三维空间中异构传感器网络的目标覆盖

在传感器随机分布的目标覆盖问题中，针对由感知属性不同的普通节点和超级节点构成的异构传感器网络，在三维空间中基于概率感知模型，结合免疫优势克隆算法来进行目标覆盖问题的优化．由免疫优势克隆算法提供的传感器节点最少等效个数的寻求方法，保证了随机分布于三维空间里的可感知的目标点能够全部被覆盖到．实验结果表明，与目前解决此问题最好的遗传算法相比，在达到与遗传算法相同覆盖质量的条件下，免疫优势克隆算法有效提高了实时性，明显降低了寻优时间．

Target coverage of heterogeneous sensor networks in three-dimensional space

As for the coverage problem when sensors are randomly distributed, aiming thatt the heterogeneous sensor network consists of normal nodes and super nodes with different attributes, the target coverage problem was optimized by immunodominance clone algorithm based on the probability perception model in the three- dimensional space. The model seeking for the number equivalent to the minimum cost of sensor nodes was supplied by immunodominance clone algorithm, and it assured all the perceptible target nodes which were randomly distributed in the 3-D space could be covered. The experimental results show that in comparison with the present best genetic algorithm for this problem, under the conditions of achieving the coverage quality the same as the genetic algorithm, the immunodominance and optimizing time is significantly reduced. clone algorithm improves the real-time property effectively,