Paulini, M. S., Duran, D., Rice, M., Andrekanic, A., & Suri, N. (2023). KENNEL Threat Detection Boxes for First Responder Situational Awareness and Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 208–219). Omaha, USA: University of Nebraska at Omaha.
Abstract: KENNEL is a deployable IoT-based system consisting of a network of unattended ground sensors, known as Threat Detection Boxes (TDBs), which may be outfitted with any variety of custom and commercial-off-the-shelf sensors for hazard detection. The KENNEL system fills a technological gap for sensor fusion, interpretation, and real-time alerting via existing information management systems, such as Team Awareness Kit (TAK). First responders face a critical need for improved situational awareness, detection, and response to hazardous events. KENNEL provides a first of its kind, low-cost sensing & data fusion platform that is highly extensible, configurable, and self-sustaining, opening a world of modernization and innovation possibilities across the first responder domain. TDBs may also be statically or ad hoc deployed, improving flexibility, stand-off hazard detection, and resilience in the operational domain. From critical infrastructure monitoring to wearables, the system affords timeliness of critical information for effective risk management and increased personnel safety.
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Lennart Landsberg, Alexandra Braun, Ompe Aimé Mudimu, & Klaus-Dieter Büttgen. (2021). Considering end user needs when developing new technologies – a new plug and play sensor technology for locating trapped victims. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 922–928). Blacksburg, VA (USA): Virginia Tech.
Abstract: Building collapses often happen unexpectedly and suddenly. Consequently, people are often buried under the debris. What follows is a complicated search by first responders, which is characterized by time pressure and danger. In the research project SORTIE, a modular and UAV-based technical system is being developed to support the first responders in their search efforts. During the first phase of this project, an extensive requirements analysis was conducted with the involvement of end users. This ensures that the developed technology meets the requirements for later use under realistic circumstance. The project consortium has good experience with this operational approach and is in close cooperation with end users who are part of the consortium. In addition to a comprehensive understanding of building collapses and prevailing conditions, the technical partners were also able to identify requirements that they might not have discovered without the involvement of end users and the appropriate methods.
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Tiina Ristmae, Dimitra Dionysiou, Miltiadis Koutsokeras, Athanasios Douklias, Eleftherios Ouzounoglou, Angelos Amditis, et al. (2021). The CURSOR Search and Rescue (SaR) Kit: an innovative solution for improving the efficiency of Urban SaR Operations. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 867–880). Blacksburg, VA (USA): Virginia Tech.
Abstract: CURSOR (Coordinated Use of miniaturized Robotic equipment and advanced Sensors for search and rescue OpeRations) is an ongoing European H2020 project with the main objective to enhance the efficiency and safety of Urban Search and Rescue (USaR) operations on disaster sites. CURSOR's approach relies on the integration of multiple mature and emerging technologies offering complementary capabilities to an USaR system, so as to address several challenges and capability gaps currently encountered during first responder missions. The project's research and development are structured around an earthquake master scenario. CURSOR aspires to advance the state-of the-art in several key aspects, including reduced time for victim detection, increased victim localization accuracy, enhanced real-time worksite information management, improved situational awareness and rescue team safety.
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Tobias Andersson Granberg, Carl-Oscar Jonson, Erik Prytz, Krisjanis Steins, & Martin Waldemarsson. (2020). Sensor Requirements for Logistics Analysis of Emergency Incident Sites. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 952–960). Blacksburg, VA (USA): Virginia Tech.
Abstract: Using sensors to collect data at emergency incident sites can facilitate analysis of the logistic operations. This can be used to improve planning and preparedness for new operations. Furthermore, real-time information from the sensors can serve as operational decision support. In this work in progress, we investigate the requirements on the sensors, and on the sensor data, to facilitate such an analysis. Through observations of exercises, the potential of using sensors for data collection is explored, and the requirements are considered. The results show that the potential benefits are significant, especially for tracking patients, and understanding the interaction between the response actors. However, the sensors need to be quite advanced in order to capture the necessary data.
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Jürgen Moßgraber, Désirée Hilbring, Hylke van der Schaaf, Philipp Hertweck, Efstratios Kontopoulos, Panagiotis Mitzias, et al. (2018). The sensor to decision chain in crisis management. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 754–763). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In every disaster and crisis, incident time is the enemy, and getting accurate information about the scope, extent, and impact of the disaster is critical to creating and orchestrating an effective disaster response and recovery effort. Decision Support Systems for disaster and crisis situations need to solve the problem of facilitating the broad variety of sensors available today. This includes the research domain of the Internet of Things and data coming from social media. All this data needs to be aggregated and fused, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. Furthermore, the interaction and integration with risk and crisis management systems are necessary for a better analysis of the situation and faster reaction times. This paper provides an insight into the sensor to decision chain and proposes solutions and technologies for each step.
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