Showing papers on "Situation awareness published in 2023"

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Abstract: Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eye-tracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.

ROAD: The Road Event Awareness Dataset for Autonomous Driving

Abstract: Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.

Analytic Review of Using Augmented Reality for Situational Awareness

Abstract: Situational awareness is the perception and understanding of the surrounding environment. Maintaining situational awareness is vital for performance and error prevention in safety critical domains. Prior work has examined applying augmented reality (AR) to the context of improving situational awareness, but has mainly focused on the applicability of using AR rather than on information design. Hence, there is a need to investigate how to design the presentation of information, especially in AR headsets, to increase users’ situational awareness. We conducted a Systematic Literature Review to research how information is currently presented in AR, especially in systems that are being utilized for situational awareness. Comparing current presentations of information to existing design recommendations aided in identifying future areas of design. In addition, this survey further discusses opportunities and challenges in applying AR to increasing users’ situational awareness.

Neurophysiological and emotional influences on team communication and metacognitive cyber situational awareness during a cyber engineering exercise

Abstract: Background: Cyber operations unfold at superhuman speeds where cyber defense decisions are based on human-to-human communication aiming to achieve a shared cyber situational awareness. The recently proposed Orient, Locate, Bridge (OLB) model suggests a three-phase metacognitive approach for successful communication of cyber situational awareness for good cyber defense decision-making. Successful OLB execution implies applying cognitive control to coordinate self-referential and externally directed cognitive processes. In the brain, this is dependent on the frontoparietal control network and its connectivity to the default mode network. Emotional reactions may increase default mode network activity and reduce attention allocation to analytical processes resulting in sub-optimal decision-making. Vagal tone is an indicator of activity in the dorsolateral prefrontal node of the frontoparietal control network and is associated with functional connectivity between the frontoparietal control network and the default mode network. Aim: The aim of the present study was to assess whether indicators of neural activity relevant to the processes outlined by the OLB model were related to outcomes hypothesized by the model. Methods: Cyber cadets (N = 36) enrolled in a 3-day cyber engineering exercise organized by the Norwegian Defense Cyber Academy participated in the study. Differences in prospective metacognitive judgments of cyber situational awareness, communication demands, and mood were compared between cyber cadets with high and low vagal tone. Vagal tone was measured at rest prior to the exercise. Affective states, communication demands, cyber situational awareness, and metacognitive accuracy were measured on each day of the exercise. Results: We found that cyber cadets with higher vagal tone had better metacognitive judgments of cyber situational awareness, imposed fewer communication demands on their teams, and had more neutral moods compared to cyber cadets with lower vagal tone. Conclusion: These findings provide neuroergonomic support for the OLB model and suggest that it may be useful in education and training. Future studies should assess the effect of OLB-ing as an intervention on communication and performance.

Attack Graph Model for Cyber-Physical Power Systems using Hybrid Deep Learning

Abstract: Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016. However, existing attack detection methods are limited. Most of them are based on power system measurement anomalies that occur when an attack is successfully executed at the later stages of the cyber kill chain. In contrast, the attacks on the Ukrainian power grid show the importance of system-wide, early-stage attack detection through communication-based anomalies. Therefore, in this paper, we propose a novel method for online cyber attack situational awareness that enhances the power grid resilience. It supports power system operators in the identification and localization of active attack locations in Operational Technology (OT) networks in near real-time. The proposed method employs a hybrid deep learning model of Graph Convolutional Long Short-Term Memory (GC-LSTM) and a deep convolutional network for time series classification-based anomaly detection. It is implemented as a combination of software defined networking, anomaly detection in communication throughput, and a novel attack graph model. Results indicate that the proposed method can identify active attack locations, e.g., within substations, control center, and wide area network, with an accuracy above 96%. Hence, it outperforms existing state-of-the-art deep learning-based time series classification methods.

