Showing papers on "Situation awareness published in 2015"

Processing Social Media Messages in Mass Emergency: A Survey

Abstract: Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

Analyzing the Cyber-Physical Impact of Cyber Events on the Power Grid

Abstract: With ongoing smart grid activities, advancements in information and communication technology coupled with development of sensors are utilized for better situational awareness, decision support, and control of the power grid However, it is critical to understand the complex interdependencies between cyber and power domains, and also the potential impacts of cyber events on the power grid In this paper, the impact of three different possible cyber events on physical power grid have been analyzed using an integrated cyber-power modeling and simulation testbed Real-time modeling of end-to-end cyber-power systems have been developed with hardware-in-the-loop capabilities Real-time digital simulator, synchrophasor devices, DeterLab, and network simulator-3 are utilized in this developed testbed with a wide-area control algorithm and associated closed-loop control DeterLab can be used to model real-life cyber events in the developed cyber-physical testbed Man-in-the-middle and denial-of-service attacks have been modeled as specific cases for the IEEE standard test cases Additionally, communication failure impact on the power grid has been analyzed using the testbed

The Benefits of Self-Awareness and Attention in Fog and Mist Computing

Abstract: Self-awareness facilitates a proper assessment of cost-constrained cyber-physical systems, allocating limited resources where they are most needed. Together, situation awareness and attention are key enablers for self-awareness in efficient distributed sensing and computing networks.

The Impact of Cooperative Perception on Decision Making and Planning of Autonomous Vehicles

Abstract: In this article, we investigate how cooperative perception gives the impact on decision making and planning of autonomous vehicles on the road. Cooperative perception is the exchange of local sensing information with other vehicles or infrastructures via wireless communications, by which the perception range can be considerably extended up to the boundary of connected vehicles. This augmented perception capability can provide oncoming traffic information beyond line-of-sight and field-of-view, which enables better control of both manned and unmanned vehicles. In this article, we first present an on-road sensing system to provide a see-through/lifted-seat/satellite view to drivers. Then, we investigate how the extended perception capability can contribute to situation awareness on the road. Finally, we provide methods for safer and smoother autonomous driving using the augmented situation awareness and perception capability. All introduced and proposed concepts are implemented and validated on autonomous vehicles.

Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness

Abstract: The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. We demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.

Haptic seat for automated driving: preparing the driver to take control effectively

Abstract: Drivers' situation awareness is known to be remarkably low in the automated driving mode, which can result in a delayed and inefficient response when requested to resume control of the vehicle. The present study examined the usefulness of a haptic seat that projects spatial information on approaching vehicles to facilitate drivers' preparedness to take control of the vehicle. The results of a simulator study on 26 participants using behavioral and eye tracking techniques showed that when required to regain control, having haptic seat led to faster reactions in scenarios requiring lane changing. The haptic seat also reduced the probability that the participants would slow down below acceptable speeds on a freeway. Eye tracking showed that drivers had a more systematic scan of the environment in the first two seconds following the transition of control with a haptic seat. Overall, these findings suggest that the haptic seat can play a significant role in keeping drivers aware of surrounding traffic during automated driving, and consequently facilitate the control transitions between the vehicle and the driver.

Human-robot Teaming for Rescue Missions: Team ViGIR's Approach to the 2013 DARPA Robotics Challenge Trials

Abstract: Team ViGIR entered the 2013 DARPA Robotics Challenge DRC with a focus on developing software to enable an operator to guide a humanoid robot through the series of challenge tasks emulating disaster response scenarios. The overarching philosophy was to make our operators full team members and not just mere supervisors. We designed our operator control station OCS to allow multiple operators to request and share information as needed to maintain situational awareness under bandwidth constraints, while directing the robot to perform tasks with most planning and control taking place onboard the robot. Given the limited development time, we leveraged a number of open source libraries in both our onboard software and our OCS design; this included significant use of the robot operating system libraries and toolchain. This paper describes the high level approach, including the OCS design and major onboard components, and it presents our DRC Trials results. The paper concludes with a number of lessons learned that are being applied to the final phase of the competition and are useful for related projects as well.

HAC-ER: A Disaster Response System based on Human-Agent Collectives

Abstract: This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emergency responders by enabling humans and agents, using state-of-the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC-ER utilises crowdsourcing combined with machine learning to extract situational awareness information from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.

