LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Algorithmic issues in modeling motion
ACM Computing Surveys (CSUR)
A Framework for Generating Network-Based Moving Objects
Geoinformatica
On the Generation of Time-Evolving Regional Data
Geoinformatica
Moving Objects Information Management: The Database Challenge
NGITS '02 Proceedings of the 5th International Workshop on Next Generation Information Technologies and Systems
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Oporto: A Realistic Scenario Generator for Moving Objects
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
A Stop-or-Move Mobility model for PCS networks and its location-tracking strategies
Computer Communications
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The issue of standardized generation scheme of spatio- temporal datasets is a research area of growing importance. In case of the lack of large real datasets, especially, benchmarking spatio-temporal database requires the generation of synthetic datasets simulating the real-word behavior of spatial objects that move and evolve over time. Recently, a few studies have been conducted on the generation of artificial datasets from a different point of view. For more realistic datasets, this paper proposes a novel framework, called state-based movement framework (SMF) to provide more generalized framework for both describing and generating the movement of complexly moving objects which simulate the movement of real-life objects. Based on Markov chain model, a well-known stochastic model, the proposed model classifies the whole trajectory of a moving object into a set of movement state. From some illustrative examples, we show that the proposed scheme is able to generate various realistic datasets with respect to the given input parameters.