在同一仿真平台上比较了3种分布式定位算法,Ad Hoc positioning,Robust positioning和N-Hopmultilateration,并介绍了每种算法的基本原理和实现方法,抽象出一种适用于大规模无线传感器网络的通用三阶段分布式定位结构体系。仿真结果显示...在同一仿真平台上比较了3种分布式定位算法,Ad Hoc positioning,Robust positioning和N-Hopmultilateration,并介绍了每种算法的基本原理和实现方法,抽象出一种适用于大规模无线传感器网络的通用三阶段分布式定位结构体系。仿真结果显示了3种算法在不同场景下的定位误差情况,比较了3种算法的优劣,同时,也对不同的网络环境参数对网络定位性能的影响做出了分析。展开更多
A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, ...A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.展开更多
提出了一种新颖的数据分发机制--match-making by geometric structure quorum(MM-GSQ).该机制使用一种称为空间邻居代理quorum的新quorum方法,可充分利用平面图的几何特性,减少quorum的规模.通过减少传输的消息数和消息碰撞,MM-GSQ改...提出了一种新颖的数据分发机制--match-making by geometric structure quorum(MM-GSQ).该机制使用一种称为空间邻居代理quorum的新quorum方法,可充分利用平面图的几何特性,减少quorum的规模.通过减少传输的消息数和消息碰撞,MM-GSQ改善了能量消耗,增加了匹配成功率,而且易于实现.理论分析和实验结果表明,新quorum方法和MM-GSQ与伪quorum方法相比有更好的可伸缩性、更高的能量效率和匹配成功率,特别适用于大规模无线传感器网络数据分发.展开更多
社会网络应用已无处不在,在健康医疗领域也是如此.同时,传感器网络的发展也面临新的形势.在真实世界中,有许多因素(如社会关系、历史健康状态和个人属性信息)都能对健康状态检测?预测结果产生影响.然而,却很少有相关文献能够系统阐述新...社会网络应用已无处不在,在健康医疗领域也是如此.同时,传感器网络的发展也面临新的形势.在真实世界中,有许多因素(如社会关系、历史健康状态和个人属性信息)都能对健康状态检测?预测结果产生影响.然而,却很少有相关文献能够系统阐述新形势下在一个动态社会网络中节点用户健康状态如何进行检测?预测以及不同因素对用户健康状态影响到何种程度.首先描述一种新颖的医疗物联网:医疗社会网络(medical social networks,MSNs);然后统一考虑社会关系、历史健康状态和用户属性对网络用户健康状态检测结果的影响,提出一种新的基于时-空概率因子图模型(temporal-spatial factorgraph model,TS-FGM)的网络用户健康状态检测?预测方法.在Twitter数据集上对所提出的模型进行了验证,并在一个真实的临床医疗数据集上与SVM基线算法进行了对比实验.实验结果表明所提出的TS-FGM模型是有效的,健康状态检测方法也在一定程度上优于基线方法.展开更多
文摘在同一仿真平台上比较了3种分布式定位算法,Ad Hoc positioning,Robust positioning和N-Hopmultilateration,并介绍了每种算法的基本原理和实现方法,抽象出一种适用于大规模无线传感器网络的通用三阶段分布式定位结构体系。仿真结果显示了3种算法在不同场景下的定位误差情况,比较了3种算法的优劣,同时,也对不同的网络环境参数对网络定位性能的影响做出了分析。
文摘A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.
文摘提出了一种新颖的数据分发机制--match-making by geometric structure quorum(MM-GSQ).该机制使用一种称为空间邻居代理quorum的新quorum方法,可充分利用平面图的几何特性,减少quorum的规模.通过减少传输的消息数和消息碰撞,MM-GSQ改善了能量消耗,增加了匹配成功率,而且易于实现.理论分析和实验结果表明,新quorum方法和MM-GSQ与伪quorum方法相比有更好的可伸缩性、更高的能量效率和匹配成功率,特别适用于大规模无线传感器网络数据分发.
文摘社会网络应用已无处不在,在健康医疗领域也是如此.同时,传感器网络的发展也面临新的形势.在真实世界中,有许多因素(如社会关系、历史健康状态和个人属性信息)都能对健康状态检测?预测结果产生影响.然而,却很少有相关文献能够系统阐述新形势下在一个动态社会网络中节点用户健康状态如何进行检测?预测以及不同因素对用户健康状态影响到何种程度.首先描述一种新颖的医疗物联网:医疗社会网络(medical social networks,MSNs);然后统一考虑社会关系、历史健康状态和用户属性对网络用户健康状态检测结果的影响,提出一种新的基于时-空概率因子图模型(temporal-spatial factorgraph model,TS-FGM)的网络用户健康状态检测?预测方法.在Twitter数据集上对所提出的模型进行了验证,并在一个真实的临床医疗数据集上与SVM基线算法进行了对比实验.实验结果表明所提出的TS-FGM模型是有效的,健康状态检测方法也在一定程度上优于基线方法.
