Distributed state estimation for autonomous vehicles in unknown environments: Enhancing situational awareness

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.

Effect of a looming visual cue on situation awareness and perceived urgency in response to a takeover request

Protecting valuable IT assets is one of the most significant challenges that organizations face today. Cyber criminals operating beyond physical boundaries, are able to disrupt and destroy cyber infrastructure, deny organizations access to IT services, and steal sensitive data. In response, enterprises organize security operations centres at the heart of their entities with the purpose of employing socio-technical systems with capabilities to detect, analyze and respond to these threats. This exploratory study examines how such capabilities are operationalized in leading “Managed Security Service Providers” (MSSPs) providing cybersecurity operations and incident response, and looks at how situation awareness knowledge is constructed through the organizational levels of the enterprise detection and response. In this context, situational awareness span over different levels in the organization starting from team personnel, ending at top management. Our work contributes to situational awareness theory in the context of cybersecurity operations and incident response. Thus, we advance the understanding of the organizational capabilities of MSSPs to develop awareness of the cyber-threat landscape and the broader operational dynamics. By introducing InCReASE, a dynamic framework towards enhancing situation awareness in Security Operations Centers (SOC) operations and incident response; we extend existing situational awareness models, combining elements of the existing body of knowledge and our empirical findings. The presented work is a reflection on the best practices adopted by MSSPs organizations operating in Norway.

We present a bionic neural wave network that uses multiple autonomous underwater vehicles to search and acquire intelligent targets in an unknown underwater environment. The neuron pheromone content is arranged according to neural wave diffusion and layer-by-layer energy attenuation, when underwater mesh space based on neural wave diffusion theory was established that the neuron nodes in the neural network structure correspond to obstacles, autonomous underwater vehicles, and targets in the environment. In order to solve the problems of over-allocation and under-allocation of the multi-autonomous underwater vehicles system during the cooperative capture of targets, a redistribution mechanism based on the improved self-organizing map algorithm is implemented and directed to rationalize task distribution. Two different taboo search methods are employed to update the autonomous underwater vehicle path in real time, and the polynomial coefficient solution method is used to fit partial path data. So that the autonomous underwater vehicle trajectory can be obtained and an interceptor position coordinate can be predicted. An auxiliary autonomous underwater vehicle is aimed to replace the intercepted autonomous underwater vehicle and the matching capture points are tracked to ensure the completion of the task so that the full range of hunting targets is identified. In order to simulate an unknown complex underwater environment, obstacles are randomly arranged around the target, the location information of the obstacle, and the target is unknown and unpredictable. Four simulation experiments were performed to verify the accuracy and efficiency of the algorithm under unknown environment. The results show that this algorithm can improve the path update average efficiency by 66% compared with other algorithms. Obviously, this algorithm is reasonable and effective.

Chapter 29 - Enhancing Situation Awareness in Power Systems: Overcoming Uncertainty and Variability with Renewable Resources

This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. The fundamental requirement for constant depth flight is the knowledge of the depth, and a properly designed controller to govern the process. With the SA strategy, we proposed a multirate depth control procedure to address the dynamics variation and performance requirement difference in various stages of AUV's trajectory for a nontrivial mid-small size AUV “r2D4” model. Two adaptive neural network controllers are designed for fast and stable diving maneuvers of this AUV model. This hybrid control strategy for chosen AUV model has been verified by simulation of diving maneuvers using software package Simulink and demonstrated good performance for fast SA in real-time search-and-rescue operations.

Age-related declines in cognition may have detrimental effects on older adults’ ability to complete everyday activities that young- and middle-aged individuals perform automatically. Theories of cognitive aging have found deficits in older adults’ fluid intelligence, capacity for inhibition, number of processing resources, and speed of processing, and in recent years, studies have proposed cognitive strategies to ameliorate these declines. However, few strategies directly train the cognitive strategies necessary to improve performance in dynamic environments and physical activities. One such strategy may be the enhancement of situation awareness, the capability to perceive and understand one’s environment. Although the term has typically been applied to pilots and other expert performers, situation awareness may also be relevant to cognitive aging, where older adults’ perception and comprehension of their environment become critical to everyday functioning and physical activities. If older adults’ situation awareness can be facilitated, then it may be possible to reduce the impact of age-related cognitive declines, allowing older adults to successfully participate in dynamic situations and sports where the environment is constantly changing (e.g., driving and tennis). The following review outlines cognitive deficits in aging, details their relation to situation awareness, and discusses how training in situation awareness may reduce cognitive declines.

Situational awareness (SAW) is a concept widely spread in application areas that require critical decision-making, such as in emergency dispatching systems. SAW is related to the level of consciousness that an individual or team has to a situation. SAW-oriented UI for critical systems require specialized user interfaces to provide operators a dynamic understanding of what is happening in an environment. The information to be managed by such interfaces affects the way operators in an emergency dispatch system acquire, maintain and recover SAW. A challenging issue on the design of SAW-oriented interfaces is how the human-system interaction process can be redesigned for the enhancement of SAW considering environments with potential large scale heterogeneous multi sensors data in complex, ever-changing situations. The problem is increased when such information is subject to uncertainty, which may compromise the acquisition of the situational awareness. Also, humans are expected to make decisions based on their own understanding of what is going on, which allied to experience and expertise can be valuable assets to be used to process refinement during the construction of an incremental knowledge. The goal of this paper is to introduce a conceptual framework to create specialized interfaces that support the participation of operators in the process of SAW acquisition. Such SAW-oriented interface presents a tight integration between the operator and the other phases of an assessment process, such as information quality assessment, information fusion and information visualization. A robbery event report, in an emergency dispatch system, is used as a case study to demonstrate practical and promising results of the applicability of our solution.

