分布式无线传感器网络容错事件边界检测

事件监测是无线传感器网络的一种重要应用。针对该应用中软故障节点提供的错误数据会降低监测的准确性的问题,提出了一种分布式的容错事件边界检测算法。节点只需与邻节点交换一次传感数据,通过简单地计算识别故障;正常的事件节点利用统计比较的方法判断其是否处于事件边界,边界宽度可根据网络用户的要求调节。该算法执行时所需的通信量小,计算复杂度低,时延小,对大规模网络具有很好的可扩展性。仿真结果表明即使节点故障率很高,应用该算法仍可以获得很好的检测效果。     

失真容忍无线传感网络中的最优传感器配置研究

研究了一维和二维无线传感器网络(WSN)在空间信号源相关条件下的最优传感器配置方法。WSN通过分布的传感器节点得到与位置相关的测量数据, 然后恢复出具有空间相关性的信号。WSN重建信号的基本准则是使重建信号和原信号在单位区域能量恒定条件下的均方误差(MSE)最小。研究了在具有有限节点的小网络和具有有限节点密度的大网络中传感器节点密度和空间数据相关性对网络性能的影响, 定量分析了各种不同网络参数间的相互作用及其对网络性能的影响。其结果为实用WSN的设计提供了基础。     

无线传感器网络中基于闭合环包围的边界节点检测

由于无线传感器网络中可能会出现覆盖漏洞,导致网络无法提供高质量的数据,所以需要检测边界节点以准确找到覆盖漏洞进行修复。已有研究大多是通过传感器的坐标或者依据大量节点信息进行检测, 现提出算法通过检测每个节点的邻居节点是否能形成包围检测节点的闭合环来判别当前节点是否为边界节点。该算法使得节点能够仅基于小邻域的信息自主地决定它是否是边界节点,使其适用于节点分布不均匀的网络中。仿真实验验证了该算法在识别准确率、降低通信量等方面的有效性。     

一种基于分簇的分布式无线传感器网络拓扑控制算法研究

拓扑控制是无线传感器网络的一个重要研究方向。无线传感器网络中一般节点数量大,分布范围广泛且不规则,难以进行集中式控制。本文提出了一种基于分簇的分布式无线传感器网络拓扑控制(CDTC)算法。利用分簇思想将网络划分为可重叠的簇,簇内各节点按照局部最小生成树算法思想确定邻居关系,调整发送功率,生成合适的网络拓扑。仿真实验证明运行CDTC算法后,网络中节点平均发送功率明显减少,平均节点度较低,节点间干扰较少。     

无线传感器网络中数据密度相关度融合算法

无线传感器网络节点部署在复杂环境时,节点间相关性无法通过节点间距离来准确描述.为了克服该缺陷,本文提出了数据密度相关度公式.该公式反映了节点数据的ε邻域内数据的聚集程度,也反映了该节点数据相对其ε邻域内数据的相对位置.同时,将数据密度相关度公式应用到代表式数据融合算法中,提出了数据密度相关度融合算法.该融合算法得到的相关区域具有相关区域内节点数据相关度大,相关区域问节点数据相关度小的优点.仿真实验结果表明了该融合算法在数据准确性和能耗方面较基于α-局部空间数据融合算法和基于皮尔森相关系数的数据融合算法优越.     

基于时空特性的无线传感器网络节点故障诊断方法

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。对于网络中存在的节点瞬时故障,通过时间冗余的检测方法,降低故障诊断的虚警率。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。     

Data aggregation in wireless sensor network using SVM-based failure detection and loss recovery

Abstract

In wireless sensor network, data aggregation can cause increased transmission overhead, failures, data loss and security-related issues. Earlier works did not concentrate on both fault management and loss recovery issues. In order to overcome these drawbacks, in this paper, a reliable data aggregation scheme is proposed that uses support vector machine (SVM) for performing failure detection and loss recovery. Initially, a group head, selected based on node connectivity, splits the nodes into clusters based on their location information. In each cluster, the cluster member with maximum node connectivity is chosen as the cluster head. When the aggregator receives data from the source, it identifies node failures in the received data by classifying the faulty data using SVM. Furthermore, a reserve node-based fault recovery mechanism is developed to prevent data loss. Through simulations, we show that the proposed technique minimises the transmission overhead and increases reliability.     