Improved space weather observations and modeling for aviation radiation

Abstract: In recent years there has been a growing interest from the aviation community for space weather radiation forecasts tailored to the needs of the aviation industry. In 2019 several space weather centers began issuing advisories for the International Civil Aviation Organization alerting users to enhancements in the radiation environment at aviation flight levels. Due to a lack of routine observations, radiation modeling is required to specify the dose rates experienced by flight crew and passengers. While mature models exist, support for key observational inputs and further modeling advancements are needed. Observational inputs required from the ground-based neutron monitor network must be financially supported for research studies and operations to ensure real-time data is available for forecast operations and actionable end user decision making. An improved understanding of the geomagnetic field is required to reduce dose rate uncertainties in regions close to the open/closed geomagnetic field boundary, important for flights such as those between the continental US and Europe which operate in this region. Airborne radiation measurements, which are crucial for model validation and improvement, are lacking, particularly during solar energetic particle events. New measurement campaigns must be carried out to ensure progress and in situ atmospheric radiation measurements made available for real-time situational awareness. Furthermore, solar energetic particle forecasting must be improved to move aviation radiation nowcasts to forecasts in order to meet customer requirements for longer lead times for planning and mitigation.

Unknown Attack Traffic Classification in SCADA Network Using Heuristic Clustering Technique

Abstract: Attack Traffic Classification (ATC) technique is an essential tool for Industrial Control System (ICS) network security, which can be widely used in active defense, situational awareness, attack source traceback and so on. At present, the state-of-the-art ATC methods are usually based on traffic statistical features and machine learning techniques, including supervised classification methods and unsupervised clustering methods. However, it is difficult for these methods to overcome the problems of lack of attack samples and high real-time requirement in ATC in Supervisory Control and Data Acquisition (SCADA) networks. In order to address the above problems, we propose a self-growing ATC model based on a new density-based heuristic clustering method, which can continuously and automatically detect and distinguish different kinds of unknown attack traffic generated by various attack tools against SCADA networks in real time. An effective representation method of SCADA network traffic is proposed to further improve the performance of ATC. In addition, a large number of experiments are conducted on a compound dataset consisting of the SCADA network dataset, the attack tool dataset and the ICS honeypot dataset, to evaluate the proposed method. The experimental results show that the proposed method outperforms existing state-of-the-art ATC methods in the crucial situation of only normal SCADA network traffic.

Layered Coordination Architecture for Resilient Restoration of Power Distribution Systems

Abstract: The current practices for restoring critical services in the distribution system during a disaster, align with the traditional centralized ideology of distribution systems operations. A central processor evaluates the distribution system after a disruption and attains a restoration plan. However, the centralized operational paradigm is susceptible to single-point failures, requires full situational awareness of the distribution system, and poses scalability challenges for large multifeeder distribution systems. This motivates a distributed decision-making paradigm where multiple agents solve smaller subproblems and jointly coordinate their individual decisions to achieve the global/network-level objective. Toward this goal, we propose a layered architecture for distributed algorithms for resilience and a two-stage distributed algorithm for distribution system restoration. The proposed distributed decision-making framework enables the bottom-up restoration of the distribution system using all available resources, including distributed generation, while only requiring local awareness and limited communications with neighboring connected regions. The proposed framework is robust to single-point failures, enables autonomy using distributed algorithms, and had reduced computational cost compared to centralized optimization solutions.

Automatic Flight Callsign Identification on a Controller Working Position: Real-Time Simulation and Analysis of Operational Recordings

Abstract: In the air traffic management (ATM) environment, air traffic controllers (ATCos) and flight crews, (FCs) communicate via voice to exchange different types of data such as commands, readbacks (confirmation of reception of the command) and information related to the air traffic environment. Speech recognition can be used in these voice exchanges to support ATCos in their work; each time a flight identification or callsign is mentioned by the controller or the pilot, the flight is recognised through automatic speech recognition (ASR) and the callsign is highlighted on the ATCo screen to increase their situational awareness and safety. This paper presents the work that is being performed within SESAR2020-founded solution PJ.10-W2-96 ASR in callsign recognition via voice by Enaire, Indra, and Crida using ASR models developed jointly by EML Speech Technology GmbH (EML) and Crida. The paper describes the ATCo speech environment and presents the main requirements impacting the design, the implementation performed, and the outcomes obtained using real operation communications and real-time simulations. The findings indicate a way forward incorporating partial recognition of callsigns and enriching the phonetization of company names to improve the recognition rates, currently set at 84–87% for controllers and 49–67% for flight crew.

