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Tracking-Based Trajectory Data Reduction in Wireless Sensor Networks

机译:无线传感器网络中基于跟踪的轨迹数据缩减

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This work addresses the problem of balancing the trade-off between the energy expenses due to communication vs. the accuracy of the trajectories representation in Wireless Sensor Networks (WSN) where the spatio-temporal data is obtained by tracking. We consider some of the approaches used by the Moving Objects Databases (MOD) and Computational Geometry (CG) communities, and we demonstrate that, with appropriate modifications, they can yield benefits in WSN in terms of energy savings. Towards that, we developed distributed algorithms that implement the Dead-Reckoning policy for managing the transient location-in-time information of mobile entities, whose localization is done by tracking sensors. In addition, we developed a distributed variant of the Douglas-Peuker heuristic for polyline reduction from CG literature, augmented with temporal awareness. Our experiments demonstrate the benefits in terms of reducing the communication overheads, while keeping the error boundaries at acceptable levels. Although it may seem counter-intuitive at first, we also demonstrate that an attempt to merge the Dead-Reckoning and Douglas-Peuker approaches, need not yield additional improvements of the in-network energy savings, due to the complementary nature of the data reduction in the two approaches.
机译:这项工作解决了在通信引起的能量消耗与无线传感器网络(WSN)中轨迹表示的准确性之间进行权衡的问题,无线传感器网络通过跟踪获得时空数据。我们考虑了移动对象数据库(MOD)和计算几何(CG)社区使用的一些方法,并且我们证明了通过适当的修改,它们可以在WSN上带来节能方面的好处。为此,我们开发了分布式算法,这些算法实现了Dead-Reckoning策略,用于管理移动实体的瞬时时间位置信息,其定位由跟踪传感器完成。此外,我们从CG文献中开发了Douglas-Peuker启发式算法的分布式变体,以减少折线,并增强了时间意识。我们的实验证明了在减少通信开销的同时将错误边界保持在可接受水平的好处。尽管乍看起来似乎是违反直觉的,但我们还证明,由于数据缩减的互补性,尝试将Dead-Reckoning和Douglas-Peuker方法进行合并,并不需要进一步提高网络内的节能效果在两种方法中。

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