Abstract: Aiming at the data gathering optimization problem in energy harvesting Wireless Sensor Network (WSN), a scheme based on dynamic sensor node sampling rate and data compression was proposed, where the spatial-temporal characteristics of energy harvested by individual sensor node was considered. To maximize the total amount of sampling data in the network, first, according to the neighbor information of the nodes, a local compression algorithm was proposed to determine the optimal compression strategy. Considering the data receiving and forwarding energy consumption of the node based on its topological position in the data aggregation tree, its sampling rate was gradually increased until its total energy consumption reached the collection energy consumption threshold. After that, a global optimization problem of network performance was constructed, and a heuristic algorithm was proposed. By iteratively solving linear programming problems, the optimal sampling rate and compression scheme were obtained. The experimental results show that compared with the existing adaptive sensing and compression rate selection scheme, the proposed two data collection optimization algorithms can maintain more stable sensor node battery levels and achieve higher network performance.

Key words: data gathering, sampling rate optimization, data compression, energy harvesting, Wireless Sensor Network (WSN)

摘要： 针对能量采集无线传感网络（WSN）中的数据收集优化问题，考虑传感器节点能量采集的时空变化特性，提出一种基于节点动态采样速率和数据压缩的策略，以实现网络中采样数据总量的最大化。首先，提出一种根据节点的邻居信息决定其最优压缩策略的本地压缩算法，基于节点在数据汇聚树中的拓扑位置考虑其数据接收和转发能耗，逐渐增加其采样速率直到其总能耗到达采集能耗阈值。接着构造网络性能的全局优化问题并提出一种启发式的算法，通过迭代求解线性规划问题计算最优的采样速率和压缩策略。实验结果表明，与现有的自适应传感和压缩率选择方案相比，所提出的两种数据收集优化算法能够维持更加稳定的传感器节点电量水平并实现更高的网络性能。

关键词: 数据收集, 采样速率优化, 数据压缩, 能量采集, 无线传感器网络