无线传感器网络多时间尺度链路估计算法研究

针对无线传感器网络链路存在的不稳定性和不对称性问题,提出多时间尺度链路估计算法,通过长期和短期两种时间尺度估计节点间的链路质量,构建稳定链路和突发链路共存的拓扑结构,并采用突发链路发现策略实现机会路由;同时又针对由突发链路的不对称性引起的复制数据包问题,提出了多路径ACK策略,通过稳定链路和突发链路共同形成的多条路径传...     

无线传感器网络中一种具有稳定链路的鲁棒可调拓扑算法

该文针对传感器网络中无线链路的不稳定性问题,基于r邻居图模型提出一种具有稳定链路的鲁棒可调拓扑控制算法RAWSL (Robust Adjustable with Steady Links)。算法以接收信号强度阈值作为链路判断条件,有效地避免了网络中不稳定链路存在的可能性,并能够通过调整参数r取值满足不同网络的需求。实验结果表明,RAWSL算法不仅能够确保全网络的连通,还具有鲁棒性高和时延较低的特点。     

无线传感器网络分布式多传感器目标检测

目标检测是无线传感器网络中的一种重要应用,文中提出了一种能量有效分布式多传感器目标检测方法,该方法区别于以往的单目标方法,通过多个节点相互协作对目标进行检测,算法1给出了时间序列离散法,求得目标被测距离,且多节点将检测到的信息在聚合节点进行聚合,并通过算法2找到一条最优传输路径将聚合数据包传送至sink节点。此外通过仿真比较了单节点检测目标和多节点协同检测目标的概率、权系数α对检测节点平均能量消耗的影响,并给出了目标检测数据的最佳采样周期。     

室内无线传感器网络链路质量特征研究

首先分析了室内无线传感网络链路特点及传统链路质量特征描述存在的问题,指出了人类活动对室内无线传感器网络链路质量的显著影响。其次提出并描述了人体室内运动模型,在此基础上提出了人体运动条件下室内无线传感器网络有效区域,过渡区域和无效区域的估计方法,给出了上述区域中人体运动造成的数据接收成功率下降的理论界限。通过构造人体运动条件下干扰实验案例,分析了上述估计方法的性能。     

无线传感器网络链路通信质量测量研究

首先分析了传统链路测量方法所存在的问题,提出了均值RSSI(接收信号强度显示)、均值LQI(链路质量显示)测量法,通过测量及计算RSSI、LQI的均值,可以较为准确地获得链路通信质量信息;其次构建了发送功率改变、硬件校准改变、周围环境改变和并发干扰等实验案例,分析了均值RSSI、均值LQI在上述案例下的测量性能,定义了链路测量灵敏度,计算了均值RSSI、均值LQI的灵敏度值,对RSSI及LQI用于链路通信质量测量和性能评估做出了全面的权衡。     

无线传感器网络分布式频谱检测研究

传统单节点频谱检测由于受到阴影效应、多径效应和隐藏终端问题的影响，使得检测性能受到影响。基于无线传感器网络的分布式频谱检测技术能有效克服这一缺陷。在简要分析频谱检测技术的特点和要求的基础上，讨论了基于无线传感器网络的分布式频谱检测系统的结构，从本地检测和融合算法两个角度，对现有的几种基于无线传感器网络的分布式频谱检测方法进行了综述，并对其发展方向给出了建议。     

无线传感器网络中基于链路质量的路径延时分析

基于链路质量给出了路径满足实时性概率的上界,并证明了计算其上界的时间复杂度为指数级。另外在考虑链路质量的基础上,提出了一种在给定的延迟阈值下最大化端到端数据分组发送成功概率的贪心算法(RROP)。根据给定的延迟阈值和链路质量,RROP算法通过设置每跳链路的最大重传次数来优化端到端数据分组发送成功的概率。证明该算法能够在多项式时间内找到最优解并且通过该最优解获得路径满足实时性概率的一个近似最优的下界。实验结果表明给出的路径延迟分析上界和下界是准确的,并且提出的RROP算法在节省能量和满足实时性上比传统的方法能够获得高出10%以上的性能。     

基于概率可通链路的无线传感器网络栅格路由

该文基于一种通用的概率可通链路模型,分析了相邻栅格可通概率和路径可通概率,研究了节点密度和栅格大小对它们的影响,分别给出了以能量效率和网络平均分组递交率为目标的最佳栅格大小,并提出了将二者结合的最佳栅格选择标准,在满足用户需求的前提下网络寿命达到最大。最后对其性能进行了仿真分析,结果表明在考虑了概率可通链路后的最佳栅格大小与分析结果非常接近,且分组递交率和能量效率相结合的栅格大小选择标准能够很好的延长网络寿命。     

