基于无线传感器网络的无线医疗监护系统的设计与实现

本文设计了基于无线传感器网络的无线医疗监护系统,利用基于TinyOS操作系统的CC2430作为无线射频模块,加速度传感器、脉搏传感器和体温传感器分别检测人体姿态、脉搏和体温。提出的基于RSSI值的定位算法可正确定位被监护者所处位置,跌倒算法可以准确判别人体跌倒状态,设计的上位机界面可以实时显示被监护人人体状态。结果表明,该系统可准确地获取被监护人脉搏、体温、人体姿态和位置等信息,实现了可靠的无线监护目标。     

基于无线传感器网络的车流量检测系统设计

由于城市中车辆增多,车流量检测在现代交通中已成为重要的一部分。掌握车流量信息,可以了解路面状况从而对做出合理决策有极大的帮助。如何准确实时地得到车流量数据成为道路交通的显著要求,本文中用TI公司的CC2530作为核心处理器单元,通过在主要路段设置热释电红外传感器检测节点,用ZigBee无线传输技术构建无线传感器网络,来实现对车流量的准确检测。本文网络版地址：http：//www.eepw.com.cn/article/276361.htm     

基于无线传感器网络的海水检测系统

针对海水检测周期长、范围小的特点,提出了使用无线传感器网络的海水指标实时在线检测方案。研究了精确检测的方法与算法,探讨了无线传感器网络节能路由协议。系统综合运用无线传感器智能信息处理技术、远程GPRS无线数据通信技术以及基于Internet显示平台的监测点管理查询技术,将功能相同或不同的传感器构成智能化传感器网络,从而全面提升了系统的自动化与监测水平。模拟实验结果表明系统具有良好的海水定量检测能力。     

基于无线传感器网络的海水检测系统设计

针对海水检测恶劣环境提出了使用无线传感器网络的海水指标实时在线检测方案;探讨了传感器网络节能路由协议;实现了系统硬件与软件设计.系统将无线检测传感器布置于所检测水域,利用远程无线分组服务数据通信技术远程传输检测数据,通过因特网显示平台的检测点管理查询技术,全面提升了系统的自动化与检测水平.模拟实验结果表明该系统可以实现远程控制中心和监测控制节点的可靠、高速率的通讯.系统体积小、成本低,适用于现场海水污染程度连续检测.     

无线传感器网络分布式多传感器目标检测

目标检测是无线传感器网络中的一种重要应用,文中提出了一种能量有效分布式多传感器目标检测方法,该方法区别于以往的单目标方法,通过多个节点相互协作对目标进行检测,算法1给出了时间序列离散法,求得目标被测距离,且多节点将检测到的信息在聚合节点进行聚合,并通过算法2找到一条最优传输路径将聚合数据包传送至sink节点。此外通过仿真比较了单节点检测目标和多节点协同检测目标的概率、权系数α对检测节点平均能量消耗的影响,并给出了目标检测数据的最佳采样周期。     

无线传感器网络分布式频谱检测研究

传统单节点频谱检测由于受到阴影效应、多径效应和隐藏终端问题的影响,使得检测性能受到影响。基于无线传感器网络的分布式频谱检测技术能有效克服这一缺陷。在简要分析频谱检测技术的特点和要求的基础上,讨论了基于无线传感器网络的分布式频谱检测系统的结构,从本地检测和融合算法两个角度,对现有的几种基于无线传感器网络的分布式频谱检测方法进行了综述,并对其发展方向给出了建议。     

无线传感器网络入侵检测系统

无线传感器网络与传统网络存在较大差异,传统入侵检测技术不能有效地应用于无线传感器网络。文中分析了无线传感器网络面临的安全威胁;总结了现有的无线传感器网络入侵检测方案;在综合现有无线传感器网络入侵检测方法的基础上,提出了一种分等级的入侵检测系统,该入侵检测体系结构通过减少错报能检测到大多数的安全威胁。     

