Smart UAV Research Articles

Leveraging lightweight blockchain for secure collaborative computing in UAV Ad-Hoc Networks

Unmanned Aerial Vehicle (UAV) Ad-Hoc Networks (UANET) enable collaborative work among UAVs for versatile task execution, but they face security challenges due to physical vulnerabilities, software issues, and dynamic wireless networks. This paper proposes a secure collaborative computing framework based on blockchain technology. Specifically, we first design a lightweight blockchain scheme suitable for UANET and present an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on trust evaluation, aiming to reduce consensus overhead and establish trust relationships among UAVs. Furthermore, we devise a smart contract-based UAV task allocation strategy that considers both task execution efficiency and offloading security. This strategy enables UAVs to make optimal task-offloading decisions and facilitates collaborative computing according to smart contract rules. Simulation results demonstrate that our proposed consensus algorithm reduces average consensus latency by 47% and message count by 76% compared to PBFT-based algorithms. Additionally, the task allocation strategy decreases task costs by 48% compared to local computing strategies, achieving efficient and secure collaboration among UAVs in UANET. The real-world experiments with a UAV swarm further validate the efficiency and security of our framework, confirming its practical applicability in UANET.

Global challenges and the ‘farm to fork’ strategies of the European Green Deal: Blessing or curse

AbstractThe article evaluates how well the goals of the European Green Deal are justified, especially considering the risks to energy and food security arising from the conflict between Russia and Ukraine. We agree with the objectives of the European Green Agreement as a whole, but whether some of the objectives which feature in the EASAC study can be achieved by 2030 is questionable, and the description of the tools necessary to achieve the objectives is incomplete. Among other things, there is hardly any mention of the role played by precision farming with digitalization, which is a revolutionary change from an ecological and economic point of view, in reducing the use of synthetic inputs, in regenerating the original state of the soil, in reducing GHG emissions, thus in increasing biodiversity, and at the same time in intensifying production, and finally in expanding the application of biotechnology. We examine these areas in our analysis. Some of the objectives of the EASAC study to be achieved by 2030 are subject to debate, and the description of the information and communication conditions necessary to achieve the objectives is incomplete. The IoT (Internet of Things) responds to global and local challenges: it integrates the precision technologies, WSNs (Wireless Sensor Networks), artificial intelligence, mobile field (Smart Small Robots) and remote data loggers (UAVs: Unmanned Air Vehicles and satellites), Big Data, and cloud computing. Consequently, decision support is increasingly developing into unmanned decision making. IoT (Internet of Things) is the basis of “Farm to Fork” and “Lab to Field” monitoring approaches.This article evaluates the implementation of European Green Agreement objectives in light of energy and food security risks arising from the Russia-Ukraine conflict. While overall support for the agreement exists, the feasibility of certain EASAC study objectives by 2030 is called into question due to insufficient tools specifications. Notably absent is the emphasis on precision farming with digitalization, which is a transformative ecological and economic practice. Our analyses look into its function in reducing synthetic inputs, soil regeneration, GHG emission reduction, biodiversity enhancement, production intensification, and biotechnology development. Debates surround EASAC study objectives for 2030, despite limited information and communication restrictions. The Internet of Things (IoT) arises as a solution, combining precision technology, WSNs (wireless sensor networks), AI (artificial intelligence), smart small robots, UAVs (unmanned aerial vehicles), satellites, big data, and cloud computing. As a result, decision support turns toward unmanned decision-making, with IoT laying the groundwork for “Farm to Fork” and “Lab to Field” monitoring systems.

Reconfigurable UAV-aided 3D sustainable surveillance in classified air-spaces

The recent advancement of smart UAVs (Unmanned Aerial Vehicles) allows its applicability to wide-range fields covering time-critical reports, emergent item delivery, virtual emotion services and so on. In particular, the surveillance can be supported by a fleet of smart UAVs in cooperation with various system components including sensors, mobile robots, autonomous vehicles and so on. Additionally, it is crucial to consider the reconfigurable capabilities of smart UAVs leveraging for rapid movement in disregarded airspace. In this paper, we introduce a reconfigurable UAV-enabled 3D sustainable surveillance in classified air-spaces to provide energy-efficient monitoring and security surveillance enforcement. We formally define a problem to maximize the reconfigurable UAV-aided sustainable surveillance efficiency with optimal height in 3D classified air-spaces avoiding excessive resource consumption and waste. To solve this issue, two different schemes of Lined-Up and Zig-Zag are proposed. Furthermore, we analyze their performances based on the gained numerical results through expanded simulations with detailed discussions.

