无线传感器网络中一种基于流量预测和相关系数的异常检测方法

几种典型的流量预测模型在无线传感器网络入侵检测系统中的应用进行了分析,结合相关系数,提出了一种异常入侵检测方法.根据WSN节点的预测流量序列和实际流量序列的相关系数变化来进行异常检测.实验结果表明了该方法的有效性,在攻击强度较弱时也具有较高的检测率.     

一种基于仿射传播聚类的入侵检测方法

针对基于无监督聚类的入侵检测需要预先指定初始聚类中心和数目的问题,提出了一种基于仿射传播聚类的入侵检测方法,采用了仿射传播聚类实现入侵检测,将每个数据点都看作潜在的聚类中心,通过信息迭代更新自动决定最后的聚类中心和数目,能够获得准确的聚类结果。在对KDD CUP99数据集的仿真实验中验证了方法的可行性,实验结果表明,相比传统方法能有效提高检测率。     

一种基于森林火险控制的自适应性WSN分簇算法

通过对森林防火现状的总结,分析了WSN在火险控制中的特点,文中提出了一种自适应分簇算法,将整个传感器网络划分成多层.在网络部署初期,为实现监测数据传输的实时性要求,各层簇首节点采用单跳方式与SINK节点直接通信;当节点对环境数据的监测达到一定程度形成预测趋势后,所有的节点可以自适应进入下一个阶段,进行多跳路由的建立,以提高网络的生命周期,延长传感器节点对火场环境监测的时间.     

基于网络摄像机的森林火灾检测系统研究

针对当前世界上发达国家采用卫星作为森林火灾的监测工具,辅以直升机补充卫星监测带的误差,不仅造价高昂,且不符合林区分布广、分布不集中的国情等缺点,提出了一种以网络摄像机作为监测工具,手机、电脑作为处理终端,开发基于Android手机客户端和PC客户端的森林火灾监测系统。利用TCP/IP协议栈设计多线程任务应用,实现火灾监测在网络摄像机平台的移植。最终将视频处理结果由以太网传至监控中心,实现对火灾的提前预警。     

基于运动特征分析的森林火灾烟云实时检测方法

本文提出了一种基于地面视频检测系统中烟云序列图像识别的新方法。利用视频图像的灰度信息,通过运动趋势的估计和相似性的测算,有效提高了具有非结构化及随机性特征的烟云图像检测的可靠性。在仿真试验中,使用此方法对美国Alaska州2005年5月1日拍摄的森林火灾序列图像进行了分析,结果表明它对早期产生的烟云区域能够实时检测和识别,并具有较好的鲁棒性。     

Shift Reduce Parser Based Malicious Sensor Detection for Predicting Forest Fire in WSNs

Nowadays wireless sensor networks (WSNs) has been used in enormous applications for data collection in an unfriendly environment. Forest fire makes vast hazard to the consuming plant of the world. To preserve the forest from fire, sensor nodes monitor the environment temperature. If the temperature is increased and it exceeds the threshold, the sensor sends the notification message to the fire monitoring system. The forest fire is measured by Fire Weather Index. Most of the existing fire monitoring systems only highlight in detection, but not the verification of the sensor. Suppose, if the attacker inserts any malicious sensor, the malicious sensor sends false information or create an additional delay in fire monitoring system. As a result, the more chances for the forest to be destroyed by fire. To solve this problem, Shift Reduce Parser based Malicious Sensor Detection (SRP_MSD) in WSN is proposed. The Bivariate Pascal Triangle method hides the original identity of nodes, data route from the malicious observer and sends confidential information to the Base Station. This method is analyzed and evaluated using network simulator-2. The results show that it is possible to detect malicious sensor nodes and send the reliable information to the forest fire monitoring system.     

A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure

This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images in the original feature space without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudotraining set that is necessary for initializing an adequately defined binary semisupervised support vector machine classifier. Starting from these initial seeds, the performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. This algorithm models the full complexity of the change-detection problem, which is only partially represented from the seed pixels included in the pseudotraining set. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings. Experimental results obtained on different multispectral remote-sensing images confirm the effectiveness of the proposed approach.     

基于视频图像的森林火灾识别算法研究

针对火焰的色彩特性和燃烧时的动态特性,提出一种面向视频图像序列的火灾识别算法.首先通过火焰颜色特征,在HSV模型基础上运用改进的区域增长法提取目标区域,然后根据火焰区域的重心和面积变化特性,通过设定模糊数学的隶属度函数来判定是否有火灾发生.实验结果表明,本算法实用有效,对火灾的识别率较高.     

基于改进SSA优化WSN分簇协议

无线传感器网络(Wireless Sensor Networks,WSN)的路由协议是无线传感器网络领域中的一个研究热点.针对LEACH协议的不足,提出一种基于自适应t分布改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)的改进LEACH协议(LEACH?ISSA),以解决...     

SC-EEDC: Similarity Based Clustering for Energy Efficient Data Collection in WSN

Wireless Personal Communications - Wireless sensor networks (WSN) are widely used in various situations. Energy saving is one of the most important issues because of the limited power....     

