Increased capacity, higher data rate, decreased latency, and better service quality are examples of the primary objectives or needs that must be catered to in the near future, i.e., fifth-generation (5G) and beyond. To fulfil these needs, cellular network design must be drastically improved. The 5G cellular network design, huge multiple-input multiple-output (MIMO) technology, and device-to-device communication are all highlighted in this comprehensive study. Hence, free-space optics (FSO) is a promising solution to address this field. However, FSO standalone is insufficient during turbulent weather conditions. FSO systems possess some limitations, such as being able to be disturbed by any interference between sender and receiver such as a flying bird and a tree, as it requires line-of-sight (LOS) connectivity. Moreover, it is sensitive to weather conditions; the FSO performance significantly decreases in bad weather conditions such as fog and snow; those factors deteriorate the performance of FSO. This paper conducts a systematic survey on the existing projects in the same area of research such as the hybrid FSO/Radio frequency (RF) communication system by listing each technique used for each model to achieve optimum performance in terms of data rate and Bit Error Rate (BER) to be implemented in 5G networks.

In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G vehicle-to-vehicle (V2V) communications. The proposed system model aims to address the challenges of highly dynamic V2V environments, including rapid changes in channel conditions, interference, and Doppler effects. By leveraging the beam-switching capabilities of switched beam antennas and the intelligent decision making of DRL, the proposed approach enhances the performance of 6G V2V communications in terms of throughput, latency, reliability, and spectral efficiency. The proposed work develops a comprehensive mathematical model that accounts for 6G channel modeling, beam-switching, and beam-alignment errors. The Proposed DRL framework is designed to learn optimal beam-switching decisions in real time, adapting to the complex and varying V2V communication scenarios. The integration of the proposed antenna system and DRL model results in a robust solution that is capable of maintaining reliable communication links in a highly dynamic environment. To validate the proposed approach, extensive simulations were conducted and performance analysis using various performance metrics, such as throughput, latency, reliability, energy efficiency, resource utilization, and network scalability, was analyzed. Results demonstrate that the proposed system model significantly outperforms conventional V2V communication systems and other state-of-the-art techniques. Furthermore, the proposed approach shows that the beam-switching capabilities of the switched beam antenna system and the intelligent decision making of the DRL model are essential in addressing the challenges of 6G V2V communications.

Attribute-based encryption (ABE) cryptography is widely known for its potential to solve the scalability issue of recent public key infrastructure (PKI). It provides a fine-grained access control system with high flexibility and efficiency by labeling the secret key and ciphertext with distinctive attributes. Due to its fine-grained features, the ABE scheme is a protection layer in securing users’ data and privacy in big data processing and analytics. However, quantum computing, new technology on the horizon that will transform the security and privacy environment, has begun to appear. Like the conventional ABE schemes, present cryptography is not excluded from the impacts of quantum technology as they are not made to be quantum-resistant. While most recent surveys generally touched on the generic features of attribute-based encryption schemes such as user revocation, scalability, flexibility, data confidentiality, and scope in pairing-based ABE schemes, this survey investigated quantum-resistant ABE schemes in securing big data. This survey reviews the challenges faced by the recent ABE cryptography in the post-quantum era and highlights its differences from the conventional pairing-based ABE schemes. Subsequently, we defined the criteria of an ideal quantum-resistant ABE scheme. Additionally, existing works on quantum-resistant ABE schemes are reviewed based on their algorithms design, security and functionalities. Lastly, we summarized quantum-resistant ABE schemes’ ongoing challenges and future works.

Traditional elderly monitoring systems may misdiagnose and treat senior citizens due to inaccurate results, resulting in higher care costs and poorer health outcomes. Thus, the Internet of Things (IoT) was introduced to provide accurate, real-time monitoring data to improve overall performance. The IoT health system connects devices wirelessly to collect and analyze health data, monitor vital signs and physical activity, and provide real-time insights to improve health management. This paper aims to monitor real-time conditions as well as ensure the safety of senior citizens by developing a low-cost wearable prototype device to measure heart pulses, detect falls, and determine their actual location in an indoor space. The data is uploaded to IoT platforms like Blynk, Firebase, and Google Assistant, providing frequent updates on the elderly's health status and conditions and sending emergency messages, if necessary, to an Android application. A home control system is also developed to control the home appliances using mobile phones or voice control. A demonstration has been conducted to showcase the operation and functionality of the prototype. The proposed system has the capability to simplify the daily routines of the elderly while also granting caregivers greater control over their health and well-being.

Since 2017, up to 41% of Malaysia's land has been cultivated for durian, making it the most widely planted crop. The rapid increase in demand urges the authorities to search for a more systematic way to control durian cultivation and manage the productivity and quality of the fruit. This research paper proposes a deep-learning approach for detecting and sizing durian fruit in any given image. The aim is to develop zero-shot learning models that can accurately identify and measure the size of durian fruits in images, regardless of the image’s background. The proposed methodology leverages two cutting-edge models: Grounding DINO and Segment Anything (SAM), which are trained using a limited number of samples to learn the essential features of the fruit. The dataset used for training and testing the model includes various images of durian fruits captured from different sources. The effectiveness of the proposed model is evaluated by comparing it with the Segmentation Generative Pre-trained Transformers (SegGPT) model. The results show that the Grounding DINO model, which has a 92.5% detection accuracy, outperforms the SegGPT in terms of accuracy and efficiency. This research has significant implications for computer vision and agriculture, as it can facilitate automated detection and sizing of durian fruits, leading to improved yield estimation, quality control, and overall productivity.

This study proposes an innovative integration of the Car-to-Car Network-Hierarchical deep neural network (CtCNET-HDRNN) model with Fifth generation (5G) and Dedicated Short-Range Communications (DSRC) systems, streamlining computational efficiency in edge computing. CtCNET-HDRNN is a specialized deep learning model designed for vehicular communication, allowing vehicles to exchange information seamlessly in a connected environment. It harnesses an adaptive learning rate and regularization within the model’s advanced training methodology, ensuring optimal data fit, superior generalization, and efficient convergence. A key novelty lies in the introduction of a Sparse Deep Recurrent Neural Network (SDRNN), which significantly reduces computational complexity by pruning insignificant connections, making it suitable for deployment on resource-constrained edge devices. SDRNN is a variant of recurrent neural networks designed to minimize computational burden while maintaining high performance in time-series data analysis. Furthermore, this research presents an original integration model, adeptly merging the CtCNET-HDRNN model with the Millimeter wave (mmWave) of 5G and Monte Carlo for DSRC systems for seamless data transmission. The mmWave technology offers high-speed communication capabilities, while Monte Carlo enables adaptive collision avoidance and efficient channel access control for vehicular networks. Beyond immediate computational gains, this integrated model also contributes significantly to edge computing research and practical applications, promising enhanced system performance and improved user experience in vehicular communication scenarios. The proposed approach opens new possibilities for efficient and reliable communication in connected vehicles, laying the foundation for safer and smarter transportation systems.