دانلود رایگان مقاله ادغام زمینه تصادفی مارکوف با زنجیره مارکوف برای تشخیص رویداد با شبکه حسگر بی سیم

Abstract

Event detection is an important task required in various applications of wireless sensor network (WSN). The existing approaches consider the spatial and temporal correlation of sensor data separately or not in a cohesive way. In this paper an event detection scheme with WSN is introduced, which adopts a hierarchical structure to efficiently integrate the spatial and temporal correlation of sensor data. Here a fusion algorithm considering both the weight of the sensors and spatial information is applied to Markov random field to properly fuse the decisions of the neighboring nodes. Markov chain is also adopted to effectively extract the temporal correlation after the spatial correlation is decided. The simulation results demonstrate that the proposed scheme can effectively increase the detection accuracy and reduce communication cost, in comparison with the existing schemes.

5. Conclusion

In this paper we have presented a novel event detection approach considering the properties of both spatial and temporal relationship in wireless sensor network. In order to increase the detection accuracy, the proposed scheme integrates spatial event detection and temporal event detection using a hierarchical structure of sensor nodes. In addition, a fusion rule considering the weight of the sensor and spatial information was employed with MRF to fuse the decisions of the sensor nodes along with the Markov chain model for temporal analysis. Computer simulation revealed that the proposed scheme significantly improves the detection accuracy and communication overhead compared with the threshold approach and MRF-based event detection approach. Energy consumption is a crucial issue for WSN since the deployed sensor nodes have limited battery power. Continuous sensing and data transmission will consume large energy, which shortens the lifetime of the nodes. The proposed scheme will be improved by controlling the node operation based on the sleep-andwakeup mechanism for energy efficiency. In addition, the mechanism for systematically determining the operation parameters required in the proposed event detection scheme will be investigated in the future. The event detection scheme with insufficient number of nodes in the target area is also important. The correlation between the spatial and temporal data needs to be handled in an integrated way. A new approach will also be researched to compare the effectiveness of different ways of integration of them.