School of Computer Science and Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China.
Key Laboratory of Intelligent IoT, Luoyang Institute of Science and Technology, Luoyang 471023, China.
Sensors (Basel). 2018 Nov 2;18(11):3749. doi: 10.3390/s18113749.
When the nodes in the network are deployed in the target area with an appropriate density, the effective aggregation and transmission of the data gathered in the monitoring area remain to be solved. The existing Compressed Sensing (CS) based on data aggregation schemes are accomplished in a centralized manner and the Sink node achieves the task of data aggregation. However, these existing schemes may suffer from load imbalance and coverage void issues. In order to address these problems, we propose a Compressed Sensing based on Fault-tolerant Correcting Data Aggregation (CS-FCDA) scheme to accurately reconstruct the compressed data. Therefore, the network communication overhead can be greatly reduced while maintaining the quality of the reconstructed data. Meanwhile, we adopt the node clustering mechanism to optimize and balance the network load. It is shown via simulation results, compared with other data aggregation schemes, that the proposed scheme shows obvious improvement in terms of the Fault-tolerant correcting capability and the network energy efficiency of the data reconstruction.
当网络节点以适当的密度部署在目标区域时,如何有效聚集和传输监测区域中采集的数据仍然有待解决。现有的基于数据聚集的压缩感知(CS)方案以集中式方式完成,汇聚节点完成数据聚集的任务。然而,这些现有的方案可能会存在负载不均衡和覆盖空洞的问题。为了解决这些问题,我们提出了一种基于容错纠错数据聚集的压缩感知(CS-FCDA)方案来准确地重构压缩数据。因此,在保持重构数据质量的同时,大大降低了网络通信开销。同时,我们采用节点聚类机制来优化和平衡网络负载。仿真结果表明,与其他数据聚集方案相比,所提出的方案在数据重构的容错纠错能力和网络能量效率方面有明显的改进。