摘要
针对无线传感器网络区域重构问题,提出了一种新的基于模糊规划算法的传感器选择方法。算法利用反距离加权插值法对插值点的数据进行预测,并且使用容斥原理计算每个节点的正常工作概率。以传感器正常工作概率,误差精度为约束条件,以传感器数量最少化为目标函数,求解0-1整数规划。进一步,考虑误差阈值和工作概率模糊的情况,将节点选择问题公式化为模糊规划求解。利用传感器温度数据对0-1整数规划和模糊规划算法进行分析评估,结果证明模糊规划算法在相同约束情况下,相较于0-1整数规划约能减少35%的传感器节点数量。
This paper presents a novel fuzzy-programming based sensor selection approach for the field reconstruction in wireless sensor networks. The information of the interpolation point is predicted by using the inverse distance weighted interpolation method and the normal operating probability of each node is computed based on the principle of inclusion-exclusion. Then,the minimal number of the required sensor nodes is computed by the utilization of the 0-1 integer programming to achieve the requirements of normal operating probability,accuracy of each node.What's more,the fuzzy-programming is used to compute the required sensor number according to the error threshold and the fuzzy operating probability. We evaluate the 0-1 integer programming and fuzzy-programming based on the temperature sensor data. The results show that the fuzzy programming algorithm can reduce the number of sensor nodes by about 35% compared with 0-1 integer programming under the same constraint.
出处
《电子科技》
2018年第3期32-35,共4页
Electronic Science and Technology
基金
国家自然科学基金(61370087)
浙江省科技项目(2013E60005
2014C01044)
关键词
传感器节点选择
误差精度
工作概率
0-1整数规划
模糊规划
sensor node selection
error accuracy
working probability
0-1 integer programming
fuzzy programming
