Power consumption and data redundancy of wireless sensor networks (WSN) are widely considered for a distributed state monitoring network. For reducing the energy consumption and data amount, we propose a topology optimisation and an iterative parameter identification method for estimating the common model factors in WSN. The former method optimises the decentralised topology such that all the leaf nodes in a community connect to the head node directly. A circle topology is built to enable the remote leaf nodes to link to the head node through two adjoining relay nodes to reduce the whole communication distance and power consumption. Based on the optimised topology, an iterative identification method is proposed to minimise the information capacity by transmitting the processed results instead of raw data to reduce the data amount for calculation and storage. Then, we prove the consensus and convergence of the proposed identification method. Finally, two simulations verify the effectiveness of the proposed method and the comparative results present the data reduction for the on-board calculation, communication, and storage in the practical use of WSN.

无线传感器网络（WSN）的功耗和数据冗余被广泛考虑用于分布式状态监测网络。为了减少能耗和数据量，我们提出了一种拓扑优化和迭代参数识别方法来估计WSN中的公共模型因素。前一种方法优化了分散的拓扑结构，使得社区中的所有叶节点都直接连接到头节点。建立环形拓扑结构，使远端叶节点通过两个相邻的中继节点链接到头节点，以减少整个通信距离和功耗。基于优化的拓扑结构，提出了一种迭代识别方法，通过传输处理后的结果而不是原始数据来最小化信息容量，以减少计算和存储的数据量。然后，我们证明了所提出的识别方法的一致性和收敛性。最后，通过两次仿真验证了所提出方法的有效性，对比结果展示了无线传感器网络实际应用中车载计算、通信和存储的数据简化。