WSNs中基于节点能耗特征的入侵检测模型研究

为了解决无线传感器网络中的拒绝服务攻击问题,利用传感器节点能耗特征,提出了基于能耗特征的无线传感器网络入侵检测模型.采用能耗检测的方式,利用马尔科夫模型为节点建立能耗预测模型,并且对节点能耗变化进行相关性检测,综合判断节点行为是否存在异常.仿真结果表明,该模型对节点的入侵行为具有较好的检测效果.     

分布式信源编码的压缩感知技术在无线传感器网络中的应用

文章提出了分布式压缩感知理论,利用传感器节点的数据相关性,把单个信号的压缩采样扩展到信号群的压缩采样,可以实现无线传感器网络的数据重构,减少节点的通信开销,降低整个网络的能耗。     

无线传感器网络中一种基于能效的入侵检测算法-3N算法研究

识别异常行为是无线传感器网络进行入侵检测的一项重要任务,当识别异常时应使网络的通信开销和能量消耗达到最小.文中提出了一种基于分簇的分布式异常检测算法-3N算法.首先对传感器的测量值进行分簇,再向其它节点传送分簇信息之前对簇进行融合.仿真结果表明,这种分布式方法与集中式方法的检测精度相当,但通信开销大大减少.     

一种新的无线传感器网络中异常节点检测定位算法

无线传感器网络中异常节点检测是确保网络数据准确性和可靠性的关键步骤。基于图信号处理理论,该文提出了一种新的无线传感器网络异常节点检测定位算法。新算法首先对网络建立图信号模型,然后基于节点域-图频域联合分析的方法,实现异常节点的检测和定位。具体而言,第1步是利用高通图滤波器提取网络信号的高频分量。第2步首先将网络划分为多个子图,然后筛选出子图输出信号的特定频率分量。第3步对筛选出的子图信号进行阈值判断从而定位疑似异常的子图中心节点。最后通过比较各子图的节点集合和疑似异常节点集合,检测并定位出网络中的异常节点。实验仿真表明,与已有的无线传感器网络中异常检测方法相比,新算法不仅有着较高的异常检测概率,而且异常节点的定位率也较高。     

无线传感器网络中基于区域通信覆盖的节点定位投放算法

针对无线传感器网络中传感器节点投放分布对投放区域有效通信信号覆盖的影响,该文提出了一种基于通信覆盖的分布式投放概率覆盖(DDCP)算法。在保证投放精度的前提下,该算法根据传感器节点在投放区域中位置的不确定性以及信号衰减特性,建立信号覆盖模型,并通过概率优化获取传感器节点的最佳投放位置和投放数目。这样改善了区域通信覆盖,同时提高了投放效率和节省网络资源。通过仿真比较了在不同定位投放方法下的各相关性数据,验证了该算法实现高效投放的优越性和正确性。     

用于区域监测的无线传感器网络数据去冗余研究

提高能量效率是无线传感器网络研究的关键技术之一.通过分析无线传感器网络监测区域的信号空时频域动态特征,给出了基于信号空间频率带宽的无线传感器网路传感器节点的放置算法.提出基于空时域频率动态带宽的数据去空时冗余算法和相对应的路由策略.该算法有效地去除了数据的空时冗余,节省了网络的通信能量.     

用于未知环境的混杂传感器网络交互策略

给出一种用于未知环境中混杂传感器网络交互策略,设计并实现一种两层网络结构平台.结合最小二乘法和改进泛洪理论实现混杂传感器网络中动态节点的定位与导航.通过无线接收信号强度(received signal strength indicator)来估计发送端与接收端的距离,并作为阈值应用于视频通信链路维护中.实验分析了真实环境中RSSI的有效性,并在该结构平台上实现了路径查找和视频通信链路维护,结果表明该方法可以有效应用于混杂传感器网络动静节点协同工作.     

无线传感器网络基于节点行为的信誉评测模型

提出了一种无线传感器网络下的节点行为评测模型MA&TP-BRSN.MA&TP-BRSN综合考虑了网内节点的多种内部攻击行为和自私行为,并给出了对应于各类攻击的节点信任值的计算和整合方法.同时,为了消除测评过程中第三方节点恶意推荐和诋毁行为对测评结果的影响,MA&TP-BRSN引入了节点评价行为的概念,将节点评价行为与通信行为区分开来,建立了对第三方节点恶意评价行为的具体测评方法,并在此基础上给出了节点间接信誉参数的更新计算方法.实验结果表明,该模型能够更快更准确地识别出具有多种恶意行为的内部节点,并在一定程度上消除了高信誉节点的恶意诽谤行为对测评结果的影响.     

