This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms.

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This paper investigates event localization in wireless sensor networks. We improve the SNAP (Subtract on Negative Add on Positive) localization algorithm and propose the MSNAP (Modified Subtract on Negative Add on Positive) localization algorithm with higher localization accuracy and better performance of fault tolerance. First, every sensor node obverses the event signal and compares its observed reading with a threshold. If the reading is above the threshold, the node will send it to the sink station. Otherwise, it remains silent. Based on the observed readings which the nodes report, the sink station constructs the likelihood matrix by simply adding ± 1 contributions in the area around the nodes, whose maximum value points to the event location. Compared with the SNAP algorithm, when constructing the likelihood matrix, MSNAP dynamically adjusts the size of estimated region depending on the observed readings the nodes reported. Experimental results show that the algorithm effectively improves the localization accuracy and fault tolerance.