Understanding situation awareness in SOCs, a systematic literature review

Abstract: Situation awareness is shown through human factors research to be a valuable construct to understand and improve how humans perform while operating complex systems in critical environments. Within cyber security one such environment is the Security Operations Center (SOC). With the increasing threat of hybrid warfare, knowledge about situation awareness within SOC environments, where human error or low performance may be detrimental, must be developed. This paper reports on the results of a Systematic Descriptive Literature Review of the current research on situation awareness within SOCs. The goal of the paper is to analyze how situation awareness is understood in the current research. To achieve this goal three aspects of understanding were addressed: Theoretical foundations; levels of conceptualization; and measurement of situation awareness. Theoretical foundations in the literature were assessed by how situation awareness was defined and the presence of references to theoretical models of SA. The results show a clear trend of basing the research on Endsley's three level situation awareness model; this model has been developed into a domain specific formulation called “Cyber Situation Awareness”. Some parts of the literature, particularly in research aimed at developing tools for improving situation awareness, lack a theoretical foundation; some refer to alternative theoretical foundations of situation awareness like Stanton et al.’s Distributed Situation Awareness. Further, a balance between conceptualizations on the individual, group and system level has been identified. Within research aimed at developing tools for improving situation awareness there are some examples of specialized and precise measurements of situation awareness, but in general the research seems too reliant on indirect measures of situation awareness. The paper concludes with the proposition of connecting the systems-based theoretical perspective of distributed situation awareness into the research, utilizing a systems level conceptualization of situation awareness. This might prove to be a useful bridge between the human cognitive perspective of situation awareness and the development of the complex technical environment of critical importance that SOCs represent.

Automated Detection of Rig Events from Real-Time Surface Data Using Spectral Analysis and Machine Learning

Abstract: The authors present a method for automated, high-fidelity detection of rig events characterized by complex temporal signals, such as downlinking, or wave-induced heave affecting floating rigs. These can adversely impact other systems utilizing relevant data streams, for example downlinking via mud pulse telemetry can interfere with detection of pressure changes that might indicate hole cleaning problems. Identifying these events using classification techniques applied to time-domain data is difficult, hence spectral (frequency domain) techniques, combined with Machine Learning (ML), were applied to solving this problem. Surface measurements from a variety of wells, fields, regions, service companies and operators were used to develop and validate the detection methods. Data was preprocessed using time-frequency analysis, and then input to discriminative classifiers to identify rig events of interest. For downlinking state detection, high recall and precision scores (both >93%) were achieved on independent holdout well data, and thus false positive rates were low. Successful detection was demonstrated on wells separate from the training data, hence the method is expected to generalize to new well operations. The detection method enhances situational awareness, and can actively support other software in improved automated decision-making by providing operational context in real-time, such as suppression of false warnings from monitoring pressure or modelled ECD for detecting signs of poor hole cleaning. These techniques are not limited to downlinking or heave detection, and can be applied more generally to scenarios with complex periodic signals.

A 3D mixed reality visualization of network topology and activity results in better dyadic cyber team communication and cyber situational awareness

