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Efficient Resource Estimation During Mass Casualty Emergency Response Based on a Location Aware Disaster Aid Network

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 4913))

Abstract

The mass casualty emergency response involves logistic impediments like overflowing victims, paper triaging, extended victim wait time and transport. We propose a new system based on a location aware wireless sensor network (WSN) to overcome these impediments and assists the emergency responders (ER) in providing efficient emergency response. We have developed a ZigBeeready acceleration sensor node hardware which is energy efficient as shown by its current consumption results. ZigBee mesh network is setup and a RSSI-based localization solution is analyzed. The main functionality of this WSN is to collect real time data - patient/emergency doctor tracking, triage information, patient vital signs/activity and communicate it to the ER’ Monitor Station device that runs the visualization software. We have implemented this software using the new ‘Care Zone Count Algorithm’ a dynamic mechanism based on localized events and data acquired from the WSN. This algorithm calculates and displays the patient count in each care zone, victim flow rate, transport capacity, thereby enabling the ER to efficiently estimate the resources required. The analysis of this algorithm verifies that the proposed system creates situation awareness to the ER.

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Roberto Verdone

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© 2008 Springer-Verlag Berlin Heidelberg

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Chandra-Sekaran, AK., Flaig, G., Kunze, C., Stork, W., Mueller-Glaser, K.D. (2008). Efficient Resource Estimation During Mass Casualty Emergency Response Based on a Location Aware Disaster Aid Network. In: Verdone, R. (eds) Wireless Sensor Networks. EWSN 2008. Lecture Notes in Computer Science, vol 4913. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77690-1_13

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  • DOI: https://doi.org/10.1007/978-3-540-77690-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77689-5

  • Online ISBN: 978-3-540-77690-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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