EFT: Novel Fault Tolerant Management Framework for Wireless Sensor Networks

Wireless Personal Communications - The main application of wireless sensor networks is monitoring, and the nodes of these networks are located usually in harsh environments. Network management is...     

分布式无线传感网络的协作目标跟踪策略

基于图像序列的目标跟踪是目标跟踪的重要研究问题之一.由于受图像解析度和跟踪范围限制,单视角跟踪准确性和鲁棒性不足.本文提出了分布式无线传感网络测量环境下的多视角协作融合跟踪方法,并引入了渐进分布式数据融合,采用基于能耗参数和信息有效性参数的综合优化函数动态选择融合节点,规划融合过程,平衡融合精度与网络能耗.通过目标跟踪准确性、网络能耗及传输延时对比实验表明:基于渐进分布式数据融合的协作信号处理方法提高了分布式多视角跟踪的准确性与实时性,减少了网络拥塞,降低了通讯能耗及延时.     

Decentralized Consensus Based Target Localization in Wireless Sensor Networks

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss–Newton algorithm.     

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.     

Lightweight Localization Using Trilateration for Sensor Networks

Localization is an unavoidable procedure in location aware sensor networks. In such networks, management of a large amount of location information along with its processing and updating is highly desirable at a central station of the network. In this paper, we have discussed the implementation of software layer to be run on various types of sensor nodes in the localization network, which has been dealt with extensively along with some of the addressed problems and their respective solutions. In addition, the article discusses implementation of an already mathematical formulation of least squares trilateration, which has not yet been attempted in the space of wireless sensor networks. To support our clam we performed experimental analysis on \(telosb\) motes. Experimental results of proposed framework shows that average error with respect to the physical location estimation can be reduced upto 46.66 % using 4 anchor node as compare to three anchor nodes at outdoor scenario and upto 40 % in indoor scenario.     

一种轻量级的无线传感器网络密钥建立协议

该文提出了一种适用于无线传感器网络的轻量级密钥建立协议。该协议以预置的瞬时初始密钥为基础,通过优化密钥建立过程中的信息交互,能够获得更好的可扩展性和更低的能量开销。对该协议的完成时间和网络的连通概率的理论分析表明,该协议是可行的。从仿真结果可以看出,该协议在典型的网络规模下可以获得超过97%的连通概率。与同类协议相比,可以在保证足够的连通概率的情况下以更短的时间完成密钥建立。当网络密度为单跳30个节点时,建立时间小于5.2 s。此外,该协议的能量开销只有同类协议的25%,因此更适合应用于资源受限的无线传感器节点。     

Lightweight Deployment-Aware Scheduling for Wireless Sensor Networks

Wireless sensor networks consist of a large number of tiny sensors that have only limited energy supply. One of the major challenges in constructing such networks is to maintain long network lifetime as well as sufficient sensing areas. To achieve this goal, a broadly-used method is to turn off redundant sensors. In this paper, the problem of estimating redundant sensing areas among neighbouring wireless sensors is analysed. We present simple methods to estimate the degree of redundancy without the knowledge of location or directional information. We also provide tight upper and lower bounds on the probability of complete redundancy and on the average partial redundancy. With random sensor deployment, our analysis shows that partial redundancy is more realistic for real applications, as complete redundancy is expensive, requiring up to 11 neighbouring sensors to provide a 90 percent chance of complete redundancy. Based on the analysis, we propose a scalable Lightweight Deployment-Aware Scheduling (LDAS) algorithm, which turns off redundant sensors without using accurate location information. Simulation study demonstrates that the LDAS algorithm can reduce network energy consumption and provide desired QoS requirement effectively. This research was partially supported by Natural Sciences and Engineering Research Council of Canada. Kui Wu received his Ph.D. in Computing Science from the University of Alberta, Canada, in 2002. He joined the Department of Computer Science at the University of Victoria, Canada in the same year and is currently an Assistant Professor there. His research interests include mobile and wireless networks, network performance evaluation, and network security. Yong Gao received his Master's degree and Ph.D. degree in computer science from University of Alberta, Canada, in 2000 and 2005 respectively. He is currently with the Irving K. Barber School of Arts and Sciences, UBC Okanagan, Canada. His research interests include search algorithms and AI, communication networks, and computational biology. Yang Xiao worked at Micro Linear as an MAC (Medium Access Control) architect involving the IEEE 802.11 standard enhancement work before he joined Department of Computer Science at The University of Memphis in 2002. Dr. Xiao is an IEEE Senior member. He was a voting member of IEEE 802.11 Working Group from 2001 to 2004. He currently serves as Editor-in-Chief for International Journal of Security and Networks (IJSN) and for International Journal of Sensor Networks (IJSNet). He serves as an associate editor or on editorial boards for the following refereed journals: (Wiley) International Journal of Communication Systems, (Wiley) Wireless Communications and Mobile Computing (WCMC), EURASIP Journal on Wireless Communications and Networking, and International Journal of Wireless and Mobile Computing. He serves as five lead/sole guest editor for five journal special issues. He serves as a referee/reviewer for many funding agencies, as well as a panelist for NSF. His research interests are Security/ Reliable Communications, Medium Access Control, Mobility/Location/Paging Managements, Cache Access and Replacement Policies, Quality of Service, Energy Efficiency, and Routing in wireless networks and mobile computing.     

