Showing papers on "Situation awareness published in 2018"

Automation and situation awareness.

Abstract: Automation represents one of the major trends of the 20th century. In many cases automation has provided the desired benefits and has extended system functionality well beyond existing human capabilities. Situation awareness, a person's mental model of the world around him or her is central to effective decision making and control in dynamic systems. This construct can be severely impacted by the implementation of automation. Complacency—overreliance on automation—is one major factor associated with a lack of vigilance in monitoring automation. Monitoring problems have also been found with systems that have a high incidence of false alarms, leading to a lack of trust in the automation. In addition to vigilance problems, the fact that operators are passive observers of automation instead of active processors of information may add to their problems in detecting the need for manual intervention and in reorienting themselves to the state of the system in order to do so.

Big Data Analysis-Based Security Situational Awareness for Smart Grid

Abstract: Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness.

Social media applications and emergency management: A literature review and research agenda

Abstract: Social media applications have proven to be a dependable communication channel even when traditional methods fail. Their application to emergency management offers new benefits to the domain. For instance, analysis of information as the event unfolds may increase situational awareness, news and alerts may reach larger audiences in less time and decision makers may monitor public activities as well as coordinate with stakeholders. With such benefits, it seems the adoption of social media applications to emergency management should be automatic. However, their implementation introduces risks as well. To better understand the benefits and challenges, a review and analysis of the literature regarding the application of social media to emergency management was conducted. Identified research gaps were mapped into social and technological challenges. These challenges were then analyzed to set research directions for practitioners and researchers.

Recent and prospective developments in power system control centers: Adapting the digital twin technology for application in power system control centers

Abstract: The development of power system control centers has always been linked to the evolving of new technologies and innovative concepts. Switching to IP/TCP-based communication was one mayor evolutionary step in the past. Now, new precise time-synchronized phasor measurement units (PMU) based monitoring tools are on the rise and enable dynamic system observation. Future control centers for power systems can profit from this development, enlarging the observability and rising operator situation awareness. The next evolutionary step in control center technology may be the utilization of the Digital Twin concept. Its abilities to depict the actual and possible future system state, makes it an inevitable solution for further improvement of power system monitoring and control systems. This paper discusses recent developments of control center technology and introduces a new concept of a Digital Twin centric control center architecture which is based on a dynamic simulation engine called dynamic digital mirror.

Design of Future UAV-Relay Tactical Data Link for Reliable UAV Control and Situational Awareness

Abstract: Unmanned aerial vehicles (UAVs) have recently been widely used in the military and commercial domains. UAVs, especially in the military domain, were initially used for gathering intelligence, surveillance, and reconnaissance data, and sending them to a command center. Their use was then expanded to the tactical level to perform military operations with manned platforms. Operating a UAV as a relay node in the air has many advantages, including coverage, recovery from disconnection from a relay, clear communication channel, and easy deployment. Thus, the UAV-Relay tactical network can support more effective military operations. However, reliable UAV control and situational awareness must be guaranteed for the use of tactical UAVs. In other words, all tactical nodes must be able to send their location and status periodically under the dynamic and unexpected situation of a battlefield. However, a jamming attack from the enemy can make UAV control and situational awareness impossible. Thus, we need a robust tactical data link (TDL) to guarantee reliable UAV control and situational awareness under a jamming attack. In this article, we first explain the requirements for reliable UAV control and situational awareness. We then design a future UAV-relay TDL, called link-situational awareness and control (Link-SAC), which satisfies the requirements. We also deal with the spectrum allocation and management of Link-SAC because Link-SAC requires large bandwidth under the scarcity of spectrum resource. Thus, we consider spectrum sharing to allocate an X-Band uplink to Link-SAC. Based on the interference analysis in a prior study, we show that spectrum sharing is possible. We also show how Link-SAC can manage the spectrum. Lastly, we present our concluding remarks.

