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.     

A distributed energy-efficient clustering protocol for wireless sensor networks

Minimizing energy dissipation and maximizing network lifetime are among the central concerns when designing applications and protocols for sensor networks. Clustering has been proven to be energy-efficient in sensor networks since data routing and relaying are only operated by cluster heads. Besides, cluster heads can process, filter and aggregate data sent by cluster members, thus reducing network load and alleviating the bandwidth. In this paper, we propose a novel distributed clustering algorithm where cluster heads are elected following a three-way message exchange between each sensor and its neighbors. Sensor’s eligibility to be elected cluster head is based on its residual energy and its degree. Our protocol has a message exchange complexity of O(1) and a worst-case convergence time complexity of O(N). Simulations show that our algorithm outperforms EESH, one of the most recently published distributed clustering algorithms, in terms of network lifetime and ratio of elected cluster heads.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

Distributed data source verification in wireless sensor networks

In-network data aggregation is favorable for wireless sensor networks (WSNs): It allows in-network data processing while reducing the network traffic and hence saving the sensors energy. However, due to the distributed and unattended nature of WSNs, several attacks aiming at compromising the authenticity of the collected data could be perpetrated. For example, an adversary could capture a node to create clones of the captured one. These clones disseminated through the network could provide malicious data to the aggregating node, thus poisoning/disrupting the aggregation process. In this paper we address the problem of detecting cloned nodes; a requirement to be fulfilled to provide authenticity of the data fusion process.First, we analyze the desirable properties a distributed clone detection protocol should meet. Specifically: It should avoid having a single point of failure; the load should be totally distributed across the nodes in the network; the position of the clones in the network should not influence the detection probability. We then show that current solutions do not meet the exposed requirements. Next, we propose the Information Fusion Based Clone Detection Protocol (ICD). ICD is a probabilistic, completely distributed protocol that efficiently detects clones. ICD combines two cryptographic mechanisms: The pseudo-random key pre-distribution, usually employed to secure node pairwise communications, with a sparing use of asymmetric crypto primitives. We show that ICD matches all the requirements above mentioned and compare its performance with current solutions in the literature; experimental results show that ICD has better performance than existing solutions for all the cost parameters considered: Number of messages sent, per sensor storage requirement, and signature verification. These savings allow to increase the network operating lifetime. Finally, note that ICD protocol could be used as an independent layer by any data aggregation mechanism.     

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。     

Collaborative signal processing for target tracking in distributed wireless sensor networks

Target tracking, especially visual target tracking, in complex situations is challenging, which is always performed in single-view system. Because of the conflict between resolution and tracking range, however, single-view tracking is not robust and accurate. This paper presents a distributed multi-view tracking system using collaborative signal processing (CSP) in distributed wireless sensor networks (DWSNs). In the proposed tracking system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas target localization algorithm is carried out by CSP between multisensor. For conquering the disadvantages of client/server based centralized data fusion, a progressive distributed data fusion are proposed. Finally, an indoor target tracking experiment is illustrated, and then tracking performance, execution time and energy consumption of progressive distributed data fusion are compared with client/server based centralized data fusion. Experimental results demonstrate that the CSP based distributed multi-view tracking system in DWSNs can accomplish multi-target extraction, classification, localization, tracking and association quickly and accurately with little congestion, energy consumption and execution time.     

Node coordination mechanism based on distributed estimation and control in wireless sensor and actuator networks

Wireless sensor and actuator networks （WSANs） have a wide range of applications. To perform effective sensing and acting tasks, multiple coordination mechanisms among the nodes are required. As attempt in this direction, this paper describes collaborative estimation and control algorithms design for WSANs. First, a sensor-actuator coordination model is proposed based on distributed Kalman filter in federated configuration. This method provides the capability of fault tolerance and multi-source information fusion. On this basis, an actuator-actuator coordination model based on even-driven task allocation is introduced. Actuators utilize fused sensory information to adjust their action that incur the minimum energy cost to the system subject to the constraints that user＇s preferences regarding the states of the system are approximately satisfied. Finally, according to system requirements, a distributed algorithm is proposed to solve the task allocation problem. Simulations demonstrate the effectiveness of our proposed methods.     

Energy-efficient distributed wireless sensor network scheme for cluster detection

Detection of an environmental phenomenon, e.g. air pollution and oil spills, occurs when a group of sensors continuously produces similar readings (i.e. data streams) over a period of time. Thus, detection of environmental phenomena is basically a process of clustering the sensors' data streams, which commonly involves the processing of hundreds and maybe thousands of data streams in real time. Since the sensor network environment is wireless, energy conservation of the sensors would be the main concern. Thus in this paper, we propose an efficient and energy friendly distributed scheme to detect phenomena in a wireless sensor network (WSN). To achieve fast response, the proposed algorithms reduce the dimensionality of the streams. Then, each stream is represented by a point in a multi-dimensional grid. The algorithm uses a grid-based clustering technique to detect clusters of similar stream values. The processing of the algorithm is distributed among different elements of the WSN in a hierarchical topology for more energy efficiency. The paper shows the feasibility of the proposed fully distributed scheme by comparing it with three other WSN schemes in terms of clustering accuracy and energy consumption.     

