In vehicular ad hoc networks (VANETs), cellular vehicle-to-everything (C-V2X) is an emerging technology for communications between vehicle-to-infrastructure, vehicle-to-pedestrian, and vehicle-to-network which improves traffic efficiency, road safety, and the availability of infotainment services. Herein, a novel V2V-enabled resource allocation scheme based on C-V2X technology is proposed to improve the reliability and latency of VANETs. The key idea is that V2V communications based on cellular-V2X technology among vehicles remove the contention latency and can assist for longer distance communications. Particularly, we propose a hybrid architecture, where the V2V links are controlled by the cellular eNodeB in the overlay scheme. In this scheme, every vehicle periodically checks its packet lifetime and requests the cellular eNodeB to determine V2V links. The optimum resource allocation problem at the cellular eNodeB is to choose optimum receiver vehicles to determine V2V links and allocate suitable channels to minimize the total latency. This problem is equivalent to the maximum weighted independent set problem (MWIS-AW) with associated weights, which is NP-hard. In order to compute the weights, an analytical approach is developed to model the expected latency and packet delivery ratio. Moreover, a greedy cellular-based V2V link selection algorithm is proposed to solve MWIS-AW problem and develop a theoretical performance lower bound. Simulation results show that the proposed scheme significantly outperforms the existing schemes in terms of latency, throughput, and packet delivery ratio.

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net’s performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges.

With the recent advancement of vehicular ad-hoc networks (VANETs) or the internet of vehicles (IoVs), vehicles are getting more powerful and generating huge amount of traffic data, including computation-intensive and delay-sensitive applications in the vehicular edge computing (VEC) networks, which are difficult to be processed by an individual vehicular node. These resource-demanding tasks can be transferred to another vehicular node with idle computing resources for processing. Due to high mobility and limited resources of vehicular nodes, it is challenging to execute lengthy computation-intensive tasks until completion within the delay constraint. There is a need to provide an efficient task offloading strategies to support these applications. In this paper, an efficient distributed task offloading scheme is proposed to select nearby vehicles with idle computing resources, to process the tasks in parallel by considering some vital metrics, including link reliability, distance, available computing resources, and relative velocity. In order to complete the lengthy computation-intensive tasks in vehicular edge computing networks, a task is divided into several subtasks before offloading. The performance of the proposed scheme is evaluated in several VEC network conditions. Results show that the proposed computation task offloading scheme achieves better performance in latency, throughput, resource utilization and packet delivery ratio than the existing schemes.