Processing Social Media Messages in Mass Emergency: Survey Summary

Abstract: Millions of people use social media to share information during disasters and mass emergencies. Information available on social media, particularly in the early hours of an event when few other sources are available, can be extremely valuable for emergency responders and decision makers, helping them gain situational awareness and plan relief efforts. Processing social media content to obtain such information involves solving multiple challenges, including parsing brief and informal messages, handling information overload, and prioritizing different types of information. These challenges can be mapped to information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. This work highlights these challenges and presents state of the art computational techniques to deal with social media messages, focusing on their application to crisis scenarios.

Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping.

Abstract: The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach

Abstract: Millimeter-wave communication is a challenge in the highly mobile vehicular context. Traditional beam training is inadequate in satisfying low overheads and latency. In this paper, we propose to combine machine learning tools and situational awareness to learn the beam information (power, optimal beam index, etc) from past observations. We consider forms of situational awareness that are specific to the vehicular setting including the locations of the receiver and the surrounding vehicles. We leverage regression models to predict the received power with different beam power quantizations. The result shows that situational awareness can largely improve the prediction accuracy and the model can achieve throughput with little performance loss with almost zero overhead.

Situation Awareness in Future Autonomous Vehicles: Beware of the Unexpected

Abstract: Vehicle autonomy is being heavily promoted as a means of improving transportation safety on the roadways. This goal, however, is highly dependent on the ability of human drivers to maintain situation awareness and intervene in circumstances that the automation cannot handle. While autonomy software is improving, it remains far less capable than human drivers. The automation conundrum shows that even as it improves, system autonomy is increasingly likely to reduce the ability of drivers to provide needed oversight. The Human-Automation System Oversight (HASO) model provides guidance on the design of vehicle autonomy to facilitate effective human-autonomy design for semi-autonomous vehicles.

Framework for Unifying 5G and Next Generation Satellite Communications

Abstract: 5G systems have started field trials, and deployment plans are being formulated, following completion of comprehensive standardization efforts and the introduction of multiple technological innovations for improving data rates and latency. Similar to earlier terrestrial wireless technologies, build-out of 5G systems will occur initially in higher population density areas offering the best business cases while not fully addressing airborne and marine applications. Satellite communications will thus continue to be indispensable as part of an integrated 5G/satellite architecture to achieve truly universal coverage. Such a unified architecture across terrestrial and satellite wireless technologies can ensure global service, support innovative 5G use cases, and reduce both capital investments and operational costs through efficiencies in network infrastructure deployment and spectrum utilization. This article presents an architectural framework based on a layered approach comprising network, data link, and physical layers together with a multimode user terminal. The network layer uses off-the-shelf building blocks based on 4G and 5G industry standards. The data link layer benefits from dynamic sharing of resources across multiple systems, enabled by intersystem knowledge of estimated and actual traffic demands, RF situational awareness, and resource availability. Communication resource sharing has traditionally comprised time, frequency, and power dimensions. Sharing can be enhanced by leveraging dynamic knowledge of communication platform location, trajectory, and antenna directivity. Logically centralized resource management provides a scalable approach for better utilization of spectrum, especially in higher bands that have traditionally been used by satellites and now are also being proposed for 5G systems. Resource sharing maximizes the utility of a multimode terminal that can access satellite or terrestrial RF links based on specific use cases, traffic demand, and QoS requirements.

Scalable Framework for Cyber Threat Situational Awareness Based on Domain Name Systems Data Analysis

