Ground Controllers Research Articles

The newest aspect of industrial espionage : Cyber threats from UAVs to non-state actors

The crime of industrial espionage to obtain trade secrets is as old as the dawn of commerce. With the development of modern technology, the techniques for obtaining secrets, or in modern terms, data, have been constantly evolving. With the advent of drones on the market, physical and cyber-attacks against critical infrastructure and non-state actors have increased significantly. The relative ease of modification of UAVs (Unmanned Aerial Vehicle) has led to the emergence of spy vehicles capable of acquiring sensitive data without the ground controller entering the area. Critical infrastructure is protected at the state level, with military and/or national security at the global level, but non-state actors are vulnerable to attack. The aim of this paper is to show how non-state actors are affected by the threat and what options they have to defend themselves. Whether EEA countries are threatened by this form of espionage.

Application of Remote Wireless Intelligent Layered Water Injection Technology in Dagang Oilfield

A remote wireless intelligent layered water injection technolo-gy is proposed to address the problems of traditional layered water in-jection well testing in oil fields, such as the inability to achieve continu-ous dynamic monitoring of injection parameters, high failure rate, and low success rate of testing and commissioning. The technology consists of a host computer client, a server, a surface controller, and an under-ground intelligent water distributor. The underground intelligent water distributor takes the injected water as the carrier, and uses the fluctua-tion of the water injection pressure in the tubing for signal transmission to realize two-way communication between the ground controller and the downhole intelligent water distributor. The underground intelligent water distributor integrates high-precision pressure gauges, controllable water nozzles, and calculation and control modules to form an intelli-gent control system, realizing the reception of ground down transmitted commands or data, the calculation and control of water injection flow rate of water nozzles, and the storage and upload of underground data. Remote wireless intelligent layered water injection technology has been applied in 43 wells, with a success rate of 100%. The maximum depth is 3897m, and the maximum wellbore inclination is 56.07°. This tech-nology realizes remote control of water injection wells, intelligent au-tomatic measurement and adjustment, real-time data monitoring, and automatic early warning functions, laying a foundation for the con-struction of “Digital Intelligence” oilfield.

Operational Considerations for Crew Fatality on the International Space Station.

BACKGROUND: While catastrophic spaceflight events resulting in crew loss have occurred, human spaceflight has never suffered an on-orbit fatality with survival of other crewmembers on board. Historical plans for management of an on-orbit fatality have included some consideration for forensic documentation and sample collection, human remains containment, and disposition of remains; however, such plans have not included granular detailing of crew or ground controller actions. The NASA Johnson Space Center Contingency Medical Operations Group, under authority from the Space and Occupational Medicine Branch, the Space Medicine Operations Division, and the Human Health and Performance Directorate, undertook the development of a comprehensive plan, including an integrated Mission Control Center response for flight control teams and Flight Surgeons for a single on-orbit crew fatality on the International Space Station (ISS) and subsequent events. Here we detail the operational considerations for a crew fatality should it occur during spaceflight onboard the ISS, including forensic and timeline constraints, behavioral health factors, and considerations for final disposition of decedent remains. Future considerations for differential survival and crewmember fatality outside of low-Earth orbit operations will additionally be discussed, including consideration of factors unique to planetary and surface operations and disposition limitations in exploration spaceflight. While the efforts detailed herein were developed within the constraints of the ISS concept of operations, future platforms may benefit from the procedural validation and product verifications steps described. Ultimately, any response to spaceflight fatality must preserve the goal of handling decedent remains and disposition with dignity, honor, and respect.Stepaniak PC, Blue RS, Gilmore S, Beven GE, Chough NG, Tsung A, McMonigal KA, Mazuchowski EL II, Bytheway JA, Lindgren KN, Barratt MR. Operational considerations for crew fatality on the International Space Station. Aerosp Med Hum Perform. 2023; 94(9):705-714.

