基于自组织图算法的水下传感器网络优化部署研究

水下传感器网络部署是开展水下传感器网络相关应用的基础,良好的传感器节点部署方案可以有效提高目标的监测质量;针对水环境中随机事件的突发性和不确定的特点,提出了基于自组织图算法的水下传感器网络优化部署方案;首先,随机部署传感器节点,预设随机事件呈L型不均匀分布,当随机事件发生在传感器覆盖漏洞处时,采用自组织图算法确定传感器节点需要移动到的目标位置;仿真结果表明,基于自组织图算法的水下传感器网络优化部署方案可以显著提高对随机事件的覆盖率,实现对水环境的有效监测。     

鱼群启发的水下传感器节点布置

水下传感器网络(Underwater sensor networks, UWSNs)可用于海洋资源勘测、污染监控和辅助导航等领域, 已成为无线传感器网络方向的研究热点. 在开放式复杂的水下环境中监测目标具有高动态和不确定的特点, 节点如何根据环境和目标的变化自主调整位置, 以达到对监测目标的优化覆盖是一个关键问题. 本文描述了水下传感器节点布置问题, 定义了性能评价指标"事件集覆盖效能", 并提出了一种鱼群启发的水下传感器节点布置算法, 通过模拟鱼群行为, 并结合拥挤度控制, 使节点自主趋向并覆盖事件, 同时实现节点分布密度与事件分布密度相匹配. 大量对比仿真实验结果表明该算 法可以有效解决水下传感器节点布置问题, 并具有复杂度低、计算量小、收敛速度快和分布式可实现的优点.     

UWSNs中基于深度的抑制空洞路由优化算法

针对传统的水下无线传感器网络(UWSNs)的位置路由存在路由空洞问题,提出了基于深度的抑制空洞路由(DSVR)的UWSNs路由协议.DSVR协议通过融合跳数、物理距离和邻居数多个指标决策路由.为了提高通信可靠和缓解路由空洞,DSVR协议选择具有最小跳数路径、最少邻居数的节点作为下一跳转发节点.同时,DSVR协议利用定时器抑制冗余数据包.仿真结果表明:提出的DSVR协议能有效地提高数据包传递率,并降低端到端传输时延以及能耗.     

一种基于顺序博弈的UWSNs覆盖控制算法

近年来,人们对海洋资源愈加重视,水下无线传感器网络在海洋数据监测、海洋军事、辅助导航等方面的应用引起了广泛关注。一个良好的水下无线传感器网络覆盖控制,首先能够降低覆盖冗余,优化网络空间资源的配置;其次可以减少节点的能耗、降低网络的成本并延长节点的生命周期,使水下无线传感器网络可以更好地完成目标水域环境感知任务。提出了一种基于顺序博弈的水下无线传感器网络覆盖控制方法,用于优化水下无线传感器网络的覆盖控制,期望能够降低节点的能耗,均衡节点之间的能量,最终实现延长网络的生命周期的目标。仿真实验表明,该算法能够提高网络覆盖率和延长网络生命周期。     

一种基于自适应代表节点选择的WSN数据收集方法

采用压缩感知的无线传感网络数据收集方法要求每个节点都参与数据收集,会造成很大的能量浪费.本文提出了一种基于自适应代表节点选择的WSN数据收集方法,在保证压缩感知数据重构精度的同时,减少参与数据收集的节点数.首先,采用主成分分析和混合压缩感知相结合的办法设计稀疏基;然后,通过分析稀疏基的框架势FP(Frame Potential)设计压缩感知的稀疏观测矩阵,从而选择代表节点,以减少参与数据收集的节点数目;最后,根据Sink处数据重构精度,自适应调整稀疏观测矩阵以用作下一时刻数据收集,从而保证数据收集的重构精度.仿真结果表明,该方法有效的降低了网络能耗和数据传输量,同时还保证了每个时刻数据重构的精度.     

