A Novel Mechanism for Fire Detection in Subway Transportation Systems Based on Wireless Sensor Networks

Fire is a common and disastrous phenomenon in subway transportation systems because of closed environment and large passenger flow. Traditional methods detect and forecast fire incidents by fusing the data collected by wireless sensor networks and compare the fusion result with a threshold. However, this approach has a significant shortcoming. Fusion of data decreases the amount of useful information since the distribution of data is not considered. In this paper, a novel mechanism based on wireless sensor networks is proposed and we can use the mechanism to detect fire in subway transportation systems. The core algorithm in this mechanism is an alternative of data fusion in wireless sensor networks. The mechanism uses the quantity information acquired from the data and also considers the distribution information of the data in order to predict the occurrence of fire incidents. The simulation results show that fire incidents can be detected fast and efficiently. 1. Introduction Wireless sensor networks (WSNs) have extensively been used due to their excellent capability of monitoring real physical environments and collecting data [1]. For example, WSNs have been used in military, medical, and environmental monitoring applications, among others. Surely, the fields in which they are applied will continue to expand. With the rapid process of urbanization, more and more people live and work in big cities. As a result, public transportation systems are under great pressure, and subway transportation systems are good choices to help relieve this pressure. However, fire is a common and disastrous phenomenon in subway transportation systems, and great attention must be paid to guarantee the safety of the public in such systems. Thus, the research on fire detection in subway transportation systems is very important. In this paper, WSNs are used in subway transportation systems to monitor fire. The sensors deployed can collect data about temperature, and then we store the data in the database. Fire incidents can be predicted by comparing the distribution of the data collected and the normal distribution of the data on the condition when there is no fire. There are two rules that will be followed when comparing the distributions—the rule of the best consistency comparison and the rule of the best squared comparison. In this paper, these two rules are the core of the mechanism for fire detection. The best consistency comparison is a good method for fire locating, but it cannot provide any indication of the situation of the fire. Conversely, the best squared