It has been observed that unmanned aerial vehicles (UAVs), also known as drones, have been used in a very different way over time. The advancements in key UAV areas include detection (including radio frequency and radar), classification (including micro, mini, close range, short range, medium range, medium-range endurance, low-altitude deep penetration, low-altitude long endurance, and medium-altitude long endurance), tracking (including lateral tracking, vertical tracking, moving aerial pan with moving target, and moving aerial tilt with moving target), and so forth. Even with all of these improvements and advantages, security and privacy can still be ensured by researching a number of key aspects of an unmanned aerial vehicle, such as through the jamming of the control signals of a UAV and redirecting them for any high-assault activity. This review article will examine the privacy issues related to drone standards and regulations. The manuscript will also provide a comprehensive answer to these limitations. In addition to updated information on current legislation and the many classes that can be used to establish communication between a ground control room and an unmanned aerial vehicle, this article provides a basic overview of unmanned aerial vehicles. After reading this review, readers will understand the shortcomings, the most recent advancements, and the strategies for addressing security issues, assaults, and limitations. The open research areas described in this manuscript can be utilized to create novel methods for strengthening the security and privacy of an unmanned aerial vehicle.

An inset fed-microstrip patch antenna (MPA) with a partial ground structure is constructed and evaluated in this paper. This article covers how to evaluate the performance of the designed antenna by using a combination of simulation, measurement, creation of the RLC equivalent circuit model, and the implementation of machine learning approaches. The MPA’s measured frequency range is 7.9–14.6 GHz, while its simulated frequency range is 8.35–14.25 GHz in CST microwave studio (CST MWS) 2018. The measured and simulated bandwidths are 6.7 GHz and 5.9 GHz, respectively. The antenna substrate is composed of FR-4 Epoxy, which has a dielectric constant of 4.4 and a loss tangent of 0.02. The equivalent model of the proposed MPA is developed by using an advanced design system (ADS) to compare the resonance frequencies obtained by using CST. In addition, the measured return loss of the prototype is compared with the simulated return loss observed by using CST and ADS. At the end, 86 data samples are gathered through the simulation by using CST MWS, and seven machine learning (ML) approaches, such as convolutional neural network (CNN), linear regression (LR), random forest regression (RFR), decision tree regression (DTR), lasso regression, ridge regression, and extreme gradient boosting (XGB) regression, are applied to estimate the resonant frequency of the patch antenna. The performance of the seven ML models is evaluated based on mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and variance score. Among the seven ML models, the prediction result of DTR (MSE = 0.71%, MAE = 5.63%, RMSE = 8.42%, and var score = 99.68%) is superior to other ML models. In conclusion, the proposed antenna is a strong contender for operating at the entire X-band and lower portion of the Ku-band frequencies, as evidenced by the simulation results through CST and ADS, it measured and predicted results using machine learning approaches.

Numerous recent innovations have been achieved with the goal of enhancing electric vehicles and the parts that go into them, particularly in the areas of managing energy, battery design and optimization, and autonomous driving. This promotes a more effective and sustainable eco-system and helps to build the next generation of electric car technology. This study offers insights into the most recent research and advancements in electric vehicles (EVs), as well as new, innovative, and promising technologies based on scientific data and facts associated with e-mobility from a technological standpoint, which may be achievable by 2030. Appropriate modeling and design strategies, including digital twins with connected Internet of Things (IoT), are discussed in this study. Vehicles with autonomous features have the potential to increase safety on roads, increase driving economy, and provide drivers more time to focus on other duties thanks to the Internet of Things idea. The enabling technology that entails a car moving out of a parking spot, traveling along a long highway, and then parking at the destination is also covered in this article. The development of autonomous vehicles depends on the data obtained for deployment in actual road conditions. There are also research gaps and proposals for autonomous, intelligent vehicles. One of the many social concerns that are described is the cause of an accident with an autonomous car. A smart device that can spot strange driving behavior and prevent accidents is briefly discussed. In addition, all EV-related fields are covered, including the likely technical challenges and knowledge gaps in each one, from in-depth battery material sciences through power electronics and powertrain engineering to market assessments and environmental assessments.

It is undeniable that the development of robot manipulators has gained significant attention due to its numerous applications in various sectors. In order to address and resolve several issues for robotic manipulators, this paper provides a review of recent control approaches that have been conducted. In the beginning, a succinct comprehensive introduction of the pertinent terminology, configurations, components, advantages/disadvantages, and applications is given. A state-of-the-art review of the various control strategies for robotic manipulator systems is then presented, along with potential solutions when the systems are faced with various obstacles and impediments. An analytical study is discussed showing percentages of the papers discussed in this study in terms of the different control strategies, link types, models, applications, and degrees of freedom of robotic manipulators. For academic, research, medical, and industrial uses, some off-the-shelf developments in robotic manipulators are also presented.

Today people are witnessing the rapid evolvement in every area. This is because of the emerging trends in communication technology and autonomous unmanned vehicles. These trends have led us towards the high standards of health, energy, transportation, monitoring, and surveillance of huge domestic and industrial projects. Thus, this review paper presents the integration of the latest trend in communication technology, i.e., Internet of things (IoT) with unmanned aerial vehicles (UAVs). This manuscript not only reviews the use of IoT-enabled unmanned aerial vehicles for inspecting the several construction sites but also emphasizes the utilization of such IoT-enabled autonomous aerial vehicles for ensuring the health and safety measures at the site. It discusses the major limitations and shortcomings of state-of-the-art techniques for the same purpose, i.e., optimization issues in path planning, lightweight artificial intelligence (AI) and computer vision algorithms, coordination in communication using IoT, and scalability of IoT network. Thus, this paper shall help the reader to explore different open research problems in-depth.

The integration of solar energy into the global energy mix is more prevalent owing to its sustainability and ecological advantages. With the fast expansion of large-scale solar farms, the necessity for efficient and dependable maintenance and monitoring of solar modules has grown significantly. This study advocates for the utilization of unmanned aerial vehicles (UAVs) outfitted with thermal imagers and visible-light cameras as an efficient method for identifying flaws in solar photovoltaic (PV) modules. In contrast to conventional inspection techniques, UAV-based photography provides continuous, real-time surveillance, facilitating the rapid detection of potential defects such as cell malfunctions, fractures, shading, and string failures, which may result in substantial financial losses. The choice to prioritize thermal and visual imaging above mathematical modeling or optimization algorithms arises from the necessity of delivering practical, scalable inspection solutions that can be readily implemented in the field. Our review paper emphasizes instant functionality and practical insights for predictive maintenance, without necessitating complex computing resources or optimization tools. This study emphasizes the significance of early defect identification on solar farm efficiency and delineates a strategy for improving the reliability and cost-effectiveness of photovoltaic module inspections via UAV technology.