首页> 外文会议>2011 IEEE International Conference on Communications >A New Ultra-Low Power Wireless Sensor Network with Integrated Energy Harvesting, Data Sensing, and Wireless Communication
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A New Ultra-Low Power Wireless Sensor Network with Integrated Energy Harvesting, Data Sensing, and Wireless Communication

机译:集成了能量收集,数据传感和无线通信功能的新型超低功耗无线传感器网络

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A new ultra-low power (ULP) wireless sensor network (WSN) structure is proposed to monitor the vibration properties of civil structures, such as buildings and bridges. The new scheme integrates energy harvesting, data sensing, and wireless communication into a unified process, and it is fundamentally different from all the existing WSNs. In the new WSN, piezoelectric sensors are employed to harvest vibration energy and measure vibration intensity simultaneously, by utilizing the fact that the harvested energy accumulated through time is proportional to the vibration amplitude and frequency. Once the harvested energy reaches a threshold, it is released as an impulse with a wireless transmitter. An estimate of the structure vibration intensity can then be obtained by measuring the intervals between the binary impulses. Such an approach does not require complicated analog-to-digital conversion or signal processing, and it can achieve an ULP performance unrivaled by existing technologies. Optimum and sub-optimum impulse density estimation algorithms are proposed for the FC to take advantage of the spatial correlation among the sensors. Exact analytical expressions of the optimum estimation mean square error (MSE) are derived. Simulation and analytical results demonstrate that the proposed scheme can achieve a MSE of $5times 10^{-5}$ at a signal-to-noise-ratio of -8 dB for a 10-node WSN.
机译:提出了一种新的超低功耗(ULP)无线传感器网络(WSN)结构,以监视诸如建筑物和桥梁之类的土木结构的振动特性。新方案将能量收集,数据感测和无线通信集成到一个统一的过程中,并且与所有现有的WSN都有根本的不同。在新的无线传感器网络中,利用压电传感器来收集振动能量并同时测量振动强度,这是利用了随着时间的推移积累的能量与振动幅度和频率成比例的事实。一旦收集到的能量达到阈值,它就会作为脉冲与无线发射器一起释放。然后可以通过测量二进制脉冲之间的间隔来获得结构振动强度的估计值。这种方法不需要复杂的模数转换或信号处理,并且可以实现现有技术无法比拟的ULP性能。针对FC,提出了最优和次最优脉冲密度估计算法,以利用传感器之间的空间相关性。推导了最佳估计均方误差(MSE)的精确解析表达式。仿真和分析结果表明,对于10节点WSN,在-8 dB的信噪比下,该方案可以实现5乘以10 ^ {-5} $的MSE。

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