, our breadcrumb system achieves 200 percent link redundancy with only 23 percent additional deployed nodes. Our deployed breadcrumb chain can achieve 90 percent PRR when one node fails in the chain. In addition, by applying the UF coordination algorithm, the system can maintain connectivity for up to 87 percent longer distances than baseline greedy coordination approach while maintaining 96 percent packet delivery ratio." /> , our breadcrumb system achieves 200 percent link redundancy with only 23 percent additional deployed nodes. Our deployed breadcrumb chain can achieve 90 percent PRR when one node fails in the chain. In addition, by applying the UF coordination algorithm, the system can maintain connectivity for up to 87 percent longer distances than baseline greedy coordination approach while maintaining 96 percent packet delivery ratio." /> , our breadcrumb system achieves 200 percent link redundancy with only 23 percent additional deployed nodes. Our deployed breadcrumb chain can achieve 90 percent PRR when one node fails in the chain. In addition, by applying the UF coordination algorithm, the system can maintain connectivity for up to 87 percent longer distances than baseline greedy coordination approach while maintaining 96 percent packet delivery ratio." /> An Automatic, Robust, and EfficientMulti-User Breadcrumb Systemfor Emergency Response Applications
The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - April (2014 vol.13)
pp: 723-736
Hengchang Liu , Dept. of Comput. Sci., Univ. of Sci. & Technol. of China, Suzhou, China
Zhiheng Xie , Dept. of Comput. Sci., Univ. of Virginia, Charlottesville, VA, USA
Jingyuan Li , Dept. of Comput. Sci., Univ. of Virginia, Charlottesville, VA, USA
Shan Lin , Dept. of Comput. Sci., Temple Univ., Philadelphia, PA, USA
David J. Siu , OCEANIT Inc., Honolulu, HI, USA
Pan Hui , Deutsche Telekom Res. Lab., Berlin, Germany
Kamin Whitehouse , Dept. of Comput. Sci., Univ. of Virginia, Charlottesville, VA, USA
John A. Stankovic , Dept. of Comput. Sci., Univ. of Virginia, Charlottesville, VA, USA
ABSTRACT
Breadcrumb systems (BCS) aid first responders by communicating their physiological parameters to remotely located base stations. In this paper, we describe the design, implementation, and evaluation of an automatic and robust multi-user breadcrumb system for indoor first response applications. Our solution includes a breadcrumb dispenser with a link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time. Moreover, we propose UF, a distributed cooperative deployment algorithm, to achieve longer breadcrumb chain lengths while maintaining fairness and high system reliability via selecting appropriate benefit and cost functions. We deployed and evaluated our system in real buildings with several different first responder mobility patterns. Experimental results from our study show that compared to the state of the art solution , our breadcrumb system achieves 200 percent link redundancy with only 23 percent additional deployed nodes. Our deployed breadcrumb chain can achieve 90 percent PRR when one node fails in the chain. In addition, by applying the UF coordination algorithm, the system can maintain connectivity for up to 87 percent longer distances than baseline greedy coordination approach while maintaining 96 percent packet delivery ratio.
INDEX TERMS
Buildings, Redundancy, Monitoring, Base stations, Measurement, Robustness,deployment and evaluation, Breadcrumb systems, emergency response applications, link monitoring, utility function
CITATION
Hengchang Liu, Zhiheng Xie, Jingyuan Li, Shan Lin, David J. Siu, Pan Hui, Kamin Whitehouse, John A. Stankovic, "An Automatic, Robust, and Efficient Multi-User Breadcrumb System for Emergency Response Applications", IEEE Transactions on Mobile Computing, vol.13, no. 4, pp. 723-736, April 2014, doi:10.1109/TMC.2013.63
144 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool