Gyeyoung Lee

Implementation of Web Services for ILBS

LBS (Location Based Service) prevails throughout the everyday life. Navigation, logistics, fleet control, POI (Point of Interest), and so on are all types of LBS. LBS is so useful that it should be available everywhere including inside of a building, subway stations, underground shopping malls, and downtown of a big city. Such LBSs provided inside of a building is called ILBS (Indoor LBS). A web service is a piece of software or a database published on Internet so that it can be used in web application development in the standard manner. Therefore, web service is deemed to be a promising technique of developing a web application. This paper introduces our implementation of web services of elementary functions of ILBS system. Indoor positioning, rendering drawings, playing contents are a few of such functions.

Kalman filter vs. particle filter in improving K-NN indoor positioning

The Kalman filter has been widely used in estimating the state of a process and it is well known that no other algorithm can out-perform it if the assumptions of the Kalman filter hold. For a non-Gaussian estimation problem, both the extended Kalman filter and particle filter have been widely used. However, no one has performed comparison test of them. In the consequence, they arbitrarily choose one of them and apply it on their estimation process. Therefore, we have compared the performance of the Kalman filter against the performance of the particle filter. One of the practical fields on which these filters have been applied is indoor positioning. As the techniques of manufacturing mobile terminals have made a big progress, the demand for LBS (location based services) also has rapidly grown. One of the key techniques for LBS is positioning, or determining the location of the mobile terminal. Outdoor positioning is not a big burden to system developers because GPS (Global Positioning System) provides pretty accurate location information of a mobile terminal if the line of sight is not blocked. On the contrary, there is no practical solution for the indoor positioning problem. We can obtain exact location of a mobile terminal if we invest large amount of money, but this is economically not practical. One of the most practical candidate solutions for the indoor positioning problem is the WLAN (Wireless Local Area Network) based positioning methods because they do not require any special devices dedicated for indoor positioning. One of the most significant shortcomings of them is inaccuracy due to the noise on measured data. In order to improve the accuracy of WLAN based indoor positioning, both the Kalman filter and the particle filter processes have been applied on the measurements. This paper introduces our experimental results of comparing the Kalman filter and the particle filter processes in improving the accuracy of WLAN based indoor positioning so that indoor LBS developers can choose appropriate one for their applications.

Petri Net Representation of Ontologies for Indoor Location- Based Services

This paper presents a Petri net representation of an ontology for indoor location-based services. The ontology is described within the Resource Description Framework (RDF). Therefore, this paper proposes a method for transforming the RDF model into a CPN (colored Petri net). The proposed method represents the semantics of the RDF model in the CPN by mapping the classes and properties of the RDF model onto CPN places and representing the relationships between those classes and properties as token transitions in the CPN. To represent an RDF statement in the CPN, the method introduces a transition that produces a (complex) token composed of two ordered (simple) tokens: one corresponding to the subject and the other corresponding to the object of the statement. Applying the proposed method, we build a sample CPN for an RDF model and perform simulations using the model to answer RDF queries. This paper also introduces a simple ontology for an indoor location- based service. Using the proposed transformation method, we transform the ontology into a CPN. Finally, we introduce our inference algorithm for the CPN and a prototype database system for demonstrating the practicality of our method. The results indicate that the proposed database system can resolve the semantic ambiguities in the query by using the ontology.

The Optimal Threshold for ECN Marking

ECN is accurate in determining traffic congestion since it explicitly notifies the incipient congestion. Therefore, ECN method has been thoroughly studied in the field of wireless TCP. This paper introduces a formula to find the optimal threshold for ECN marking. We have implemented a Petri net model of `TCP with ECN strategy` and performed simulations on it in order to verify the validity of the formula. We have also introduced ideas of applying the formula in practice. The primary contribution of this paper is proposing a formula to find the optimal threshold for ECN marking. However, introducing the Petri net model of `TCP with ECN strategy` is no less valuable contribution because it can be helpfully used by the researchers in studying network protocols. We have built the Petri net model by modifying the existing Petri net model of TCP. In order to add ECN strategy to the existing model, we have mainly modified the network part. We have also modified sender part and receiver part as well.