Improved Algorithm for Distributed Points Positioning Using Uncertain Objects Clustering

• Main Authors: Mirko Köhler, Tomislav Galba, Ivica Lukić
• Format: Article
• Language: English
• Published: Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2019-01-01
• Series: Tehnički Vjesnik
• Subjects: clustering; data mining; Euclidian distance; information service; positioning
• Online Access: https://hrcak.srce.hr/file/320392
• ISSN: 1330-3651; 1848-6339

Positioning of mobile objects that require communication with some kind of online service application is a very challenging task. Proper positioning with minimal deviation is an important mobile service system (MSS), e.g. taxi service used in this paper. It will perform all tasks for the users and reduce the overall travel distance. This paper is focused on the development of an algorithm that will find the optimal position for an MSS object and upgrade the system quality using uncertain data clustering. If the best position for the MSS is found, then the response time is short, and the system tasks could also be performed in usable time. The improved bisector pruning method is used for clustering stored data of mobile service system objects to provide the best position of system objects. As the best position of MSS objects, we use cluster centres. Using clustering, the total expected distance from end users to the service system is minimal. Therefore, the MSS is more efficient and has more time to fulfil additional tasks at the same time.