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首页> 外文期刊>Vehicular Technology, IEEE Transactions on >Predictive Data Delivery to Mobile Users Through Mobility Learning in Wireless Sensor Networks
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Predictive Data Delivery to Mobile Users Through Mobility Learning in Wireless Sensor Networks

机译:通过无线传感器网络中的移动性学习向移动用户进行预测性数据传输

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摘要

We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smartphone-class device require access to sensor network data measured in their proximity. Specifically, we focus on efficient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes with which the user is likely to associate. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, i.e., , which uses the predictions to minimize network and energy overheads of packet transmissions. We show that data stashing significantly decreases routing cost for delivering data from stationary sensor nodes to multiple mobile users compared with routing protocols where sensor nodes immediately deliver data to the last known association nodes of mobile users. We also show that the scheme provides better load balancing, avoiding collisions and consuming energy resources evenly throughout the network, leading to longer overall network lifetime. Finally, we demonstrate that even limited knowledge of the location of future users can lead to significant improvements in routing performance.
机译:我们考虑了诸如室内导航,疏散或定向广告之类的应用,其中配备了智能手机级设备的移动用户需要访问在其附近测量的传感器网络数据。具体来说,我们专注于静态传感器与位置变化的用户之间的有效通信协议。我们的主要贡献是为每个用户预测一组可能的未来路径,并将数据存储在用户可能与之关联的传感器节点上。我们使用用户与静态传感器节点之间的无线电连接的历史数据来预测未来的用户节点关联,并提出一种网络优化过程,即使用该预测来最大程度地减少数据包传输的网络和能源开销。我们显示,与其中传感器节点立即将数据传递到移动用户的最后一个已知关联节点的路由协议相比,数据存储显着降低了从固定传感器节点向多个移动用户传递数据的路由成本。我们还表明,该方案提供了更好的负载平衡,避免了冲突,并在整个网络中均匀地消耗了能源,从而延长了整个网络的使用寿命。最后,我们证明,即使对未来用户位置的了解有限,也可以显着提高路由性能。

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