The explosive development of electrical engineering in the early 19th century marked the birth of the 2nd industrial revolution, with the use of electrical energy in place of steam power, as well as changing the history of human development. The versatility of electricity allows people to apply it to a multitude of fields such as transportation, heat applications, lighting, telecommunications, and computers. Nowadays, with the breakout development of science and technology, electric energy sources are formed by many different technologies such as hydroelectricity, solar power, wind power, coal power, etc. These energy sources are connected to form grid systems to transmit electricity to cities, businesses and homes for life and work. Electrical energy today has become the backbone of all modern technologies. To ensure the safe, reliable and energy-efficient operation of the grid, a wide range of grid management applications have been proposed. However, a significant challenge for monitoring and controlling grids is service response time. In recent times, to solve this problem, smart grid management applications based on IoT and edge computing have been proposed. In this work, we perform a comprehensive survey of edge computing for IoT-enabled smart grid systems. In addition, recent smart grid frameworks based on IoT and edge computing are discussed, important requirements are presented, and the open issues and challenges are indicated. We believe that in the Internet of Things era, the smart grid will be the future of energy. We hope that these study results will contribute important guidelines for in-depth research in the field of smart grids and green energy in the future.

While the fifth-generation mobile network is being commercialized worldwide, researchers have recently started looking towards the next generation, called the 6G network. Unlike 5G and previous generations of wireless technologies designed to improve network performance for greater bandwidth, lower latency and greater reliability, 6G ecosystems is considered a platform conducive to innovations in the fields of computing, artificial intelligence, connectivity and sensors, virtualization and more. It is designed to meet the requirements of higher global coverage, greater spectral efficiency, a reduced carbon footprint, with an emphasis on sustainability, equity, trust and security through unprecedented architectural evolutions and technology. 6G will be an integrated network system that includes a traditional terrestrial mobile network, space network, and underwater network to provide ubiquitous network access. Even if there are studies on vision of 6G network that have already been published, there is still a significant amount of ground to cover. There is no decision made yet regarding anything and nothing has been ruled out. The focus of this study is to identify a complete picture of changes in architectures, technologies, and challenges that will shape the 6G network. We hope the research results will provide indications for further studies on 6G ecosystems.

With the development of society, technological progress, and new needs, autonomous driving has become a trendy topic in smart cities. Due to technological limitations, autonomous driving is used mainly in limited and low-speed scenarios such as logistics and distribution, shared transport, unmanned retail, and other systems. On the other hand, the natural driving environment is complicated and unpredictable. As a result, to achieve all-weather and robust autonomous driving, the vehicle must precisely understand its environment. The self-driving cars are outfitted with a plethora of sensors to detect their environment. In order to provide researchers with a better understanding of the technical solutions for multi-sensor fusion, this paper provides a comprehensive review of multi-sensor fusion 3D object detection networks according to the fusion location, focusing on the most popular LiDAR and cameras currently in use. Furthermore, we describe the popular datasets and assessment metrics used for 3D object detection, as well as the problems and future prospects of 3D object detection in autonomous driving.

Large cities’ expanding populations are causing traffic congestion. The maintenance of the city’s road network necessitates ongoing monitoring, growth, and modernization. An intelligent vehicle detection solution is necessary to address road traffic concerns with the advancement of automatic cars. The identification and tracking vehicles on roads and highways are part of intelligent traffic monitoring while driving. In this paper, we have presented how You Only Look Once (YOLO) v5 model may be used to identify cars, traffic lights, and pedestrians in various weather situations, allowing for real-time identification in a typical vehicular environment. In an ordinary or autonomous environment, object detection may be affected by bad weather conditions. Bad weather may make driving dangerous in various ways, whether due to freezing roadways or the illusion of low fog. In this study, we used YOLOv5 model to recognize objects from street-level recordings for rainy and regular weather scenarios on 11 distinct classes of vehicles (car, truck, bike), pedestrians, and traffic signals (red, green, yellow). We utilized freely available Roboflow datasets to train the proposed system. Furthermore, we used real video sequences of road traffic to evaluate the proposed system’s performance. The study results revealed that the suggested approach could recognize cars, trucks, and other roadside items in various circumstances with acceptable results.

With the accelerated development of artificial intelligence, remote-sensing image technologies have gained widespread attention in smart cities. In recent years, remote sensing object detection research has focused on detecting and counting small dense objects in large remote sensing scenes. Small object detection, as a branch of object detection, remains a significant challenge in research due to the image resolution, size, number, and orientation of objects, among other factors. This paper examines object detection based on deep learning and its applications for small object detection in remote sensing. This paper aims to provide readers with a thorough comprehension of the research objectives. Specifically, we aggregate the principal datasets and evaluation methods extensively employed in recent remote sensing object detection techniques. We also discuss the irregularity problem of remote sensing image object detection and overview the small object detection methods in remote sensing images. In addition, we select small target detection methods with excellent performance in recent years for experiments and analysis. Finally, the challenges and future work related to small object detection in remote sensing are highlighted.