Comparison of logistic regression and artificial neural network model for apron allocation assignment

Management of the parking apron is one of the most essential airport ground service operations for flight operations to run smoothly. Effective airport ground service management will have a direct effect on the cost and duration of flights. Therefore, in this paper, we address the issue of using machine learning techniques, such as logistic regression analysis and artificial neural network (ANNs) models, for classified targets of stand locations assignment of an arriving flight. Also, this could assist ground controllers to assign apron allocation and improve the efficiency and predictability of airport operations which reduce the time required for airport ground processing to increase flight capacity. In order to evaluate the performance of the proposed method, simulation results reveal that ANN has the lowest error rate and the highest accuracy. Therefore, ANN is the effective classification technique for this data set.

Selection of Factors for Performance Evaluation of Fire-fighting Drones based on Delphi Techniques

This study aimed to identify the factors that can be used to evaluate the performance standards of fire-fighting drones employed in various fire-fighting missions. For this purpose, several common and uncommon performance factors were obtained by analyzing prior data from domestic and foreign research, analyses, and evaluations and three rounds of Delphi surveys were conducted. Through the consensus of a panel of experts, a total of 32 fire drone performance evaluations were classified into four evaluation categories: airframe (basic performance), battery, Ground Control System and controller, and mission equipment. This study identifies metrics for assessing the performance of fire-fighting drones and provides recommendations for their evaluation. The outcome of this study is expected to significantly contribute in future to the development of drone performance evaluation manuals with established criteria and methods for each item.

SAS-SEINet: A SNR-Aware Adaptive Scalable SEI Neural Network Accelerator Using Algorithm-Hardware Co-Design for High-Accuracy and Power-Efficient UAV Surveillance.

As a potential air control measure, RF-based surveillance is one of the most commonly used unmanned aerial vehicles (UAV) surveillance methods that exploits specific emitter identification (SEI) technology to identify captured RF signal from ground controllers to UAVs. Recently many SEI algorithms based on deep convolution neural network (DCNN) have emerged. However, there is a lack of the implementation of specific hardware. This paper proposes a high-accuracy and power-efficient hardware accelerator using an algorithm–hardware co-design for UAV surveillance. For the algorithm, we propose a scalable SEI neural network with SNR-aware adaptive precision computation. With SNR awareness and precision reconfiguration, it can adaptively switch between DCNN and binary DCNN to cope with low SNR and high SNR tasks, respectively. In addition, a short-time Fourier transform (STFT) reusing DCNN method is proposed to pre-extract feature of UAV signal. For hardware, we designed a SNR sensing engine, denoising engine, and specialized DCNN engine with hybrid-precision convolution and memory access, aiming at SEI acceleration. Finally, we validate the effectiveness of our design on a FPGA, using a public UAV dataset. Compared with a state-of-the-art algorithm, our method can achieve the highest accuracy of 99.3% and an F1 score of 99.3%. Compared with other hardware designs, our accelerator can achieve the highest power efficiency of 40.12 Gops/W and 96.52 Gops/W with INT16 precision and binary precision.

VaCoChain: Blockchain-Based 5G-Assisted UAV Vaccine Distribution Scheme for Future Pandemics.

This paper proposes a generic scheme VaCoChain, that fuses blockchain (BC) and unmanned aerial vehicles (UAVs) underlying fifth-generation (5G) communication services for timely vaccine distribution during novel coronavirus (COVID-19) and future pandemics. The scheme offers 5G-tactile internet (5G-TI) based services for UAV communication networks (UAVCN) monitored through ground controller stations (GCs). 5G-TI enabled UAVCN supports real-time dense connectivity at ultra-low round-trip time (RTT) latency of [Formula: see text] and high availability of 99.99999%. Thus, it can support resilient vaccine distributions in a phased manner at government-designated nodal centers (NCs) with reduced round trip delays from vaccine production warehouses (VPW). Further, UAVCNs ensure minimizes human intervention and controls vaccine health conditions due to shorter trip times. Once vaccines are supplied at NCs warehouses, then the BC ensures timestamped documentation of vaccinated persons with chronology, auditability, and transparency of supply-chain checkpoints from VPW to NCs. Through smart contracts (SCs), priority groups can be formed for vaccination based on age, healthcare workers, and general commodities. In the simulation, for UAV efficacy, we have compared the scheme against fourth-generation (4G)-assisted long term evolution-advanced (LTE-A), orthogonal frequency division multiplexing (OFDM) channels, and traditional logistics for round-trip time (RTT) latency, logistics, and communication costs. In the BC setup, we have compared the scheme against the existing 5G-TI delivery scheme (Gupta et al.) for processing latency, packet losses, and transaction time. For example, in communication costs, the proposed scheme achieves an average improvement of 9.13 for block meta-information. For 4000 transactions, the proposed scheme has a communication latency of 16 s compared to 36 s. The packet loss is significantly reduced to 2.5% using 5G-TI compared to 16% in 4G-LTE-A. The proposed scheme has a computation cost of 1.6 ms and a communication cost of 157 bytes, which indicates the scheme efficacy against conventional approaches.