无线传感器网络中联合路由优化的高能效链路调度

链路调度技术是提高无线传感器网络数据传输可靠性的重要途径.传统的链路调度算法主要关注如何寻找无干扰传输条件下的最小调度帧,而忽视路径选择对链路调度的影响,会导致部分链路的干扰集过大,降低了时隙复用性.针对这一难题,系统分析了链路调度与路径选择及网络能耗的关系,提出联合路径优化的高能效链路调度模型.为加快求解速度,首先通过变量删减及模型转化,提出基于整数规划的最优路由树构建算法;进一步,根据整数规划求解的路由树与链路需求,提出基于最大干扰度优先的启发式链路调度算法.大量仿真实验验证了本文算法的有效性.     

新的无线传感器网络分簇算法

针对无线传感器网络节点能量受限的特点,提出了一种响应式分布分簇算法(RDCA,responsive distributedclustering algorithm).该算法不需预先得知节点自身及其他节点的位置信息,而仅根据局部拓扑信息快速进行分布式的簇头选举,并根据代价函数进行簇的划分,适用于周期性获取信息的无线传感器网络.分析与仿真表明,该算法具有良好的负载平衡性能和较小的协议开销,与LEACH协议相比,能够减少能量消耗,网络生存期大约延长了40%.     

Localized algorithms for coverage boundary detection in wireless sensor networks

Connected coverage, which reflects how well a target field is monitored under the base station, is the most important performance metric used to measure the quality of surveillance that wireless sensor networks (WSNs) can provide. To facilitate the measurement of this metric, we propose two novel algorithms for individual sensor nodes to identify whether they are on the coverage boundary, i.e., the boundary of a coverage hole or network partition. Our algorithms are based on two novel computational geometric techniques called localized Voronoi and neighbor embracing polygons. Compared to previous work, our algorithms can be applied to WSNs of arbitrary topologies. The algorithms are fully distributed in the sense that only the minimal position information of one-hop neighbors and a limited number of simple local computations are needed, and thus are of high scalability and energy efficiency. We show the correctness and efficiency of our algorithms by theoretical proofs and extensive simulations. Chi Zhang received the B.E. and M.E. degrees in Electrical Engineering from Huazhong University of Science and Technology, Wuhan, China, in July 1999 and January 2002, respectively. Since September 2004, he has been working towards the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Florida, Gainesville, Florida, USA. His research interests are network and distributed system security, wireless networking, and mobile computing, with emphasis on mobile ad hoc networks, wireless sensor networks, wireless mesh networks, and heterogeneous wired/wireless networks. Yanchao Zhang received the B.E. degree in computer communications from Nanjing University of Posts and Telecommunications, Nanjing, China, in July 1999, the M.E. degree in computer applications from Beijing University of Posts and Telecommunications, Beijing, China, in April 2002, and the Ph.D. degree in electrical and computer engineering from the University of Florida, Gainesville, in August 2006. Since September 2006, he has been an Assistant Professor in the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark. His research interest include wireless and Internet security, wireless networking, and mobile computing. He is a member of the IEEE and ACM. Yuguang Fang received the BS and MS degrees in Mathematics from Qufu Normal University, Qufu, Shandong, China, in 1984 and 1987, respectively, a Ph.D. degree in Systems and Control Engineering from Department of Systems, Control and Industrial Engineering at Case Western Reserve University, Cleveland, Ohio, in January 1994, and a Ph.D. degree in Electrical Engineering from Department of Electrical and Computer Engineering at Boston University, Massachusetts, in May 1997. From 1987 to 1988, he held research and teaching position in both Department of Mathematics and the Institute of Automation at Qufu Normal University. From September 1989 to December 1993, he was a teaching/research assistant in Department of Systems, Control and Industrial Engineering at Case Western Reserve University, where he held a research associate position from January 1994 to May 1994. He held a post-doctoral position in Department of Electrical and Computer Engineering at Boston University from June 1994 to August 1995. From September 1995 to May 1997, he was a research assistant in Department of Electrical and Computer Engineering at Boston University. From June 1997 to July 1998, he was a Visiting Assistant Professor in Department of Electrical Engineering at the University of Texas at Dallas. From July 1998 to May 2000, he was an Assistant Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology, Newark, New Jersey. In May 2000, he joined the Department of Electrical and Computer Engineering at University of Florida, Gainesville, Florida, where he got early promotion to Associate Professor with tenure in August 2003, and to Full Professor in August 2005. His research interests span many areas including wireless networks, mobile computing, mobile communications, wireless security, automatic control, and neural networks. He has published over one hundred and fifty (150) papers in refereed professional journals and conferences. He received the National Science Foundation Faculty Early Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002. He also received the 2001 CAST Academic Award. He is listed in Marquis Who’s Who in Science and Engineering, Who’s Who in America and Who’s Who in World. Dr. Fang has actively engaged in many professional activities. He is a senior member of the IEEE and a member of the ACM. He is an Editor for IEEE Transactions on Communications, an Editor for IEEE Transactions on Wireless Communications, an Editor for IEEE Transactions on Mobile Computing, an Editor for ACM Wireless Networks, and an Editor for IEEE Wireless Communications. He was an Editor for IEEE Journal on Selected Areas in Communications:Wireless Communications Series, an Area Editor for ACM Mobile Computing and Communications Review, an Editor for Wiley International Journal on Wireless Communications and Mobile Computing, and Feature Editor for Scanning the Literature in IEEE Personal Communications. He has also actively involved with many professional conferences such as ACM MobiCom’02 (Committee Co-Chair for Student Travel Award), MobiCom’01, IEEE INFOCOM’06, INFOCOM’05 (Vice-Chair for Technical Program Committee), INFOCOM’04, INFOCOM’03, INFOCOM’00, INFOCOM’98, IEEE WCNC’04, WCNC’02, WCNC’00 Technical Program Vice-Chair), WCNC’99, IEEE Globecom’04 (Symposium Co-Chair), Globecom’02, and International Conference on Computer Communications and Networking (IC3N) (Technical Program Vice-Chair).     