Abstract: Background Cyber defense decision-making during cyber threat situations is based on human-to-human communication aiming to establish a shared cyber situational awareness. Previous studies suggested that communication inefficiencies were among the biggest problems facing security operation center teams. There is a need for tools that allow for more efficient communication of cyber threat information between individuals both in education and during cyber threat situations. Methods In the present study, we compared how the visual representation of network topology and traffic in 3D mixed reality vs. 2D affected team performance in a sample of cyber cadets (N = 22) cooperating in dyads. Performance outcomes included network topology recognition, cyber situational awareness, confidence in judgements, experienced communication demands, observed verbal communication, and forced choice decision-making. The study utilized network data from the NATO CCDCOE 2022 Locked Shields cyber defense exercise. Results We found that participants using the 3D mixed reality visualization had better cyber situational awareness than participants in the 2D group. The 3D mixed reality group was generally more confident in their judgments except when performing worse than the 2D group on the topology recognition task (which favored the 2D condition). Participants in the 3D mixed reality group experienced less communication demands, and performed more verbal communication aimed at establishing a shared mental model and less communications discussing task resolution. Better communication was associated with better cyber situational awareness. There were no differences in decision-making between the groups. This could be due to cohort effects such as formal training or the modest sample size. Conclusion This is the first study comparing the effect of 3D mixed reality and 2D visualizations of network topology on dyadic cyber team communication and cyber situational awareness. Using 3D mixed reality visualizations resulted in better cyber situational awareness and team communication. The experiment should be repeated in a larger and more diverse sample to determine its potential effect on decision-making.

Sensing and Navigation of Wearable Assistance Cognitive Systems for the Visually Impaired

Abstract: This article develops a wearable vision-based assistance system to provide situational awareness for blind and visually impaired (BVI) people in indoor scenarios. The system is built upon nonintrusive wearable devices, including an RGB-D camera, an embedded computer, and haptic modules. First, the depth map and color images of the scene are obtained from an RGB-D camera, which provides 3-D environmental information. The modular work modes are then designed for different tasks, such as navigation and multitarget recognition. Then, the cognition results are summarized and presented to the user through verbal or haptic feedback. Our system is evaluated by a pilot test to validate its effectiveness of improving the navigation capabilities and multitarget recognition capabilities for the BVI in indoor environments. We present study results with different tasks, including navigation, object localization, face recognition, and text reading. The experiments prove that the system can meet the needs of the BVI in daily use.

Human Factors While Using Head-Up-Display in Low Visibility Flying Conditions

Abstract: Flying an aircraft in low visibility is still a challenging task for the pilot. It requires precise and accurate situational awareness (SA) in real-time. A Head-up Display (HUD) is used to project collimated internal and external flight information on a transparent screen in the pilot’s forward field of view, which eliminates the change of eye position between Head-Down-Display (HDD) instruments and outer view through the windshield. Implementation of HUD increases the SA and reduces the workload for the pilot. But to provide a better flying capability for the pilot, projecting extensive information on HUD causes human factor issues that reduce pilot performance and lead to accidents in low visibility conditions. The literature shows that human error is the leading cause of more than 70% of aviation accidents. In this study, the ability of the pilot able to read background and symbology information of HUD at a different level of background seen complexity, such as symbology brightness, transition time, amount of Symbology, size etc., in low visibility conditions is discussed. The result shows that increased complexity on the HUD causes more detection errors.

Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness

Abstract: Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.

Cascading Fault Early Warning and Location Method of Transmission Networks Based on Wide Area Time-Series Power System State

Abstract: With the promotion of energy security strategy and the access of the high penetration of renewable energy, the related methods of situational awareness in the traditional transmission network may be inapplicable for modern power grids. For the early warning and location of the cascading faults caused by power flow shift, a novel method for security situation prediction in the transmission network is put forward based on Long Short Term Memory. Firstly, the power flow model is used to construct the early warning framework of transmission network security situation based on LSTM, and ADASYN oversampling is used for sample equilibrium. Secondly, the validity of the proposed method is verified by the open-source platform “Grid2Op” in the modified IEEE 36-bus system. Results indicate that the early warning accuracy for cascading failure can achieve around 90% with prediction time less than 1 second. Various simulation results demonstrate that the proposed method can improve the accuracy of security situation warning, shorten the time for identifying abnormality of the transmission network and provide reliable support for the operation and maintenance of the transmission network.

