provided to operate the data mining of the mobile agent on the database of LBS. Also these proposed middleware is a compliant of wireless CORBA [9] to support the features of the CORBA environment. The key components in wCORBA are Mobile Interoperable Object Reference (IOR) which is a relocatable object reference, a home location agent which keep track of the current location of the mobile terminal, an Access Bridge and Terminal Bridge which are the network side end point of the General Inter-ORB Protocol (GIOP) tunnel. The architecture is shown in [9]. This integration provides transparency and simplicity of the middleware for mobile agent.

Mobility middleware Mobility virtual terminal

User virtual environment

Virtual resource management

Mobile agent services Communication

Migration

Naming

Security

Interoperation

Persistency

Data mining Support

Heterogeneous location based services

Fig. 3. Proposed mobile agent middleware

4.2

User Agent

The proposed system consists of a single agent which resides on a PDA. The PDA is J2ME and CLDC complaint to support the proposed user agent. This agent has the

profile of the mobile user and associated with additional role. The user agent represents the virtual personality of the mobile use. The service of t

User Agent Jones Data Mining Profile

College Student

Interest: Computers Address: Chicago, USA

Fig. 4. User profile of mobile agent Jones

4.3 Process of Mobile Agent Data Mining The user agent triggers a location-awareness by deploying a mobile agent at the LBS database. The security configuration of deployment is also…

Mobile code

LBS Database

Data Pre-process

Information awareness

Processed data

Classification Algorithm

Fig. 5. Propose two-phase data mining of mobile agent

4

Location-aware Data Mining Model

We present a data mining model for our database. 4.1

Data Mining

The optimal search method is used in the hierarchical location management. Research study using data mining in [11] is a method of mining the user mobility pattern and predicting the movement of the mobile object and optimizes the search method. It presents a sequential mining of patterns in the cell to cell movement. In our proposal, the agent mines from the previous requests of a mobile object by using the Apriori algorithm providing an optimal search method. The function of optimal search has 3 phases. In the first phase, the previous data requests of mobile object are retrieved, collected and preprocess by deleting the attributes that has a missing value. This preprocessing prepares the data from mining the patterns. Other preprocessing is also available like filling up the missing values of the attributes but this method will consume more time to process. Phase 1. Preprocessing Steps in preprocessing 1. User profile selection – selecting the tuple(s) that has a particular value on the use profile. 2. Trim – this will delete the missing values of the tuple(s) to make the process faster.

Phase 2. Classification Algorithm.

5

Experiment Evaluation

The simulation used a database consisting of 31 nodes and 15 mobile ID in each were stored within the network. The simulation platforms used in the research were IBM compatible PC with Windows and Linux operating systems for the nodes, Borland Visibroker, C/C++ and Java as development of software agents, MySQL for the database and HBE-EMPOS II as collector of mobile objects. Figure 8 shows the interface of location agent manager implemented in CORBA.

5.1 Results Case 1: Classification Tree BookID = 600: 15 (5.0/3.0) BookID = 601: 13 (5.0/3.0) BookID = 602: 16 (7.0/3.0) BookID = 603: 14 (2.0/1.0) BookID = 604: 14 (1.0) BookID = 605: 10 (0.0) BookID = 606 totaldays 5: 9 (6.0/2.0) BookID = 607: 10 (1.0) BookID = 608: 16 (3.0/1.0) BookID = 609: 12 (4.0/2.0) BookID = 610: 8 (3.0/2.0)

Case 2: Classification Tree BookID = 600: 12 (2.0/1.0) BookID = 601: 10 (0.0) BookID = 602: 16 (5.0/1.0) BookID = 603: 16 (1.0) BookID = 604: 10 (0.0) BookID = 605: 10 (0.0) BookID = 606: 8 (7.0/4.0) BookID = 607: 10 (0.0) BookID = 608: 10 (0.0) BookID = 609: 12 (2.0/1.0) BookID = 610: 18 (1.0)

6

Conclusion and Recommendations

In this paper, we proposed the approach of hierarchical structured agents for location management of location-based services. It used location agents that perform data replication. A hierarchical process of updating and searching mobile objects was also presented. There was an improvement in location management by implementing the nearest neighbor algorithm. The searching does not need to go to all of the parent nodes when the mobile identification was found on the nearest nodes of the source node which implies efficient search. The simulation result implies that the technique using NN algorithm minimizes the database operations. The research is only limited on mobile tracking and does not include physical and logical mapping of the location. The additional functions of the LBS should be studied

in the future. The cache and other auxiliary storage were not given emphasis but will be the subject of our future works.

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