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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks
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Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

机译:节能数据采集,可在无线传感器网络中进行准确的信号估计

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Long‐term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long‐term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point‐source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption.
机译:长期监视环境是大多数无线传感器网络的基本要求。由于传感器节点的能量预算有限,因此延长使用寿命至关重要,以便进行长期监控。此外,许多应用需要传感器节点来获得对点源信号的准确估计(例如,动物叫声或地震活动)。通常,多个传感器节点会同时采样,然后协作以估计事件信号。合作节点的选择对于减少估计误差,同时节省网络能量很重要。在本文中,我们提出了一种用于传感器数据采集和信号估计的新方法,该方法考虑了估计精度,节能和能量平衡。该方法使用“虚拟群集”的概念,形成了具有相同时空属性的传感器节点组。使用两种算法来提供功能。 “分布式形成”算法会自动形成虚拟集群并对其进行分类。 “循环采样方案”安排虚拟群集依次采样事件信号。当与广义感测模型一起使用时,通过分析和仿真评估该方法的估计误差和能耗。结果表明,该方法可以在减少和平衡能量消耗的同时实现改进的信号估计。

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