Air traffic modernization and control: ADS-B system implementation update 2022: A review

Abstract: Automatic Dependent Surveillance-Broadcast (ADS-B) is a multiparameter surveillance system designed to improve key segments of air traffic: enabling real-time surveillance, raising safety and efficiency levels, and improving flight information and weather services. ADS-B consists of two subsystems, ADS-B Out and ADS-B In. Although only a complete system, ADS-B In/Out provides numerous benefits (additional situational awareness, more efficient oceanic routing, etc.) FAA and EASA only require ADS-B Out (by January and June 2020, respectively), whereby ADS-B In remains optional. Because of its many advantages, ADS-B In/Out will be popular, but there are some weaknesses, which are primarily related to its cyber vulnerabilities due to insufficient authentication and encryption in the applied protocol. In this paper, an overview of the ADS-B system is presented as an aid to understanding the security problems and the different ways of potential attack. In addition, this review deals with the current state of ADS-B deployment and its future perspective and challenges.

GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements

Abstract: This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. Our goal is to address two fundamental challenges in this field: a) scarcity in measurement locations due to the high cost of purchasing, installing, and streaming data from D-PMUs; b) limited prior knowledge about the event signatures due to the fact that the events are diverse and infrequent, and have unknown characteristics. To tackle these challenges, we propose an unsupervised graph-representation learning method, called GraphPMU, to significantly improve the performance in event clustering under locationally-scarce data availability by proposing the following two new directions: 1) using the topological information about the relative location of the few available phasor measurement units on the graph of the power distribution network; 2) utilizing not only the commonly used fundamental phasor measurements, bus also the less explored harmonic phasor measurements in the process of analyzing the signatures of various events. Through a detailed analysis of several case studies, we show that GraphPMU can highly outperform the prevalent methods in the literature.

AIS-based maritime anomaly traffic detection: A review

Abstract: Maritime transportation plays an essential role in global trade. Due to the huge number of vessels worldwide, there is also a non-negligible volume of Maritime incidents such as collisions/sinking and illegal events (e.g., piracy, smuggling, and unauthorized fishing). Electronic equipment/systems, such as radars and Automatic Identification Systems (AIS), have contributed to improving maritime situational awareness. AIS provides one of the fundamental sources of vessel kinematics and static data. Today, many approaches are focused on automatically detecting the vessels’ traffic behavior and discovering useful patterns and deviations from those data. These studies contribute to detecting suspicious activities and anomalous trajectories, whose developed techniques could be applied in the surveillance systems, helping the authorities to anticipate proper actions. Several concerns and difficulties are involved in the analyses of vessel kinematics data: how to deal with big data generated, inconsistencies, irregular updates, dynamic data, unlabeled data, and evaluation. This article presents the approaches, constraints, and challenges in maritime traffic anomaly detection research, presenting a review, a taxonomy, and a discussion of the proposed approaches.

End-to-End Detection of a Landing Platform for Offshore UAVs Based on a Multimodal Early Fusion Approach

Abstract: A perception module is a vital component of a modern robotic system. Vision, radar, thermal, and LiDAR are the most common choices of sensors for environmental awareness. Relying on singular sources of information is prone to be affected by specific environmental conditions (e.g., visual cameras are affected by glary or dark environments). Thus, relying on different sensors is an essential step to introduce robustness against various environmental conditions. Hence, a perception system with sensor fusion capabilities produces the desired redundant and reliable awareness critical for real-world systems. This paper proposes a novel early fusion module that is reliable against individual cases of sensor failure when detecting an offshore maritime platform for UAV landing. The model explores the early fusion of a still unexplored combination of visual, infrared, and LiDAR modalities. The contribution is described by suggesting a simple methodology that intends to facilitate the training and inference of a lightweight state-of-the-art object detector. The early fusion based detector achieves solid detection recalls up to 99% for all cases of sensor failure and extreme weather conditions such as glary, dark, and foggy scenarios in fair real-time inference duration below 6 ms.

Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

Abstract: This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin’s core capabilities for supporting decision-making in smart urban mobility management.