多传感器节点分布式协作调制识别算法

调制方式的自动识别是保证合法通信的关键措施之一,在民用和军用领域都有重要的作用。本文提出了一种在无线传感器网络中多个传感器节点分布式协作识别数字调制信号的新方法。为了克服在衰落信道中低信噪比时单接收节点调制识别率低的缺点,实现对MASK,MFSK,BPSK,QPSK以及OFDM这几种典型调制方式的正确识别,首先利用网络中相互协作的多个传感器,从提高网络识别性能出发,在每个传感器节点能耗最小的前提下,根据接收信噪比的大小设计有效的协作方案,得到反映调制类型显著差异的特征参数的新组合,然后利用径向基神经网络对数字调制信号进行识别,并就不同的网络条件,给出了不同的协作方案。仿真结果表明,与单节点调制识别相比,本文设计的调制识别方法在衰落信道中具有更高的识别率,并且节点系统更灵活可靠。      

无线传感器网络复件攻击的移动检测方法

无线传感器网络无人值守的特性使得它易于遭受复件攻击从而造成严重的安全隐患。提出了一个移动检测复件攻击的方法,通过检测节点的移动使得网络内的每个节点都能直接与检测节点通信,从而使得检测节点与一跳范围内的传感器节点能够直接通信,从而全局地检测到网络中的复件攻击节点,并采用更新网络对称密钥的方法防御逃避检测的节点。实验表明该方法能够检测到网络内的所有复件攻击节点,和其他方法相比,本方法不需要检测消息的转发,检测开销小并且实现了开销在网络中的均衡。     

Maximizing the lifetime of energy‐constrained cooperative spectrum sensing sensor network

Adding the cognitive capability to wireless sensor networks allows the sensors to monitor the spectrum and identify the spectrum holes to operate in different frequencies according to the radio environment which result in better spectrum utilization. Spectrum sensing is a main component in any cognitive radio network. In this paper, we propose a new cooperative sensing scheme based on energy detection for cognitive sensor networks which is constrained by the energy limitation of the wireless sensor elements. The proposed scheme minimizes the sensing energy for individual sensor and carefully selects the suitable participant sensors in each cooperative sensing process. This results in maximizing the lifetime of energy‐constrained wireless sensor networks. The proposed scheme also takes into consideration the constraints on the detection accuracy. The simulation results show that the proposed scheme prolonged the lifetime of the cognitive network, makes efficient usage of available spectrum by secondary users, and satisfy the target detection performance.     

Robust Range-Free Localization in Wireless Sensor Networks

In wireless sensor networks, sensors should have some mechanisms to learn their locations since sensed data without associated location information may be meaningless. While many sensor localization algorithms have been proposed, security issues in sensor localization are usually not addressed in their original design. Secure sensor localization is very challenging due to limited computation and energy resources in sensors. It is highly desirable that a localization scheme is robust and is able to detect malicious attacks without using complex cryptographic operations. In this paper, we present and analyze detection methods purely based on geometric constraints in sensor networks. Our detection methods can protect the localization algorithm from malicious attacks by detecting and eliminating the negative impact of fake information.     

Target localization based on energy considerations in distributed sensor networks

Wireless distributed sensor networks are important for a number of strategic applications such as coordinated target detection, surveillance, and localization. Energy is a critical resource in wireless sensor networks and system lifetime needs to be prolonged through the use of energy-conscious sensing strategies during system operation. We propose an energy-aware target detection and localization strategy for cluster-based wireless sensor networks. The proposed method is based on an a posteriori algorithm with a two-step communication protocol between the cluster head and the sensors within the cluster. Based on a limited amount of data received from the sensor nodes, the cluster head executes a localization procedure to determine the subset of sensors that must be queried for detailed target information. This approach reduces both energy consumption and communication bandwidth requirements, and prolongs the lifetime of the wireless sensor network. Simulation results show that a large amount of energy is saved during target localization using this strategy.     

