Situational Awareness Data Research Articles

Kernel-Based Edge-Preserving Methods for Abrupt Change Detection

Abrupt change detection is critical to monitor the occurrence of abnormal events from sensor data for situational awareness of complex systems. However, various disturbances and noises applied to the data observations may pose significant challenges to the robustness of many abrupt change detection methods. Recent researches have shown that bilateral filter can acquire outstanding performance on removing noises from images while preserving edge information. In this letter, we propose two improved edge-preserving memory-based cumulative sum (MB-CUSUM) methods that are able to make the abrupt change detection method more robust against noises. Our experimental studies show that the proposed methods can achieve superior performance over state-of-the-art methods to detect abrupt changes, which demonstrates the effectiveness and feasibility of their practical use.

Coordinating the real-time use of global influenza activity data for better public health planning.

Health planners from global to local levels must anticipate year‐to‐year and week‐to‐week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real‐time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.

Determining disaster severity through social media analysis: Testing the methodology with South East Queensland Flood tweets

Social media was underutilised in disaster management practices, as it was not seen as a real-time ground level information harvesting tool during a disaster. In recent years, with the increasing popularity and use of social media, people have started to express their views, experiences, images, and video evidences through different social media platforms. Consequently, harnessing such crowdsourced information has become an opportunity for authorities to obtain enhanced situation awareness data for efficient disaster management practices. Nonetheless, the current disaster-related Twitter analytics methods are not versatile enough to define disaster impacts levels as interpreted by the local communities. This paper contributes to the existing knowledge by applying and extending a well-established data analysis framework, and identifying highly impacted disaster areas as perceived by the local communities. For this, the study used real-time Twitter data posted during the 2010–2011 South East Queensland Floods. The findings reveal that: (a) Utilising Twitter is a promising approach to reflect citizen knowledge; (b) Tweets could be used to identify the fluctuations of disaster severity over time; (c) The spatial analysis of tweets validates the applicability of geo-located messages to demarcate highly impacted disaster zones.

Leveraging Crowdsourcing and Crowdsensing Data for HADR Operations in a Smart City Environment

The future of the world's population concentration lies within the bounds of urban cities. Citizens, or humans, are the most important tangible resources in a smart city environment, and they need to be served as well as protected. The concept of smart cities is trying to accomplish the idea of serving the citizens by leveraging the potential of information and communications technology assets. Citizens have access to smart technologies and applications, and thus they form an indispensable component to complement and supplement a smart city's operation. Especially in humanitarian assistance and disaster recovery (HADR) operations, where a smart city's core infrastructure might be compromised, the assets of citizens can be put to use. This article aims to describe the current state of affairs for safety in cities and humanitarian assistance in emergency situations, which require leveraging situational awareness data. We discuss and propose mechanisms for connecting to and utilizing crowdsourcing and crowdsensing data in a smart city environment, which can assist in efficient HADR operations.

Real-time monitoring of a mass K2-related overdose outbreak – Connecticut, 2018

ObjectiveTo describe the use of syndromic surveillance data for real-time situational awareness of emergency department utilization during a localized mass overdose event related to the substance K2.IntroductionOn August 15, 2018, the Connecticut Department of Public Health (DPH) became aware of a cluster of suspected overdoses in an urban park related to the synthetic cannabinoid K2. Abuse of K2 has been associated with serious adverse effects and overdose clusters have been reported in multiple states. This investigation aimed to characterize the use of syndromic surveillance data to monitor a cluster of suspected overdoses in real time.MethodsThe EpiCenter syndromic surveillance system collects data on all emergency department (ED) visits at Connecticut hospitals. ED visits associated with the event were identified using ad hoc keyword analyses. The number of visits by facility location for the state, county, and city were communicated to state and local partners in real time. Gender, age, and repeated ED visits were assessed. After the event, surveillance findings were summarized for partnersResultsDuring the period of August 15–16, 2018 the number of ED visits with a mention of K2 in the chief complaint increased from three to 30 in the impacted county, compared to a peak of 5 visits during the period of March–July, 2018. An additional 25 ED visits were identified using other related keywords (e.g., weed). After the event, 72 ED visits were identified with K2 and location keywords in the chief complaint or triage notes. These 72 visits comprised 53 unique patients, with 12 patients returning to the ED 2–5 times over the two day period. Of 53 patients, 77% were male and the median age was 40 years (interquartile range 35–51 years). Surveillance findings were shared with partners in real time for situational awareness, and in a summary report on August 21.ConclusionsData from the EpiCenter system were consistent with reports from other data sources regarding this cluster of suspected drug overdoses. Next steps related to this event involve: monitoring data for reference to areas of concentrated substance use, enabling automated alerts to detect clusters of interest, and developing a plan to improve coordinate real-time communication with stakeholderswithin DPH and with external partners during events.

