• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Systems Journal, IEEE >Biometric Sensor Management: Tradeoffs in Time, Accuracy and Energy
【24h】

Biometric Sensor Management: Tradeoffs in Time, Accuracy and Energy

机译：生物识别传感器管理：时间，准确性和能源之间的权衡

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper a novel sensor management algorithm is presented for a biometric sensor network. A distributed detection framework is managed for different energy, accuracy and time requirements. The design variables include sensor thresholds, fusion rule, sensor selection and sensor mode selection. Different sensors are associated with different transaction times. Hence, varying sensor modes can affect the accuracy and energy consumption. Once the sensors and their modes are selected, the accuracy achieved by this subset of sensors is maximized by managing the thresholds and the fusion rule. Risk, time and energy are the three objectives that the system attempts to minimize. The three objectives are tied into a single objective function by weighting them. A hybrid particle swarm optimization algorithm is design the system. The algorithm is a hybrid of continuous, discrete and binary particle swarm. The continuous particle swarm is used to manage the thresholds. The binary particle swarm is used to manage the fusion rule. The discrete particle swarm is used to select the sensors and the sensor mode. The system is adapted for different threat levels that depend on the a priori of imposter in the network. Results show the effectiveness of the proposed method in adapting the system to different requirements under different threat situations.
机译：在本文中，提出了一种针对生物传感器网络的新型传感器管理算法。管理分布式检测框架以满足不同的能量，准确性和时间要求。设计变量包括传感器阈值，融合规则，传感器选择和传感器模式选择。不同的传感器与不同的交易时间相关联。因此，变化的传感器模式会影响精度和能耗。一旦选择了传感器及其模式，就可以通过管理阈值和融合规则来最大程度地提高传感器子集的准确性。风险，时间和精力是系统试图最小化的三个目标。通过加权将这三个目标捆绑为一个目标函数。系统设计了一种混合粒子群优化算法。该算法是连续，离散和二进制粒子群的混合体。连续粒子群用于管理阈值。二元粒子群用于管理融合规则。离散粒子群用于选择传感器和传感器模式。该系统适用于不同的威胁级别，具体取决于网络冒名顶替者的先验知识。结果表明，该方法在不同威胁情况下使系统适应不同需求的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号