传感网络中误差有界的分段逼近数据压缩算法

Piecewise Approximation Based Data Compression Algorithm with Error Bound in Wireless Sensor Networks

无线传感器网络通常能量、带宽有限.一个关键而实用的需求是,在保证数据质量的情况下,对持续到达的采样数据进行在线式压缩.主要贡献:①利用传感器节点内置的缓冲区,提出了单传感器节点上基于分段常量逼近的准在线式数据压缩算法(PCADC-sensor),并给出了在无穷范数误差度量下的实现;②提出了单传感器节点上基于分段线性逼近的在线式数据压缩算法(PLADC-sensor).分别在无穷范数和2范数误差度量的情况下给出了计算PLA的两种简单快速算法,推导了分段线性一致逼近的充要条件;③簇头或基站无需接收原始采样数据,提出了基于原始数据的分段线性表示的压缩算法(PLRDC-cluster),推导了同一节点不同时段、不同节点相同时段两种情况下的计算公式.实验结果表明,这些算法较好地匹配了传感器数据流模型,显著减少了冗余数据传输.

Wireless sensor networks usually have limited energy and transmission capacity. A critical and practical demand is to online compress sensor data streams continuously. This paper makes the following contributions. First, using the built-in buffer of sensor node, a piecewise constant approximation based data compression algorithm with infinite norm error bound is presented, which is named PCADC-sensor and is a near online algorithm. Second, with infinite norm and square norm error bound respectively, this study proposes two online piecewise linear approximation based data compression algorithms in sensor node, named PLADC-sensor. A necessary and sufficient condition of PLA uniform approximation is given. Third, a piecewise linear representations based data compression algorithm in cluster head or sink, named PLRDC-cluster is presented. It does not need raw sensory data and can be applied to calculate aggregate functions. Last, the experiments on real-world sensor dataset show that the proposed algorithms match the sensor data stream model and can achieve significant data reduction.