Knowledge-based vessel position prediction using historical AIS data

Abstract: The improvement in Maritime Situational Awareness (MSA), or the capability of understanding events, circumstances and activities within and impacting the maritime environment, is nowadays of paramount importance for safety and security. Enhancing coverage of existing technologies such as Automatic Identification System (AIS) provides the possibility to integrate and enrich services and information already available in the maritime domain. In this scenario, the prediction of vessels position is essential to increase the MSA and build the Maritime Situational Picture (MSP), namely the map of the ships located in a certain Area Of Interest (AOI) at a desired time. The integration of de-facto maritime traffic routes information in the vessel prediction process has the appealing potential to provide a more accurate picture of what is happening at sea by exploiting the knowledge of historical vessel positioning data. In this paper, we propose a Bayesian vessel prediction algorithm based on a Particle Filter (PF). The system, aided by the knowledge of traffic routes, aims to enhance the quality of the vessel position prediction. Experimental results are presented, evaluating the algorithm in the specific area between the Gibraltar passage and the Dover Strait using real AIS data.

Cyber Defense and Situational Awareness

Abstract: This book is the first publication to give a comprehensive, structured treatment to the important topic of situational awareness in cyber defense. It presents the subject in a logical, consistent, continuous discourse, covering key topics such as formation of cyber situational awareness, visualization and human factors, automated learning and inference, use of ontologies and metrics, predicting and assessing impact of cyber attacks, and achieving resilience of cyber and physical mission. Chapters include case studies, recent research results and practical insights described specifically for this book. Situational awareness is exceptionally prominent in the field of cyber defense. It involves science, technology and practice of perception, comprehension and projection of events and entities in cyber space. Chapters discuss the difficulties of achieving cyber situational awareness along with approaches to overcoming the difficulties - in the relatively young field of cyber defense where key phenomena are so unlike the more conventional physical world. Cyber Defense and Situational Awareness is designed as a reference for practitioners of cyber security and developers of technology solutions for cyber defenders. Advanced-level students and researchers focused on security of computer networks will also find this book a valuable resource.

Situation awareness: operationally necessary and scientifically grounded

Abstract: Dekker (Cogn Tech Work, doi: 10.1007/s10111-015-0320-8 , 2015) provides a stilted account of situation awareness (SA) in which it is only (or primarily) used to explain away human error. He ignores the large body of work over the past 25 years that has clearly defined SA, builds a strong foundation of cognitive theory on how it works in the brain, and provides a substantial base of scientifically grounded principles for how to improve SA through improvements in display design, automation, and training (see Endsley and Jones, Designing for situation awareness: an approach to human-centered design, 2nd edn. Taylor and Francis, London, 2012). He also ignores work on human error that delves in detail into the reasons for losses of SA in accidents and uses that knowledge to provide needed system improvements. SA is widely recognized by operators and practitioners across aviation, power systems, emergency management, military, medical, and many other domains as being critical to effective decision making and performance. It is incumbent on the human factors profession to respond to this need with improved systems and approaches for enhancing SA in the difficult and complex worlds where it is so essential.

Providing Information on the Spot: Using Augmented Reality for Situational Awareness in the Security Domain

Abstract: For operational units in the security domain that work together in teams, it is important to quickly and adequately exchange context-related information to ensure well-working collaboration. Currently, most information exchange is based on oral communication. This paper reports on different scenarios from the security domain in which augmented reality (AR) techniques are used to support such information exchange. The scenarios have been designed with a User Centred Design approach, in order to make the scenarios as realistic as possible. To support these scenarios, an AR system has been developed and evaluated in two rounds. In the first round, the usability and feasibility of the AR support has been evaluated with experts from different operational units in the security domain. The second evaluation round then focussed on the effect of AR on collaboration and situational awareness within the expert teams. With regard to the usability and feasibility of AR, the evaluation shows that the scenarios are well defined and the AR system can successfully support information exchange in teams operating in the security domain. The second evaluation round showed that AR can especially improve the situational awareness of remote colleagues not physically present at a scene.

Autonomous vehicles: human factors issues and future research

Abstract: Automated vehicles are those in which at least some aspects of a safety-critical control function occur without direct driver input. It is predicted that automated vehicles, especially those capable of “driving themselves”, will improve road safety and provide a range of other transport and societal benefits. A fundamental issue, from a human factors perspective, is how to design automation so that drivers understand fully the capabilities and limitations of the vehicle, and maintain situational awareness of what the vehicle is doing and when manual intervention is needed – especially for first generation vehicles that require drivers to resume manual control of automated functions when the vehicle is incapable of controlling itself. The purpose of this paper is to document some of the human factors challenges associated with the transition from manually driven to self-driving vehicles, and to outline what we can be doing in Australia, through research and other means, to address them.

