The notion of smart cities has remained under evolution as its global implementations are challenged by numerous technological, economic, and governmental obstacles. Moreover, the synergy of the Internet of Things (IoT) and big data technologies could result in promising horizons in terms of smart city development which has not been explored yet. Thus, the current research aims to address the essence of smart cities. To this end, first, the concept of smart cities is briefly overviewed; then, their properties and specifications as well as generic architecture, compositions, and real-world implementations are addressed. Furthermore, possible challenges and opportunities in the field of smart cities are described. Numerous issues and challenges such as analytics and using big data in smart cities introduced in this study offers an enhancement in developing applications of the above-mentioned technologies. Hence, this study paves the way for future research on the issues and challenges of big data applications in smart cities.

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

The unmanned aerial vehicles (UAVs) in military and civilian systems access sensitive data via cellular networks and are ubiquitous. Therefore, the number of handovers may increase, and the process of sharing authentication keys between drones to new drones may be vulnerable to eavesdropping due to the wireless connectivity. For example, a UAV initially served by cell A may be handed over to cell B after a certain point. Therefore, Security and privacy concerns raise if there is a change in the flight path of the UAV due to wide-area connectivity traffic management, including authentication threats as well as the leakage of identity, location, and flying routes. Also, drones are resource-constrained devices that cannot perform extensive operations, leading to the inability to resist drone capture attacks and impersonation attacks. Therefore, this paper presents a high-performance and efficient anonymous handover authentication protocol to secure the drone traveling path. The protocol utilizes the advantages of the AES-RSA algorithm to provide efficient and secure key management and a fast verification and signature generation that speeds up drone authentication. The protocol's security was proved using the formal verification ROR model and automated security verifier ProVerif tool to validate the protocol resistance against various attacks and provided forward secrecy. In addition, the performance of the proposed protocol is evaluated using the OMNeT++ simulator in terms of communication, computation cost, energy consumption, packet drop ratio, end-to-end delay, and throughput. The results showed that the protocol achieves better security, efficiency, and performance than other protocols.

Many countries have allocated new frequency bands for fifth generation (5G) communication systems. In this paper, a wideband continuous class-F (CCF) radio frequency power amplifier (RFPA) is presented for the new 5G frequency band, from 3.3 GHz to 4.3 GHz using a 10 W Cree CGH40100F device. A unique wideband RFPA design approach for the output matching network (OMN) is also presented by applying a harmonic tuning network (HTN) for the harmonics and a low pass matching technique (LPMT) for the fundamental frequency. The RFPA is fabricated, and promising measurement results show a drain efficiency of 55.9% to 65.3% is achieved at an output power of 40 dBm (± 0.3 dBm) over a frequency range of 3.3 GHz to 4.3 GHz. A two-tone signal with a 10 MHz spacing was also applied to investigate the linearity of the RFPA.

The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network’s edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham’s logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes.

Smart Cities (SCs) have recently opened new lifestyles as they introduce effective approaches for improving urban management. These cities integrate Industry 4.0 technologies and new organizations into a social and technical system. A Smart City (SC) finally aims to integrate urban business, transportation, water, energy, and other subsystems by analyzing the data collected from sensors and Information and Communication Technology (ICT). In this regard, the current article proposes a hybrid structure based on Cloud Computing (CC), cloud processing, and internet technology to develop and manage SCs and urban planning. Moreover, the development and analysis of sensors to construct an SC, as well as supporting technologies, are addressed. Therefore, the present research is focused on defining and reviewing large data and objects of the Internet and CC to identify the current challenges and limitations. This research, thus, introduced a novel approach in the context of the Internet of objects, whose data are gathered from different geographic locations through devices and sensors to be used in a new system. CC services in a city can analyze and make smart decisions to optimally manage SCs and improve the welfare of citizens. This study consequently sets the path for further investigation into the problems and difficulties associated with Big Data (BD) applications in SCs.