SAFER: Crowdsourcing Based Disaster Monitoring System Using Software Defined Fog Computing

In the occurrence of natural or artificial disasters, conveying immediate and precise situation awareness (SA) information from the salient disaster scenes to the first responders is vital. Crowdsourcing can offer enormous heterogeneous data collected from diverse sources such as sensors, smartphones, vehicles, IoT devices, buildings, and social media. Sending the crowdsourced SA data to the Cloud for analyzation is futile as the opportunity to act immediately may be lost due to delay constraints. However, analyzation of the data-intensive and media-rich contents impose accessibility to massive computational resources, which may be unresponsive in the disaster zone. Fog computing allows quick analyzation of the heterogeneous SA data on the site closer to the disaster scene. This article describes an architecture named SAFER (SDN Assisted Fog computing for Emergency Resilience) which provisions Fog computing and Software-Defined Networking for efficient disaster management. We evaluated the SAFER architecture using simulation tools consuming heterogeneous data and verified that better Quality of Service is achieved by reducing the Service Delay in both transmission and computation. Using SAFER architecture disasters can be detected early than conventional Cloud-based disaster management schemes so that more human lives can be saved.

IoT Green Corridor

IoT Green Corridor is a distributed sensor network system, a working prototype of which has been designed in-house and using low-cost hardware for economically-viable implementation. This paper outlines the development of the prototype and describes its details. This proposed system combines the existing conventional traffic signal lights with networked-sensors across length and breadth of connected and usually-congested city roads. Being connected to the internet, the sensors are better-enabled and capable of synchronizing with each other for sharing localized traffic information including the presence of emergency vehicles like ambulances. Having been equipped with the situational awareness data, the sensor network in an IoT framework is capable of collective decision to manage a given set of the traffic signal lights in tandem to achieve the singular aim of providing smooth passage to the arriving ambulances or in general to any Emergency Vehicle (EMV). Every traffic-signal junction is to be retrofitted with the Green Corridor system prototype, which is required to sense the presence of an Emergency Vehicle (EMV) within a specified distance in the field of view of the sensors. Based on a positive identification of such vehicles in distress, the traffic flow is designed to quickly enable the EMV to cross the junction smoothly in least time as possible. In the absence of a system of this kind, a manual intervention would have to be made by the concerned agency or valuable golden hour time to save lives would be spent in the traffic junctions. Besides, the prototype communicates with the inter-connected adjacent sensor-network elements through the cloud network to initiate similar action at each of the traffic signals enroute the desired destination of the emergency vehicle.

Search and rescue with autonomous flying robots through behavior-based cooperative intelligence

A swarm of autonomous flying robots is implemented in simulation to cooperatively gather situational awareness data during the first few hours after a major natural disaster. In computer simulations, the swarm is successful in locating over 90% of survivors in less than an hour. The swarm is controlled by new sets of reactive behaviors which are presented and evaluated. The reactive behaviors integrate collision avoidance, battery recharge, formation control, altitude maintenance, and a variety of search methods to optimize the coverage area of camera and heart-beat locator sensors mounted on the robots. The behaviors are implemented in simulation on swarms of sizes from 1 to 20 robots. The simulation uses actual location data, including post-disaster satellite imagery, real locations of damaged and inundated buildings, and realistic victim locations based on personal interviews and accounts. The results demonstrate the value of using behavior-based swarming algorithms to control autonomous unmanned aerial vehicles for post-disaster search and assessment. Three examples of algorithms that have been effective in simulation are presented.

