An All-Time-Domain Moving Object Data Model, Location Updating Strategy, and Position Estimation. (1st November 2015)

• Record Type: Journal Article
• Authors: Wu, Qunyong; Huang, Junyi; Luo, Jianping; Yang, Jianjun
• Other Names: Cai Lin Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2015)
• Publication Date: 2015-11-01
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/463749
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : To solve the problems from the existing moving objects data models, such as modeling spatiotemporal object continuous action, multidimensional representation, and querying sophisticated spatiotemporal position, we firstly established an object-oriented all-time-domain data model for moving objects. The model added dynamic attributes into object-oriented model, which supported all-time-domain data storage and query. Secondly, we proposed a new dynamic threshold location updating strategy. The location updating threshold was given dynamically in accordance with the velocity, accuracy, and azimuth positioning information from the GPS. Thirdly, we presented several different position estimation methods to estimate the historical location and future location. The cubic Hermite interpolation function is used to estimate the historical location. Linear extended positioning method, velocity mean value positioning method, and cubic exponential smoothing positioning method were designed to estimate the future location. We further implemented the model by abstracting the data types of moving object, which was established by PL∖SQL and extended Oracle Spatial. Furthermore, the model was tested through the different moving objects. The experimental results illustrate that the location updating frequency can be effectively reduced, and thus the position information transmission flow and the data storage were reduced without affecting the moving objects trajectory precision.