Optimization Based Multi-Objective Weighted Clustering For Remote Monitoring System in WSN

Wireless Sensor Network (WSN) is generally considered as a standout amongst the most critical advancements for the twenty-first century, it normally comprises multifunctional wireless sensor nodes, with detecting, communications, and calculation capacities. Clustering the random nodes in WSN is a challenging task with high performance. This paper presents the new clustering model to monitor the eco-friendly mobile network by clustering the sensor nodes and to enhance the Quality of Service of that optimal network in WSN. The proposed Multi-Objective Weighted Clustering model groups the arbitrary nodes and afterward the optimal network is achieved by the optimization of network parameters. For optimizing the network parameters, a metaheuristic algorithm i.e. Improved Fruit Fly Optimization is introduced. With the goal of assessing the Coverage Efficiency (CE) and network user satisfaction of the accomplished optimal mobile network in WSN, the remote sensor monitoring process is applied. Sensor monitoring helps to know the network users and furthermore to improve the CE of WSN, contrasted with existing work.

     

Improved K-Means Based Q Learning Algorithm for Optimal Clustering and Node Balancing in WSN

Wireless Personal Communications - A wireless sensor network is a potential technique which is most suitable for continuous monitoring applications where the human intervention is not possible. It...     

Optimal Scheme for the Detection and Classification of Clone Node Attack in WSN Using TAIGBRFCNIA

Wireless Personal Communications - Wireless Sensor Network is an isolated set of low cost hardware sensor nodes that supply nodes, memory and processing resources to limit battery life for...     

Enhancing Clustering Stability in VANET: A Spectral Clustering Based Approach

Vehicles can establish a collaborative environment cognition through sharing the original or processed sensor data from the vehicular sensors and status map. Clustering in the vehicular ad-hoc network(VANET) is crucial for enhancing the stability of the collaborative environment. In this paper, the problem for clustering is innovatively transformed into a cutting graph problem. A novel clustering algorithm based on the Spectral Clustering algorithm and the improved force-directed algorithm is designed. It takes the average lifetime of all clusters as an optimization goal so that the stability of the entire system can be enhanced. A series of close-to-practical scenarios are generated by the Simulation of Urban Mobility(SUMO). The numerical results indicate that our approach has superior performance in maintaining whole cluster stability.     

A Low-Cost Microwave Radiometer for the Detection of Fire in Forest Environments

This paper deals with the development of a microwave noise-adding radiometer, which is purposely designed for the fire detection in forest environments. The sensor operates at 12.65 GHz and exploits commercial Satellite Television (SAT-TV) components such as a parabolic dish and a low-noise block. First, a simple system model is presented to estimate the radiometric contrast due to the presence of fire (increase in the antenna noise temperature with respect to the background) at a certain distance from the receiving antenna. Then, the design of the sensor is addressed, underlining the key technologies that allow the required performance to be attained at low industrial costs. An experimental characterization of the developed radiometer is reported focusing on both accuracy and sensitivity issues. Due to a continuous gain calibration based on the noise-adding procedure, the antenna noise temperature can be retrieved with an absolute error of 4 K without any thermal stabilization of the instrument electronics. Last, real fire detection experiments have been carried out both in laboratory and open-space environments. A radiometric contrast of 8.8 K has been recorded for a wooden fire of 0.2 $hbox{m}^{2}$ placed at a distance of about 30 m from the antenna.      

基于聚类算法实现信号盲分类

本文解决了信号处理、工业控制等领域存在的非平稳信号盲分类问题。在聚类中广泛应用的K-Means算法及其它基于中心的聚类算法有两个共同的缺陷-需要预先确定类数目且随机初始化中心引起性能不稳定。本文提出的算法较好地解决了这两个问题，提高了算法稳定性，实现了非平稳信号盲分类。提取非平稳信号的小波系数作为聚类的样本空间，分析聚类结果的统计偏差以估计类的数目，采用调和均值准则进行分类。最后给出的仿真结果表明本文提出的方法较传统的K-Means算法明显降低分类错误率。     

一种基于位置和能量的WSN改进分簇协议

针对LEACH分簇路由协议中簇首随机选择,没有考虑节点的剩余能量和地理位置,可能导致分簇和网络能耗不均衡这一问题,研究了一种同时考虑了节点位置和剩余能量信息的改进分簇路由协议,使成为簇头的节点在簇的中心位置,从而使簇头在簇内的位置更加合理,避免了簇内的部分节点由于离簇头过远,增加传输损耗的缺点,有利于网络性能的提高。仿真结果表明,研究的分簇路由协议能使簇的划分更加均匀,能量的消耗更加节省,从而延长了WSN网络的生存时间。     

基于多轮询的WSN节点捕获检测方法

在无线传感器网络(Wireless Sensor Network,WSN)内部攻击3个阶段的第1个阶段,针对节点被捕获时检测方案误报率和漏报率高导致检测率差的问题,提出一种在节点异步休眠时融合多轮询机制与节点协同决策的检测方案.该方案将节点划分为底层节点(Bottom node,BN)、监控节点(Monitoring node,MN)及决策节点(Decision node,DN)3类,组成3级体系(THS)并构成蛛网结构(Cobweb),采用多轮询机制完成节点协同决策并降低方案的误报率.同时,该方案引入异步休眠机制保证节点的休眠不影响信息的发送与接收,降低方案的漏报率.仿真实验结果表明,该方案对于WSN内部攻击的第1阶段攻击检测具有很低的误报率和漏报率,检测率准确度较高.