TDMA scheduling algorithms for wireless sensor networks

Algorithms for scheduling TDMA transmissions in multi-hop networks usually determine the smallest length conflict-free assignment of slots in which each link or node is activated at least once. This is based on the assumption that there are many independent point-to-point flows in the network. In sensor networks however often data are transferred from the sensor nodes to a few central data collectors. The scheduling problem is therefore to determine the smallest length conflict-free assignment of slots during which the packets generated at each node reach their destination. The conflicting node transmissions are determined based on an interference graph, which may be different from connectivity graph due to the broadcast nature of wireless transmissions. We show that this problem is NP-complete. We first propose two centralized heuristic algorithms: one based on direct scheduling of the nodes or node-based scheduling, which is adapted from classical multi-hop scheduling algorithms for general ad hoc networks, and the other based on scheduling the levels in the routing tree before scheduling the nodes or level-based scheduling, which is a novel scheduling algorithm for many-to-one communication in sensor networks. The performance of these algorithms depends on the distribution of the nodes across the levels. We then propose a distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10–70 over centralized algorithms for 1000 nodes. We also obtain upper bound for these schedules as a function of the total number of packets generated in the network.     

Anomaly detection in wireless sensor networks

Anomaly detection in wireless sensor networks is an important challenge for tasks such as fault diagnosis, intrusion detection, and monitoring applications. The algorithms developed for anomaly detection have to consider the inherent limitations of sensor networks in their design so that the energy consumption in sensor nodes is minimized and the lifetime of the network is maximized. In this survey article we analyze the state of the art in anomaly detection techniques for wireless sensor networks and discuss some open issues for research.     

Efficient clustering algorithms for self-organizing wireless sensor networks

Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local “growth budgets” to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time. However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.     

Energy constraint clustering algorithms for wireless sensor networks

Using partitioning in sensor networks to create clusters for routing, data management, and for controlling communication has been proven as a way to ensure long range deployment and to deal with sensor network shortcomings such as limited energy and short communication ranges. Choosing a cluster head within each cluster is important because cluster heads use additional energy for their responsibilities and that burden needs to be carefully passed around among nodes in a cluster. Many existing protocols either choose cluster heads randomly or use nodes with the highest remaining energy. We present an Energy Constrained minimum Dominating Set based efficient clustering called ECDS to model the problem of optimally choosing cluster heads with energy constraints. Our proposed randomized distributed algorithm for the constrained dominating set runs in O(log n log Δ) rounds with high probability where Δ is the maximum degree of a node in the graph. We provide an approximation ratio for the ECDS algorithm of expected size 8H_Δ∣OPT∣ and with high probability a size of O(∣OPT∣log n) where n is the number of nodes, H is the harmonic function and OPT means the optimal size. We propose multiple extensions to the distributed algorithm for the energy constrained dominating set. We experimentally show that these extensions perform well in terms of energy usage, node lifetime, and clustering time in comparison and, thus, are very suitable for wireless sensor networks.     

