基于改进MPA的分层无线传感器网络优化部署

分层的无线传感器网络中,汇聚层节点的部署位置,对感知层节点的发射功率起到决定性影响。因此,本文对3D环境下的分层无线传感器网络的优化部署进行研究。以感知层节点与汇聚层节点的全连接以及感知层节点发射功率的平衡为前提,对汇聚层节点的部署位置和感知层节点的发射功率进行联合优化。为了求解上述优化问题,本文提出了一种结合Tent混沌序列的海洋捕食者(MPA)改进算法:TMPA。多个测试函数的数值计算结果表明,TMPA比MPA具有更好的收敛性能。在此基础上,本文提出了一种基于TMPA的分层无线传感器网络的优化部署机制。仿真结果表明,本文的优化部署机制能够在保证感知层节点全连接的前提下,极大地降低并平衡感知层节点的能耗,从而延长了网络的寿命。     

一种结合粒子群和虚拟力的动态节点部署策略

无线传感器节点部署是无线传感器网络研究的关键问题,面对工作在复杂环境下的众多传感器节点,模拟了一个由随机部署的固定节点和移动节点构成的无线传感器网络环境。为了优化节点的布局,将粒子群算法与虚拟力相结合,提出了一种虚拟力扰动指数权值递减型粒子群算法,该策略通过改进粒子群算法加快了粒子进入局部搜索的速度,并异构了节点间虚拟力来影响粒子群算法中粒子的进化过程,提高算法收敛速度。仿真结果表明,和传统的粒子群算法相比,提出的算法可以得到更高的覆盖率,且收敛速度更快。     

无线传感网络移动节点位置并行微粒群优化策略

网络节点位置优化是无线传感网络研究的核心问题之一.无线传感网络通常由固定节点和少量移动节点构成,传统的虚拟力导向算法无法解决固定节点对移动节点优化的约束.该文针对这一问题,提出了基于并行微粒群算法的优化策略.微粒群算法具有适于解决连续空间多维函数优化问题、能快速收敛至全局最优解的特点.并行框架提高了算法的运行效率,降低了算法的运算复杂度,使算法能够满足无线传感网络的需求.通过并行微粒群算法搜索不同状态下无线传感节点的最优位置,使无线传感网络能够利用移动节点实现网络结构的动态重组,最大化网络覆盖范围,提高网络测量可靠性.实验证明,并行微粒群优化策略能快速有效地实现无线传感网络移动节点位置优化.     

A relay node deployment method for disconnected wireless sensor networks: Applied in indoor environments

In this paper, a 2-D geometric and adaptive relay node deployment method with polynomial-time complexity is proposed to reduce relay nodes for bridging all disconnected node pairs of a wireless sensor network (WSN). In this method, proper locations for placing relay nodes are discovered on a plane, which gives a much larger degree of freedom to compare with the traditional 1-D search algorithm. In this way, the deployed relay node can bridge as many disconnected node pairs as possible around it. Besides, the method can also adapt to radio environments because that a sophisticated propagation model including large-scale path loss, shadowing, and multipath fading effects is used to estimate link connectivity. Simulation result validates its robustness and efficiency compared with the existing relay node deployment methods. The proposed method significantly reduces the total number of relay nodes compared with the related works in all of the investigated indoor environments.     

改进QGA在WSNs节点部署中的应用

对含有障碍区域的无线传感器网络（WSNs）节点部署问题进行研究。建立节点探测模型和网络覆盖率评价方法,基于概率传感器模型提出一种部署方式,即对障碍区域进行随机布撒节点,确定区域采用量子遗传算法（QGA）寻找最优节点部署位置,实现对同构WSNs节点构成的目标区域的高效覆盖。仿真结果与GA,QGA相比：改进QGA有效提高了算法整体的搜索能力和收敛速度。     

Analysis of low power wireless links in smart grid environments

Recently, wireless sensor networks (WSNs) have been used in various smart grid applications, including remote power system monitoring and control, power fraud detection, wireless automatic metering, fault diagnostics, demand response, outage detection, overhead transmission line monitoring, load control, and distribution automation. However, harsh smart grid environment propagation characteristics cause great challenges in the reliability of WSN communications in smart grid applications. To this end, the analysis of wireless link reliability and channel characterizations can help network designers to foresee the performance of the deployed WSN for specific smart grid propagation environments, and guide the network engineers to make design decisions for the channel modulation, encoding schemes, output power, and frequency band. This paper presents a detailed analysis of low power wireless link reliability in different smart grid environments, such as 500 kV outdoor substation environment, indoor main power control room, and underground network transformer vaults. Specifically, the proposed analysis aims to evaluate the impact of different sensor radio parameters, such as modulation, encoding, transmission power, packet size, as well as the channel propagation characteristics of different smart grid propagation environments on the performance of the deployed sensor network in smart grid. Overall, the main objective of this paper is to help network designers quantifying the impact of the smart grid propagation environment and sensor radio characteristics on low power wireless link reliability in harsh smart grid environments.     