基于无线传感器网络的农田土壤温湿度检测

通过现代信息处理技术和现代传感器技术,结合实际农田环境因素,创造出的一种智能检测系统称作农田土壤温湿度智能检测。本文研究分析了关于常规农田环境温湿度检测的方法,根据电测法来设计无线传感网络的温湿度农田环境智能检测系统模式,以达到有效提高精细农业的灌溉策略和时空差异性的目的。     

基于无线传感器网络的路边停车检测系统设计

为了构建基于无线传感器网络的大规模、低成本、低功耗的城市路边停车检测系统,本文提出了一种停车检测传感器节点硬件及软件设计方案。该系统硬件部分利用磁阻传感器实现车辆磁信号检测．软件部分主要实现基于磁信号的车辆检测算法。实地部署了80多个传感器节点的路边停车检测系统,实际应用表明该系统车辆检测精度高,达到了设计要求。     

无线传感器网络的异常检测

本文重要阐述了在进行无线传感器网络得到的异常数据时,如何针对其异常数据进行检测。也阐述了一般情况下,出现异常数据时,无线传感器网络的大致范围,并利用图模型、相邻节点之间的属性相似、当前接收到的数据与历史数据进行的对比验证,以此来推断出,异常数据出现的大致因素、并借此能准确推断出相关的地理区域出现异常信息的大概位置,又或者是设备本身可能出现故障的大概范围,为维护管理人员在网络异常判识和原因分析方面提供指导。     

基于局部联系的无线传感网络异常入侵检测

提出一种局部联系对比搜索算法.通过把节点刷新定位过程,与其相邻的小范围分布网络的均值特征节点做比较,利用局部无线网络节点最优信息,检测异常入侵节点信息,避免了传统集中式方法对全部节点搜索的耗时.实验证明,这种局部联系对比定位算法能够有效利用网络信息,对异常节点实现准确入侵检测,缩短了检测时间.     

基于可变阈值的无线传感网络异常值检测

定义了新的异常因子,将数据分为正常、异常、临界3种状态,并在此基础上构建了一个基于动态阈值的异常值检测模型。在修正马尔科夫假设的基础上,给出动态阈值的更新方法。算法在无需训练集的条件下,实现了在线的实时异常值检测。仿真实验表明,算法在保持较高检测精度的同时,维持了较低的误报率。     

无线传感器网络研究概述

集中传感器、计算机以及通信技术的无线传感器网络技术由于其良好的性能以及广阔的运用空间,得到了广大学者的关注和研究。基于此介绍了无线传感器网络的基本概念、属性,讨论了无线传感器网络的关键技术,并就使用领域进行了阐述。     

Using Information Fusion to Assist Data Dissemination in Wireless Sensor Networks

Data dissemination is a fundamental task in wireless sensor networks. Because of the radio range limitation and energy consumption constraint, sensor data is commonly disseminated in a multihop fashion (flat networks) through a tree topology. However, to the best of our knowledge none of the current solutions deals with the moment when the dissemination topology needs to be rebuilt. This work addresses such a problem introducing the use of information fusion mechanisms, where the traffic is handled as a signal that is filtered and translated into evidences that indicate the likelihood of critical failures occurrence. These evidences are combined by a Dempster-Shafer engine to detect the need for a topology reconstruction. Our solution, called Topology Rebuilding Algorithm (TRA), is evaluated through a set of simulations. In the experiments, TRA showed to be efficient in avoiding unnecessary topology reconstructions. Compared to the periodic rebuilding, in some cases, TRA could reduce the traffic overhead in nearly 35% of the traffic produced by the periodicu rebuilding.     

无线传感器网络系统综述

介绍了无线传感器网络的概念、几种主要的无线传感器网络协议，尤其是MAC层协议的研究情况，给出了一些比较经典的无线传感器网络MAC协议的思想。     

Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

A forest fire is a severe threat to forest resources and human life. In this paper, we propose a forest?fire detection system that has an artificial neural network algorithm implemented in a wireless s...     

