Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network

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.