Search Results (27)

This study proposes a novel optical wireless communication system for high-speed, large-capacity data transmission, supporting underwater IoT devices in shallow seas. The system employs a mirror-equipped aerial drone as a relay between underwater drones and a terrestrial station, using 850 nm optical signals for low atmospheric loss and enhanced confidentiality. Adaptive modulation optimizes transmission capacity based on SNR, accounting for air and underwater channel characteristics. Experiments confirmed an exponential SNR decrease with distance (0.6–1.8 m) and demonstrated successful 4K UHD video streaming in shallow seawater (turbidity: 2.2 NTU) without quality loss. The design ensures cost-effectiveness and stable optical alignment using advanced posture control. Full article

A novel common-aperture miniaturized antenna with wideband and dual-polarized characteristics is proposed, which consists of a circularly polarized (CP) and a linearly polarized (LP) antenna. The circularly polarized antenna stacked on the upper layer adopts asymmetrical ground and introduces the patch and T-type feed network. On this basis, the meshed reflector structure, which also works as a ground plane for the LP antenna, is added to reduce the influence on circular polarization and achieve directional radiation. The LP antenna stacked in the lower layer uses a monopole structure, and the coaxial feed line perforates the reflector, and thereby the common-aperture antennas are tightly stacked together from top to bottom. Simulation and test are in good accordance, and the results show that the two ports of the antenna are well matched in the range of 5.5 GHz to 7.8 GHz, where peak gains of 8.5 dB and 6 dB are realized for circular polarization and linear polarization, respectively. Moreover, the 3 dB axial ratio (AR) bandwidth of the CP antenna is 34.3% and the isolation between the two ports is better than 15 dB, suggesting potential applications in the relay platform or drone detection for signal transmission and reception. Full article

Using drone cells to optimize Radio Access Networks is an exemplary way to enhance the capabilities of terrestrial Radio Access Networks. Drones fitted with communication and relay modules can act as drone cells to provide an unobtrusive network connection. The multi-drone-cell placement problem is solved using adapted Dispersive Flies Optimization alongside other meta-heuristic algorithms such as Particle Swarm Optimization and differential evolution. A home-brewed simulator has been used to test the effectiveness of the different implemented algorithms. Specific environment respective parameter tuning has been explored to better highlight the possible advantages of one algorithm over the other in any particular environment. Algorithmic diversity has been explored, leading to several modifications and improvements in the implemented models. The results show that by using tuned parameters, there is a performance uplift in coverage probability when compared to the default meta-heuristic parameters while still remaining within the constraints implied by the problem’s requirements and resource limitation. This paper concludes by offering a study and comparison between multiple meta-heuristic approaches, investigating the impact of parameter tuning as well as analyzing the impact of intermittent restarts for the algorithms’ persistent diversity. Full article

To address the issues of communication failure and inefficiency in clustered drone relay communication due to external malicious interference, this paper proposes a joint optimization method for relay communication rates under interference conditions for clustered drones. This method employs the following two-step processing framework: Firstly, the Discrete Soft Actor-Critic (DSAC) algorithm is used to train the relay drones for dynamic channel selection, effectively avoiding various types of interference. Simultaneously, the Bayesian optimization algorithm is applied to optimize the hyperparameters of the DSAC algorithm, further enhancing its performance. Subsequently, the modulation order, transmission power, trajectory of the relay drones, and power allocation factors of the clustered drones are jointly optimized. This complex problem is transformed into a convex subproblem for determining a solution, aiming to maximize the communication rate of the clustered drones. The simulation’s results demonstrate that the proposed algorithm exhibits excellent performances in terms of anti-interference capability, solution convergence, and stability. It effectively improves the mission efficiency of clustered drones under interference conditions and enhances their adaptability to dynamic environments. Full article

