The concept of smart city architecture requires a comprehensive solution that can combine real-time response applications for cyber-physical systems. However, the architecture faces challenges that can obstruct the operations in terms of systems, processes, and data flow as far as the breach risk is concerned. Though the field has been researched with the existence of centralized and distributed architectures to support smart cities. Research gaps regarding security concerns, platform assistance, and resource management continue to persist. This research article presents a novel blockchain-based architecture that proposes expansion in the non-fungible tokens (NFTs) to cater to the expansion of IoT-enabled smart assets. It enables NFTs to employ fog computing for all users and smart devices connected to a fog node in a cyber-physical system. The proposed expansion suggested in Non-Fungible Tokens (NFTs) for IoT assets representation in a cyber-physical system, provides devices and user identification and authentication functionality. The proposed NFT architecture has been designed to provide a smart city solution for cyber-physical systems that ensures robust security features (such as CIA) by introducing new attributes and functions for Owner, User, Fog, and IoT device/s authentication. The validation and rigor of the security services, efficiency, and latency have been achieved by deployments on private and public ledgers. The efficiency, and cost-effectiveness of the suggested functions and components have been evaluated in terms of evaluation cost and time complexity which resulted in promising results, obtained and validated on a testnet. The evaluation cost for the devised mint component was approximately 81%, and devisedapprove()was approximately 23% more efficient than other solutions.

With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing other non-plant objects such as color charts, barcodes, and labels. Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity of leaf trait extraction and analysis. Focusing on segmentation and trait extraction on individual intact herbarium leaves, this study proposes a pipeline consisting of deep learning semantic segmentation model (DeepLabv3+), connected component analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits. The proposed method achieved a higher F1-score for both the in-house dataset (96%) and on a publicly available herbarium dataset (93%) compared to object detection-based approaches including Faster R-CNN and YOLOv5. Furthermore, using the proposed approach, the phenotypic measurements extracted from the segmented individual leaves were closer to the ground truth measurements, which suggests the importance of the segmentation process in handling background noise. Compared to the object detection-based approaches, the proposed method showed a promising direction toward an autonomous tool for the extraction of individual leaves together with their trait data directly from herbarium specimen images.

The blueprint of the proposed Decentralized Smart City of Things (DSCoT) has been presented with smart contracts development and deployment for robust security of resources in the context of cyber-physical systems (CPS) for smart cities. Since non-fungibility provided by the ERC721 standard for the cyber-physical systems (CPSs) components such as the admin, user, and IoT-enabled smart device/s in literature is explicitly missing, the proposed DSCoT devised the functionality of identification and authentication of the assets. The proposed identification and authentication mechanism in cyber-physical systems (CPSs) employs smart contracts to generate an authentication access code based on extended non-fungible tokens (NFTs), which are used to authorize access to the corresponding assets. The evaluation and development of the extended NFT protocol for cyber-physical systems have been presented with the public and private blockchain deployments for evaluation comparison. The comparison demonstrated up to 96.69% promising results in terms of execution cost, efficiency, and time complexity compared to other proposed NFT-based solutions.