The rapid advancement of the Internet of Vehicles (IoV) has led to a massive growth in data received from IoV networks. The cloud storage has been a timely service that provides a vast range of data storage for IoV networks. However, existing data storage and access models used to manage and protect data in IoV networks have proven to be insufficient. They are centralized and usually accompanied by a lack of trust, transparency, security, immutability, and provenance. In this paper, we propose VBlock, a blockchain-based system that addresses the issues of illegal modification of outsourced vehicular data for smart city management and improvement. We introduce a novel collusion-resistant model for outsourcing data to cloud storage that ensures the network remains tamper-proof, has good data provenance and auditing, and solves the centralized problems prone to the single point of failure. We introduced a key revocation mechanism to secure the network from malicious nodes. We formally define the system model of VBlock in the setting of a consortium blockchain. Our simulation results and security analysis show that the proposed model provides a strong security guarantee with high efficiency and is practicable in the IoV environment.

Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platform’s expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.

Conventional visual inspection of concrete structures in Structural Health Monitoring (SHM) ascertains the health status of concrete structures but due to subjectivity and intense labor on the part of experts, the dynamics have almost shifted to automation which heavily dwells on detectors. Although object detectors accelerate the task of SHM, in concrete crack detection, some challenges include the asymmetrical shapes, hairline/salient cracks, and varying scales of cracks. To mitigate these challenges, we introduce image denoising and attention mechanisms as enhancements for a one-stage detector. An extensive experiment on the SDNET2018 dataset indicates improvement on the adapted detector which attains a little over 2% mAP than two baseline detectors.

Vision Transformers use self-attention techniques to learn long-range spatial relations to focus on the relevant parts of an image. They have achieved state-of-the-art results in many computer vision tasks. Recently, some methods have to leverage Vision Transformer-based models to tackle tasks in medical imaging. However, Vision Transformer emphasizes the low-resolution features due to the repetitive downsamplings, which result in a loss or lack of detailed localization information, making it highly unfit for low-level image recognition. In this paper, we investigate the performance of Vision Transformer on low-level medical images and contrast it with convolutional neural networks. The experimental results show that Convolutional Neural Network outperforms the Vision Transformer-based models on all four datasets.