Abstract
The high energy density of sunlight makes solar wireless sensor networks (WSN) have advantages in outdoor monitoring applications. Two key technologies are applied: 1) Solar energy prediction and 2) Energy-aware routing strategy. Affected by frequently changing weather, shadows of buildings, trees, and other factors, the accuracy of existing prediction algorithms is relatively low, and the lifetime is not very satisfactory when the existing predictions are used as a support basis for routing. Thus, this paper proposes a prediction algorithm based on revised machine learning, and propose an adjustable routing strategy to achieve long-term stable monitoring. Experimental results show that the accuracy of prediction can be increased by up to 72.7%, and the network lifetime can be extended by at least 9.8%.
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