A moving object database (MODB), a database representing information on moving objects, has many uses in a wide range of applications, such as the digital battlefield and transportation systems. In the transportation system, an MODB processes queries such as ''How long should I wait until the next bus arrives here?'' Therefore, location information on moving objects reflects the most important data the MODB has to manipulate. Most moving objects are equipped with a GPS (Global Positioning System) unit that sends location information to the MODB. However, GPS signals are usually very weak inside enclosed structures; thus, locating indoor moving objects requires more than the GPS. In this regard, indoor positioning for location-based services (LBSs) has been an important research topic for the last decade. There are many other differences between indoor and outdoor MODBs. For examples, the area where the indoor moving objects are moving around is much smaller than where the outdoor moving objects are moving around, and the speed of indoor moving objects is much slower than that of outdoor ones. Therefore, the indoor moving object database (IMODB) should be studied separately from the outdoor MODB or the MODB. One of the most important problems that the MODB has to solve is the updating problem. In this regard, this paper proposes an updating method of IMODBs for location-based services. Our method applies the Kalman filter to the most recently collected series of measured positions to estimate the moving object's position and velocity at the last moment of the series of the measurements and extrapolates the current position with the estimated position and velocity. If the difference between the extrapolated current position and the measured current position is less than the threshold, that is, if the two positions are close, we skip updating the IMODB. When the IMODB requires information on the moving object's position at a certain moment T, it applies the Kalman filter to the series of the recorded measurements at the moments before T and extrapolates the position at T with the Kalman filter in the same manner as the updating process described earlier. To verify the efficiency of our updating method, we applied our method to a series of measured positions obtained by employing the fingerprinting indoor positioning method while we walked through the test bed. We then analyzed the test results to calculate savings of communication cost and error.

A Kalman filter updating method for the indoor moving object database

The location determination in obstructed area can be very challenging especially when the Global Positioning System is blocked. Disable users will find it difficult to navigate directly on-site in such condition, particularly in obstructed environment. Sometimes, it needs to integrate with other sensors and positioning methods in order to determine the location with more intelligent, reliable and ubiquity. By using ubiquitous positioning, it provides the location technique inside the wheelchair navigation system that needed for disable people. In this paper, we utilizes the integration of wireless local area network and the Global Positioning System which receive signal strength from access point and at the same time, it retrieve Global Navigation System Satellite signal. This positioning information will be switched based on type of environment in order to ensure the ubiquity of wheelchair navigation system. Finally, we present our results by illustrating the performance of the system for an indoor/ outdoor environment set-up.

Ubiquitous positioning: integrated GPS/Wireless LAN positioning for wheelchair navigation system

This paper present our mobile u-navigation system. This approach utilizes hybridization of wireless local area network and Global Positioning System internal sensor which to receive signal strength from access point and the same time retrieve Global Navigation System Satellite signal. This positioning information will be switched based on type of environment in order to ensure the ubiquity of positioning system. Finally we present our results to illustrate the performance of the localization system for an indoor/ outdoor environment set-up.

Performance Evaluation of Mobile U-Navigation based on GPS/WLAN Hybridization

Performance evaluation of mobile U-navigation based on GPS/WLAN hybridization

Location-Based Services (LBSs) have long been studied by many researchers and they nowadays provide very useful information to us, making our daily lives convenient. Vehicle navigation systems are an example of an LBS. As people frequently visit huge unfamiliar buildings, the demand for Indoor LBSs (ILBSs) has rapidly increased. Since indoor positioning is a key technique needed to implement an ILBS system, it has been studied by many researchers. However, a universal solution for indoor positioning has not been found yet. Smartphones are one of the best devices with which interactions between humans and ILBS systems occur because they are equipped with screens, sensors, processors and telecommunications gadgets. Therefore, many research results from smartphone-based indoor positioning studies have been published. One of the most popular methods of indoor positioning is the fingerprint method. The smartphonebased fingerprint method cannot be accurate if the smartphone sensor values collected at one spot are not different from smartphone sensor values collected at other spots. This paper outlines experiments done to show which smartphone sensors can be used in fingerprint indoor positioning.

Smartphone Sensor Value Pattern Analysis with Neural Network

Location-based service is one of the most popular buzzwords in the field of U-cities. Positioning a user is an essential ingredient of a location-based system in a U-city. For outdoor positioning, GPS based practical solutions have been introduced. However, the measurement error of GPS is too big for it to be used for U-campus services, because the size of a campus is smaller than that of a city. We propose the Relative-Interpolation Method to improve the accuracy of outdoor positioning. However, indoor positioning is also necessary for a U-campus because the GPS signal is not available inside buildings. For indoor positioning, various systems including Cricket, Active Badge, and so on have been introduced. These methods require special equipment dedicated to positioning. Our method does not require such equipment because it determines the user's position based on the received signal strength indicators (RSSIs) from access points (AP) which are already installed for WLAN. The algorithm we use for indoor positioning is a kind of fingerprinting method. However, our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, the proposed method is faster than the existing indoor positioning methods in the real-time phase. We integrated our indoor and outdoor positioning methods and implemented a prototype indoor-outdoor positioning system on a laptop. The experimental results are discussed in this paper. In implementing the prototype, we also implemented a C# library function which can be used to read the RSSIs from the APs.

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Implementation of a Prototype Positioning System for LBS on U-campus

Location-based service is one of the most popular buzzwords in the field of U-city. Positioning a user is an essential ingredient of a location-based system on a U-city. For the outdoor positioning, GPS based practical solutions have been introduced. However, GPS measurement error is too big to be used for U-campus services because the size of a campus is relatively smaller than a city. We propose Relative-Interpolation Method in order to improve the correctness of outdoor positioning. Besides, indoor positioning is necessary for U-campus while GPS signal is not available inside buildings. For the indoor positioning, Cricket, Active Badge, and so on have been introduced. These methods require special equipments dedicated for positioning. Our method does not require such equipments because it determines the user's position based on the receiver signal strength indicators (RSSI) from access points (AP) which are already installed for WLAN. The algorithm we are using for indoor positioning is a kind of finger prints method. However our algorithm builds a decision treeinstead of a look-up table in the off-linephase. Therefore, our method is faster than existing indoor positioning methods in the real-timephase. We have integrated our indoor and outdoor positioning methods and implemented a prototype of our indoor-outdoor positioning method on a laptop. Our experimental results are discussed in this paper.

Jaesu Do

Papers

Implementation of a Prototype Positioning System for LBS on U-campus

Strategy of Positioning for LBS on U-Campus