Abstract
Various search methods combined with frontier technology have been utilized to save lives in rescue situations throughout history. Today, new networked technology, cyber-physical system platforms, and algorithms exist which can coordinate rescue operations utilizing swarm intelligence with Rapid Alert Sensor for Enhanced Night Vision (RASEN). We will also introduce biologically inspired algorithms combined with proposed fusion night vision technology that can rapidly converge on a near optimal path between survivors and identify signs of life trapped in rubble. Wireless networking and automated suggested path data analysis is provided to rescue teams utilizing drones as first responders based on the results of swarm intelligence algorithms coordinating drone formations and triage after regional disasters requiring Big Data analytic visualization in real-time. This automated multiple-drone scout approach with dynamic programming ability enables appropriate relief supplies to be deployed intelligently by networked convoys to survivors continuously throughout the night, within critical constraints calculated in advance, such as projected time, cost, and reliability per mission. Rescue operations can scale according to complexity of Big Data characterization based on data volume, velocity, variety, variability, veracity, visualization, and value.
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Tanik, U.J., Wang, Y., Güldal, S. (2017). Swarm Intelligence Applied to Big Data Analytics for Rescue Operations with RASEN Sensor Networks. In: Suh, S., Anthony, T. (eds) Big Data and Visual Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-63917-8_2
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