Abstract: There are myriad of security solutions that have been developed to tackle the Cyber Security attacks and malicious activities in digital world. They are firewalls, intrusion detection and prevention systems, anti-virus systems, honeypots etc. Despite employing these detection measures and protection mechanisms, the number of successful attacks and the level of sophistication of these attacks keep increasing day-by-day. Also, with the advent of Internet-of-Things, the number of devices connected to Internet has risen dramatically. The inability to detect attacks on these devices are due to (1) the lack of computational power for detecting attacks, (2) the lack of interfaces that could potentially indicate a compromise on this devices and (3) the lack of the ability to interact with the system to execute diagnostic tools. This warrants newer approaches such as Tier-1 Internet Service Provider level view of attack patterns to provide situational awareness of Cyber Security threats. We investigate and explore the event data generated by the Internet protocol Domain Name Systems (DNS) for the purpose of Cyber threat situational awareness. Traditional methods such as Static and Binary analysis of Malware are sometimes inadequate to address the proliferation of Malware due to the time taken to obtain and process the individual binaries in order to generate signatures. By the time the Anti-Malware signature is available, there is a chance that a significant amount of damage might have happened. The traditional Anti-Malware systems may not identify malicious activities. However, it may be detected faster through DNS protocol by analyzing the generated event data in a timely manner. As DNS was not designed with security in mind (or suffers from vulnerabilities), we explore how the vast amount of event data generated by these systems can be leveraged to create Cyber threat situational awareness. The main contributions of the book chapter are two-fold: (1). A scalable framework that can perform web scale analysis in near real-time that provide situational awareness. (2). Detect early warning signals before large scale attacks or malware propagation occurs. We employ deep learning approach to classify and correlate malicious events that are perceived from the protocol usage. To our knowledge this is the first time, a framework that can analyze and correlate the DNS usage information at continent scale or multiple Tier-1 Internet Service Provider scale has been studied and analyzed in real-time to provide situational awareness. Merely using a commodity hardware server, the developed framework is capable of analyzing more than 2 Million events per second and it could detect the malicious activities within them in near real-time. The developed framework can be scaled out to analyze even larger volumes of network event data by adding additional computing resources. The scalability and real-time detection of malicious activities from early warning signals makes the developed framework stand out from any system of similar kind.

A Design Framework for Awareness Cues in Distributed Multiplayer Games

Abstract: In the physical world, teammates develop situation awareness about each other's location, status, and actions through cues such as gaze direction and ambient noise. To support situation awareness, distributed multiplayer games provide awareness cues - information that games automatically make available to players to support cooperative gameplay. The design of awareness cues can be extremely complex, impacting how players experience games and work with teammates. Despite the importance of awareness cues, designers have little beyond experiential knowledge to guide their design. In this work, we describe a design framework for awareness cues, providing insight into what information they provide, how they communicate this information, and how design choices can impact play experience. Our research, based on a grounded theory analysis of current games, is the first to provide a characterization of awareness cues, providing a palette for game designers to improve design practice and a starting point for deeper research into collaborative play.

Strategies for coordinated multirobot exploration with recurrent connectivity constraints

Abstract: During several applications, such as search and rescue, robots must discover new information about the environment and, at the same time, share operational knowledge with a base station through an ad hoc network. In this paper, we design exploration strategies that allow robots to coordinate with teammates to form such a network in order to satisfy recurrent connectivity constraints—that is, data must be shared with the base station when making new observations at the assigned locations. Current approaches lack in flexibility due to the assumptions made about the communication model. Furthermore, they are sometimes inefficient because of the synchronous way they work: new plans are issued only once all robots have reached their goals. This paper introduces two novel asynchronous strategies that work with arbitrary communication models. In this paper, ‘asynchronous’ means that it is possible to issue new plans to subgroups of robots, when they are ready to receive them. First, we propose a single-stage strategy based on Integer Linear Programming for selecting and assigning robots to locations. Second, we design a two-stage strategy to improve computational efficiency, by separating the problem of locations’ selection from that of robot-location assignments. Extensive testing both in simulation and with real robots show that the proposed strategies provide good situation awareness at the base station while efficiently exploring the environment.

Drone-Aided Border Surveillance with an Electrification Line Battery Charging System

Abstract: This paper proposes to develop a drone-aided border surveillance system with electrification line battery charging systems (DABS-E). Currently, mobile and fixed border surveillance systems such as truck-mounted video recording units, agent portable surveillance units, aerostats, and fixed towers are often used to enhance the comprehensive situational awareness along the U.S. border lines. However, a few drawbacks of the existing systems include limited operating capability, blind spots, physical fatigue of field agents, and lack of fast-responding situational awareness capability. The use of drones and mobile technologies are an ideal way to overcome these issues in border patrol activities. Even though drones bring numerous technical advantages (i.e., short response time, being able to access dangerous areas, and no on-board pilot required) for the border patrol mission, a relatively short flight duration is the main concern for the full implementation for patrol at this time. Therefore, this paper proposes a new concept that is built on electrification line (E-line) systems to wirelessly charge drones during the flight to extend flight duration. As a result, extra power can be provided for drones without the need of landing, stopping or returning back to ground control centers. To accomplish our goal, this paper proposes an optimization model and algorithm to schedule drone flights for a DABS-E. Through a numerical example, this paper shows the feasibility of our proposed method and corresponding economic benefits.

