The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

Segmentation of trees in urban settings is important for tracking vegetation and managing green infrastructure in urban areas. However, there are numerous factors that make it difficult to obtain precise results, such as differences in tree species, season, and environmental variables. Street-view imagery can provide a valuable database to be used for the detection and identification of trees. Unfortunately, the process of labelling images manually is time-consuming and inconsistent. In recent years, deep learning models have provided better automation and precision. However, difficulties in obtaining results persist due to factors such as occlusion, background, and changes in lighting conditions. Therefore, this paper presents an overview of the current trends in the use of deep learning for the detection and segmentation of trees. Specifically, the following aspects will be discussed in this work: preprocessing of images, semantic and instance segmentation, models based on deep learning, and transformers. This literature review also analyzes current datasets, evaluation measures, and applications, offering knowledge about existing challenges and future directions for further studies. The article is designed to be a source of reference for scientists and practitioners working on problems related to urban vegetation classification and computer vision.