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Kuntke, F., Bektas, M., Buhleier, L., Pohl, E., Schiller, R., & Reuter, C. (2023). How Would Emergency Communication Based On LoRaWAN Perform? Empirical Findings of Signal Propagation in Rural Areas. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1042–1050). Omaha, USA: University of Nebraska at Omaha.
Abstract: Low Power Wide Area Network (LPWAN) technologies are typically promoted for Internet-of-Things (IoT) applications, but are also of interest for emergency communications systems when regular fixed and mobile networks break down. Although LoRaWAN is a frequently used representative here, there are sometimes large differences between the proposed range and the results of some practical evaluations. Since previous work has focused on urban environments or has conducted simulations, this work aims to gather concrete knowledge on the transmission characteristics in rural environments. Extensive field studies with varying geographic conditions and comparative tests in urban environments were performed using two different hardware implementations. Overall, it was found that the collected values in rural areas are significantly lower than the theoretical values. Nevertheless, the results certify that LoRaWAN technology has a high range that cannot be achieved with other common technologies for emergency communications.
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Muhammad Imran, Carlos Castillo, Jesse Lucas, Patrick Meier, & Jakob Rogstadius. (2014). Coordinating human and machine intelligence to classify microblog communications in crises. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 712–721). University Park, PA: The Pennsylvania State University.
Abstract: An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowd sourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real world datasets.
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Reem Abbas, Tony Norris, & Dave Parry. (2018). Disaster Healthcare: An Attempt to Model Cross-Agency CommunicationIn Disasters. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 504–515). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: In disasters, several national, international, and non-governmental organisations such as police, health, ambulance, fire and civil defence services are usually involved in the response process. Therefore, it is crucial that responding agencies communicate effectively to avoid fragmentation and duplication in services, to harmonise separate activities, and to clarify roles and responsibilities. Central to communication is information exchange. Effective information exchange enhances not only the appropriateness and success of disaster response, it also ensures timeliness. However, cross-agency communication is extremely challenging especially at times when there are high stress levels, incomplete data, and minimum time to make critical decisions. This paper attempts to specify a 'best-practice' model for cross-agency communication built around the specific information requirements of disaster management and disaster medicine agencies, with the aim of improving the overall quality of healthcare services provided to disaster victims.
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Rode-Hasinger, S., Haberle, M., Racek, D., Kruspe, A., & Zhu Xiao Xiang. (2023). TweEvent: A dataset of Twitter messages about events in the Ukraine conflict. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 407–416). Omaha, USA: University of Nebraska at Omaha.
Abstract: Information about incidents within a conflict, e.g., shelling of an area of interest, is scattered amongst different data or media sources. For example, the ACLED dataset continuously documents local incidents recorded within the context of a specific conflict such as Russia’s war in Ukraine. However, these blocks of information might be incomplete. Therefore, it is useful to collect data from several sources to enrich the information pool of a certain incident. In this paper, we present a dataset of social media messages covering the same war events as those collected in the ACLED dataset. The information is extracted from automatically geocoded Twitter text data using state-of-the-art natural language processing methods based on large pre-trained language models (LMs). Our method can be applied to various textual data sources. Both the data as well as the approach can serve to help human analysts obtain a broader understanding of conflict events.
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