An efficient approach for outlier detection in big sensor data of health care

In recent years, wireless sensor networks are pervasive and are generating tons of data every second. Performing outlier detection to detect faulty sensors from such a large amount of data becomes a challenging task. Most of the existing techniques for outlier detection in wireless sensor networks concentrate only on contents of the data source without considering correlation among different data attributes. Moreover, these methods are not scalable to big data. To address these 2 limitations, this paper proposes an outlier detection approach based on correlation and dynamic SMO (sequential minimal optimization) regression that is scalable to big data. Initially, correlation is used to find out strongly correlated attributes and then the point anomalous nodes are detected using dynamic SMO regression. For fast processing of big data, Hadoop MapReduce framework is used. The experimental analysis demonstrates that the proposed approach efficiently detects the point and contextual anomalies and reduces the number of false alarms. For experiments, real data of sensors used in body sensor networks are taken from Physionet database.     

Exploiting Reactive Mobility for Collaborative Target Detection in Wireless Sensor Networks

Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service requirements including high detection probability, low false alarm rate, and bounded detection delay. Although a dense all-static network may initially meet these Quality-of-Service requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes.     

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。     

信号强度和运动向量结合的无线传感器网络移动节点定位

无线传感器网络的定位是近年来无线传感器网络研究的重要课题.本文首先介绍了无线传感器网络的来源、重要性以及无线传感器网络定位的分类.然后提出了一种全新定位算法,信号强度和运动向量结合的无线传感器网络移动节点定位,简称SSMV算法,在外围布置四个锚节点,得用信号强度和未知节点在运动中向量的变化,对锚节点在内的未知节点进行定位,并对该算法进行了仿真和总结.通过与凸规划法进行比较,仿真结果表明,该算法有更高的定位精度.     

应用三维弹簧系统模型的锚节点部署优化

为使随机部署的三维无线传感器网络中锚节点的分布更加合理,提高未知节点定位精度,针对锚节点部署进行优化。通过构建弹簧系统模型,将锚节点抽象为通过弹簧相连接的点,使部分锚节点在合力作用下进行伸缩运动,达到提高网络性能的目的。当锚节点部署优化完成后,应用近似三角形内点测试( APIT)和DV-HOP( Distance Vector-hop)算法测试优化前后的节点定位精度。仿真结果表明,三维空间下的锚节点经过弹簧系统模型的部署优化后,锚节点网络覆盖率和定位覆盖率均得到了提高,网络平均连通度有所提升,且定位精度显著提高。     

Optimal barrier coverage for critical area surveillance using wireless sensor networks

Intrusion detection using barrier coverage is one of many applications existed in wireless sensor networks. The main purpose of using barrier coverage is to monitor the borders of a specific area against the intruders that are trying to penetrate this critical area by ensuring the total coverage with a low cost and extending the lifetime of the network, many solutions have been proposed in the literature in order to solve the coverage problem in wireless sensor networks, which became a vital field of research. In this paper, we present a new technique based on geometric mathematical models, in order to guarantee the total coverage of our deployed barriers with a minimum possible number of sensors. The idea is to calculate the number of sensors adequate to cover a barrier before deployment. We then formulate the problem to minimize the number of sensors to be deployed to properly cover a barrier; the calculation makes it possible to solve this problem in polynomial using our own heuristic. Additionally, we propose a new mechanism for ensuring a fault‐tolerant network by detecting the faulty sensors and select other suited sensors to close the existing gaps inside the barriers and detecting the sensors whose energy is low before the failure. The obtained simulation results prove the effectiveness of the proposed algorithms.     

On Prolonging the Lifetime for Wireless Video Sensor Networks

This paper investigates strategies for prolonging the system lifetime for wireless video sensor networks, by adopting a mobile sink and solar-powered video sensors. Issues of tracking moving objects in wireless video sensor networks are studied, and the effectiveness of adopting a mobile sink is evaluated. This paper applies a power-rate-distortion analysis framework, which provides a theoretical fundamental to quantify various properties of wireless video sensor networks. The performance of wireless video sensor networks is evaluated with a mobile sink versus a static sink, under different cluster sizes and number of sensors. Comparisons of network lifetime, tracking error, video distortion, are also covered in this paper. In addition, this paper also evaluates the performance of solar-powered video sensors under an unequal layered clustering topology.