一种无线传感器网络节点故障的检测方法

To reduce excessive computing and communication loads of traditional fault detection methods, a neighbor-data analysis based node fault detection method is proposed. First, historical data is analyzed to confirm the confidence level of sensor nodes. Then a nodes reading data is compared with neighbor nodes which are of good confidence level. Decision can be made whether this node is a failure or not. Simulation shows this method has good effect on fault detection accuracy and transient fault tolerance, and never transfers communication and computing overloading to sensor nodes.     

Directional Controlled Fusion in Wireless Sensor Networks

Though data redundancy can be eliminated at aggregation point to reduce the amount of sensory data transmission, it introduces new challenges due to multiple flows competing for the limited bandwidth in the vicinity of the aggregation point. On the other hand, waiting for multiple flows to arrive at a centralized node for aggregation not only uses precious memory to store these flows but also increases the delays of sensory data delivery. While traditional aggregation schemes can be characterized as “multipath converging,” this paper proposes the notation of “multipath expanding” to solve the above problems by jointly considering data fusion and load balancing. We propose a novel directional-controlled fusion (DCF) scheme, consisting of two key algorithms termed as directional control and multipath fusion. By adjusting a key parameter named multipath fusion factor in DCF, the trade-offs between multipath-converging and multipath-expanding can be easily achieved, in order to satisfy specific QoS requirements from various applications. We present simulations that verify the effectiveness of the proposed scheme.

Lightweight Deployment-Aware Scheduling for Wireless Sensor Networks

Wireless sensor networks consist of a large number of tiny sensors that have only limited energy supply. One of the major challenges in constructing such networks is to maintain long network lifetime as well as sufficient sensing areas. To achieve this goal, a broadly-used method is to turn off redundant sensors. In this paper, the problem of estimating redundant sensing areas among neighbouring wireless sensors is analysed. We present simple methods to estimate the degree of redundancy without the knowledge of location or directional information. We also provide tight upper and lower bounds on the probability of complete redundancy and on the average partial redundancy. With random sensor deployment, our analysis shows that partial redundancy is more realistic for real applications, as complete redundancy is expensive, requiring up to 11 neighbouring sensors to provide a 90 percent chance of complete redundancy. Based on the analysis, we propose a scalable Lightweight Deployment-Aware Scheduling (LDAS) algorithm, which turns off redundant sensors without using accurate location information. Simulation study demonstrates that the LDAS algorithm can reduce network energy consumption and provide desired QoS requirement effectively. This research was partially supported by Natural Sciences and Engineering Research Council of Canada. Kui Wu received his Ph.D. in Computing Science from the University of Alberta, Canada, in 2002. He joined the Department of Computer Science at the University of Victoria, Canada in the same year and is currently an Assistant Professor there. His research interests include mobile and wireless networks, network performance evaluation, and network security. Yong Gao received his Master's degree and Ph.D. degree in computer science from University of Alberta, Canada, in 2000 and 2005 respectively. He is currently with the Irving K. Barber School of Arts and Sciences, UBC Okanagan, Canada. His research interests include search algorithms and AI, communication networks, and computational biology. Yang Xiao worked at Micro Linear as an MAC (Medium Access Control) architect involving the IEEE 802.11 standard enhancement work before he joined Department of Computer Science at The University of Memphis in 2002. Dr. Xiao is an IEEE Senior member. He was a voting member of IEEE 802.11 Working Group from 2001 to 2004. He currently serves as Editor-in-Chief for International Journal of Security and Networks (IJSN) and for International Journal of Sensor Networks (IJSNet). He serves as an associate editor or on editorial boards for the following refereed journals: (Wiley) International Journal of Communication Systems, (Wiley) Wireless Communications and Mobile Computing (WCMC), EURASIP Journal on Wireless Communications and Networking, and International Journal of Wireless and Mobile Computing. He serves as five lead/sole guest editor for five journal special issues. He serves as a referee/reviewer for many funding agencies, as well as a panelist for NSF. His research interests are Security/ Reliable Communications, Medium Access Control, Mobility/Location/Paging Managements, Cache Access and Replacement Policies, Quality of Service, Energy Efficiency, and Routing in wireless networks and mobile computing.