BlockFedL: a blockchain-based federated learning framework for securing smart UAV delivery systems at the edge

BlockFedL: a blockchain-based federated learning framework for securing smart UAV delivery systems at the edge

Affective Surveillance Management in Virtual Emotion Based Smart Complex Infrastructure

Affective computing has evolved substantially thanks to recent great advances for numerous applications including emotion sensing, human interactions, emotion-enabled communications, eHealth, metaverse. Also, it is highly anticipated that such an evolved affective computing and its technology can be applied to secure surveillance based on virtual emotion detection in advanced smart cities. In this paper, we design an energy-aware affective surveillance management system for virtual emotion based smart complex architecture. With an affective surveillance perspective in smart complex environment with multi levels of ground and underground, the proposed system basically provides low resource affective surveillance which is formed by various system components such as IoT devices, mobile robots and smart UAVs. Then, we present a formal representation of the problem that is to minimize the number of non-working affective components. Three different approaches are developed to resolve the problem as well as their performances are evaluated through extensive experiments. Besides, the open research issues are presented for new research branch of affective surveillance.

Optimizing Performance in Federated Person Re-Identification through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems

In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.

GeoSS: Geographic Segmentation Security Barriers for Virtual Emotion Detection With Discriminative Priorities in Intelligent Cooperative Vehicular System

A development of the integrated vehicular system through ground and aerial cooperation using intelligent mobile robots and smart UAVs is required to support various applications including intelligent transportation, secure service, surveillance reinforcement. Also, it is highly anticipated that the applicability of virtual emotion is expanded continuously in smart systems. In this paper, we introduce a geographic segmentation security barrier system for the purpose of secure surveillance through virtual emotion detection in intelligent cooperative vehicular area with mobile robots and UAVs. The proposed system is necessary to provide discriminative priorities in the requested cooperative vehicular area consisting of mobile robots and UAVs with rapid construction. Moreover, with ILP formulation, we formally defined a problem whose goal is to maximize a total number of geographic segmentation security barriers such that the completed establishment of those barriers is done by a specific group of mobile robots and UAVs within the requested various districts where they are divided according to differential security priorities. To solve the problem, we devise novel schemes to return the maximum number of geographic segmentation security barriers so that it pursues the maximization of system lifetime consequently. Then, the proposed approaches are implemented through expansive simulations and their performances based on numerical results are evaluated clearly.

Development of Smart UAV (Drone) ozone (O3) monitoring system in Port Harcourt, Rivers State, Nigeria

We hereby report a detailed design and fabrication of our indigenously developed smart UAV (drone) Ozone, O3, monitoring system, using locally sourced materials in Nigeria. Our device is locally built from scratch to finish using materials available to us. Our design comprises a small lightweight (Polystyrene), Rotors, Arduino Joystick, Arduino Nano Board, 434 MHz Transmitter Module, 1 KΩ Resistor, Vero Board, Connecting wires, 9 Volts Battery, Antenna, simple and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related climate information. We have used a long-range wireless communication module with real-time monitoring and visualizing software applications embedded on our device to access live data, track trends, and archive historical data for comparisons (data logger). We have also obtained original ozone datasets after conducting test flight measurement experiments at the launch site (Rivers State University Campus) Port Harcourt, Nigeria.

EETO-GA: Energy Efficient Trajectory Optimization of UAV-IoT Collaborative System Using Genetic Algorithm

Unmanned aerial vehicle (UAVs) is capable of adding significant potential to the internet of thing (IoT) devices and hence smart UAV–IoT collaborative system has attracted the attention of many researchers. This system has to be energy efficient for its nature and functionalities. Optimized trajectory planning is a significant area of research for any automatic movable device. In this article, we propose a technique, called EETO-GA for energy-efficient trajectory optimization of UAV–IoT using a genetic algorithm (GA). This technique prescribes each device of: (i) the next timestamp of arrival on the present cluster of IoT devices, at which the task queue of its header contains the highest possible number of tasks, and (ii) the minimum amount of energy that requires to complete all the tasks present in task queue of the IoT device. This technique uses a GA for optimization where the fitness function is designed by optimizing objectives: (i) the total number of tasks that can be completed, (ii) minimization of consumed energy, and (iii) the number of devices that could be served. A GA is applied here to accommodate a large number of IoT devices. A binary method of encoding is applied and methods like cross-over and mutation are used to arrive at the optimal solution. Through a simulation study, the proposed technique shows significant improvement in terms of UAV energy saved (UAVE), energy saving in IoT devices (IoTDEC), the average delay in execution of the task (ADET), and the percentage of tasks that could be completed (PTSK). Proposed EETO-GA improved average UAVE: 43%, IoTDEC: 56%, PTSK: 7.5%, and ADET: 38% over the state of the art.

