Empirical Analysis and Ranging using Environment and Mobility Adaptive RSSI Filter for Patient Localization during Disaster Management

During emergency response to mass casualty disasters, one of the main logistic impediments faced by the On-site Organization Chief is to track the patients at the disaster site: We had proposed a new system based on a location aware wireless sensor network (WSN) to overcome these impediments and assist the responders in providing efficient emergency response. In this paper we have implemented a new ranging algorithm called Ranging using Environment and Mobility Adaptive RSSI (REAM) Filter which will provide the distance estimates and yield the real time localization of patients at the disaster site. We have conducted Ranging experiments both in indoor and outdoor environments and have built an offline database which is given as input for the REAM filter simulation. The output of REMA filter is given as input to the new position estimation algorithm currently being developed by us and the position estimation error is calculated using simulation. The REAM filter simulation results show the suitability of the algorithm for ranging during tracking of patients at the disaster site.