无线传感器网络中基于分簇的节点定位异常检测*

在引入WSN分簇结构基础上,提出一种分布式的节点定位异常检测方法,利用聚类拓扑减少通信量,同时降低以往集中式检测存在的单点风险。该方法不需要任何已知的部署知识或额外的硬件,每个簇的簇头节点只需根据该簇节点报告的位置和邻居表信息进行过滤计算,更新权值,即可确定和撤销定位异常的节点。通过理论分析和仿真模拟验证了这种基于分簇的节点定位异常检测方法的正确性和有效性。     

Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling

Modern infrastructure increasingly depends on large computerized systems for their reliable operation. Supervisory Control and Data Acquisition (SCADA) systems are being deployed to monitor and control large scale distributed infrastructures (e.g. power plants, water distribution systems). A recent trend is to incorporate Wireless Sensor Networks (WSNs) to sense and gather data. However, due to the broadcast nature of the network and inherent limitations in the sensor nodes themselves, they are vulnerable to different types of security attacks. Given the critical aspects of the underlying infrastructure it is an extremely important research challenge to provide effective methods to detect malicious activities on these networks. This paper proposes a robust and scalable mechanism that aims to detect malicious anomalies accurately and efficiently using distributed in-network processing in a hierarchical framework. Unsupervised data partitioning is performed distributively adapting fuzzy c-means clustering in an incremental model. Non-parametric and non-probabilistic anomaly detection is performed through fuzzy membership evaluations and thresholds on observed inter-cluster distances. Robust thresholds are determined adaptively using second order statistical knowledge at each evaluation stage. Extensive experiments were performed and the results demonstrate that the proposed framework achieves high detection accuracy compared to existing data clustering approaches with more than 96% less communication overheads opposed to a centralized approach.     

基于RSSI的无线传感器网络节点定位算法

节点定位技术是无线传感器网络关键技术之一,介绍了节点定位技术的基本原理,提出了一种新的基于接收信号强度(RSSI)的无线传感器网络定位算法.该算法在第一阶段对节点初始位置进行初步估计,第二阶段对节点初始位置进行求精.仿真结果表明,在锚节点比例较低的情况下,该算法仍然可以实现较高的定位精度,并且与dv-distance定位算法比较,表明该算法在相同条件下精度更高.     

基于DV-HOP的无线传感器网络定位算法

通过对DV-HOP算法的理论分析,找出其产生误差的主要原因,提出相应的改进算法,即对未知节点与锚节点之间的估计距离做出修正,该修正值由多跳的校正值和锚节点的平均每跳距离误差所组成,同时将总体最小二乘法（TLS）应用于定位中,进一步提高了定位精度。选用Matlab进行相关仿真,结果表明：改进算法的定位精度比原算法及部分现有改进算法有明显的提高。     

Perimeter detection in wireless sensor networks

For a mobile robotic agent to bridge the gaps between disconnected networks, it is beneficial for the robot to first determine the network coverage boundary. Several techniques have been introduced to determine the boundary nodes of a network, but the correctness of these techniques is often ill-defined. We present a technique for obtaining boundary node ground truth from region adjacency analysis of a model-based image created from a network graph. The resulting ground truth baseline is then used for quantitative comparison of several boundary detection methods including a local application of the image region adjacency analysis and the computation of the local convex hull with the addition of a perturbation value to overcome small boundary concavities in the node location point set. Given our proposed metrics of the techniques evaluated, the perturbed convex hull technique demonstrates a high success rate for boundary node identification, particularly when the convex hull is formed using two-hop neighborhoods. This technique was successfully implemented on a physical 25-node network, and the performance of this network implementation is evaluated.     

无线传感器网络入侵检测系统模型

为了提高无线传感器网络的安全性,针对无线传感器网络的自身特性,设计了一种入侵检测系统模型.该模型按照聚类的方法,将区域划分成簇;在每个簇中选举簇头,簇头需定期轮换;采用基于相关向量机(RVM)的入侵检测方案.实验表明:所提出的模型与其它检测模型相比具有更高的平均检测率和更低的平均误检率,且具有低能耗的特点.     

