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Due to the energy and resource constraints of a wireless sensor node in a wireless sensor network (WSN), design of energy-efficient multipath routing protocols is a crucial concern for WSN applications. To provide high-quality monitoring information, many WSN applications require high-rate data transmission. Multipath routing protocols are often used to increase the network transmission rate and throughput. Although large-scale WSN can be supported by high bandwidth backbone network, the WSN remains the bottleneck due to resource constraints of wireless sensors and the effects of wireless interference. In this paper, we propose a multipath energy-efficient routing protocol for WSN that considers wireless interference. In the proposed routing protocol, nodes in the interference zone of the discovered path are marked and not allowed to take part in the subsequent routing process. In this way, the quality of wireless communication is improved because the effects of wireless interference can be reduced as much as possible. The network load is distributed on multiple paths instead of concentrating on only one path, and node energy cost is more balanced for the entire wireless network. The routing protocol is simulated in NS2 software. Simulation result shows that the proposed routing protocol achieves lower energy cost and longer network lifetime than that in the literature.

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License plate location is a key part of license plate recognition, and its positioning accuracy seriously determines the final result of license plate recognition. Firstly, the traditional license plate location methods are compared and analyzed. Secondly, in order to solve the localization problem in low resolution and multi-vehicle environment, a license plate method based on YOLOv5s was proposed by using deep learning image recognition technology, and data enhancement was introduced to improve YOLOv5s. Finally, the YOLOv5 algorithm is applied to real vehicle images under complex environmental conditions for experimental verification, and the results show that the accuracy is 99.12%. The experimental results show that the license plate location model based on YOLOv5S can effectively solve the influence of illumination, image quality and other factors on license plate location, improve the efficiency and accuracy compared with the traditional license plate location method, the algorithm has good robustness and fast calculation speed.

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