移动无线传感器网络节点的定位

文中在MCB（Monte—Carlo Localization Boxed）定位算法的基础上提出了一种新的移动无线传感器网络（Mobile Wireless Sensor Networks）节点的定位算法——权重MCB算法。MCB算法在定位过程中,在采样和滤波阶段用到了一阶锚节点和二阶锚节点的位置信息,而没有应用到邻居节点的位置信息。权重MCB在定位过程中不仅用到了一阶锚节点和二阶锚节点的位置信息,还应用到了一阶邻居节点的采样集合里的采样点（即一阶邻居节点的估计位置）,从而改进了定位精度。对比MCB算法,权重MCB算法对定位精度的改进为13％～18％。     

基于压缩感知的无线传感器网络多目标定位算法

目标定位是无线传感器网络的重要应用场景。该文提出了一种将压缩感知应用于无线传感器网络多目标定位的方法,把基于网格的多目标定位问题转化为压缩感知问题。应用多分辨率分析的思想,设计了迭代回溯的压缩感知算法,该方法的特点是可同时进行多目标定位,并且大大减少了网络通信的数据量从而延长网络寿命,代价是融合中心的算法复杂度的增加。仿真结果显示,采用迭代回溯算法定位精度提高了50%以上,具有较好的多目标定位效果。     

Dynamic Collaborative Support Vector Machine for Target Classification in Wireless Sensor Networks

Target classification is a significant topic addressed in Wireless sensor networks （WSNs）. The distributed learning for target classification is desired for highly-constrained WSN since the processing ability, energy and bandwidth are strictly limited. This paper presents a dynamic data fusion based collaborative Support vector machine （SVM） for target classification in WSN. With the collaboration of multiple sensor nodes, the dynamic collaborative SVM can independently select the optimal set of sensor nodes according to the estimated energy consumption and information contribution. Then the training process of SVM is incrementally carried out in the dynamic data fusion framework with the consideration of tradeoff between energy consumption and information contribution. Because of the purposeful sensor nodes selection strategy, the dynamic collaborative SVM can conquer the inevitable missing rate and false rate of samples in WSN and save the energy for prolonging the lifetime of WSN. Furthermore, the dynamic collaborative SVM can also improve the robustness in target classification, and achieve the tradeoff between the performance and the cost. The experimental results demonstrate that the proposed dynamic collaborative SVM can effectively implement target classification in WSN. It is also verified that the proposed dynamic collaborative SVM has outstanding performance in energy efficiency and time delay.     

An Inter-cluster Communication based Energy Aware and Fault Tolerant Protocol for Wireless Sensor Networks

The ambient intelligence paradigm is built upon Ubiquitous Computing (UC), in which the computing devices are embedded in the environment with the purpose of enhancing the human experience at home, workplace/office, learning, health care etc. The UC applications aim at providing services to the users anywhere, anytime in an unobtrusive, seemingly invisible way. Wireless sensor networks (WSNs) have great potential for UC applications and are envisioned to revolutionize them. This paper presents a clustering routing protocol for event-driven, query-based and periodic WSNs. The protocol aims at optimizing energy dissipation in the network as well as providing network’s fault tolerance and connectivity. Message propagation is accomplished by using short distance transmissions by employing nearest neighbor nodes between neighboring clusters. Moreover, the algorithm proposes using an energy efficient approach by alternating the nodes responsible for inter-cluster communication inside one cluster. The algorithm also aims at even energy dissipation among the nodes in the network by alternating the possible routes to the Sink. This helps to balance the load on sensor nodes and increases the network lifetime, while avoiding congested links at the same time. We discuss the implementation of our protocol, present its proof of correctness as well as the performance evaluation through an extensive set of simulation experiments. This work is partially sponsored by Grants from the NSERC, Canada Research Chairs Program, ORNEC, the Ontario Distinguished Researcher Award and the EAR Award.     

无线传感器网络中基于压缩感知的动态目标定位算法

传统的动态目标定位算法需要采集、存储和处理大量数据,并不适用于能量受限的无线传感器网络。针对该缺陷,该文提出一种基于压缩感知的动态目标定位算法。该算法利用目标的运动规律设计稀疏表示基,从而将动态目标定位问题转化为稀疏信号恢复问题。针对传统观测矩阵难以实现的缺陷,该算法设计可实现且与稀疏表示基相关性低的稀疏观测矩阵,从而保证了算法的重构性能。该算法的特点是可利用较少的数据采集实现动态目标定位,从而大大延长无线传感器网络的寿命。仿真结果表明,该文所提出的基于压缩感知的动态目标定位算法具有较好的定位性能。     

A Distributed Node Localization Scheme for Wireless Sensor Networks

A distributed node localization scheme for wireless sensor networks (WSNs) is presented in this paper, and it includes three generic phases: (1) determine node-beacon distances, (2) compute node positions, and (3) refine the positions. Different from previous researches, we propose an algorithm combination Min–max + LI for the position derivation and SD method for the refinement in our scheme. Simulation shows that our proposed scheme can perform more robust than some representative distributed node localization schemes presented in previous researches in terms of the trade-off among accuracy, coverage, computation cost, and communication overhead.     