Alarms, alerts, and warnings in air traffic control: An analysis of reports from the Aviation Safety Reporting System

Signals (alarms, alerts, and warnings) are essential for alerting air traffic controllers to potential collisions and other adverse events. Excessive or misleading signals can increase response times and decrease their response rates. We used reports from the Aviation Safety Reporting System and structured interviews to understand the complexity of the controller’s tasks in the context of potentially high-consequence situations and events, and to develop design strategies to enhance the effectiveness of signals in the ATC environment. MethodsWe reviewed ASRS reports over a 6-year interval from 2015 to 2020, searching for reports that mentioned alarm, alert, or warning and were submitted by air traffic controllers. We found 370 relevant reports that we analyzed for hits, misses, false alarms, and correct rejections. Structured interviews with former controllers further explored the role of signals in air traffic control. ResultsThe most common signals in reports were MSAW (139), ASDE-X and ASSC (27), CA (195), and AMASS (4). ASDE-X, ASSC, or AMASS were mentioned in 30 reports by ground or local controllers. TRACON controllers reported events involving MSAW 70 times and CA 51 times; these were also implicated by local controllers. CA was most mentioned, cited a total of 195 times in the reports. ConclusionsThis information may help us to develop strategies that can enhance signaling modality (e.g., new auditory, visual, and tactile signals). Trust in automation may be improved by using strategies such as indicating the automation’s level of confidence in situations like impending loss of separation.

Deep Reinforcement Learning-Based Resource Allocation in Cooperative UAV-Assisted Wireless Networks

We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network consisting of multiple cooperative UAVs, whose operations are coordinated by a central ground controller using wireless fronthaul links, to serve multiple ground user equipments (UEs). A problem of jointly designing UAVs’ positions, transmit beamforming, as well as UAV-UE association is formulated in the form of mixed integer nonlinear programming (MINLP) to maximize the sum UEs’ achievable rate subject to limited fronthaul capacity constraints. Solving the considered problem is hard owing to its non-convexity and the unavailability of channel state information (CSI) due to the movement of UAVs. To tackle these effects, we propose a novel algorithm comprising of two distinguishing features: (i) exploiting a deep Q-learning approach to tackle the issue of CSI unavailability for determining UAVs’ positions, (ii) developing a difference of convex algorithm (DCA) to efficiently solve for the UAV’s transmit beamforming and UAV-UE association. The proposed algorithm recursively solves the problem of interest until convergence, where each recursion executes two steps. In the first step, the deep Q-learning (DQL) algorithm allows UAVs to learn the overall network state and account for the joint movement of all UAVs to adapt their locations. In the second step, given the determined UAVs’ positions from the DQL algorithm, the DCA iteratively solves a convex approximate subproblem of the original non-convex MINLP problem with the updated parameters, where the problem’s variables are transmit beamforming and UAV-UE association. Numerical results show that our design outperforms the existing algorithms in terms of algorithmic convergence and network performance with a gain of up to 70%.