In the evolving landscape of cyber threats, traditional threat intelligence methods are increasingly inadequate for addressing the complexity and speed of modern attacks. This paper explores the transformative impact of Artificial Intelligence (AI) on enhancing cyber security threat intelligence and situational awareness. By leveraging advanced AI technologies—such as machine learning, natural language processing, and data analytics—organizations can significantly improve their ability to detect, analyze, and respond to threats. We provide a comprehensive review of current AI applications in threat intelligence, illustrating how these technologies enable proactive threat management and enhance situational awareness. Through detailed case studies, we demonstrate the effectiveness of AI-driven solutions in various sectors, including finance and healthcare. The paper also addresses key challenges such as data privacy, system integration, and adversarial AI, offering recommendations for future research and development. This study underscores the critical role of AI in advancing cyber security practices and provides insights into how organizations can harness AI to achieve a more robust and responsive threat intelligence framework.

EpiNATO-2 is the only interoperable health surveillance system that is defined in North Atlantic Treaty Organization (NATO) doctrine. It was first implemented in the Kosovo Force and European Union Training Mission Mali in 2013. EpiNATO-2 is mandated for use during all NATO operations. Its coverage has steadily increased and now includes all NATO Joint and Component Command Operations and several non-NATO operations. The system monitors morbidity predominately for Role 1 sites by using weekly reports from the medics and other medical providers. The reports for all sites in theater are sent to the Combined Joint Medical (CJMED), which consolidates and submits them to NATO Deployment Health Surveillance Capability (DHSC), the satellite branch of NATO Centre of Excellence for Military Medicine (MILMED COE), for analysis and feedback. Although EpiNATO-2 will likely have a number of overlaps with most nations' disease and nonbattle injury trackers, a distinguishing characteristic is that it has specific categories for classifying more clinical activity. Sustaining the quality of data collection is paramount and achieved through contemporaneous analysis and feedback that are disseminated via CJMED to all providers. This enhances situational awareness about evolving trends in health issues across the deployed force and is intended to provide information for action and medical decision-making and force health protection assurance at the local and theater levels. The awareness imparted by this article can add to the Special Operations Forces (SOF) medics' tool kit to ensure success for the SOF medic and SOF community while deployed or collaborating with NATO and NATO partner nation militaries at any level in theater.

People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound–Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.

Autonomous vehicles can reduce labor power during cargo transportation, and then improve transportation efficiency, for example, the automated guided vehicle (AGV) in the warehouse can improve the operation efficiency. To overcome the limitations of traditional path planning algorithms in unknown environments, such as reliance on high-precision maps, lack of generalization ability, and obstacle avoidance capability, this study focuses on investigating the Deep Q-Network and its derivative algorithm to enhance network and algorithm structures. A new algorithm named APF-D3QNPER is proposed, which combines the action output method of artificial potential field (APF) with the Dueling Double Deep Q Network algorithm, and experience sample rewards are considered in the experience playback portion of the traditional Deep Reinforcement Learning (DRL) algorithm, which enhances the convergence ability of the traditional DRL algorithm. A long short-term memory (LSTM) network is added to the state feature extraction network part to improve its adaptability in unknown environments and enhance its spatiotemporal sensitivity to the environment. The APF-D3QNPER algorithm is compared with mainstream deep reinforcement learning algorithms and traditional path planning algorithms using a robot operating system and the Gazebo simulation platform by conducting experiments. The results demonstrate that the APF-D3QNPER algorithm exhibits excellent generalization abilities in the simulation environment, and the convergence speed, the loss value, the path planning time, and the path planning length of the APF-D3QNPER algorithm are all less than for other algorithms in diverse scenarios.

<p>With digital technologies now being part of the fabric of our societies, identifying and managing cybersecurity threats becomes imperative. Within the European Union, several initiatives are underway, aiming to motivate, regulate and eventually orchestrate the establishment of capacity and enhancement of situational awareness, incident response, and preparedness capabilities, with an expected emphasis on operators of essential services and state actors entrusted with cybersecurity. In this context, the institution of cooperation and information exchange channels to allow for coordinated cross-border responses to large-scale incidents is particularly prioritized. Motivated by the above, this work presents a conceptual blueprint in support of architecting and establishing interoperable Cyber Security Operations Centres that combine capacity for situational awareness, incident response, and preparedness, also benefiting from the interplay between them, ultimately enhancing national cybersecurity capabilities, cross-border collaboration, and national supervision of their critical sectors, in line with current and upcoming regulatory requirements and the ever-increasing need for national and international cooperation.</p>

One major task of the control centre operator is to identify the global system state of the transmission system. However, during the past couple of years, the complexity of transmission system operation has increased significantly. The operator in the control room has to observe wider external areas to fully evaluate system security. Along with the development of information technology and functionality of SCADA systems, the amount of data the operator has to monitor has augmented notably. The focus of this work is to develop a visualization of the global system state and to analyse and enhance situation awareness in wide area electrical transmission systems. In addition first versions of this new MMI are implemented into the SCADA system of the German TSO, Amprion. Thereby the study also enables usability engineering of variant new visualizations in the control centre. The proposed solution allows the operator to observe the global system state at a glance and enables intuitive situation awareness by improving visual perception like pattern recognition.

Augmented visualization cues on primary flight display facilitating pilot's monitoring performance