Vehicular communication networks is a powerful tool that enables numerous vehicular data services and applications. The rapid growth in vehicles has also resulted in the vehicular network becoming heterogeneous, dynamic, and large-scale, making it hard to meet the strict requirements, such as extremely latency, high mobility, top security, and enormous connections of the fifth-generation network. Previous studies have shown that with the increase in the application of Software-Defined Networking (SDN) on Vehicular Adhoc Network (VANET) in industries, researchers have exerted considerable efforts to improve vehicular communications. This study presents an exhaustive review of previous works by classifying them based on based on wireless communication, particularly VANET. First, a concise summary of the VANET structure and SDN controller with layers and details of their infrastructure is provided. Second, a description of SDN-VANET applications in different wireless communications, such as the Internet of Things (IoT) and VANET is provided with concentration on the examination and comparison of SDN-VANET works on several parameters. This paper also provides a detailed analysis of the open issues and research directions accomplished while integrating the VANET with SDN. It also highlights the current and emerging technologies with use cases in vehicular networks to address the several challenges in the VANET infrastructure. This survey acts as a catalyst in raising the emergent robustness routing protocol, latency, connectivity and security issues of future SDN-VANET architectures.

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

Ultra-wideband (UWB) technology can offer broad capacity, short-range communications at a relatively low level of energy usage, which is very desirable for wireless body area networks (WBANs). The involvement of the human body in such a device poses immense difficulties for both the architecture of the wearable antenna and the broadcast model. Initially, the bonding between the wearable antenna and the human body should also be acknowledged in the early stages of the design, so that both the potentially degrading output of the antenna as a consequence of the body and the possibility of exposure for the body may be handled. Next, the transmission path in WBAN is affected by the constant activity of the human body, leading to the time-varying dispersion of electromagnetic waves. Few researchers were interested in this field, and some substantial progress has recently been considered. On the other hand, this paper covered both wearable and Non-wearable UWB antenna designs and applications with respect to their substrate characteristics. Finally, this review prospectively exposes the upgraded developments of (ON-OFF) body antennas in the area of wearable and Non-wearable UWB and their implementations in the WBAN device and aims to evaluate the latest design features that inspire the performance of the antennas.

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.

Advancements in Wireless Sensor Network (WSN) technology has allowed lightweight, inexpensive sensor nodes to track diverse physical and environmental environments, data, and so forth. Wireless correspondence, and delivery. The clustering of WSNs has proved to be an efficient method for enhancing data processing and scalability for broad WSNs. Clustering often retains the sensors’ minimal energy supplies. The aim of the paper to present the concept of WSN and their Characteristics and discuss the concept of the cluster in a wireless sensor network.