## 1 Introduction  
In the present age, natural gas has become one of the most important energy resources in the World, which primarily depends on the pipeline transportation. However, with the widespread demand for natural gas supplies, the pipeline leak detection problem has become increasingly prominent [1,2]. Conventional leak detection methods mainly rely on periodical inspections conducted by maintenance personnel, which needs intensive human involvement, but periodical inspection does not provide real time monitoring of the pipeline. Accordingly, a leak may not be detected in time and this may bring about a great deal of economic losses and environmental pollution. Real time pipeline monitoring systems based on wired or wireless sensors have been studied in [3-5]. The wire-based techniques connect the sensors along the pipelines with wires. Monitoring information measured by each sensor is transmitted to the monitoring control center through these wires. Nevertheless, the wire-based monitoring systems are vulnerable to damage in any part of the networks and the deployment cost in an underground scenario is very expensive. Wireless sensor networks (WSNs) [6,7], on the other hand, are a much more robust and efficient option to monitor pipelines. Since 2006, many studies have been carried out on WSNs for pipeline monitoring. [PERSON] _et al._[8] proposed a framework for pipeline infrastructure monitoring using WSNs and designed a routing protocol for transmitting the information from sensor nodes to the sink. [PERSON] _et al._[9] put forward a distributed sensing data propagation algorithm based on graded residual energy used in pipeline monitoring sensor networks. The algorithm can achieve balanced energy consumption among sensor nodes and prolong the network lifetime. [PERSON] _et al._[10] described a system called PipeNet used for detecting and localizing leaks and failures in water transmission pipelines. The system is composed of a host of sensor nodes with perception, computation and wireless communication capacities by self-organization. According to the acoustic signals, velocity (flow) signals and pressure transient signals captured by nodes, the system can diagnose the current state of the pipeline more accurately [10]. Although some monitoring systems based on WSNs have been developed in the last several years, research on information processing in this type of networks is relatively scarce. [PERSON] _et al._[11] analyzed the features of monitoring information in pipeline networks in detail. [PERSON] _et al._[12] presented a model of in-network information processing for pipeline monitoring based on WSNs, but did not give any specific algorithms. This is mainly because monitoring information shows uncertainty and diversity in expressive form, huge amounts in numbers and complicated relationships [11]. Consequently, so far there has not been a kind of method which can effectively process monitoring information of natural gas pipelines. This mainly reflects the following aspects [13,14]: in the first place, under urban area circumstances, sensors may be affected by background noise so that detection information has great ambiguity. This is due to the fact that noise may submerge the useful signals, which will result in a false diagnosis by the monitoring system. In addition, the information provided by single-type sensors is always incomplete or inaccurate as a result of their own deficiencies and environmental interference, so each node may integrate several different types of sensors, such as acoustic sensor, flow sensor and pressure sensor and so on. Thus we need to design a reliable method for multi-source information processing. Moreover, to avoid the monitoring blind zone, when a node is out of action, the detection range among nodes should have a certain degree of overlap.