Institute of Information and Control, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2010;10(10):9084-101. doi: 10.3390/s101009084. Epub 2010 Oct 11.
A kind of data compression algorithm for sensor networks based on suboptimal clustering and virtual landmark routing within clusters is proposed in this paper. Firstly, temporal redundancy existing in data obtained by the same node in sequential instants can be eliminated. Then sensor networks nodes will be clustered. Virtual node landmarks in clusters can be established based on cluster heads. Routing in clusters can be realized by combining a greedy algorithm and a flooding algorithm. Thirdly, a global structure tree based on cluster heads will be established. During the course of data transmissions from nodes to cluster heads and from cluster heads to sink, the spatial redundancy existing in the data will be eliminated. Only part of the raw data needs to be transmitted from nodes to sink, and all raw data can be recovered in the sink based on a compression code and part of the raw data. Consequently, node energy can be saved, largely because transmission of redundant data can be avoided. As a result the overall performance of the sensor network can obviously be improved.
本文提出了一种基于次优聚类和簇内虚拟地标路由的数据压缩算法。首先，消除了同一节点在连续瞬间获取的数据中的时间冗余。然后对传感器网络节点进行聚类。基于簇头建立簇内虚拟节点地标。通过结合贪婪算法和泛洪算法实现簇内路由。第三，建立基于簇头的全局结构树。在节点到簇头以及簇头到汇聚节点的数据传输过程中，消除数据中的空间冗余。仅需从节点向汇聚节点传输原始数据的一部分，并且可以根据压缩码和原始数据的一部分在汇聚节点恢复所有原始数据。因此，可以节省节点能量，这主要是因为可以避免传输冗余数据。从而可以明显提高传感器网络的整体性能。