Mobility-assisted minimum connected cover in a wireless sensor network

All properties of mobile wireless sensor networks (MWSNs) are inherited from static wireless sensor networks (WSNs) and meanwhile have their own uniqueness and node mobility. Sensor nodes in these networks monitor different regions of an area of interest and collectively present a global overview of monitored activities. Since failure of a sensor node leads to loss of connectivity, it may cause a partitioning of the network. Adding mobility to WSNs can significantly increase the capability of the WSN by making it resilient to failures, reactive to events, and able to support disparate missions with a common set of sensor nodes. In this paper, we propose a new algorithm based on the divide-and-conquer approach, in which the whole region is divided into sub-regions and in each sub-region the minimum connected sensor cover set is selected through energy-aware selection method. Also, we propose a new technique for mobility assisted minimum connected sensor cover considering the network energy. We provide performance metrics to analyze the performance of our approach and the simulation results clearly indicate the benefits of our new approach in terms of energy consumption, communication complexity, and number of active nodes over existing algorithms.     

无线传感器网络技术在海洋平台结构安全检测领域的应用研究

无线传感器网络能够很好地解决海洋平台结构检测中遇到的很多问题。对无线传感器网络这一无线通信中的热点领域在无线传感器网络体系构架、大规模组网技术及网络管理、环境感知技术、数据采集技术以及信息处理技术等方面进行了应用研究,以期能将该项技术应用于我国海洋平台结构的安全检测中。     

Effective target tracking mechanism in a self-organizing wireless sensor network

Target tracking is an important sensing application of wireless sensor networks. In these networks, energy, computing power, and communication bandwidth are scarce. We have considered a random heterogeneous wireless sensor network, which has several powerful nodes for data aggregation/relay and large number of energy-constrained sensor nodes that are deployed randomly to cover a given target area. In this paper, a cooperative approach to detect and monitor the path of a moving object using a minimum subset of nodes while maintaining coverage and network connectivity is proposed. It is tested extensively in a simulation environment and compared with other existing methods. The results of our experiments clearly indicate the benefits of our new approach in terms of energy consumption.     

基于遗传算法的无线传感器/执行器网络故障检测滤波器设计

为提高无线传感器/执行器网络(WSAN)的可靠性,提出一种基于遗传算法(GA)的WSAN故障检测滤波器的优化设计方法。在系统建模时将无线网络传输延迟对网络控制系统的影响建模为一种系统扰动噪声,将由敏感度和鲁棒性构成的复合优化指标作为故障检测滤波器的优化目标函数,即适应度函数。同时根据优化目标在自动控制系统中的数值特性,选择与之相适应的实数编码、均匀变异和算术交叉等处理方法,以期在加快收敛速度的同时也兼顾计算结果的精确度。所提的优化滤波器设计,不仅能抑制滤波器信号中的噪声分量,而且能放大故障信号。最后,通过Matlab/OMNET++的仿真平台验证了这一设计的有效性。     

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。     

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

In this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.     

An efficient trajectory design for mobile sink in a wireless sensor network

Mobile sink trajectory plays a pivotal role for network coverage, data collection and data dissemination in wireless sensor networks. Considering this, we propose a novel approach for mobile sink trajectory in wireless sensor networks. Our proposed approach is based on Hilbert Space Filling Curve, however, the proposed approach is different from the previous work in a sense that the curve order changes according to node density. In this paper, we investigate the mobile sink trajectory based on Hilbert Curve Order which depends upon the size of the network. Second, we calculate the Hilbert Curve Order based on node density to re-dimension the mobile sink trajectory. Finally, we perform extensive simulations to evaluate the effectiveness of proposed approach in terms of network coverage and scalability. Simulation results confirm that our proposed approach outperforms with size based Hilbert Curve in terms of network coverage, packet delivery ratio and average energy consumption.     

Understanding optimal data gathering in the energy and latency domains of a wireless sensor network

The problem of optimal data gathering in wireless sensor networks (WSNs) is addressed by means of optimization techniques. The goal of this work is to lay the foundations to develop algorithms and techniques that minimize the data gathering latency and at the same time balance the energy consumption among the nodes, so as to maximize the network lifetime. Following an incremental-complexity approach, several mathematical programming problems are proposed with focus on different network performance metrics. First, the static routing problem is formulated for large and dense WSNs. Optimal data-gathering trees are analyzed and the effects of several sensor capabilities and constraints are discussed, e.g., radio power constraints, energy consumption model, and data aggregation functionalities. Then, dynamic re-routing and scheduling are considered. An accurate network model is proposed that captures the tradeoff between the data gathering latency and the energy consumption, by modeling the interactions among the routing, medium access control and physical layers.For each problem, extensive simulation results are provided. The proposed models provide a deeper insight into the problem of timely and energy efficient data gathering. Useful guidelines for the design of efficient WSNs are derived and discussed.