MFALM:一种水下动态传感器网络主动定位方法

针对水下动态传感器网络定位的特点及问题,提出了一种基于到达时间差(TDoA)的多频率主动定位方法MFALM(multi-frequency active localization method base on TDoA)。该方法将网络节点分为3组:信标节点、中继节点和普通节点。节点之间通过高、低两种频率分别进行近、远距离通信。为减少通信开销,定义了精简的报文格式和有效的通信方式。分析表明,该定位方法由于采用事件驱动、多频通信、精简报文,因此在定位过程中不但能够减少节点的定位开销,延长网络生存时间,而且还有定位精度高、节点密度可调等优点。     

基于链接预测的水下WSN消息转发算法

随着移动自组织网络的发展以及为了更加便捷地监测和探索水下环境,水下无线传感器网络开始出现并逐渐受到研究人员的重视.水下无线传感器网络可广泛应用于海洋环境监测、资源开采、水下生物研究、海难搜救等诸多水下场景.与传统的无线传感器网络不同,通常,水下无线传感器网络中存在锚定节点和移动节点两种类型的节点,并且由于水声通信的不规...     

水下传感器网络中的多路径路由技术研究

水下声信道是一种复杂信道,由于各方面因素影响,在数据传输过程中,水下传感器网络数据链路层会有可能产生随机中断.研究了一种虚拟汇聚节点的多路径水下传感器网络路由技术,在随机布设的传感器节点中选取了缓存节点,并创建了局部的汇聚节点,通过多路径的路由技术,有效减少了网络吞吐率受链路中断的影响,并且避免了汇聚节点处的数据冲突.通过网络的冗余性,增强了网络稳定性、鲁棒性.     

基于洋流模型的水下传感器网络实时定位算法

针对近海监控领域内水下传感器网络的节点随着海洋运动具有流动性的问题,提出了基于洋流模型的水下传感器网络实时定位算法(RTLC).利用洋流模型表示节点的运动模型消除流动性对定位造成的影响,成功模拟了节点在水中的移动速度.通过卡尔曼预测方法结合观测值与估计值优化运动模型,使其更贴近节点运动规律.采用信息记录机制及列表更新机制保证信息的时效性从而提高了定位准确度.通过仿真分析,RTLC算法性能优于基于移动预测的大规模水下传感器网络可扩展定位算法(SLMP),具有较高的定位覆盖度,较低的平均定位误差及平均通信能耗.     

基于信息量的分布式协同自组织算法

无线传感器网络中节点协同自组织主要涵盖传感器管理和状态估计,也就是如何选择传感器节点、设置传感器参数并估计被监测系统的状态,因此协同白组织为决策与估计的联合优化．本文提出了一种自适应动态协同自组织算法,以量测所提供的信息量和节点自身的剩余能量做为节点选择的综合指标,根据设定的感知精度,自适应地选择参与感知任务的节点集合,在信息滤波的融合框架下完成状态的分布式估计．相比信息驱动传感器查询（information-driven sensor querying,IDSQ）,算法具有精度可调、强鲁棒,同时尽可能地延长了网络的生命周期．以目标跟踪为应用背景,其仿真结果表明：以跟踪精度、失跟率和网络生命周期作为评价指标,该算法优于IDSQ．     

基于水平集的围猎式水下传感器节点布置研究

水下传感器网络作为一种探索和开发海洋的新方法,在人类不易接触的水下区域的探测和监测中发挥着重要作用,是无线传感器网络领域研究的热点.在水下空间中传感器节点如何自主调整位置实现对"兴趣域"的覆盖和监视是一个重要课题.该文引入水平集理论LSM,提出了一种基于水平集的围猎式水下传感器节点布置方法.在该方法中网关节点根据传感器...     

