中文摘要：

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．

英文摘要：

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.