Wu Mou, Tan Liansheng, Xiong Naixue
Department of Computer Science, Central China Normal University, Wuhan 430079, China.
School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China.
Sensors (Basel). 2014 Dec 25;15(1):248-73. doi: 10.3390/s150100248.
One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.
无线传感器网络（WSNs）中最广泛且重要的应用之一是连续数据采集，例如监测环境温度和湿度的变化。由于传感器节点的能量供应有限，在对物理现象进行连续观测时降低能量消耗对于延长无线传感器网络的寿命起着重要作用。然而，由于传感器节点过度部署，传感数据的高度冗余导致了能量的大量浪费。在本文中，我们开发了一种结构保真度数据采集（SFDC）框架，利用节点之间的空间相关性来减少活跃传感器节点的数量，同时保持所采集数据的低结构失真。基于图像质量评估方法的结构失真用于执行节点的工作/睡眠调度，以便在采样期间减少工作节点的数量，同时其余节点可以进入低功耗睡眠模式。SFDC的主要贡献在于为如何在无线传感器网络的连续传感应用中基于结构相似性保持数据保真度提供了独特的视角。基于合成数据集和真实世界数据集的仿真结果验证了SFDC框架在节能和数据保真度方面的有效性。
