摘要
建立有向传感器节点模糊感知模型,利用模糊数据融合规则减少网络不确定区域.对于有向传感器网络路径覆盖问题,提出基于模糊粒子群算法的有向传感器网络路径覆盖增强算法,将n维求解问题转化为一维求解问题,以提高单个传感器节点净覆盖域为目的,提高网络覆盖率.仿真结果表明,对于感知方向可连续调节的有向传感器网络节点,在随机部署情况下与现有算法对比,文中算法能有效提高有向传感器网络路径覆盖率,并且具有较快的收敛速度,延长网络生存期.
By utilizing fuzzy data fusion rules, a fuzzy perception model for directional sensor nodes is built to reduce the network uncertain region. Aiming at path coverage problems of directional sensor networks, a path coverage enhancement algorithm of the directional sensor networks based on fuzzy particle swarm optimization is proposed. The formed n-dimension problem is transformed to one-dimension problem toimprove the coverage area of single sensor node and thereby increase the network coverage. For the directional sensor network nodes of adjustable perception direction, the simulation experiment is carried out by comparing the proposed algorithm with the existing algorithms under random deployment. The results show that the proposed algorithm can effectively improve the path coverage of the directional sensor networks, have a faster convergence rate and prolong the network life time.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2017年第2期183-192,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61304144
61172014
61040010)
航空科学基金项目(No.20115142005)
国家国际科技合作与交流专项(No.2013DFA11040)资助~~
关键词
有向传感器网络
路径覆盖
数据融合
粒子群优化
Directional Sensor Networks, Path Coverage, Data Fusion, Particle Swarm Optimization