Using mobile data collectors to improve network lifetime of wireless sensor networks with reliability constraints

In this paper, we focus on maximizing network lifetime of a Wireless Sensor Network (WSN) using mobile Data Collectors (DCs) without compromising on the reliability requirements. We consider a heterogeneous WSN which consists of a large number of sensor nodes, a few DCs, and a static Base Station (BS). The sensor nodes are static and are deployed uniformly in the terrain. The DCs have locomotion capabilities and their movement can be controlled. Each sensor node periodically sends sensed event packets to its nearest DC. The DCs aggregate the event packets received from the sensor nodes and send these aggregate event packets to the static BS. We address the following problem: the DCs should send the aggregate event packets to the BS with a given reliability while avoiding the hotspot regions such that the network lifetime is improved. Reliability is achieved by sending each aggregate event packet via multiple paths to the BS. The network lifetime is maximized by moving the DCs in such a way that the forwarding load is distributed among the sensor nodes. We propose both centralized and distributed approaches for finding a movement strategy of the DCs. We show via simulations that the proposed approaches achieve the required reliability and also maximize the network lifetime compared to the existing approaches.     

Node localization algorithm for wireless sensor networks using compressive sensing theory

In recent years, localization has been recognized as an important supporting technology for wireless sensor networks (WSNs). Along with the increase in WSN indoor applications, indoor localization has become a hot research topic and many localization algorithms have been studied. Among these algorithms, the localization method based on compressive sensing theory emerges as a popular approach to indoor localization. In this approach, the nodes are sparse when compared to the number of grids utilized to represent the locations of the nodes, so the locations are considered as sparse signal and can be reconstructed using the compressive sensing techniques. The localization problem is formulated as the sparse reconstruction of sparsifying matrix which is comprised of measurement of received signal at grids. In order to improve the localization accuracy and meet the real-time requirement of localization applications in large indoor area, an indoor localization algorithm based on dynamic measurement compressive sensing for wireless sensor networks is proposed. Using the bounding-box method, we firstly identify a potential area that possesses the independent features. Instead of using the entire node deployment region as the measurement area, our method can decrease the number of meshing and also the dimension of measurement matrix. Meanwhile, we assume that only the anchor nodes which have communication relationship with the unknown nodes can be used as the measuring nodes; the measurement matrix of unknown nodes which need to be localized can be dynamically constructed according to the potential area and the received anchor node information, and the maximum number of measurement is decided by the number of grids of potential area. The proposed algorithm can mitigate the measurement redundancy and improve the real-time feature. Simulation results indicate that the proposed algorithm can reduce the time complexity and also maintain good localization accuracy and localization efficiency.     

基于过完备字典的体域网压缩感知心电重构

针对体域网远程监护中心对重构的心电信号（Electrocardiogram,ECG）精度要求高和体域网（Body sensor network,BSN）低功耗问题,提出基于过完备字典的体域网压缩感知心电重构方法. 该方法利用压缩感知理论,在传感节点端利用随机二进制矩阵对心电信号进行观测,观测值被传送至远程监护中心后,再利用基于K-SVD算法训练得到的过完备字典和块稀疏贝叶斯学习重构算法对心电信号进行重构. 仿真结果表明,当心电信号压缩率在70%～95%时,基于K-SVD过完备字典比基于离散余弦变换基的压缩感知心电重构信噪比高出5～22dB. 该方法具有信号重构精度高、功耗低和易于硬件实现的优点.     

Sensor placement for robust burst identification in water systems: Balancing modeling accuracy,parsimony, and uncertainties

