Most surveillance applications in wireless sensor network (WSN) have stringent accuracy requirements in targets surveillance with maximized system lifetime, while large amount of continuous sensing data and limited resource in WSNs pose great challenges. So it is necessary to select appropriate sensors that can collaboratively work with each other in order to obtain balance between accuracy and system lifetime. However, because of sensing diversity and big data from WSN, most existing methods can not select appropriate sensors to cover all critical monitoring locations in large scale real deployments. Accordingly, an AdaBoost based algorithm is first proposed to identify valid sensors with contribution towards accuracy improvement, which can reduce computation and communication overhead by excluding invalid sensors. The valid sensors are combined and work in a collaborative way, which can obtain better performance than other ways. Then, because of independence of each monitoring location, a divide-and-conquer architecture based method (EasiSS) is proposed to select the most informative sensor clusters from the valid sensors for critical monitoring locations. EasiSS can obtain higher classification accuracy at different user requirement. Finally, according to the experiment on real data, we demonstrate that our proposed method can get a better performance of sensor selection, comparing with traditional methods.
['Kejiang Xiao', 'Rui Wang', 'Tun Fu', 'Jian Li', 'Pengcheng Deng']
Divide-and-conquer architecture based collaborative sensing for target monitoring in wireless sensor networks