Review on computer vision techniques in emergency situations

Abstract: In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.

A Federated Capability-based Access Control Mechanism for Internet of Things (IoTs)

Abstract: The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide intelligent services with or without human intervention. While leveraging the large-scale IoT-based applications like Smart Gird and Smart Cities, IoT also incurs more concerns on privacy and security. Among the top security challenges that IoTs face is that access authorization is critical in resource and information protection over IoTs. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanisms to meet requirement of IoT systems. The extraordinary large number of nodes, heterogeneity as well as dynamicity, necessitate more fine-grained, lightweight mechanisms for IoT devices. In this paper, a federated capability-based access control (FedCAC) framework is proposed to enable an effective access control processes to devices, services and information in large scale IoT systems. The federated capability delegation mechanism, based on a propagation tree, is illustrated for access permission propagation. An identity-based capability token management strategy is presented, which involves registering, propagation and revocation of the access authorization. Through delegating centralized authorization decision-making policy to local domain delegator, the access authorization process is locally conducted on the service provider that integrates situational awareness (SAW) and customized contextual conditions. Implemented and tested on both resources-constrained devices, like smart sensors and Raspberry PI, and non-resource-constrained devices, like laptops and smart phones, our experimental results demonstrate the feasibility of the proposed FedCAC approach to offer a scalable, lightweight and fine-grained access control solution to IoT systems connected to a system network.

An HMI Concept to Improve Driver's Visual Behavior and Situation Awareness in Automated Vehicle

Abstract: At a level-3 or higher level automation [1], a driver does not have to constantly monitor the vehicle and environment while driving, which enables the driver to conduct non-driving-related tasks (NDRTs) and be out of the control loop. This may influence a driver's visual behavior, cognitive states, which leads to loss of situation awareness (SA) and skills. This is dangerous if the automated system reaches its boundaries: the driver must take-over the driving task in a critical situation within a limited period of time. In this paper, a concise HMI concept of the LED ambient light positioned at the bottom of the windscreen is presented, which contains information about the status and intention of the automation, detected potential hazards and the warning for a take-over request (TOR) by varying the LED's color, frequency, lighting position and animation. The goal is to increase the SA during automated driving and improve the take-over quality while allowing the driver to perform NDRTs without distraction and annoyance. In this between-subject-design experiment in a static driving simulator, 50 participants performed a visual-motoric task on a smartphone during a 45-min automated drive with or without the new HMI. Compared to the baseline, results show significant improvements in the gaze behavior and take-over quality. The new HMI also shows a high acceptance and increases the trust in automation while avoiding overtrust.

Intelligent smart home energy efficiency model using artificial TensorFlow engine

Abstract: Smart home and IoT-related technologies are developing rapidly, and various smart devices are being developed to help users enjoy a more comfortable lifestyle. However, the existing smart homes are limited by a scarcity of operating systems to integrate the devices that constitute the smart home environment. This is because these devices use independent IoT platforms developed by the brand or company that developed the device, and they produce these devices based on self-service modules. A smart home that lacks an integrated operating system becomes an organizational hassle because the user must then manage each device individually. Furthermore, this leads to problems such as excessive traffic on the smart home network and energy wastage. To overcome these problems, it is necessary to build an integrated management system that connects IoT devices to each other. To efficiently manage IoT, we propose three intelligent models as IoT platform application services for a smart home. The three models are intelligence awareness target as a service (IAT), intelligence energy efficiency as a service (IE2S), and intelligence service TAS (IST). IAT manages the "things" stage. IAT uses intelligent learning to acquire a situational awareness of the data values generated by things (sensors) to collect data according to the environment. IE2S performs the role of a server (IoT platform) and processes the data collected by IAT. The server uses Mobius, which is an open-source platform that follows international standards, and an artificial TensorFlow engine is used for data learning. IE2S analyzes and learns the users' usage patterns to help provide service automatically. IST helps to provide, control, and manage the service stage. These three intelligent models allow the IoT devices in a smart home to mutually cooperate with each other. In addition, these intelligent models can resolve the problems of network congestion and energy wastage by reducing unnecessary network tasks to systematically use energy according to the IoT usage patterns in the smart home.