In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and non-line-of-sight environments that may cause service degradation or even failure. This paper presents a single-station emergency solution that integrates communication and TT&C (IC&T) functions based on radar chaff cloud technology. Firstly, a suitable selection of frequency bands and modulation methods is provided for the emergency IC&T system to ensure compatibility with existing communication and TT&C systems while catering to the future needs of IC&T. Subsequently, theoretical analyses are conducted on the communication link transmission loss, data transmission, code tracking accuracy, and anti-multipath model of the emergency IC&T system based on the chosen frequency band and modulation mode. This paper proposes a dual-way asynchronous precision ranging and time synchronization (DWAPR&TS) system employing dual one-way ranging (DOWR) measurement, a dual-way asynchronous incoherent Doppler velocity measurement (DWAIDVM) system, and a single baseline angle measurement system. Next, we analyze the physical characteristics of the radar chaff and establish a dynamic model of spherical chaff cloud clusters based on free diffusion. Additionally, we provide the optimal strategy for deploying chaff cloud. Finally, the emergency IC&T application based on the radar chaff cloud relay is simulated, and the results show that for severe interference, taking drones as an example, under a measurement baseline of 100 km, the emergency IC&T solution proposed in this paper can achieve an accuracy range of approximately 100 m, a velocity accuracy of 0.1 m/s, and an angle accuracy of 0.1°. In comparison with existing TT&C system solutions, the proposed system possesses unique and potential advantages that the others do not have. It can serve as an emergency IC&T reference solution in denial environments, offering significant value for both civilian and military applications. Full article

Unmanned aerial vehicle (UAV) swarms serve as a dynamic platform for diverse missions, including communication relays, search and rescue operations, and environmental monitoring. The success of these operations crucially depends on the resilience of their electrical support systems, especially in terms of battery management. This paper examines the reliability of electrical support for UAV swarms engaged in missions that require prioritization into high and low categories. The paper proposes a dynamic resource allocation strategy that permits the flexible reassignment of drones across different-priority tasks, ensuring continuous operation while optimizing resource use. By leveraging the Markov chain theory, an analytical model for the evaluation of the resilience of the battery management system under different operational scenarios was developed. The paper quantitatively assesses the impact of different operational strategies and battery management approaches on the overall system resilience and mission efficacy. This approach aims to ensure uninterrupted service delivery for critical tasks while optimizing the overall utilization of available electrical resources. Through modeling and analytical evaluations, the paper quantifies the impact of various parameters and operating strategies on overall system resilience and mission availability, considering the utilization strategies of batteries and their reliability and maintenance metrics. The developed models and strategies can inform the development of robust battery management protocols, resource allocation algorithms, and mission planning frameworks, ultimately enhancing the operational availability and effectiveness of UAV swarms in critical special missions. Full article

This study presents a performance analysis for an Internet-of-Things wildlife radio-tracking mission using drones, satellites and stratospheric platforms for data relay with Spread Spectrum Modulation devices. The performance analysis is presented with link and data budgets, calculations of the area coverage, an estimation of the time resolution and allowable data amount of each collar, a power and energy budget and consequent battery pack and collar weight estimations, cost budgets, and considerations on synergetic approaches to incorporate more mission segments together. The paper results are detailed with example species to target with each collar weight range, and with design drivers and guidelines to implement improved mission segments. Full article

Integrating the relaying drone and non-orthogonal multiple access (NOMA) technique into post-disaster emergency communications (PDEComs) is a promising way to accomplish efficient network recovery. Motivated by the above, by optimizing the drone three-dimensional (3D) deployment optimization and spectrum allocation, this paper investigates a quality of service (QoS)-driven sum rate maximization problem for drone-and-NOMA-enhanced PDEComs that aims to improve the data rate of cell edge users (CEUs). Due to the non-deterministic polynomial (NP)-hard characteristics, we first decouple the formulated problem. Next, we obtain the optimal 3D deployment with the aid of a long short-term memory (LSTM)-based recurrent neural network (RNN). Then, we transform the spectrum allocation problem into an optimal matching issue, based on which the Hungarian algorithm is employed to solve it. Finally, the simulation results show that the presented scheme has a significant performance improvement in the sum rate compared with the state-of-the-art works and benchmark scheme. For instance, by adopting the NOMA technique, the sum rate can be increased by 9.72% and the needs of CEUs can be satisfied by enabling the relaying drone. Additionally, the convergence, complexity, and performance gap caused by iterative optimization are discussed and analyzed. Full article

