Sparsest Random Sampling for Cluster-Based Compressive Data Gathering in Wireless Sensor Networks
Sparsest Random Sampling for Cluster-Based Compressive Data Gathering in Wireless Sensor Networks
复制标题
无线传感器网络中基于集群的压缩数据收集的最稀疏随机采样
DOI：
10.1109/access.2018.2846815
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Zhi
中科院分区：
文献类型：
--
作者：
Sun, Peng;Wu, Liantao;Wang, Zhi
压缩数据收集（CDG）已被认为是一种有前途的技术，收集传感器网络（WSNs）的传感器数据，降低能源成本和更好的流量负载平衡。此外，通常将分簇集成到CDG中以进一步提高网络性能。然而，现有的基于簇的CDG方法通常需要大量的传感器节点参与每个压缩感知（CS）测量收集，并且很少考虑由于功率耗尽或恶意攻击而可能导致的节点故障，导致能量效率不足和系统鲁棒性差。在本文中，我们提出了一个稀疏的随机抽样方案，基于簇的CDG（SRS-CCDG）在无线传感器网络中实现能源效率和强大的数据收集。具体而言，传感器节点被组织成集群。在每一轮数据收集中，传感器节点的随机子集感测监测到的字段，并将其测量结果发送到相应的簇头（CH）。然后，每个CH将在其集群内收集的数据发送到信宿。在SRS-CCDG中，每个传感器阅读被视为一个CS测量，并且簇内和簇间数据传输都可以通过两种方法来实现，即，中继或直接传输。此外，我们提出了分析模型，研究集群的大小和使用不同的簇内和簇间传输方案时的能量成本之间的关系，旨在找到最佳的集群和传输方案，可以导致最小的能量成本。然后，在理论分析的基础上，提出了一种集中式聚类算法。最后，我们研究了SRS-CCDG在节点失效时信号恢复性能的鲁棒性。大量的仿真结果表明，SRS-CCDG可以显着降低能量成本，提高系统的鲁棒性节点故障。
Compressive data gathering (CDG) has been recognized as a promising technique to collect sensory data in wireless sensor networks (WSNs) with reduced energy cost and better traffic load balancing. Besides, clustering is often integrated into CDG to further facilitate the network performance. However, existing cluster-based CDG methods generally require a large number of sensor nodes to participate in each compressive sensing (CS) measurement gathering and rarely consider possible node failures due to power depletion or malicious attacks, leading to insufficient energy efficiency and poor system robustness. In this paper, we propose a sparsest random sampling scheme for cluster-based CDG (SRS-CCDG) in WSNs to achieve energy efficient and robust data collection. Specifically, sensor nodes are organized into clusters. In each round of data gathering, a random subset of sensor nodes sense the monitored field and transmit their measurements to the corresponding cluster heads (CHs). Then, each CH transmits the data gathered within its cluster to the sink. In SRS-CCDG, each sensor reading is regarded as one CS measurement, and both intra-cluster and inter-cluster data transmissions can be realized by two methods, i.e., relaying or direct transmission. Furthermore, we propose analytical models that study the relationship between the size of clusters and the energy cost when using different intra-cluster and inter-cluster transmission schemes, aimed at finding the optimal size of clusters and transmission schemes that could lead to minimum energy cost. Then, we present a centralized clustering algorithm based on the theoretical analysis. Finally, we investigate the robustness of signal recovery performance of SRS-CCDG when node failures happen. Extensive simulations demonstrate that SRS-CCDG can significantly reduce the energy cost and improve the system robustness to node failures.