Pulse-coupled time synchronization for distributed acoustic event detection using wireless sensor networks

Time synchronization has proven to be critical in sensor fusion applications where the time of arrival is utilized as a decision variable. Herein, the application of pulse-coupled synchronization to an acoustic event detection system based on a wireless sensor network is presented. The aim of the system is to locate the source of acoustic events utilizing time of arrival measurements for different formations of the sensor network. A distributed localization algorithm is introduced that solves the problem locally using only a subset of the time of arrival measurements and then fuses the local guesses using averaging consensus techniques. It is shown that the pulse-coupled strategy provides the system with the proper level of synchronization needed to enable accurate localization, even when there exists drift between the internal clocks and the formation is not perfectly maintained. Moreover, the distributed nature of pulse-coupled synchronization allows coordinated synchronization and distributed localization over an infrastructure-free ad-hoc network.     

无线传感器网络中基于空间关联性的聚类异常检测算法

针对无线传感器网络节点定位场景中的环境变化、障碍物及传感器节点失效等异常问题, 提出了一种基于空间关联性的聚类异常检测算法（ODCASC算法）。该算法通过聚类技术完成节点的异常数据判断, 同时还利用邻居节点间感知数据的空间关联性过滤噪声数据, 提取异常事件信息, 辅助系统决策。室内实测结果显示, ODCASC算法弥补了时空关联检测算法的不足, 可以有效地检测并区分网络中的环境噪声及异常事件。     

Link scheduling in wireless sensor networks: Distributed edge-coloring revisited

We consider the problem of link scheduling in a sensor network employing a TDMA MAC protocol. Our algorithm consists of two phases. The first phase involves edge-coloring: an assignment of a color to each edge in the network such that no two edges incident on the same node are assigned the same color. Our main result for the first phase is a distributed edge-coloring algorithm that needs at most (Δ+1) colors, where Δ is the maximum degree of the network. To our knowledge, this is the first distributed algorithm that can edge-color a graph using at most (Δ+1) colors. The second phase uses the edge-coloring solution for link scheduling. We map each color to a unique timeslot and attempt to assign a direction of transmission along each edge such that the hidden terminal problem is avoided; an important result we obtain is a characterization of network topologies for which such an assignment exists. Next, we consider topologies for which a feasible transmission assignment does not exist for all edges, and obtain such an assignment using additional timeslots. Finally, we show that reversing the direction of transmission along every edge leads to another feasible direction of transmission. Using both the transmission assignments, we obtain a TDMA MAC schedule which enables two-way communication between every pair of adjacent sensor nodes. For acyclic topologies, we prove that at most 2(Δ+1) timeslots are required. Results for general topologies are demonstrated using simulations; for sparse graphs, we show that the number of timeslots required is around 2(Δ+1). We show that the message and time complexity of our algorithm is O(nΔ²+n²m), where n is the number of nodes and m is the number of edges in the network. Through simulations, we demonstrate that the energy consumption of our solution increases linearly with Δ. We also propose extensions to account for non-ideal radio characteristics.     

A framework for use of wireless sensor networks in forest fire detection and monitoring

Forest fires are one of the main causes of environmental degradation nowadays. Current surveillance systems for forest fires lack in supporting real-time monitoring of every point of a region at all times and early detection of fire threats. Solutions using wireless sensor networks, on the other hand, can gather sensory data values, such as temperature and humidity, from all points of a field continuously, day and night, and, provide fresh and accurate data to the fire-fighting center quickly. However, sensor networks face serious obstacles like limited energy resources and high vulnerability to harsh environmental conditions, that have to be considered carefully. In this paper, we propose a comprehensive framework for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes proposals for the wireless sensor network architecture, sensor deployment scheme, and clustering and communication protocols. The aim of the framework is to detect a fire threat as early as possible and yet consider the energy consumption of the sensor nodes and the environmental conditions that may affect the required activity level of the network. We implemented a simulator to validate and evaluate our proposed framework. Through extensive simulation experiments, we show that our framework can provide fast reaction to forest fires while also consuming energy efficiently.     