Amphibious (air and water) drones, capable of both aerial and aquatic operations, have the potential to provide valuable drone applications in aquatic environments. However, the limited range of wireless data transmission caused by the low antenna height on water and reflection from the water surface (e.g., 45 m for vertical half-wave dipole antennas with the XBee S2C^TM, estimated using the two-ray ground reflection model) persists as a formidable challenge for amphibious systems. To overcome this difficulty, we developed a wireless data relay system for amphibious drones using the mesh-type networking functions of the XBee^TM. We then conducted field tests of the developed system in a large marsh pond to provide experimental evidence of the efficiency of the multiple-drone network in amphibious settings. In these tests, hovering relaying over water was attempted for extension and bypassing obstacles using the XBee S2C^TM (6.3 mW, 2.4 GHz). During testing, the hovering drone (<10 m height from the drone controller) successfully relayed water quality data from the transmitter to the receiver located approximately 757 m away, but shoreline vegetation decreased the reachable distance. A bypassing relay test for vegetation indicated the need to confirm a connected path formed by pair(s) of mutually observable drones. Full article

Networks constructed in the sky are known as non-terrestrial networks (NTNs). As an example of an NTN, relay transmission using drones as radio stations enables flexible network construction in the air by performing handovers with ground stations. However, the presence of structures or obstacles in the flight path causes multipath interference; consequently, the propagation environment fluctuates significantly based on the flight. In such a communication environment, it is difficult for a drone to select an optimal ground station for a handover. Moreover, unlike a terrestrial network, the propagation environment of a flying drone is affected by structures and other factors that cause multipaths based on the flight speed and altitude, making the conditions of the propagation environment even more complex. To solve these problems, we propose handover schemes between drones and the ground that consider the multipath interference caused by obstacles. The proposed methods are used to perform handovers based on an optimal threshold of received power considering interference and avoid unnecessary handovers based on the moving speed, which makes the handover seamless. Finally, we develop a simulator that evaluates the cross layer from propagation to upper network protocols in a virtual space, including buildings, evaluate the communication quality of a drone flying in a three-dimensional space, and confirm the effectiveness of the proposed methods as well as the evaluation of the real environment. Full article

The evolution of agriculture towards a modern, intelligent system is crucial for achieving sustainable development and ensuring food security. In this context, leveraging the Internet of Things (IoT) stands as a pivotal strategy to enhance both crop quantity and quality while effectively managing natural resources such as water and fertilizer. Wireless sensor networks, the backbone of IoT-based smart agricultural infrastructure, gather ecosystem data and transmit them to sinks and drones. However, challenges persist, notably in network connectivity, energy consumption, and network lifetime, particularly when facing supernode and relay node failures. This paper introduces an innovative approach to address these challenges within heterogeneous wireless sensor network-based smart agriculture. The proposed solution comprises a novel connectivity management scheme and a dynamic clustering method facilitated by five distributed algorithms. The first and second algorithms focus on path collection, establishing connections between each node and m-supernodes via k-disjoint paths to ensure network robustness. The third and fourth algorithms provide sustained network connectivity during node and supernode failures by adjusting transmission powers and dynamically clustering agriculture sensors based on residual energy. In the fifth algorithm, an optimization algorithm is implemented on the dominating set problem to strategically position a subset of relay nodes as migration points for mobile supernodes to balance the network’s energy depletion. The suggested solution demonstrates superior performance in addressing connectivity, failure tolerance, load balancing, and network lifetime, ensuring optimal agricultural outcomes. Full article

