Data generated by sensors are inherently apt to spatial and temporal redundancy owing to the proximity of sensors that could sense the same environment or react to the same event. The massively generated data leads to reducing the life span of the sensors in particular, and the network in general. To minimize the effect of such generated data, we develop an approach to reducing the spatial and temporal data redundancy while maintaining the life of the sensor that results in prolonging the lifetime of the network with balancing data reliability. The proposed approach relies on two levels. The first level represents the end node, and it is responsible for reducing the temporal data redundancy and minimizing the data transmission using the Kalman filter for data estimation. The second level represents the sink or base station, which works in synchronization with the end nodes. This level is responsible for minimizing the spatial data redundancy based on two algorithms, namely Sink Level Grouping Algorithm (SLGA) and Sink Level Aggregation Algorithm (SLAA). The obtained results demonstrate that the proposed approach outperformed Prefix Frequency Filtering (PFF) and Redundancy Elimination Data Aggregation (REDA) algorithms in terms of spatial and temporal data redundancy and accuracy with acceptable results of energy consumption.

传感器生成的数据由于可以感应相同环境或对相同事件做出反应的传感器的接近性，因而固有地倾向于空间和时间冗余。大量生成的数据导致特别是缩短传感器的寿命，并缩短整个网络的寿命。为了最大程度地减少此类生成的数据的影响，我们开发了一种在保持传感器寿命的同时减少空间和时间数据冗余的方法，从而可以在平衡数据可靠性的同时延长网络的使用寿命。提议的方法有两个层次。第一级代表端节点，它负责减少时间数据冗余并使用卡尔曼滤波器进行数据估计，以最小化数据传输。第二层代表接收器或基站，与终端节点同步工作。该级别负责基于两种算法（即接收器级别分组算法（SLGA）和接收器级别聚合算法（SLAA））最小化空间数据冗余。获得的结果表明，该方法在时空数据冗余性和准确性方面都优于前缀频率过滤（PFF）和冗余消除数据聚合（REDA）算法，并且在能耗方面可以接受。