Urban water systems are seeing an uptake in using advanced sensing technology. Incorporating sensors for monitoring water distribution systems (WDSs) provides promising benefits to water utilities by enabling a shift from reactive to proactive pipe failure detection and from delayed water loss management to automatic sense-and-respond capabilities. Despite the opportunities that new sensing technologies create, a budget-constrained utility is challenged with identifying sensing locations in the WDS that will maximize information gain. To address this gap, this paper studies the problem of optimal placement of high-frequency pressure sensors in WDSs for pipe burst identification. This paper proposes a sensor placement strategy to address the challenges of data and modeling uncertainty by incorporating robust representation and tolerance analysis into an optimization framework with the objective of achieving the best detection and identification of burst events. Transient simulations are first used to predict system’s response to burst events, demonstrating the importance of modeling accuracy over approximation methods. A robust event representation approach is then presented to summarize system response to pipe bursts using signature matrices. Subsequently, the identification problem is cast as a minimum test cover problem when the number of available sensors is unlimited, and as the maximum covering test problem when the number of available sensors is limited. The optimization problems are then formulated and solved using mixed integer linear programming. Four complementary metrics are suggested to evaluate the performance of the sensor placement designs. Multiple criteria decision analysis is then applied to select the placement design while balancing information gain and cost. The results show that incorporating more information can improve event identification, but sufficient accuracy of the extracted information is required to accrue the benefits.     

基于分组的无线传感器网络入侵检测方案

为了有效地检测无线传感器网络所面临的各种恶意攻击,提出了一种轻量、高效、灵活的分组入侵检测方案.在该方案中,整个传感器网络被划分成若干物理位置临近、具有相似观测结果的分组,组内各传感器节点同时观测其它节点的多个属性,以便精确地检测各种攻击行为.实验结果表明,与传感器网络中现有的入侵检测方案相比,本方案具有较低的误报率和较高的检测精度.同时消耗更少的能量.     

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。     

基于多AUV协作的稀疏水下传感网定位技术研究

如何在稀疏部署的水下传感器网络中实现传感器节点的高效定位是一个研究热点.提出了一种基于多个移动AUV协作的水下传感器网络内节点定位机制,利用AUV的精确自导航功能实现对网内未知位置节点的定位协助.提出的协作定位算法扩展了水下传感器网络的网内节点位置迭代估计方法,将信标节点和多AUV联合作为定位参考点,然后推导了基于最小二乘法的定位估计方程.仿真结果验证了该方法可以在定位节点比例、归一化定位误差和平均置信度等几个方面提高定位性能.     

Topology control algorithm for underwater wireless sensor networks using GPS-free mobile sensor nodes

Underwater wireless sensor networks (UWSNs) have been developed for underwater applications, such as resource exploration, pollution monitoring, and tactical surveillance. The topology control techniques of UWSNs and terrestrial wireless sensor networks are significantly different because of the particularity of underwater environments and acoustic communication, such as mobility pattern, propagation delay and energy consumption. Due to multifarious factors of underwater environments, node mobility becomes a non-negligible issue. However, GPS may not be feasible because of the limitations of satellite coverage or obstructions in adverse underwater environments. In particular, anchored sensor nodes towed by wires are prone to offset around their static positions, causing each node to move within a spherical crown surface (spherical crown mobility pattern). Nevertheless, most previous studies have not focused on this specific mobility pattern. In the current paper, a mobility model for UWSNs nodes is constructed, and three representative topology control objectives are attained. A distributed radius determination algorithm is designed for the mobility-based topology control problem. Results of theoretic analysis prove that the proposed algorithm is convergent, and it has preferable approximate ratios and polynomial complexity. Performance of the algorithm is analyzed through simulation experiments, which indicate a well-constructed topology. Every objective can still be upgraded without the dynamic location information of mobile nodes.     

针对未知稀疏信号的压缩自相关检测算法

针对未知的宽频带稀疏信号检测问题,提出了一种直接基于非重构采样值 的压缩自相关检测算法。首先利用压缩感知技术以远低于奈奎斯特采样速率获取信号,在自 相关矩阵检测信号理论的基础上,利用压缩感知中传感矩阵的严格等距特性,推导出基于统 计分布的信号稀疏系数自相关检测算法,从理论上给出了判决门限的选取和虚警概率之间的 关系,并进行了算法复杂度分析。由于无需重构原始信号,该算法直接利用少量的压缩测量 值进行检测,可以有效地提高检测过程的时效性。仿真表明在较低的信噪比时,该算法对未 知信号仍有良好的检测性能。