Graph database-based network security situation awareness data storage method

With the rapid development of the Internet, network security situation awareness has attracted tremendous attention. In large-scale complex networks, network security situation awareness data presents the characteristics of large-scale, multi-source, and heterogeneous. Recently, much research work have been done on network security situation awareness. However, most of the existing methods store different types of data in different ways, which makes data query and analysis inefficient. To solve this problem, we propose a graph database-based hierarchical multi-domain network security situation awareness data storage method. In our scheme, we build a hierarchical multi-domain network security situation awareness model to divide the network into different domains, which can collect and dispose the awareness data more efficiently. Meanwhile, to unify our storage mode, we also define network security situation awareness data storage rules and methods based on graph database. Finally, extensive experiments on real datasets show that our proposed method is efficient compared to state-of-the-art storage models.

Maritime anomaly detection: A review

The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, this study presents a review of maritime anomaly detection. The found articles are categorized into four groups (a) data, (b) methods, (c) systems, and (d) user aspects. We present a comprehensive summary of the works found in each category, and finally, outline possible paths of investigation and challenges for maritime anomaly detection.This article is categorized under: Application Areas > Government and Public Sector Algorithmic Development > Spatial and Temporal Data Mining

Should they stay or should they go now? Exploring the impact of team familiarity on interprofessional team training outcomes

IntroductionAlthough simulation is an effective method for enhancing team competencies, it is unclear how team familiarity impacts this process. We examined how team familiarity impacted team competencies. MethodsTrainees were assigned to stable or dynamic teams to participate in three simulated cases. Situation awareness (SA) data was collected through in-scenario freezes. The recorded performances were assessed for clinical effectiveness (ClinEff) and teamwork. All data are reported on a 1–100% (100% = perfect performance) scale. ResultsForty-six trainees (23 General Surgery; 23 Emergency Medicine) were randomized by specialty into stable (N = 8) or dynamic (N = 7) groups. Overall changes from Sim 1 to Sim3 were 12.2% (p < 0.01), −1.1% (ns), and 7.1% (p < 0.01) for SA, ClinEff, and Teamwork, respectively. However, improvements differed by condition, with stable teams reflecting improvements in ClinEff (15.2%; p < 0.05), whereas dynamic team ClinEff improvement (8.7%) was not significant. Both groups demonstrated improvements in teamwork (stable = 9%, p < 0.05; dynamic = 4.9%, p < 0.05). ConclusionsTeams who continued to work together demonstrated increased improvements in clinical effectiveness and teamwork, while dynamic teams only demonstrated improvements in teamwork.

EOS situational data shared service mechanism

With the rapid development of aerospace and remote sensing technology, various high-resolution Earth Observation Systems (EOS) are widely used in economic, social, military and other fields and playing an increasingly prominent role in the construction of Digital Earth and national strategic planning. The normal operation of the system is the premise of high quality data acquisition. Compared with the ground observation mode, EOS itself and the surrounding environment are more complex, and its operation control mainly depends on all kinds of Space Situational Awareness (SSA) data acquisition and analysis. SSA data has more extensive sources, larger volume, stronger time-effectiveness and more complicated structure than traditional geographical spatial data. For effective data sharing and utilization, combined with the analysis of data types and structures, a SSA data sharing identity language SSDSML is designed based on the extensible mark-up language XML, which realizes a comprehensive description of satellites and their attributes, space environment, ground stations, etc. Then EOS situational data shared service mechanism is established and provides a powerful data support for the normal operation of the system.

The early bird catches the term: combining twitter and news data for event detection and situational awareness.

BackgroundTwitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. They can therefore be highly useful for event detection and situational awareness applications.ResultsIn this paper we apply customised filtering techniques to existing bio-surveillance algorithms to detect localised spikes in Twitter activity, showing that these correspond to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which best describe the event and by linking to highly relevant news articles. This news linkage is accomplished by identifying terms occurring more frequently in the event tweets than in a baseline of activity for the area concerned, and using these to search for news. We apply our methods to outbreaks of illness and events strongly affecting sentiment and are able to detect events verifiable by third party sources and produce high quality summaries.ConclusionsThis study demonstrates linking event detection from Twitter with relevant online news to provide situational awareness. This builds on the existing studies that focus on Twitter alone, showing that integrating information from multiple online sources can produce useful analysis.

Design projects motivated and informed by the needs of severely disabled autistic children.

Technology can positively impact the lives of severely disabled autistic children if used to (a) gather situational awareness data regarding their health, development, and behavior and (b) assist them with learning and day-to-day activities. This paper summarizes student design projects in the Kansas State University (KSU) College of Engineering that are motivated and informed by the needs of severely disabled children at Heartspring, Wichita, KS. These efforts are supported through the National Science Foundation's General and Age-Related Disabilities Engineering (GARDE) program. Projects relate thematically to (1) facets of a bed sensor system that unobtrusively tracks nighttime health parameters and child activity and (2) miscellaneous resources geared toward paraeducator ("para") and child well-being and development.

