Zou Tengyue, Li Zhenjia, Li Shuyuan, Lin Shouying
College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel). 2017 May 4;17(5):1028. doi: 10.3390/s17051028.
Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on -means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.
目标检测是一种广泛应用于区域监控、老年人护理和火灾报警的技术；其目的是在感兴趣的区域中找到特定的物体或事件。通常，固定观测站或静态传感器节点均匀地布置在该区域。然而，该区域的每个部分被侵入的概率不同；如果一个物体突然进入一个守护者设备较少的区域，就会出现检测失误，而安全区域的监测站将长时间消耗能量却毫无发现。因此，移动无线传感器网络在检测中可能会因适应性和针对性而受益。配备轮子的传感器节点能够通过基于贝叶斯网络的自适应学习过程向危险区域移动。此外，采用基于k均值++的聚类算法和能量控制机制来降低节点的能量消耗。扩展卡尔曼滤波器和投票数据融合方法被用于提高目标的定位精度。仿真和实验结果表明，这种采用自适应节能方法的新系统能够比传统系统实现更好的性能。
