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The emerging field of the Internet of Vehicles (IoV) has garnered significant attention due to its potential to revolutionize transportation and mobility. IoV enables the development of innovative services and applications that can enhance the efficiency, safety, and sustainability of transportation systems. However, ensuring secure and reliable communication among different components of an IoV system poses a critical challenge. This study proposes a blockchain-based communication framework for secure and trustworthy IoV applications. The framework utilizes blockchain technology's decentralization and security features to create secure communication channels between IoV system components, including vehicles, infrastructure, and service providers. An identity management system is also integrated into the framework to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To assess the proposed framework's effectiveness, real-world IoV scenarios were used to conduct experiments, and the results demonstrate that the framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework provides a promising solution for addressing security and trust challenges in IoV communication systems. [ABSTRACT FROM AUTHOR]

The production rate of crops is significantly declining due to natural disasters, animal interventions and plant diseases. Internet of things (IoT) and wireless sensor networks are widely applied in crop field monitoring systems to observe the quality of each plant and the field. This work proposes IoT based crop field protection system (ICFPS) that monitors and protects the crop fields from animal intrusions. This proposed system uses ultrasonic sensors, hyperspectral cameras, voice recorded buzzers and other agriculture sensors to protect the entire crop field. This system uses numerous sensor nodes and cameras for gathering field objects (images and environmental objects). The proposed ICFPS creates deep learning techniques such as recurrent convolutional neural networks (RCNN) and recurrent generative adversarial neural networks (RGAN) for feature extraction, disease detection and field data monitoring practices. This proposed work develops a smart city‐based agriculture system using cognitive learning approaches. This proposed system analyses crop field data and provide automatic alerts regarding animal interferences and crop diseases. Moreover, the cognitive smart crop field system observes various field conditions which support for good production rate. In this system, sensors and camera‐enabled agriculture drones are coordinated with each other to collect the field data regularly. At the same time, the proposed work trains the RCNN and RGAN units using effective crop field datasets to attain realistic decisions within minimal time intervals. The experiment details and results show the proposed ICFPS works with 8%–10% of more classification accuracy than existing systems. [ABSTRACT FROM AUTHOR]

In this Letter, a novel approach is used for improving the Bit Error Rate (BER) for non-stationary environment based spectrum sensing. Here, we have considered Converged Welch's Power Spectral Density (PSD) for analyzing of power. The signal of interest is thus further studied in terms of BER keeping the antenna and channel parameters constant. The proposed method yields an improvement of 34.6% as compared with the conventional method. [ABSTRACT FROM AUTHOR]

This Letter addresses the problem of dynamic power allocation based on spectrum sensing outputs for non-ergodic systems. On such type of systems, the power allocation for each corresponding nodes to be sensed by cognitive transmitter may not require all the time. The dynamic power allocation in nodes of each channel in cognitive radio (CR) network using vector quantization based on autocorrelation error is proposed. [ABSTRACT FROM AUTHOR]