Machine learning for relevance of information in crisis response

Abstract: Efficient communication during crisis response situations is a major challenge for involved emergency responders. Lack of relevant information or too much irrelevant information hampers the emergency responders’ decision-making process, workflow and situational awareness. Despite efforts to better centralize relevant information during crisis response, a gap still exists between the information supply and information needs of responders. Our contribution to bridge the information gap is a software system that monitors communication and may send information to emergency responders that were not addressed in the initial communication. The system, Task-Adaptive Information Distribution (TAID) is capable of disseminating information in a timely manner and adapting itself to the fast-changing information needs in a crisis response environment. This TAID system was trained with practical examples for which information relevance is known. To assess relevance, TAID uses a built relevance model for crisis response using methods from machine learning. In TAID this was set up as a classification task in which input information and knowledge about (ir)relevance of information was used. The technical results of the built relevance models are promising. The TAID-effect on crisis response was measured with simulation experiments which show that the expected effect of the TAID system on crisis response is restricted to specific circumstances. In a number of cases the intervening of TAID improved the situational awareness of emergency responders and reduced the duration of the crisis with very low additional costs, which results from time taken due to reading additional messages.

Design and Implementation of a Real-Time Off-Grid Operation Detection Tool from a Wide-Area Measurements Perspective

Abstract: Real-time situational awareness tools are of critical importance to power system operators, especially during emergencies. The availability of electric power has become a linchpin of most post-disaster response efforts, because public and private sector services depend upon it. Knowledge of the scope and extent of facilities impacted, as well as the duration of their dependence on backup power, enables emergency response officials to plan for contingencies and provide a better overall response. Based on the measurement data acquired by the frequency disturbance recorders deployed in the North American power grids, an off-grid detection method is proposed and implemented. This method monitors the critical electrical loads and detects the transition of these loads from an on-grid operation to an off-grid operation, during which the loads are fed by an uninterrupted power supply or a backup generation system. The details of the detection algorithm are presented, and some case studies and off-grid detection scenarios are also provided to verify the effectiveness and robustness. This paper also presents the real-time implementation of this method and several effectively detected off-grid situations. Moreover, two visualization tools are developed to display the real-time system operation condition in an intuitive manor.

Situational awareness in smart environments: socio-mobile and sensor data fusion for emergency response to disasters

Abstract: Traditional situational awareness services in disaster management are mainly focused on the institutional warning response and not fully exploit the active participation of citizens involved. This paper presents an advanced system for emergency management (ASyEM) which fuses the potentiality offered by mobile social data and bottom-up communication with smart sensors. The proposed architecture model is organized into four different layers: (1) sensor, (2) local transmission, (3) network and (4) management. ASyEM is able to capture and aggregate two different kind of data: (a) user generated content produced by citizens during or immediately after the disaster and shared online through socio-mobile applications and (b) data acquired by smart sensors distributed on the environment (i.e., intelligent cameras, microphones, acoustic arrays, etc.). Data are selected, analysed, processed and integrated in order to increase the reliability and the efficiency of whole situational awareness services, localize the critical areas and obtain in this way some relevant information for emergency response and completion of search and rescue operations.

Using social media to enhance emergency situation awareness

Abstract: Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.

PERCIVAL: proactive and reactive attack and response assessment for cyber incidents using visual analytics

Abstract: Situational awareness is a key concept in cyber-defence. Its goal is to make the user aware of different and complex aspects of the network he or she is monitoring. This paper proposes PERCIVAL, a novel visual analytics environment that contributes to situational awareness by allowing the user to understand the network security status and to monitor security events that are happening on the system. The proposed visualization allows for comparing the proactive security analysis with the actual attack progress, providing insights on the effectiveness of the mitigation actions the system has triggered against the attack and giving an overview of the possible attack’s evolution. Moreover, the same visualization can be fruitfully used in the proactive analysis since it allows for getting details on computed attack paths and evaluating the mitigation actions that have been proactively computed by the system. A preliminary user study provided a positive feedback on the prototype implementation of the system. A video of the system is available at: https://youtu.be/uMpYCJCX95k.

A multi-agent fuzzy consensus model in a Situation Awareness framework

Abstract: Graphical abstractDisplay Omitted In order to define systems enabling the automatic identification of occurring situations, numerous approaches employing intelligent software agents to analyse data coming from deployed sensors have been proposed. Thus, it is possible that more agents are committed to monitor the same phenomenon in the same environment. Redundancy of sensors and agents is needed, for instance, in real world applications in order to mitigate the risk of faults and threats. One of the possible side effects produced by redundancy is that agents, observing the same phenomenon, could provide discordant opinions. Indeed, solid mechanisms for reaching an agreement among these agents and produce a shared consensus on the same observations are needed. This paper proposes an approach to integrate a fuzzy-based consensus model into a Situation Awareness framework. The main idea is to consider intelligent agents as experts claiming their opinions (preferences) on a phenomenon of interest.

