Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
Mobile anchor-free localization for wireless sensor networks
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
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The problem of reconstruction and mining object trajectories is of interest in the applications of mining transport enterprise data concerning with the route followed by its delivery vans in order to optimize time and space deliveries. The paper investigates the case of Wireless Sensor Network (WSN) technology, not primarily designed for localization, and reports a technique based on recurrent neural networks to reconstruct the trajectory shape of a moving object (a sensor on a Lego train) from the sensor accelerometer data and to recover its localization. The obtained patterns are thus mined to detecting periodic or frequent patterns, exploiting a recently proposed technique based on clustering algorithms and associative rules to assert the ability of the proposed approach to track WSN mote localizations.