无线传感器网络中节点故障诊断方法的研究

无线传感器网络中故障节点会产生并传输错误数据,这将消耗节点的能量和带宽,同时会形成错误的决策。利用节点感知数据的空间相似性,提出了节点故障诊断的算法,通过对邻节点所感知的传感数据进行比较,从而确定检测节点的状态,并将测试状态向网络中其他相邻节点进行扩散。该算法对实现故障节点的检测具有较好的性能,实验结果验证了算法的可行性和有效性。     

无线传感网离群点检测技术研究综述

无线传感器网络(wireless sensor network,WSN)受电池能量、计算能力、通信能力和内存空间及传感数据多维特征的限制,传统的离群点检测技术不能直接应用于WSN,因此出现了一系列针对WSN的离群点检测技术.对已有的WSN离群点检测技术进行了概述,根据各离群点检测技术的特征进行了分类和分析,并结合现有技术的缺陷和需求,展望了WSN离群点检测技术的未来研究方向和目标.     

基于功率控制的无线传感器网络定位算法

针对无线传感器网络自身定位问题,提出了一种基于功率控制的定位算法(PCLA),算法通过计算信号覆盖区域的交叉点重心确定节点位置.在算法中,每个信标节点广播不同功率等级的无线信号信息,且节点之间不需要严格的时间同步和相互协作.传感器节点只是简单的接收并保存信标节点传播的有用信息,不需要和邻居节点进行信息交换,复杂的计算由汇聚节点完成,减少了节点的能量消耗,延长了网络寿命.最后仿真结果表明了该算法的有效性.     

Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach

Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.     

A novel intrusion detection framework for wireless sensor networks

Vehicle cloud is a new idea that uses the benefits of wireless sensor networks (WSNs) and the concept of cloud computing to provide better services to the community. It is important to secure a sensor network to achieve better performance of the vehicle cloud. Wireless sensor networks are a soft target for intruders or adversaries to launch lethal attacks in its present configuration. In this paper, a novel intrusion detection framework is proposed for securing wireless sensor networks from routing attacks. The proposed system works in a distributed environment to detect intrusions by collaborating with the neighboring nodes. It works in two modes: online prevention allows safeguarding from those abnormal nodes that are already declared as malicious while offline detection finds those nodes that are being compromised by an adversary during the next epoch of time. Simulation results show that the proposed specification-based detection scheme performs extremely well and achieves high intrusion detection rate and low false positive rate.     

In-network outlier detection in wireless sensor networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance by simulation, using real sensor data streams. Our results demonstrate that our approach is accurate and imposes reasonable communication and power consumption demands.     

Distributed routing for signal detection in wireless sensor networks

We study the problem of energy-efficient routing for signal detection in wireless sensor networks. Generic routing protocols use networking-centric measures such as minimum hop or minimum energy to establish routes. These schemes do not take into account the performance of application-specific algorithms that is achievable from the data collected by the nodes along the routes. Routing protocols for signal detection have recently been proposed to facilitate joint optimization of detection performance and energy efficiency by developing metrics that connect detection performance with energy consumption of each link along the routes. In existing routing for signal detection (RSD) schemes, however, the routes are computed centrally requiring complex optimization algorithms and global information such as locations and observation coefficients of all nodes in the network. Clearly, for large-scale networks, or networks with dynamically changing topologies, distributed routing schemes are more practical due to their better flexibility and scalability. We present a distributed RSD protocol where each node, based on locally available information, selects its next-hop with the goal of maximizing the detection performance associated with unit energy expenditure. We show that the proposed protocol is readily implementable in ZigBee networks, and present simulation results that reveal its significant improvements in detection performance and energy efficiency over generic routing protocols.     

Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance

Heterogeneous wireless sensor networks are a source of large amount of different information representing environmental aspects such as light, temperature, and humidity. A very important research problem related to the analysis of the sensor data is the detection of relevant anomalies. In this work, we focus on the detection of unexpected sensor data resulting either from the sensor system itself or from the environment under scrutiny. We propose a novel approach for automatic anomaly detection in heterogeneous sensor networks based on coupling edge data analysis with cloud data analysis. The former exploits a fully unsupervised artificial neural network algorithm, whereas cloud data analysis exploits the multi-parameterized edit distance algorithm. The experimental evaluation of the proposed method is performed applying the edge and cloud analysis on real data that has been acquired in an indoor building environment and then distorted with a range of synthetic impairments. The obtained results show that the proposed method can self-adapt to the environment variations and correctly identify the anomalies. We show how the combination of edge and cloud computing can mitigate the drawbacks of purely edge-based analysis or purely cloud-based solutions.     

管网检测的无线传感网络信息采集同步

无线传感网络的时钟校准决定了各节点信息采集的同步性。在供水管网泄漏检测中,各节点信息采集的时间同步误差将引入漏点的定位误差。在泛洪时间同步协议的基础上,提出拨号时间同步方案,在同步过程中采用定向拨号和线性回归相结合的方法,周期补偿终端节点间因本地时钟源频差而产生的时钟偏移,实现网络时间校准。对单跳网络,每2分钟进行1次校正,测试表明节点间的同步误差每秒在10微秒以内,累积误差不超过1毫秒,这样引起的管网漏点定位误差在1米以内,可以满足供水管网泄漏检测的要求。     

Low-priced and efficient replica detection framework for wireless sensor networks

A replica attack is one of the most challenging problems in wireless sensor networks. Once a node is physically captured, it can be reprogrammed and replicated into a large number of replicas, and thus maliciously occupying the network. Although until now various schemes have been proposed to detect replicas, most of them suffer from high energy costs and additional device such as a global positioning system. In this paper, we introduce the efficient replica detection framework without the need of additional device. The framework consists of key establishment and replica detection protocols. The proposed replica detection protocol is executed on the proposed key establishment protocol. Furthermore, through simulation experiments, we show that the proposed replica detection protocol provides high replica detection ratio, faster replica detection, and higher energy saving than existing protocols.     

无线传感器网络基于移动信标动态选择的定位算法

无线传感器网络节点定位是许多应用的基础.DV-Hop 是一种无需测距的定位算法,但其定位精度依赖于网络的联通状况,对于不规则拓扑的网络定位误差较大.针对这种情况,提出一种新的基于移动信标动态选择的改进 DV-Hop 定位算法,利用一个移动信标在网络中漫游并广播定位分组信息,并在每个虚拟信标中计算当前位置的平均跳距离.未...     

A destination-based approach for cut detection in wireless sensor networks

Wireless sensor networks often suffer from disrupted connectivity caused by its numerous aspects such as limited battery power of a node and unattended operation vulnerable to hostile tampering. The disruption of connectivity, often referred to as network cut, leads to ill-informed routing decisions, data loss and waste of energy. A number of protocols have been proposed to efficiently detect network cuts; they focus solely on a cut that disconnects nodes from the base station. However, a cut detection scheme is truly useful when a cut is defined with respect to multiple destinations (i.e. target nodes), rather than a single base station. Thus, we extend the existing notion of cut detection, and propose an algorithm that enables sensor nodes to autonomously monitor the connectivity to multiple target nodes. We introduce a novel reactive cut detection solution, the point-to-point cut detection, where given any pair of source and destination, a source is able to locally determine whether the destination is reachable or not. Furthermore, we propose a lightweight proactive cut detection algorithm specifically designed for a network scenario with a small set of target destinations. We prove the effectiveness of the proposed algorithms through extensive simulations; specifically, in our network configurations, proposed cut detection algorithms achieve more than an order of magnitude improvement in energy consumption, when coupled with an underlying routing protocol.