Research of UAV Controller-pilot Data Link Real-time Processing Technique

In traditional flight tests, ground monitoring is based on on-board telemetry data. With the rapid development of UAV design, the monitoring data source transitions from radio telemetry transmission to ground-to-air data link transmission gradually. Traditional ground real-time processing system can’t meet the new requirements. UAV controller-pilot data link real-time processing technique doesn’t rely on traditional PCM telemetry, via controller-pilot data link which is integrated into the UAV, ground controller system and flight tests data processing system, which promotes the efficiency of flight test dramatically, the data processing duration is also reduced significantly.

An enhanced AHP-TOPSIS-based clustering algorithm for high-quality live video streaming in flying ad hoc networks

Flying ad hoc networks (FANETs) consist of unmanned aerial vehicles (UAVs) with energy limitations which have the capability of sending recorded live video stream to supervise their surroundings completely and intelligently. Although significant efforts have been made by previous researchers to increase the quality of received video stream as a main mission of a UAV, challenges like energy consumption, effective use of bandwidth, effective clustering among UAVs and their intelligent communication with ground stations especially at the same time have not been noticed in the past research studies simultaneously. Therefore, in the proposed method, for the first time, a low complex AHP-TOPSIS hybrid algorithm has been used for effective clustering in FANETs. Cluster heads (CHs), in addition to imaging, receive the recorded videos frames by other UAVs through Wi-Fi and send them to the ground station through 5G connection. Using AHP-TOPSIS algorithm, the ground controller intelligently specifies which UAVs should be CH in regular intervals. Therefore, because of UAVs’ swarm reduction and, at the same time, effective use of bandwidth, traffic and delay in transferring live video frames are reduced which leads to achieving high video quality in ground station and, at the same time, reduction UAV energy consumption. The results of numerous simulations in OMNET + + under different conditions show that the parameters of video quality percentage, UAV average energy consumption and the number of necessary cluster head have been significantly improved when two famous mobility models including Paparazzi and Random Waypoint are considered comparing other methods.

A new provable hierarchical anonymous certificateless authentication protocol with aggregate verification in ADS-B systems

Automatic Dependent Surveillance-Broadcast (ADS-B) technology is a new solution for communication among aircraft and ground controller stations. In this new surveillance technology, critical messages (e.g., the location of aircraft) are achieved by navigation satellites, and then an on-board equipment multicasts the unencrypted messages twice per second to the others. The former ADS-B protocols suffer from weak authentication protocols with a few security vulnerabilities and privacy issues including key-escrow problem, user profiling, time consuming verification processes, and difficulties with certificate management. In this paper, we propose a new hierarchical authentication protocol used Certificateless Public Key Cryptography (CL-PKC) technique to avoid using Public Key Infrastructure (PKI) certificate management and solve the key-escrow concern in Identity-based Public Key Cryptography (ID-PKC). Further, unlike many of certificateless schemes, our proposed scheme is secure against malicious-but-passive Key Generation Center (KGC). We prove that our scheme preserves conditional privacy, which means that distinct identities map to varied pseudonyms. The security and privacy features of our scheme are provably modeled under the widely-accepted random oracle model by computational Diffie-Hellman (CDH) assumption against adaptive chosen-message attack. Finally, we show that the time needed to aggregate verification of 50 messages reduced by 84% and 48% compared to those of Yang et al.’s and He et al.’s schemes respectively.

Research and Application of Fine Intelligent Injection Technology of Cable Type in Dagang Oilfield

Dagang oilfield belongs to typical multi-layer system, heterogeneity of complex fault block reservoir, reservoir and the large difference of physical property of crude oil, water, etc, the longitudinal permeability difference is obvious, separate injection interval is not detailed, The existing bridge eccentric note, bridge concentric injection technology can’t meet the needs of the fine water injection, real-time measurement and control because of drift, well depth, pressure and other factors, and there is poor adaptability, measuring problem of long time and low efficiency. Cable type fine intelligent water injection technology in underground intelligent water distributor, the ground controller as the core tools, cable as transmission medium, the computer network communication technology, intelligent control technology combined with oil field water injection demand, developed a new and reliable water injection system, realized the layered injection allocation under different deviation, full automatic control and monitoring data, and direct reading test. The technology in the implementation of 5 Wells in dagang oilfield, the implementation of maximum deviation 80 °, the maximum depth of 3661.52 m, the highest water flooding pressure 25 mpa, construction success rate reaches 100%, downhole test times 5 Wells, inspection accuracy of 100%. The results show that the cable type intelligent water flooding technology can not only realize downhole data real-time monitoring, real-time traffic regulation, and can complete the packer direct reading test, and the measurement process is reliable and efficient, at the same time solve the high Angle well, horizontal well can’t test the technical problem, provides the high Angle well, horizontal well fine water injection technology support.

