The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT) where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers which are Vehicle Identification Layer, Object Layer, Inter-Intra Devices Layer, Communication Layer, Servers and Cloud Services Layer, Big Data and Multimedia Computation Layer, and Application Layer. The final part of the paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario.

With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications.

Recent advances in large-scale networked sensor technologies and the explosive growth in big data computing have made it possible for new application deployments in smart cities ecosystems. In this paper, we define big sensor data systems, and survey progress made in the development and applications of big sensor data research. We classify the existing research based on their characteristics and smart city layer challenges. Next, we discuss several applications for big sensor data systems, and explore the potential of large-scale networked sensor technologies for smart cities in the big data era. We conclude this paper by discussing future work directions highlighting some futuristic applications. The aim of this survey paper is to be useful for researchers to get insights into this important area, and motivate the development of practical solutions toward deployment in smart cities.

This paper presents a new approach to simplify the design of class-E power amplifier (PA) using hybrid artificial neural-optimization network modeling. The class-E PA is designed for wireless power transfer (WPT) applications to be used in biomedical or internet of things (IoT) devices. Artificial neural network (ANN) models are combined with optimization algorithms to support the design of the class-E PA. In several amplifier circuits, the closed form equations cannot be extracted. Hence, the complicated numerical calculations are needed to find the circuit elements values and then to design the amplifier. Therefore, for the first time, ANN modeling is proposed in this paper to predict the values of the circuit elements without using the complex equations. In comparison with the other similar models, high accuracy has been obtained for the proposed model with mean absolute errors (MAEs) of 0.0110 and 0.0099, for train and test results. Moreover, root mean square errors (RMSEs) of 0.0163 and 0.0124 have been achieved for train and test results for the proposed model. Moreover, the best and the worst-case related errors of 0.001 and 0.168 have been obtained, respectively, for the both design examples at different frequencies, which shows high accuracy of the proposed ANN design method. Finally, a design of class-E PA is presented using the circuit elements values that, first, extracted by the analyses, and second, predicted by ANN. The calculated drain efficiencies for the designed class-E amplifiers have been obtained equal to 95.5% and 91.2% by using analyses data and predicted data by proposed ANN, respectively. The comparison between the real and predicted values shows a good agreement.

Wireless rechargeable sensor networks (WRSNs) have emerged with strong potential to address the bottlenecks of energy/lifetime of a wireless sensor network. Recent techniques have shown the efficacy of multiple mobile elements (MMEs) in terms of energy consumption optimization. However, new challenges for energy-efficient MMEs scheme have emerged due to the emergence of large-scale WRSNs (LS-WRSNs) with big sensor-based data systems. Thus, large-scale deployments are currently limited owing to the bottlenecks of energy/lifetime, and mode of deployments of the sensor nodes. This article proposes a deadline-based MMEs (DB-MMEs) model exploiting the efficacies of the MMEs scheme to optimize energy consumption and provisioning. The DB-MMEs scheme exploits multifunctional wireless mobile charging vehicles (MCVs) for both wireless charging and data collection via a single-hop transmission. The scheme is specifically designed for delay-intolerant applications. None of the existing techniques has considered this approach to minimize latency and optimize energy consumption and provisioning for LS-WRSNs scenarios. The proposed scheme first organizes the sensors into several clusters for wireless charging and data collection. To optimize energy consumption and provisioning and address the challenges of energy/lifetime for LS-WRSNs scenarios, this article proposes analytical-based approaches to address some critical tradeoffs including: 1) determining the optimal amount of energy available for the MCVs; 2) finding the optimal number of MCVs deployed within a given deadline; and 3) finding the optimal number of data collection and charging points (DCCPs). Finally, the performance of the proposed approach is evaluated through experimental simulations, and the results validate the efficacy of the analytical-based method.