Measures of Day and Night Times Ozone (O3) Concentration in Parts of Port Harcourt, Rivers State, Nigeria

This work was aimed at taking measures of ozone concentration during day and night times in Port Harcourt. To this end, we employed a smart unmanned aerial vehicle (UAV) that we had previously designed and constructed to take measures of atmospheric zone concentration in mg/m3. We collected data, in 5-minutes intervals, at noon for 245 minutes within 1pm-3:45pm and at night for 180minutes within 8pm-10pm on the 18/11/2022 on our pilot launch. The data collected was determined by how long our device battery could last on the pilot launch. Our results from the day time measures of ozone concentration showed that the concentration of ozone was quite stable, remaining between 0.1 and 0.2 mg/m3 the whole period while the night time observation shows a natural rise in ozone levels throughout this time period, with concentrations ranging from 0.03 to 0.06 mg/m3. This shows that the ozone concentration was higher during the night time, which could be attributed to Port Harcourt being an urban area and the weather condition in Port Harcourt during the period of this study.

A W-Band Active Phased Array Miniaturized Scan-SAR with High Resolution on Multi-Rotor UAVs

The smart unmanned aerial vehicle (UAV) with a mini-SAR payload provides an advanced earth observation capability for target detection and imaging. Simiar to large-scale SAR, mini-SAR also has an increasing requirement for high resolution and wide swath. However, due to the low cruising altitude of UAVs, small coverage angles in the direction range, and the insufficient operating range of mini-SAR, expanding the swath has become an urgent problem for mini-SAR. To solve this problem, this paper proposes a W-Band active phased array miniaturized SAR (APA mini-SAR), whose scanning capacity has been proven on the multi-rotor UAV platform. Many efforts, including the novel active phased array antenna scheme, the sparse triangular arrangement of antenna elements, the wideband chirp source, and the three-dimensional integration technology, have been made to develop this APA mini-SAR for the first time in the W-Band. The volume of this APA mini-SAR is 69 × 82 × 87 mm3, with a weight of 600 g. Combined with a new motion compensation strategy and the ω-k imaging algorithm, the focused image is finally obtained. Experiments have been conducted, and the results indicate that this APA mini-SAR has a resolution of 4.5 cm, the imaging swath is three times that of the traditional single-channel mini-SAR, and the operating range is increased to 800 m.

Aerial Drone Survey

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more than $45 Billion market value of UAV usage. Unmanned aerial vehicles often are employed in civil engineering for a variety of purposes. Drones as instruments that improve communication between construction participants, improve site safety, use topographic measurements of huge regions, and produce buildings aerial surveying, bridges, roads, and highways utilising principles of aerial photogrammetry, saving project time and costs, and so on.

RESEARCH OF THE INERTIAL DEVIATION MEASUREMENT SYSTEM UAV CARGO FROM ZERO POINT

Inertial measurement units (IMUs) can consist of one or more sensors that collect data designed to measure inertial motions in a specific frame of reference. Acceleration, rotational speed are examples of data that can be obtained from the sensors contained in the IMU. IMUs can be found in a variety of smart devices, medical rehabilitation, general robotics, UAV control and navigation enhancement systems, sports devices, and virtual reality systems. Some engineering solutions and displacement sensors have a number of limitations, such as systems that do not have a clear initial reference point. In such systems, it is difficult to track deviations from the starting point. However, the acceleration of a moving object is usually easy to measure, so the given deflection of the object can easily be obtained using the integral of the acceleration. To solve such problems, a distance measurement system based on a microelectromechanical system (MEMS) accelerometer has been developed. The hardware structure of the system includes a data measurement module and integrated data collection and data processing modules. Due to the physical properties of the sensor, such a system can be used in a small space, with large overloads and other adverse conditions. This article describes a system calibration method and proposes a deviation tracking algorithm based on acceleration integration. Fixed output error caused by sensor mounting position is analyzed. The analysis shows that if this factor is not taken into account, the error will lead to a serious error. The experiment of measurement of the deviation from the zero point is carried out on a horizontal, flat surface, and the calculation results show that the average accuracy of the deviation measurement of this system can reach 99.05%. The results of the experiment indicate the feasibility of the system in the conditions of short-term free fall and the validity of the data processing algorithm. The accuracy of the distance measurement system can meet most engineering needs.

A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling.

The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods—such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys—are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.