Mining crisis information: A strategic approach for detection of people at risk through social media analysis

Abstract: Situational awareness of the rapidly changing environment in the event of disaster is vital for effective response and recovery management. The major challenges in achieving such awareness are lack of access to various sources of information and tools. Social media plays a vital role in understanding the real situation at the place of disaster as the information is received directly from the affected people. If the collected information is leveraged effectively, the crisis situation can be brought under control and the risks of the disaster affected people or disaster prone areas are reduced. This indeed minimizes the casualty and helps the affected people in serving with their basic needs and medical emergencies. In this paper, we propose a hybrid method for segregating and classifying the texts 1 received from the people who are at risk in the affected region. The proposed hybrid method combines rule based methodology and machine learning algorithms with linguistic features for segregating the texts and classifying them according to the needs. The results of the real-time text classification algorithm help the emergency responders to locate the people at risk and reach them during the hour of their need.

Come See This! Augmented Reality to Enable Human-Robot Cooperative Search

Abstract: Ahstract-Robots operating alongside humans in field environments have the potential to greatly increase the situational awareness of their human teammates. A significant challenge, however, is the efficient conveyance of what the robot perceives to the human in order to achieve improved situational awareness. We believe augmented reality (AR), which allows a human to simultaneously perceive the real world and digital information situated virtually in the real world, has the potential to address this issue. Motivated by the emerging prevalence of practical human-wearable AR devices, we present a system that enables a robot to perform cooperative search with a human teammate, where the robot can both share search results and assist the human teammate in navigation to the search target. We demonstrate this ability in a search task in an uninstrumented environment where the robot identifies and localizes targets and provides navigation direction via AR to bring the human to the correct target.

Development and Evaluation of an Intuitive Flexible Interface for Teleoperating Soft Growing Robots

Abstract: Mobility by growth is a new paradigm in robotic systems design and their applications in the real world. Soft, tip-extending, or “growing”, robots have potential applications including inspection and navigation in disaster scenarios. However, due to their growing capability, such robots create unique challenges for intuitive human control. In this paper, a new flexible interface is proposed to intuitively map human bending commands into movements of the growing robot while providing shape information of the robot in order to improve situational awareness. Several command mappings are proposed, and a subjective study was conducted to assess the intuitiveness of the developed interface and mappings compared with other commercially available interfaces. The interfaces were evaluated using four metrics in two virtual task scenarios. The proposed interface with shape mapping performed better than the other interfaces, especially when the vine robot rolls over unintentionally during complex tasks.

Evaluating perception systems for autonomous vehicles using quality temporal logic

Abstract: For reliable situation awareness in autonomous vehicle applications, we need to develop robust and reliable image processing and machine learning algorithms. Currently, there is no general framework for reasoning about the performance of perception systems. This paper introduces Timed Quality Temporal Logic (TQTL) as a formal language for monitoring and testing the performance of object detection and situation awareness algorithms for autonomous vehicle applications. We demonstrate that it is possible to describe interesting properties as TQTL formulas and detect cases where the properties are violated.

Enhancing Navigator Competence by Demonstrating Maritime Cyber Security

Abstract: As technology continues to develop, information and communication technology and operational technology on board ships are increasingly being networked, and more frequently connected to the Internet. The introduction of cyber systems changes the work environment with the aim of decreasing the workload for the navigator, but at the same time introduces more complexity and vulnerabilities that in turn may alter the competencies needed to perform safe and efficient navigation. Contemporary examples of how cyber-attacks can distort situational awareness and interfere with operations are needed to enhance the navigator's competence through increased system awareness. This paper demonstrates some of the possible attack vectors that a cyber-attack can present to a ship, as well as discussing the plausibility and consequences of such attacks. In this study we provide a practical example to better understand how one can demystify cyber threats in order to enhance the navigators' competence.