An Implementation of Location Trajectory Data Reduction

Byon, Sungwon; Kwon, Eunjung; Jung, Eui-Suk; Lee, Yong-Tae

doi:10.1109/ictc.2018.8539709

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…In another aspect addressed in this work, it is noted that handling of multi-user location information over extended periods of time generates large volumes of spatio-temporal data, which highlights the need for compact data representations [25][26][27][28]. Processing techniques applied to proximity and community detection [29][30][31], and trajectory similarities and cluster formations [32][33][34] have also been of interest recently.…”

Section: Introductionmentioning

confidence: 99%

Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

2022

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The paper considers mobile location tracking and trajectory data reduction techniques for applications pertaining to mobile contact tracing and proximity detection in wireless cellular networks. Unscented Kalman filtering with non-line-of-sight bias mitigation is first applied for robust mobile trajectory estimation. An approach for modeling and analysis of pair-wise proximity and multi-mobile clustering scenarios is then introduced within a hypothesis testing framework, and a thorough performance evaluation is presented to assess the achievable detection and false alarm probabilities based on factors pertaining to proximity distance and timespan, ranging accuracy and bias statistics. For scenarios of practical interest, results show that correct proximity detection rates in excess of 70-to-80% range can be achieved while maintaining very low false alarm rates. Data reduction using the discrete Haar transform is subsequently applied for efficient storage of trajectory data. An analysis of the tradeoffs between reduction level and proximity detection reliability is presented to demonstrate the viability of the proposed approach with its low complexity and good performance at moderate reduction levels. Additional comparative analysis is presented to assess the impact of specific distance measures and wavelet types, and it is found that the Chebyshev distance offers improvements in detection accuracy compared to Euclidean and Manhattan measures, while wavelet change, when retaining short support, didn't have a significant impact.

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Section: Introductionmentioning

confidence: 99%

Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

2022

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A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

Diri,

Yildirim

2024

Arab J Sci Eng

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Highly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e$$-$$ - 04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential.

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An Implementation of Location Trajectory Data Reduction

Cited by 2 publications

References 3 publications

Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

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