Being Aware of the World: Toward Using Social Media to Support the Blind With Navigation

Abstract: This paper lays the ground work for assistive navigation using wearable sensors and social sensors to foster situational awareness for the blind. Our system acquires social media messages to gauge the relevant aspects of an event and to create alerts. We propose social semantics that captures the parameters required for querying and reasoning an event-of-interest, such as what, where, who, when, severity, and action from the Internet of things, using an event summarization algorithm. Our approach integrates wearable sensors in the physical world to estimate user location based on metric and landmark localization. Streaming data from the cyber world are employed to provide awareness by summarizing the events around the user based on the situation awareness factor. It is illustrated using disaster and socialization event scenarios. Discovered local events are fed back using sound localization so that the user can actively participate in a social event or get early warning of any hazardous events. A feasibility evaluation of our proposed algorithm included comparing the output of the algorithm to ground truth, a survey with sighted participants about the algorithm output, and a sound localization user interface study with blind-folded sighted participants. Thus, our framework supports the navigation problem for the blind by combining the advantages of our real-time localization technologies so that the user is being made aware of the world, a necessity for independent travel.

Application of Waveform Analytics for Improved Situational Awareness of Electric Distribution Feeders

Abstract: Over the past several years, distribution utilities have invested heavily in installations of “smart-meter” advanced metering initiative (AMI) systems. Among the anticipated benefits of these systems, utilities with smart-meter deployments are generally able to quickly assess which portions of their systems are operating normally and which customers are experiencing an outage. Projects at multiple utilities have focused on integrating AMI information, along with traditional supervisory control and data acquisition data sources, into utility distribution management systems to improve situational awareness on distribution feeders. Despite the clear benefits each of these systems offer, their ability to provide utilities with broad awareness of events affecting the health and status of the distribution system is limited, and often reactive in nature. This paper presents never-before-published cases observed in real-world field trials, detailing how integration of waveform analytics into utility operational practice leads to improved situational awareness.

Human Factors in Automotive Engineering and Technology

Abstract: This book offers a unique perspective on vehicle design and new developments in vehicle technology. The table of contents lists the titles of the chapters as: The Car of the Future, Here Today; A Technology Timeline; Lessons from Aviation; Defining Driving; Describing Driver Error; Examining Driver Error and Its Causes; A Psychological Model of Driving; Vehicle Feedback and Driver Situational Awareness; Vehicle Automation and Driver Workload; Automation Displays; Trust in Vehicle Technology; A Systems View of Vehicle Automation; and Conclusions. The book also provides an Appendix; Further Readings; References; Bibliography; and an Index.

Gaining an Edge in Cyberspace with Advanced Situational Awareness

Abstract: Organizations that rely on cyberspace as a mission-critical asset require advanced situational awareness to maintain a tactical advantage over emerging threats. A new cyber-situational awareness framework relies on the OODA (observe, orient, decide, act) cycle to provide near real-time cognitive mapping for corporate environments.

Event stream processing for improved situational awareness in the smart grid

Abstract: Deployment of PMUs in grid has brought a new data stream to be processed.New method of stream processing is proposed to improve awareness in power systems.Phasor data stream is mined to minimize wide spread outages and cascading failures.Synchrophasor data is processed within acceptable time, memory, and accuracy.Operators can be alerted quickly to improve the future power systems' reliability. Deployment of Phasor Measurement Units (PMU) in the United States transmission grid has brought a new data stream to be processed and an opportunity to improve situational awareness on the grid. This new data stream offers opportunity for a faster detection and response algorithm to minimize wide spread outages. High rate of data collection of PMU systems has also brought a challenge on how to extract information from fast moving PMU data stream in real time to improve situational awareness inside a control room. Despite the fact that mathematical and probabilistic methods are the most accurate methods of stability analysis, online decision making algorithms cannot afford the latency brought by those methods. Traditional batch processing Artificial Intelligence (AI) techniques have been extensively studied as potential replacements for these approaches, however conventional AI techniques do not deal with continuous streams of fast moving phasor data. This paper presented a novel application of the stream mining algorithms for synchrophasor data to meet quick decision making requirement of future situational awareness applications in power systems. To prove that the proposed methods are efficient and capable of handling huge amounts of data with reasonable accuracy and within limited resources of memory and computational power, four different experiments with different conditions (changing/unchanging the load conditions of Real Power and Reactive Power, fixing the size of memory, and comparing the performance of non-adaptive Hoeffding tree with traditional decision tree algorithms) were conducted. The algorithms discussed in this paper support decisions inside the control rooms helping stakeholders make informed decisions to improve reliability of the future smart grid.