无线磁阻传感器网络停车检测技术研究

随着机动车保有量的高速增长,停车混乱问题日益严重,各大中城市对停车实时监控及管理有迫切的建设需求。为了构建基于无线传感器网络的大规模、低成本、低功耗的停车管理系统,本文基于磁场干扰车辆检测原理,提出了一种停车检测算法,实际应用表明该算法车辆检测精度高,达到了设计要求。     

无线传感器网络中一种能量高效的分布式分簇算法

提出了一种适用于无线传感器网络的能量高效的分布式分簇算法（ＥＥＤＣ）,该算法使具有较高剩余能量及距离基站较近的节点有更大的机会成为簇头．理论分析表明该算法通信开销较小,而且有效地均衡了节点的能量消耗．为了确保ＥＥＤＣ 的正确性、完整性和可靠性,利用形式化方法———着色网对其关键属性进行建模和分析．仿真结果表明,ＥＥＤＣ 有效地延长了网络生命周期,提高了网络的能耗效率．     

基于节点分布密度的传感器网络查询处理机制

为了使传感器网络在进行数据查询时降低能耗和提高网络生命期,引入了一种分布式查询处理机制。这种机制是先将查询分发到网络后再进行优化,这种方法更具有针对性,优化效果也更明显。分簇路由协议与分布式查询有着天然的结合点。每个簇头相当于传统数据库中的一个索引,负责查询的分析、优化和数据融合。簇头根据本区域的节点分布和数据特性可以自主地选择区域内结构而不受其他区域的影响,这样就可以把每个区域看成一个自治系统,而整个传感器网络就是多个自治系统的集合。结果表明：设计查询处理机制时考虑这些因素可以降低能耗和提高网络生命期。     

Theoretical analysis of the lifetime and energy hole in cluster based wireless sensor networks

Cluster based wireless sensor networks have been widely used due to the good performance. However, in so many cluster based protocols, because of the complexity of the problem, theoretical analysis and optimization remain difficult to develop. This paper studies the performance optimization of four protocols theoretically. They are LEACH (Low Energy Adaptive Clustering Hierarchy), MLEACH (Multi-hop LEACH), HEED (Hybrid Energy-Efficient Distributed Clustering Approach), and UCR (Unequal Cluster based Routing). The maximum FIRST node DIED TIME (FDT) and the maximum ALL node DIED TIME (ADT) are obtained for the first time in this paper, as well as the optimal parameters which maximize the network lifetime. Different from previous analysis of network lifetime, this paper analyzes the node energy consumption in different regions through the differential analysis method. Thus, the optimal parameters which maximize the lifetime can be obtained and the detailed energy consumption in different regions at different time can be also obtained. Moreover, we can obtain the time and space evolution of the network, from a steady state (without any death) to a non-steady state (with some death of nodes), and then to the final situation (all nodes die). Therefore, we are fully aware of the network status from spatial and temporal analysis. Additionally, the correctness of the theoretical analysis in this paper is proved by the Omnet++ experiment results. This conclusion can be an effective guideline for the deployment and optimization of cluster based networks.     

无线传感器网络下分布式决策融合方法综述

分布式决策融合方法以其成本低廉、可靠性高、生存能力强、带宽要求低等优点而在多传感器检测系统特别是无线传感器网络中具有广阔的应用前景,但是传感器网络具有有限的能量、有限的计算能力、有限的通讯带宽,以及存在无线信道衰落、传输错误和干扰噪声对分布式决策融合理论带来挑战。介绍了分布式检测和决策融合的理论基础,对近年来无线传感器网络下的分布式决策融合理论方法及其研究进展进行了详细的综述,分析并展望了该领域存在的问题和前景,探讨了进一步的研究方向。     

Ubiquitous tutoring in laboratories based on wireless sensor networks

Industrial education places a high premium on experiences in practical hands-on training, though it is commonly recognized for the deprivation in its opportunities of student performance analysis due to lack of recorded data as such. In the present study, the technology of wireless sensor networks has been implemented in the tutoring in courses offered in laboratory skills, where the students’ experimental skills and learning inclinations have been examined. Two sets of questionnaires in the respective aspects have been developed to be administered in order to have pretest–posttest scores obtained. As illustrated in the results derived from the study, a learning environment where wireless sensor networks provide a significant degree of support during laboratory sessions, student performance had significantly improved, and this has been proven evidently for the students in the low-achievement group when being compared to the results delivered by the students in the high-achievement group.     

无线传感器网络中一种有效的分布式簇划分算法

提出了一种快速有效的分布式簇划分算法，为每个节点设定一个初始时间，最先到期的节点成为簇头。考虑到簇头选举的合理性，时间衰减与节点连通度相关，并辅以随机化的方法消除时间同步对算法的影响。通过仿真验证该簇划分算法的有效性，并定量分析了通信半径与平均簇头个数的关系。