In order to facilitate collaborative decision-making in the Trajectory-Based Operation context, a strategic collaborative trajectory deconfliction method in Free Route Airspace is proposed. To achieve good trade-off between the objectives of safety, efficiency and equity, a multi-objective nonlinear integer model is formulated for the trajectory deconfliction problem by modifying the user-preferred trajectories with the combination of ground delay, rerouting and flight level allocation maneuvers. The Gini coefficient-based metric is introduced to quantify the inter-airline equity, and the airline priorities are incorporated in the model with the maximum allowed extra operation cost of each flight. Considering the large-scale and multi-objective characteristics of the problem, a decomposition-based memetic algorithm hybridized with local search operators is proposed. To balance the exploitation and exploration of the search process, A special hybridization scheme is employed for the setting of local search intensity and frequency. A traffic scenario in western China airspace with several free-routing sectors is used to validate the effectiveness of the proposed method. The results show that the proposed collaborative trajectory deconfliction method can provide high quality solutions to decision support in strategic trajectories planning.
Many overall safety factors need to be considered in the next generation of Urban Air Mobility (UAM) systems and addressing these can become the anchor point for such technology to reach consent for worldwide application. On the other hand, fulfilling the safety requirements from an exponential increase of prolific UAM systems, is extremely complicated, and requires careful consideration of a variety of issues. One of the key goals of these Unmanned Air Systems (UAS) is the requirement to support the launch and control of hundreds of thousands of these advanced drones in the air simultaneously. Given the impracticalities of training the corresponding number of expert pilots, achieving this goal can only be realized through safe operation in either fullautonomous or semi-autonomous modes. According to many recent studies, the majority of flight accidents are concentrated on the last three stages of a flight trip, which include the Initial Approach, Final Approach, and Landing Phases of an airplane trip. Therefore, this paper proposes a novel decentralized processing system for enhancing the safety factors during the critical phases of Vertical and/or Short Take-Off and Landing (V/STOL) drones. This has been achieved by adopting several processing and control algorithms such as an Open Fuzzy Logic System (FLS) integrated with a Flight Rules Unit (FRU), FIR filters, and a novel Prognostic Malfunction processing unit. After applying several optimization techniques, this novel coarse-grained Autonomous Landing Guidance Assistance System (ALGAS3) processing architecture has been optimized to achieve a maximum computational processing performance of 70.82 Giga Operations per Second (GOPS). Also, the proposed ALGAS3 system shows an ultra-low dynamic thermal power dissipation (I/O and core) of 145.4 mW which is ideal for mobile avionic systems using INTEL 5CGXFC9D6F27C7 FPGA chip.
This paper suggests a new authentication model using Online Certificate Status Protocol (OCSP) stapling with trusted responders to navigate a Public Key Infrastructure (PKI) trust tree in a constrained computing environment. This paper also suggests a model of how the trusted responders could be deployed and how to establish and maintain authentication between clients' applications and the trusted responder. Aircraft oftentimes work in limited RF bandwidth environments sharing a single frequency between many aircraft. Maximizing the efficiency of transmission time is paramount to servicing all aircraft communications needs. One of the large contributors to long transmission time in an IPS network is the login process, which requires the exchanging and verification of certificate chains. OCSP can be used to verify individual certificates, however it likely requires a request per certificate to the certificate authorities. Traditional OCSP responds with the validity information of a certificate, leaving the communicating counter parties, the constrained client and server, responsible for determining if the PKI tree between them is sufficiently strong to establish trust. The OCSP trusted responder model proposed by this paper would offload the PKI tree determination to trusted ground entities. The trusted ground entities would determine the level of trust, if any, for communication between the counter parties and forward only the necessary information for cryptographic exchange to the communicating counter parties. In an OCSP trusted responder model, a pre-configured list of trusted OCSP responders resides with the constrained client. During the authentication process the constrained client submits the list of OCSP trusted responders to the counter party server. The server is required to prove validity of its own PKI certificate using one of the OCSP trusted responders supplied by the client. The OCSP trusted responder would fetch any necessary intermediate certificates, walk the Public Key Infrastructure tree, and determine the level of trust, if any, between the counter parties and forward the information to the server. Thereby removing the need to exchange full certificate chains between constrained client and server. The server will then respond to the client with its own certificate, and the validation response from the OCSP trusted responder if any. The OCSP trusted responder model is adaptable and may be used in conjunction with, or in replace of, other verification methods of models.
The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network.
Aviation performance depends on education and training of pilots and air traffic controllers (ATCOs) on technical, procedural, crisis management, decision making, leadership and communication skills. Recent incidents and studies, however, show crew members may be caught unaware and errors in human judgement can still occur in air traffic control (ATC) systems even more so in the presence of cyberattacks. This paper focuses on raising situation awareness and decision making of ATCOs with cyberattacks impacting operations in next-generation ATC systems. Our goal is to investigate crew-based cyber security as a layer in making ATC systems resilient to cyber and cyber- physical attacks. Automatic Dependent Surveillance Broadcast (ADS-B) technology is used as the basis of our preliminary investigation due to its widely known security concerns and central role in ATC systems. We present experimental study considerations for assessing cyber readiness and improving training of ATCOs. We propose a Kalman filter based cyber alarm solution approach for a machine learning based aid for ATCOs towards early cyberattack detection and incident response in ATC systems.