Carbon Footprint and Total Fuel Consumption Benefit Evaluation of a Proposed Decision Support System Algorithm for Istanbul Ataturk Airport Surface Movement Area

The continuous growth in air traffic volume causes extra operational fuel consumption due to serious ground delays at major airports. Effective ground movement management of aircraft plays an essential role in reducing or even eliminating these ground holds and congestions. Ground movement of aircraft is conventionally done by ground controllers using conventional techniques, which rely on static and short-term (dynamic) planning. Such planning usually overlooks possible in-flight delays, which lead to gate assignment problems (GAP) at destination airports. Ground movement decision support system is proposed as an alternative method to mitigate these problems. This system allocates available taxi paths to arriving and departing aircraft. Such a dynamic planning provides more robust taxi path assignment and delay management at the airports movement area especially in the peak hours. This study analyzes time benefits and time cost effects of this system using SIMMOD, which is a fast time simulation tool. In order to demonstrate the fuel consumption benefits of tactical taxi path allocation, two scenarios (baseline and alternative), have been generated and tested for Istanbul Ataturk Airport. Keywords: Airport Modeling, Carbon Footprint, Heuristic Algorithm, Airport Ground Delays, Air Traffic Management DOI: 10.7176/JSTR/6-04-06

Testing of a Cloud-Controlled Unmanned Aircraft System

During the last years, cloud controlled unmanned aircraft systems (UAS) have become reality. Keeping up with the trend, my research focused on design, development and testing of such a system, while keeping security concerns in mind. The present article summarises the final testing phase, including flight tests and test in lab environment. The most interesting results include successful flight tests with more than 15,000 km communication roundtrip between the ground controller station, the cloud server and the drone; evidences of the importance of georedundant installation of compute hosts to increase survivability of the service; and the demonstration of automatic horizontal scaling of the system depending on performance demands.

Synthetic-analytic behavior-based control framework: Constraining velocity in tracking for nonholonomic wheeled mobile robots

This work presents a genetic programming control design methodology that extends the traditional behavior–based control strategy towards a synthetic-analytic perspective. The proposed approach considers the internal and external dynamics of the system, providing solutions to a general structure, and including analytic functions, which can be studied within the Control Theory framework. The method is illustrated for the tracking control problem under bounded velocity restrictions of a nonholonomic wheeled mobile robot. A classic Control Theory (CT) based controller that solves the tracking problem (but not the velocity constraint requirement) is chosen from the literature; based on its stability properties, a modified structure where the search of suitable analytic basis behaviors, fulfilling both control objectives simultaneously, can be introduced. The proposed framework takes the form of a learning process based on Genetic Programming (GP) which generates a set of nonlinear tracking controllers satisfying pre-specified velocity bounds. A collection of 9113 suitable nonlinear solutions were obtained to augment the ground controller. Simulations and real–time experiments are performed to illustrate the effectiveness of the methodology through the testing of the models with the best performance, as well as those with lower structural complexity.