无线传感器网络中的定位异常检测

在无线传感器网络中位置信息有着重要应用,但是定位过程容易受到恶意攻击者的攻击或环境因素的干扰。为了增加节点定位的安全性。本文提出一种基于节点部署模型的检测方法用来提高定位的安全性能,该方法独立于节点定位过程,根据节点位置的邻居发现和部署知识的一致性判断节点位置是否异常。在具体的异常判断过程中使用一种由马氏距离定义差异矩阵作为比较工具。最后通过Matlab仿真实验分别从检测率,错误警报率两个方面评估验证了该方法的正确性。     

Channel-aware distributed detection in wireless sensor networks

This paper reviews the classical decentralized decision theory in the light of new constraints and requirements. The central theme that transcends various aspects of signal processing design is that an integrated channel-aware approach needs to be taken for optimal detection performance given the available resources.     

Approximation algorithms for broadcasting in duty cycled wireless sensor networks

Broadcast is a fundamental operation in wireless sensor networks (WSNs). Given a source node with a packet to broadcast, the aim is to propagate the packet to all nodes in a collision free manner whilst incurring minimum latency. This problem, called minimum latency broadcast scheduling (MLBS), has been studied extensively in wireless ad-hoc networks whereby nodes remain on all the time, and has been shown to be NP-hard. However, only a few studies have addressed this problem in the context of duty-cycled WSNs. In these WSNs, nodes do not wake-up simultaneously, and hence, not all neighbors of a transmitting node will receive a broadcast packet at the same time. Unfortunately, the problem remains NP-hard and multiple transmissions may be necessary due to different wake-up times. Henceforth, this paper considers MLBS in duty cycled WSNs and presents two approximation algorithms, BS-1 and BS-2, that produce a maximum latency of at most \((\Delta -1) TH\) and \(13TH\) respectively. Here, \(\Delta\) is the maximum degree of nodes, \(T\) denotes the number of time slots in a scheduling period, and \(H\) is the broadcast latency lower bound obtained from the shortest path algorithm. We evaluated our algorithms under different network configurations and confirmed that the latencies achieved by our algorithms are much lower than existing schemes. In particular, compared to OTAB, the best broadcast scheduling algorithm to date, the broadcast latency and transmission times achieved by BS-1 is at least \(\frac{1}{17}\) and \(\frac{2}{5}\) that of OTAB respectively.     

Gravitational outlier detection for wireless sensor networks

Accuracy of sensed data and reliable delivery are the key concerns in addition to several other network‐related issues in wireless sensor networks (WSNs). Early detection of outliers reduces subsequent unwanted transmissions, thus preserving network resources. Recent techniques on outlier detection in WSNs are computationally expensive and based on message exchange. Message exchange‐based techniques incur communication overhead and are less preferred in WSNs. On the other hand, machine learning‐based outlier detection techniques are computationally expensive for resource constraint sensor nodes. The novelty of this paper is that it proposes a simple, non message exchange based, in‐network, real‐time outlier detection algorithm based on Newton's law of gravity. The mechanism is evaluated for its accuracy in detecting outliers, computational cost, and its influence on the network traffic and delay. The outlier detection mechanism resulted in almost 100% detection accuracy. Because the mechanism involves no message exchanges, there is a significant reduction in network traffic, energy consumption and end‐to‐end delay. An extension of the proposed algorithm for transient data sets is proposed, and analytic evaluation justifies that the mechanism is reactive to time series data. Copyright © 2016 John Wiley & Sons, Ltd.     

Performance of some metaheuristic algorithms for localization in wireless sensor networks

In this paper we propose two novel and computationally efficient metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) principles for locating the sensor nodes in a distributed wireless sensor network (WSN) environment. The WSN localization problem is formulated as a non‐linear optimization problem with mean squared range error resulting from noisy distance measurement as the objective function. Unlike gradient descent methods, both TS and PSO methods ensure minimization of the objective function without the solution being trapped into local optima. We further implement a refinement phase with error propagation control for improvement of the results. The performance of the proposed algorithms are compared with each other and also against simulated annealing based WSN localization. The effects of range measurement error, anchor node density and uncertainty in the anchor node position on localization performance are also studied through various simulations. The simulation results establish better accuracy, computational efficiency and convergence characteristics for TS and PSO methods. Further, the efficacy of the proposed methods is verified with data collected from an experimental sensor network reported in the literature. Copyright © 2008 John Wiley & Sons, Ltd.