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.

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.

SPIE Digital Library Proceedings

Waypoints have enhanced the prospect of fully autonomous drone applications. However, Geographical Position System (GPS) spoofing and signal interferences are key issues in waypoint-based drone applications. Also, conceptual waypoint-based drone applications require accurate awareness of waypoints based on environmental cues and integration of additional sensing modalities. Additional sensor modalities may overwhelm drones’ processing resources, reducing operational time. This study proposes W-MobileNet, a denoising model for autonomous trajectory trail navigation based on precision control of a path planner, denoising capabilities of Weiner filters, and perceptual knowledge of convolutional neural networks. Creatively integrating the modules of W-MobileNet results in an intuitive drone navigation controller characterized by position, orientation, and speed estimation. Further, a generic loss function that significantly aids models to converge faster during training is proposed based on adaptive weights. An extensive evaluation of a simulated and real-world experiment shows that W-MobileNet is more favorable in precision and robustness than contemporary state-of-the-art models. W-MobileNet has the potential to become one of the standards for autonomous drone applications.

Resource-constrained edge nodes are ubiquitous in industrial settings yet are challenged by limited computing resources. Leveraging computational advantage and perceptual awareness of Gabor filters, a hybrid classifier to mitigate computational requirements in the context of classification of key components of civil structures which are essential in the proactive structural assessment, specific repairs, and maintenance post-construction is given. Deployment of the hybrid classifier using the CoreML framework exhibits favorable classification accuracy and robustness as compared to contemporary state-of-the-art classifiers.