Enhancement of the Advanced Weather Awareness System for the development of an Integrated Mission Management System in the COAST project

Abstract: The COAST (Cost Optimized Avionics SysTem) project, funded by Clean Aviation Joint Undertaking, works toward the realization of cost-effective key technologies for cockpit and avionics of Small Air Transport (SAT) aircraft. In 2020 the design of a new technology started, the Integrated Mission Management System (IMMS), devoted to automatically optimize the trajectory while considering air-traffic, weather conditions, terrain and obstacles. It is aimed to implement into a unique system the functionalities of Trajectory Planning, Flight Reconfiguration, Tactical Separation and Weather Awareness, benefitting from the integration and interaction on-board of three technologies, individually developed and tested in the first phases of the COAST project: Flight Reconfiguration System (FRS, managing pilot’s incapacitation emergency), Tactical Separation System (TSS, managing tactical traffic separation and enhanced situational awareness) and Advanced Weather Awareness System (AWAS, devoted to provide on-board updated weather data). The present work focuses on the last one and more in detail on the description of new functionalities introduced to the baseline AWAS system, already demonstrated in flight in 2021, in order to allow its integration in IMMS. Specifically, enhancements to the AWAS technology were required to integrate new input weather data and generate additional information, needed for IMMS purposes. These data are produced on-ground and sent through satellite link to the AWAS on-board segment, which has been updated to manage and exchange them with the other components on the aircraft. All the achieved progresses in the development of the evolved version of the AWAS system presented in this work will be demonstrated and tested in flight during a dedicated campaign planned in 2023 in the framework of COAST project.

Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking for Disaster Response

Abstract: In a disaster response management (DRM) scenario, communication and coordination are limited, and absence of related infrastructure hinders situational awareness. Unmanned aerial vehicles (UAVs) or drones provide new capabilities for DRM to address these barriers. However, there is a dearth of works that address multiple heterogeneous drones collaboratively working together to form a flying ad-hoc network (FANET) with air-to-air and air-to-ground links that are impacted by: (i) environmental obstacles, (ii) wind, and (iii) limited battery capacities. In this paper, we present a novel environmentally-aware and energy-efficient multi-drone coordination and networking scheme that features a Reinforcement Learning (RL) based location prediction algorithm coupled with a packet forwarding algorithm for drone-to-ground network establishment. We specifically present two novel drone location-based solutions (i.e., heuristic greedy, and learning-based) in our packet forwarding approach to support application requirements. These requirements involve improving connectivity (i.e., optimize packet delivery ratio and end-to-end delay) despite environmental obstacles, and improving efficiency (i.e., by lower energy use and time consumption) despite energy constraints. We evaluate our scheme with state-of-the-art networking algorithms in a trace-based DRM FANET simulation testbed featuring rural and metropolitan areas. Results show that our strategy overcomes obstacles and can achieve 81-to-90% of network connectivity performance observed under no obstacle conditions. In the presence of obstacles, our scheme improves the network connectivity performance by 14-to-38% while also providing 23-to-54% of energy savings in rural areas; the same in metropolitan areas achieved an average of 25% gain when compared with baseline obstacle awareness approaches with 15-to-76% of energy savings.

Asset management of flood defences as a co‐production—An analysis of cooperation in five situations in the Netherlands

Abstract: Flood defences are in practice often multi-used, multi-managed and multi-financed. Flood defence asset management contains technical, organizational and spatial complex issues involving multiple organizations. In the literature, little attention has been given to the conditions for successful cooperation between organizations in flood defence asset management. This paper elaborates on this aspect of mature asset management from a practical point of view. Although the importance of a fit-for-purpose cooperation seems trivial, practice shows that the shape of cooperation is often the coincidental result of implicit or ad-hoc choices and is not deliberately designed. This paper reports on empirical data gathered in a case consisting of five different situations related to collaboration in flood defence management. The management context consists of three main tasks: performance assessment, reinforcement and daily management, and three decision levels: strategic, tactical and operational, resulting in nine different management environments and related interfaces. For effectively achieving desired outcomes, the shape of cooperation has to be explicitly chosen dependent on the complexity of content and organizational context, and relevant external circumstances: situational cooperation.