The high density of solar energy makes outdoor application of wireless sensor networks (WSNs) possible. In recent years, some advanced technologies have emerged in solar WSNs. However, there are still some problems: (1) existing energy prediction algorithms are difficult to deploy in scenarios with many small nodes in different locations; (2) how and when to adjust network topology. To solve the two problems, this paper proposes a solar WSN adaptation framework (SWAF). SWAF includes the proposed shadow judgment method (SJM) based on geographic and geometric theories and the solar-aware routing strategy (SAR) based on integer programming. SJM is a key adaptation algorithm that facilitates the integration of existing prediction techniques and enables the solar WSN to be deployed in practice. SAR is modeled with the goal of reducing node mortality. The AKS-based algorithm is introduced to cope with the expansion of network scales. Experimental results show that SJM can improve the accuracy by 20%-50%. The further introduction of SAR can prolong network lifetime by 12%-35%.

太阳能的高密度使无线传感器网络 (WSN) 的户外应用成为可能。近年来，太阳能无线传感器网络中出现了一些先进技术。但是，仍然存在一些问题：（1）现有的能量预测算法难以部署在不同位置的小节点较多的场景中；(2) 如何以及何时调整网络拓扑。针对这两个问题，本文提出了一种太阳能无线传感器网络适配框架（SWAF）。SWAF 包括提出的基于地理和几何理论的阴影判断方法 (SJM) 和基于整数规划的太阳能感知路由策略 (SAR)。SJM 是一种关键的自适应算法，可促进现有预测技术的集成，并使太阳能 WSN 能够在实践中部署。SAR 的建模目标是降低淋巴结死亡率。引入基于AKS的算法来应对网络规模的扩大。实验结果表明，SJM 可以将准确率提高 20%-50%。SAR的进一步引入可以将网络寿命延长12%-35%。