Abhish Khanal, Deepak Chand, Prakash Chaudhary, Subash Timilsina, Sanjeeb Prasad Panday, Aman Shakya, et al. (2020). Search Disaster Victims using Sound Source Localization. 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. 1022–1030). Blacksburg, VA (USA): Virginia Tech.
Abstract: Sound Source Localization (SSL) are used to estimate the position of sound sources. Various methods have been used for detecting sound and its localization. This paper presents a system for stationary sound source localization by cubical microphone array consisting of eight microphones placed on four vertical adjacent faces which is mounted on three wheel omni-directional drive for the inspection and monitoring of the disaster victims in disaster areas. The proposed method localizes sound source on a 3D space by grid search method using Generalized Cross Correlation Phase Transform (GCC-PHAT) which is robust when operating in real life scenario where there is lack of visibility. The computed azimuth and elevation angle of victimized human voice are fed to embedded omni-directional drive system which navigates the vehicle automatically towards the stationary sound source.
|
Anjum, U., Zadorozhny, V., & Krishnamurthy, P. (2023). Localization of Events Using Neural Networks in Twitter Data. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 909–919). Omaha, USA: University of Nebraska at Omaha.
Abstract: In this paper, we develop a model with neural networks to localize events using microblogging data. Localization is the task of finding the location of an event and can be done by discovering event signatures in microblogging data. We use the deep learning methodology of Bi-directional Long Short-Term Memory (Bi-LSTM) to learn event signatures. We propose a methodology for labeling the Twitter date for use in Bi-LSTM However, there might not be enough data available to train the Bi-LSTM and learn the event signatures. Hence, the data is augmented using generative adversarial networks (GAN). Finally, we combine event signatures at different temporal and spatial granularity to improve the accuracy of event localization. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods.
|
Imane Benkhelifa, Samira Moussaoui, & Nadia Nouali-Taboudjemat. (2013). Locating emergency responders using mobile wireless sensor networks. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 432–441). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Emergency response in disaster management using wireless sensor networks has recently become an interest of many researchers in the world. This interest comes from the growing number of disasters and crisis (natural or man-made) affecting millions of lives and the easy-use of new and cheap technologies. This paper details another application of WSN in the post disaster scenario and comes up with an algorithm for localization of sensors attached to mobile responders (firefighters, policemen, first aid agents, emergency nurses, etc) while assisted by a mobile vehicle (fire truck, police car, or aerial vehicle like helicopters) called mobile anchor, sent to supervise the rescue operation. This solution is very efficient and rapidly deployable since no pre-installed infrastructure is needed. Also, there is no need to equip each sensor with a GPS receiver which is very costly and may increase the sensor volume. The proposed technique is based on the prediction of the rescuers velocities and directions considering previous position estimations. The evaluation of our solution shows that our technique takes benefit from prediction in a more effective manner than previous solutions. The simulation results show that our algorithm outperforms conventional Monte Carlo localization schemes by decreasing estimation errors with more than 50%.
|
Dahlke, D., Kaiser, S., & Bayer, S. (2023). Self-Localization: A proposal to equip first responders with a robust and accurate GNSS device. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 242–251). Omaha, USA: University of Nebraska at Omaha.
Abstract: In this paper we explore the GNSS positioning capabilities in the context of search and rescue operations. Our contribution is a tool that robustly receives and precisely evaluates GNSS signals. The final positioning information is then transmitted to an orchestrator where other tools like augmented reality utilities or the command and control have access to. During the time from the project start in September 2021 to December 2022 the components have been chosen, and the design and software of the tool have been developed. Furthermore, some of the tool’s capabilities have been tested and compared during field trials with first responders and measurement campaigns. The developed tool outperforms the commonly used smartphone localization in terms of accuracy, operation time and time to get a GNSS fix. This reliability improvement helps to identify someones position in adverse conditions.
|
Volkmar Schau, Christian Erfurth, Gerald Eichler, Steffen Späthe, & Wilhelm Rossak. (2011). Geolocated communication support in rescue management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Efficient communication on base of consistent and up to date information is the key factor to cope with hard rescue missions. With the new generation of mobile devices local peer-to-peer communication in conjunction with geolocated information is promising to improve information's quality. Thereby, the routing of information in ad-hoc networks is very dynamic. This contribution, based on work of the SpeedUp project, analyses protocols and presents an approach which combines mobile software agents, routing in ad-hoc networks, and geolocated information to build up a reliable communication infrastructure. The 2MANS simulator allows efficient graphical model building. Geolocated information will be utilized as a map representation to improve the overall situation for unified rescue forces management.
|
