National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China.
Sensors (Basel). 2018 Sep 24;18(10):3221. doi: 10.3390/s18103221.
Reliability and energy efficiency are two key considerations when designing a compressive sensing (CS)-based data-gathering scheme. Most researchers assume there is no packets loss, thus, they focus only on reducing the energy consumption in wireless sensor networks (WSNs) while setting reliability concerns aside. To balance the performance⁻energy trade-off in lossy WSNs, a distributed data storage (DDS) and gathering scheme based on CS (CS-DDSG) is introduced, which combines CS and DDS. CS-DDSG utilizes broadcast properties to resist the impact of packet loss rates. Neighboring nodes receive packets with process constraints imposed to decrease the volume of both transmissions and receptions. The mobile sink randomly queries nodes and constructs a measurement matrix based on received data with the purpose of avoiding measuring the lossy nodes. Additionally, we demonstrate how this measurement matrix satisfies the restricted isometry property. To analyze the efficiency of the proposed scheme, an expression that reflects the total number of transmissions and receptions is formulated via random geometric graph theory. Simulation results indicate that our scheme achieves high precision for unreliable links and reduces the number of transmissions, receptions and fusions. Thus, our proposed CS-DDSG approach effectively balances energy consumption and reconstruction accuracy.
在设计基于压缩感知 (CS) 的数据采集方案时，可靠性和能效是两个关键考虑因素。大多数研究人员假设没有数据包丢失，因此，他们只关注在将可靠性问题搁置一旁的情况下降低无线传感器网络 (WSN) 的能量消耗。为了在有丢包的 WSN 中平衡性能-能量折衷，引入了一种基于 CS 的分布式数据存储 (DDS) 和采集方案 (CS-DDSG)，它结合了 CS 和 DDS。CS-DDSG 利用广播特性来抵抗数据包丢失率的影响。邻居节点接收带有过程约束的数据包，以减少传输和接收的数量。移动汇聚节点随机查询节点，并根据接收的数据构建一个测量矩阵，目的是避免测量有丢包的节点。此外，我们展示了这种测量矩阵如何满足受限等距性质。为了分析所提出方案的效率，通过随机几何图理论，制定了一个反映总传输和接收数量的表达式。仿真结果表明，我们的方案在不可靠链路中实现了高精度，并减少了传输、接收和融合的数量。因此，我们提出的 CS-DDSG 方法有效地平衡了能量消耗和重构准确性。