无线传感器网络中的鲁棒区域定位算法

大多数传统的方法并不能处理一些影响定位算法性能的因素,如各向相异的投放环境,不精确的锚节点位置以及带误差的距离测量。该文提出一种鲁棒的区域定位算法,通过建立一个全局约束集来处理如上所述的影响因素。使用可行解区域投影方法计算每个节点的可行地理区域,将传感器节点的真实位置限定于该区域中,同时利用非凸约束计算其存在的内部空洞。此外为了提高该方法的实用性,提出了一种基于分簇的分布式迭代算法。仿真结果表明算法受地理环境,测量误差等因素的影响较小,能适用于传感器网络应用。     

无线传感器网络中移动目标跟踪算法研究

由于单个传感器节点的通信、处理和感知能力有限,所以传感器节点必须协同工作才能够完成任务。本文介绍了无线传感器网络的研究现状,阐述了目前成形的无线传感器网络协议与体系结构,阐述了针对面向目标跟踪的无线传感器网络涉及到的一些关键技术,并对现有的一些无线传感器网络中移动目标协同跟踪算法以及未来的发展方向进行了论述。     

无线传感器网络中一种协同源定位算法

针对无线传感器网络传统目标定位过程中,数据在大量传感器节点间传输使得节点能量迅速耗尽,同时远距离低信噪比节点对定位精度的影响等问题。基于分区域协同工作的思想,提出了一种协同源定位算法。对该算法进行仿真试验,结果显示,该算法明显降低了网络能耗,且具有较高的定位精度和稳健性。     

无线传感器网络中基于网格的目标跟踪算法

提出了一种适用于无线传感器网络中基于网格的目标跟踪算法，以解决在目标跟踪过程中信任度（belief）更新和传感器节点信息贡献量估计问题。该算法对信任度进行非参数化表示，用基于网格的算法对序列贝叶斯滤波过程进行实现。并且利用目标位置预测和基于网格的算法在不预先获知传感器节点测量数据的情况下，对节点的信息贡献量进行估算。在资源受限的无线传感器网络中，该算法在降低计算复杂度、提高算法适用范围方面都有显著改进。最后在仿真环境中验证了基于网格的目标跟踪算法的有效性。     

A Cross-Layer Architecture of Wireless Sensor Networks for Target Tracking

We propose the Low Energy Self-Organizing Protocol (LESOP) for target tracking in dense wireless sensor networks. A cross-layer design perspective is adopted in LESOP for high protocol efficiency, where direct interactions between the Application layer and the Medium Access Control (MAC) layer are exploited. Unlike the classical Open Systems Interconnect (OSI) paradigm of communication networks, the Transport and Network layers are excluded in LESOP to simplify the protocol stack. A lightweight yet efficient target localization algorithm is proposed and implemented, and a Quality of Service (QoS) knob is found to control the tradeoff between the tracking error and the network energy consumption. Furthermore, LESOP serves as the first example in demonstrating the migration from the OSI paradigm to the Embedded Wireless Interconnect (EWI) architecture platform, a two-layer efficient architecture proposed here for wireless sensor networks     

Sequence-Based Localization in Wireless Sensor Networks

We introduce a novel sequence-based localization technique for wireless sensor networks. We show that the localization space can be divided into distinct regions that can each be uniquely identified by sequences that represent the ranking of distances from the reference nodes to that region. For n reference nodes in the localization space, combinatorially, O(n") sequences are possible, but we show that, due to geometric constraints, the actual number of feasible location sequences is much lower: only O(n ⁴). Using these location sequences, we develop a localization technique that is robust to random errors due to the multipath and shadowing effects of wireless channels. Through extensive systematic simulations and a representative set of real mote experiments, we show that our lightweight localization technique provides comparable or better accuracy than other state-of-the-art radio signal strength-based localization techniques over a range of wireless channel and node deployment conditions.     

无线传感器网络的目标跟踪算法

为了解决无线传感器网络跟踪非线性运动目标的分布式数据融合问题,使用了基于扩展信息滤波器(EIF)的分布式估计算法.对于活跃传感器的选择方法,采用了基于与目标位置接近程度的近邻选择算法和基于信息贡献的信息选择算法.仿真结果表明,与分布式扩展信息滤波器(DEIF)算法相比,近邻选择算法和信息选择算法得到了相似的响应曲线,且具有减少能量消耗和简化计算的优点.