Institute of Information and Control, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2010;10(10):9065-83. doi: 10.3390/s101009065. Epub 2010 Oct 11.
In many wireless sensor network applications, the possibility of exceptions occurring is relatively small, so in a normal situation, data obtained at sequential time points by the same node are time correlated, while, spatial correlation may exist in data obtained at the same time by adjacent nodes. A great deal of node energy will be wasted if data which include time and space correlation is transmitted. Therefore, this paper proposes a data compression algorithm for wireless sensor networks based on optimal order estimation and distributed coding. Sinks can obtain correlation parameters based on optimal order estimation by exploring time and space redundancy included in data which is obtained by sensors. Then the sink restores all data based on time and space correlation parameters and only a little necessary data needs to be transmitted by nodes. Because of the decrease of redundancy, the average energy cost per node will be reduced and the life of the wireless sensor network will obviously be extended as a result.
在许多无线传感器网络应用中，异常情况发生的可能性相对较小，因此在正常情况下，同一节点在连续时间点获得的数据是时间相关的，而相邻节点在同一时间获得的数据则可能存在空间相关性。如果传输包含时间和空间相关性的数据，将会浪费大量节点能量。因此，本文提出了一种基于最优顺序估计和分布式编码的无线传感器网络数据压缩算法。通过探索传感器获得的数据中包含的时间和空间冗余，接收器可以基于最优顺序估计获得相关参数。然后，接收器根据时间和空间相关参数来恢复所有数据，并且仅需要由节点传输少量必要的数据。由于冗余的减少，每个节点的平均能量消耗将会降低，从而明显延长无线传感器网络的寿命。