Unmanned Aerial Vehicles: A Literature Review

In recent years, Unmanned Aerial Vehicles (UAVs) have grown and increased in applications because of computational simplicity and adaptive control capacity with strong support from both civilian and military sectors. The applications of UAVs in various military, commercial and civilian areas have led to sustainable results. The application areas include but are not limited to oil & gas, cargo transport, geographic mapping, aerial photography, health care, and disaster management. The success of the UAV application missions is completely dependent on the accuracy in control provided by the flight controllers. Thus, there is a need for accurate, robust, and adaptive flight controllers. UAV dynamics modeling and identification and control of these vehicles are still major active areas of research and development. They pose severe challenges due to the vehicle's complex design, inherently nonlinear, and time-varying dynamics. The main goal of this paper is to identify the past research trends and recent improvements in UAVs. Furthermore, this paper discusses a comprehensive literature review according to the optimized objectives, solution techniques, and applications of UAVs such as Cargo Transport, Disaster Management, etc. According to the literature review, aerial photography is one of the applications of smart UAVs. The reliability of image matching across multiple camera perspectives, angles, and positions encourages computer vision approaches for UAV navigation, opening the way for future researchers to develop vision applications. This article presents a comprehensive literature review discussing the importance of UAV applications related to cost-effectiveness and versatility. Furthermore, a detailed survey of system modeling identification and control techniques is presented.

Air Pollution Monitoring in Remote Areas using Smart UAV based System

Real time monitoring of air pollution and measurement of pollutant gases is very challenging task and needs more numbers of sensors and lots of observation. In this paper, Unmanned Aerial Vehicle (UAV) based system equipped with different micro sensor, is introduced for monitoring of air quality. It will offer a new approach in environmental pollution assessment instead of ground based monitoring system. Air pollution concentration data is collected by different sensors present in UAV, are effectively monitored in personal computer or mobile devices. The main objective of this paper is to elaborate the performance capability of UAV for effective monitoring of air pollution and measure health hazard air pollutants with high sensitivities in a particular area where human cannot reach. As it is quite recent field, a fruitful effort has dedicated to develop an integrated sensing system and optimization of its crucial features as dimension, weight and energy autonomy. The effectiveness of the developed system is evaluated by performing some field experiments using a hardware prototype UAV model.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Machine learning techniques in internet of UAVs for smart cities applications

Recently, there were much interest in technology which has emerged greatly to the development of smart unmanned systems. Internet of UAV (IoUAV) enables an unmanned aerial vehicle (UAV) to connect with public network, and cooperate with the neighboring environment. It also enables UAV to argument information and gather data about others UAV and infrastructures. Applications related to smart UAV and IoUAV systems are facing many impairments issues. The challenges are related to UAV cloud network, big data processing, energy efficiency in IoUAV, and efficient communication between a large amount of different UAV types, in addition to optimum decisions for intelligence. Artificial Intelligence (AI) technologies such as Machine Learning (ML) mechanisms enable to archives intelligent behavior for unmanned systems. Moreover, it provides a smart solution to enhance IoUAV network efficiency. Decisions in data processing are considered one of the most problematic issues related to UAV especially for the operations related to cloud and fog based network levels. ML enables to resolve some of these issues and optimize the Quality of UAV network experience (QoE). The paper provides theoretical fundamentals for ML models and algorithms for IoUAV applications and recently related works, in addition to future trends.

Study and Comparison Analysis of Conventional Light Weight UAV Airfoils using XFLR Analysis

In Aeronautical industries one of the very important issues in smart UAV is the duration of flight since the overall time uses standard power that may be an unusable product, includes a restricted life, and is cost. But, there's an enormous demand for exploitation, a limitless non-exhaustible supply of power. So an alternative source of power calculation is required to get efficient flying conditions for a UAV. This work is to study and analysis of solar energy UAV airfoil using XFLR analysis. A UAV with 2.98Kg of weight is studied for power calculation, implementation of solar cells, and design aspects, including airfoil, fuselage, and tail section. From analysis it is found that UAV flight performance at solar radiation intensities above 451.23 W/m2 for power system which worked to increase the battery life and works more efficiently at solar radiation intensities above 666.5 W/m2.

A PUF‐based lightweight authentication and key agreement protocol for smart UAV networks

With the advancement of information technology and the reduction of costs, the application of unmanned aerial vehicle (UAV) has gradually expanded from the military field to the industrial field and civilian field. It brings great convenience to people in surveillance, detection, transportation, emergency rescue etc. However, UAVs usually work in harsh natural environments, and their communication security confronts various challenges. Due to UAVs' limited resources, such as computing capability, storage space, and energy, traditional security protection schemes based on complex cryptographic algorithms are not suitable for UAV systems directly. Therefore, a two-stage lightweight identity authentication and key agreement protocol for UAV is proposed in this paper. The entire process only uses hash and XOR operations, which significantly improves the authentication efficiency. Simultaneously, the physical unclonable function (PUF) is introduced and embedded into the UAV hardware to ensure UAV network communication security when a UAV suffers a physical capture attack. In the paper, the security of the proposed protocol is proved with Burrows–Abadi–Needham (BAN) logic, Real-or-Random (ROR) model, and AVISPA simulation tools. An informal security analysis is also provided to illustrate that the protocol satisfies the security requirements of UAV networks. Finally, the protocol is compared with other existing protocols regarding function properties, computation cost, and communication cost, which shows that the proposed protocol has effectiveness and practicality.