Low‐altitude UAV air‐ground propagation channel measurement and analysis in a suburban environment at 3.9 GHz

Unmanned aerial vehicles (UAVs) have been applied to various promising applications because of their features of high-mobility, portability, rapid deployment, and modest power consumption. Small UAVs fly at low altitudes and typically short distances from the ground controller. It is of interest and value to model the propagation channel between low-altitude UAVs and ground stations. This article presents results of channel measurements at 3.9 GHz carried out in a suburban environment with a small-size UAV flying at low altitudes, up to 40 m. The authors also present comparative results from ray-tracing simulations. Height-dependent models for path loss, root mean square (RMS) delay spread, and the number of multipath components are provided. The results illustrate that although multipath effects exist at low altitudes in this environment, the two-ray path loss model works reasonably well in describing the channel behaviour. Initial ray tracing simulations show reasonable agreement with measurements. In addition, RMS delay spread results indicate that distant scatterers have an appreciable effect on the UAV air-ground (AG) propagation channels, which can be different from some terrestrial channels. Authors' results should be of use in the modelling of low-altitude AG propagation channels and in the performance analysis of UAV-enabled AG communication systems.

FOCUS: Fog Computing in UAS Software-Defined Mesh Networks

Unmanned aerial systems (UASs) allow easy deployment, three-dimensional maneuverability and high reconfigurability, as they sustain communication network in the absence of pre-installed infrastructure. The proposed FOg Computing in UAS Software-defined mesh network (FOCUS) paradigm aims to realize an implementable network design that considers practical issues of aerial connectivity and computation. It allocates UASs to the tasks of data forwarding and in-network fog computing while maximizing number of ground-users in UAS coverage. FOCUS improves efficient utilization of network resources by introducing on-board computation and innovates on top of software-defined networking stack by integrating the capabilities of network and ground controllers to enable simultaneous orchestration of both UASs and communication flows. There are three main contributions of the paper: First, a SDN-based architecture is designed enabling autonomous configuration of computation and communication as well as managing multi-hop aerial links. Second, a global optimization problem to achieve optimal forwarding and computational allocation is formulated using Open Jackson Network model and solved via a heuristic approach with well defined complexity. Third, FOCUS framework is implemented on a small-scale testbed of Intel® Aero UASs performing image analysis with a full software stack. Experiments reveal at least 32% latency improvement in computation service time compared to traditional centralized computation at the end-server or greedy task allocation schemes within the network.

Multi-channel wireless sensor network for Heavy-Lift Launch Vehicles

A typical new generation Heavy-Lift Launch Vehicle (HLV) has more than 600 sensors on board. More than 80% of these sensors are used for non-critical operational and technological data. This data is sent to the ground launching controllers allowing them to supervise, in real time or a posteriori, information concerning the state of the HLV and the progress of the launching operation. Sensors are traditionally connected to a concentrator entity using cables. The concentrator is a special node in charge of collecting data that is generated by sensors and transmit it to the ground controllers unit. The use of wireless technology to interconnect sensor nodes with the concentrator would allow an ease of deployment and a weight gain which will help achieve assembly time reduction and a gain in terms of generated power. Nevertheless, of-the-shelf wireless technologies cannot guarantee a wired equivalent reliability in terms of packet delivery ratio or end-to-end delay. Indeed, when transmitting over wireless communication links it is challenging to find a solution that avoids data loss especially in high data rate scenarios. This is essentially due to the nature of the wireless medium that is very sensitive to the surrounding environment and to the possible simultaneous multiple accesses to the medium by the different sensor nodes. In this paper, we propose a wireless solution based on the physical layer of IEEE 802.15.4 standard in the 2.4 GHz frequency band that enhances packet delivery ratio under an acceptable end-to-end delay threshold during all phases of the launching operation. The proposal is a multi-channel and multi-hop protocol that allows sensors that are deployed close to the interstage of the HLV to communicate with a multi-radio interface concentrator node. Simulation results show promising performance in terms of packet delivery rate and end-to-end delay.

Low-Complexity Portable Passive Drone Surveillance via SDR-Based Signal Processing

This article presents an overview on passive drone detection. An SDR-based, portable universal software radio peripheral (USRP) prototype is developed for detection in two scenarios. In the scenario in which a drone communicates with the ground controller, the cyclostationarity signature of the drone signal and pseudo Doppler principle are employed. For the scenario in which a drone is not sending any signal, a micro Doppler signature generated by the RF signal is exploited for detection and identification.