Every second counts when a patient who requires an organ transplant is finally matched with a donor. The organ’s post-transplant performance declines with the increasing time between the organ’s removal and transplantation into the recipient. Organs must be transported from point A to B as quickly and safely as possible to improve the chances of success. In addition to delivering medical goods or vaccines to hard-to-reach places, drones can help us to save lives across the world, but there, are some issues to address, one of which is maintaining container temperature and humidity and monitoring it. Further, drones carrying medical containers flying at different altitudes causes temperature changes, which may affect the organs. To tackle such difficulties, in this work a smart container embedded with a Peltier module (thermoelectric cooler) and a temperature sensor has been developed to maintain the temperature thereby providing safety for healthcare products or organs. Further, the relay module is utilized to control the Peltier module and ESP8266 WIFI Microcontroller (MCU) which also enables the user to send live data to the cloud and also allows the user to monitor and control the temperature remotely. The Blynk Internet of Things (IoT) platform is used to monitor the temperature. Results show that the proposed system is highly efficient at monitoring and controlling temperature changes accurately according to user-defined values. For demonstration purposes, the temperature of the container is maintained at 12 degrees Celsius and the performance of the system is presented. The medical cargo drone carrying healthcare products is tested in real time and at different altitude levels to examine the performance of the developed system. Full article

With the rapid development of smart grids, the deployment number of transmission lines has significantly increased, posing significant challenges to the detection and maintenance of power facilities. Unmanned aerial vehicles (UAVs) have become a common means of power inspection. In the context of drone power inspection, drone clusters are used as relays for long-distance communication to expand the communication range and achieve data transmission between patrol drones and base stations. Most of the communication occurs in the air-to-air channel between UAVs, which requires high reliability of communication between drone relays. Therefore, the main focus of this paper is on decoding schemes for drone air-to-air channels. Given the limited computing resources and battery capacity of a drone, as well as the large amount of power data that needs to be transmitted between drone relays, this paper aims to design a high-accuracy and low-complexity decoder for LDPC long-code decoding. We propose a novel shared-parameter neural-network-normalized minimum sum decoding algorithm based on codebook quantization, applying deep learning to traditional LDPC decoding methods. In order to achieve high decoding performance while reducing complexity, this scheme utilizes codebook-based weight quantization and parameter sharing methods to improve the neural-network-normalized minimum sum (NNMS) decoding algorithm. Simulation experimental results show that the proposed method has a better BER performance and low computational complexity. Therefore, the LDPC decoding algorithm designed effectively meets the drone characteristics and the high channel decoding performance requirements. This ensures efficient and reliable data transmission on the data link between drone relays. Full article

The effectiveness of human security-based guard patrol systems often faces challenges related to the consistency of perimeter checks regarding timing and patterns. Some solutions use autonomous drones for monitoring assistance but primarily optimize their camera-based object detection capabilities for favorable lighting conditions. This research introduces an innovative approach to address these limitations—a flying watchdog designed to augment patrol operations with predetermined flight patterns, enabling checkpoint identification and position verification through vision-based methods. The system has a laser-based data transmitter to relay real-time location and timing information to a receiver. The proposed system consists of drone and ground checkpoints with distinctive shapes and colored lights, further enhanced by solar panels serving as laser data receivers. The result demonstrates the drone’s ability to detect four white dot LEDs with square configurations at distances ranging from 18 to 20 m, even under deficient light conditions based on the OpenCV detection algorithm. Notably, the study underscores the significance of achieving an even distribution of light shapes to mitigate light scattering effects on readings while also confirming that ambient light levels up to a maximum of 390 Lux have no adverse impact on the performance of the sensing device. Full article

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, especially in resource availability. This context underscores an urgent need for solutions that synergize low latency, high precision, and computational efficiency. Previous methodologies have primarily depended on high-resolution images, leading to considerable computational burdens. To enhance the efficiency and accuracy of object detection in drone aerial images, and building on the YOLOv7, we propose the Efficient YOLOv7-Drone. Recognizing the common presence of small objects in aerial imagery, we eliminated the less efficient P5 detection head and incorporated the P2 detection head for increased precision in small object detection. To ensure efficient feature relay from the Backbone to the Neck, channels within the CBS module were optimized. To focus the model more on the foreground and reduce redundant computations, the TGM-CESC module was introduced, achieving the generation of pixel-level constrained sparse convolution masks. Furthermore, to mitigate potential data losses from sparse convolution, we embedded the head context-enhanced method (HCEM). Comprehensive evaluation using the VisDrone and UAVDT datasets demonstrated our model’s efficacy and practical applicability. The Efficient Yolov7-Drone achieved state-of-the-art scores while ensuring real-time detection performance. Full article