Development of the Decoupled Discreet-Time Jacobian Eigenvalue Approximation for Situational Awareness Utilizing Open PDC

With the increased number of PMUs in the power grid, effective high speed, realtime methods to ascertain relevant data for situational awareness are needed. Several techniques have used data from PMUs in conjunction with state estimation to assess system stability and event detection. However, these techniques require system topology and a large computational time. This paper presents a novel approach that uses real-time PMU data streams without the need of system connectivity or additional state estimation. The new development is based on the approximation of the eigenvalues related to the decoupled discreet-time power flow Jacobian matrix using direct openPDC data in real-time. Results are compared with other methods, such as Prony’s method, which can be too slow to handle big data. The newly developed Discreet-Time Jacobian Eigenvalue Approximation (DDJEA) method not only proves its accuracy, but also shows its effectiveness with minimal computational time: an essential element when considering situational awareness.

NextSim

The Federal Aviation Administration expects a large increase in air traffic over the next 15 to 20 years. In response, the Next Generation Air Transportation System (NextGen) has been proposed, which will use newer technologies and automation to shift the way air traffic is managed. Many of the proposed changes need to be tested before implantation begins, but it is difficult to conduct human factors tests on an environment that does not yet exist. We describe an air traffic control (ATC) simulator developed for this purpose. NextSim is an ATC research simulator that collects performance, workload, and situation awareness data to address human factors/ergonomics issues that might arise in NextGen.

Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness

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.

移动社交信息智能态势感知分析

With the rapid rise of microblogging, social networking sites, and mobile Internet, users access to information, public participation, and expression of their demands is increasing significantly. Thus, the challenges faced by public opinion monitoring and guidance are becoming increasingly serious. To address the problems of mobile social network public opinion monitoring, this paper presents an intelligent situation awareness analysis. Intelligent situation awareness combines with the timing of memory hierarchy, extracting information flow characteristics better suited to a highly uncertain, scale-free dynamic information environment, effectively reducing the interference of false alarm history data; is based on robust self-learning screening methods to achieve automatic screening of the node data extraction features and to facilitate the automatic aggregation of situation awareness data in mobile social networking application server environments and unattended scenarios; builds situation knowledge to improve robustness and not lose the characteristics or cause false alarms because of dynamic changes in the network topology of small-world, making situation awareness more suitable for small-world network environments; makes empty inspection matrix scouring the results and eliminating false match points to accelerate visualization. With the maturing of the mobile social networking environment and doubling of the number of users, intelligent situation awareness provides protection to further enhance the level of intelligence of the emergency information platform and facilitate effective monitoring public opinion.

지상 무인체계용 다중입출력 통신 시스템 설계 및 성능시험 분석

High-capacity video, control and situation awareness data should be transmitted efficiently to control robots properly in the ground unmanned system, which requires the technology maximizing the communication range and the data transmission throughput. This technology is connected to the OFDMA(Orthogonal Frequency Division Multiple Access)-MIMO(Multiple Input Multiple Output) transmission technology under the limited bandwidth and transmission power. In this paper, we design MIMO communication system for ground unmanned systems, and investigate the data reception performance experimentally, comparing with SISO(Single Input Single Output) system. Experiment results show that the data reception performance of MIMO is significantly improved compared to that of SISO, e.g. 4dB gain of sensitivity and 5dB of SNR at the value MPR = 1, for the mobile stations with <TEX>$2{\times}2$</TEX> STBC diversity.

Using Big Healthcare Data for ILI Situational Awareness in Georgia

We describe how high-volume electronic healthcare reimbursement claims from providers' offices and retail pharmacies can be used to provide timely and accurate influenza-like illness (ILI) situational awareness at state and CBSA levels. We focused on the 2006-2010 influenza seasons and the 39 CBSAs in the State of GA. A sufficient proportion of claims for services within any 7-day service period can be accumulated from both providers' offices and retail pharmacies to generate useful CBSA-level ILI metrics, dispelling the common perception that claims data accumulate too slowly to be useful for public health decision making in sub-state geographic areas.