基于动态近邻反馈修正的室内定位算法

针对目前无线传感器网络（WSN）室内接收信号强度（RSSI）测距算法中RSSI易受到信道干扰和传播环境影响从而导致定位精度低的问题,提出一种动态近邻反馈修正的室内定位优化算法FC-DNN,以实现无线传感器室内节点精确定位。首先,通过对环境进行Voronoi图分割确定最小定位区域;然后计算每个区域的路径损耗模型参数得到节点间的精确距离;最后利用Spearman等级相关系数动态选择邻居锚节点,根据邻节点反馈修正进一步提高未知节点的定位精度。仿真结果表明,FC-DNN算法复杂度低、计算开销小、能耗较低,与典型的RSSI测距差分修正定位算法（DDLA）和受限三维空间传感器定位算法（CO-3D）相比,节点的平均定位误差降低了约15个百分点,能够很好地满足室内环境定位要求。     

一种基于改进蚁群算法的三维K-栅栏覆盖算法

为解决三维环境下无线传感器网络的K-栅栏覆盖问题,提出一种改进的蚁群优化算法3D-ACO。将三维表面映射到二维平面进行网格划分,通过计算网格梯度并引入空间权重及部署方向角来改进蚁群算法寻找最短路径构建栅栏,采用移动节点填补栅栏间隙以确保构建强栅栏。实验结果表明,与strong optimal和strong greedy算法相比,该算法能够在有效提高节点利用率的同时降低节点能耗,并且在三维环境下所构建的栅栏覆盖具有较强的自适应性。     

Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks

In a wireless sensor network (WSNs), probability of node failure rises with increase in number of sensor nodes within the network. The, quality of service (QoS) of WSNs is highly affected by the faulty sensor nodes. If faulty sensor nodes can be detected and reused for network operation, QoS of WSNs can be improved and will be sustainable throughout the monitoring period. The faulty nodes in the deployed WSN are crucial to detect due to its improvisational nature and invisibility of internal running status. Furthermore, most of the traditional fault detection methods in WSNs do not consider the uncertainties that are inherited in the WSN environment during the fault diagnosis period. Resulting traditional fault detection methods suffer from low detection accuracy and poor performance. To address these issues, we propose a fuzzy rule-based faulty node classification and management scheme for WSNs that can detect and reuse faulty sensor nodes according to their fault status. In order to overcome uncertainties that are inherited in the WSN environment, a fuzzy logic based method is utilized. Fuzzy interface engine categorizes different nodes according to the chosen membership function and the defuzzifier generates a non-fuzzy control to retrieve the various types of nodes. In addition, we employed a routing scheme that reuses the retrieved faulty nodes during the data routing process. We performed extensive experiments on the proposed scheme using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the effectiveness of the proposed algorithm in terms of various important performance metrics.     

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.     

煤矿井下无线传感器网络节点三维定位算法

现有煤矿无线传感器网络节点定位存在定位精度差、功耗高等问题,提出一种基于信标节点规则部署的煤矿井下无线传感器网络节点三维定位算法,根据井下巷道特征成对部署信标节点,在定位估算时先将信标节点投影到与未知节点同一高度的水平面,再利用三边测量法进行平面定位,然后结合未知节点与信标节点的高度差即可实现三维定位。理论分析和仿真结果表明,该算法具有计算量小、通信量小、定位精度较高以及稳定性较好等特点。     

基于改进引力搜索算法的室内定位锚节点部署方案

在室内定位场景中,锚节点的部署对定位性能具有重要影响.考虑存在障碍物的室内环境,基于缺少环境详细信息或者缺少设备模拟室内信号传播的基本场景,综合考虑可定位率、定位精度以及是否冗余部署的指标,设计一种新的评价函数,并利用改进的引力搜索算法(GSA)进行求解,在可接受时间成本内得到室内定位锚节点部署问题的较优解.仿真实验中将所提出算法与3种参考算法进行对比,在定位率、几何精度因子(GDOP)值、定位误差等方面所提出算法都表现出较优的性能;同时,针对考虑部分因素的评价函数进行比较,通过对多个指标项的分析,证实所提出的评价函数可以获得较优的锚节点部署方案,从而能够有效改善锚节点拓扑,提高定位性能.     

无线传感器网络中节点的动态部署

作为无线传感器网络中节点部署的新兴技术,动态部署得到了越来越多的重视并在近年来取得了理论和应用上的一系列进展。首先介绍了动态部署的基本概念和设计难点,并根据移动主体的不同,将动态部署分为节点自主部署和机器人辅助部署两种方式。然后,依据静态和动态两种部署场景,分类论述了节点自主部署的代表性成果。接着从网络构建和网络维护两个阶段介绍机器人辅助部署的主要方案。最后总结了节点动态部署亚待解决的问题,并探讨了进一步研究的方向。     

Energy-aware node placement,topology control and MAC scheduling for wireless sensor networks

In the WSNs, the nodes closer to the sink node have heavier traffic load for packet forwarding because they do not only collect data within their sensing range but also relay data for nodes further away. The unbalanced power consumption among sensor nodes may cause network partition. This paper proposes efficient node placement, topology control, and MAC scheduling protocols to prolong the sensor network lifetime, balance the power consumption of sensor nodes, and avoid collision. Firstly, a virtual tree topology is constructed based on Grid-based WSNs. Then two node-placement techniques, namely Distance-based and Density-based deployment schemes, are proposed to balance the power consumption of sensor nodes. Finally, a collision-free MAC scheduling protocol is proposed to prevent the packet transmissions from collision. In addition, extension of the proposed protocols are made from a Grid-based WSN to a randomly deployed WSN, enabling the developed energy-balanced schemes to be generally applied to randomly deployed WSNs. Simulation results reveal that the developed protocols can efficiently balance each sensor node’s power consumption and prolong the network lifetime in both Grid-based and randomly deployed WSNs.     

一种无线传感器网络节点随机部署策略

针对无线传感器网络节点随机部署的盲目性,提出一种按随机均匀分布规律部署无线传感器网络节点的策略。证明在感知区域内,随机均匀部署的大量相互独立的传感器节点数目服从泊松分布,通过建立无线传感器网络节点分布模型,得到面积覆盖率与目标区域节点分布密度之间的关系,设计在目标区域内传感器节点数量的估计方法。实验结果表明,该策略能保证无线传感器网络的覆盖性和连通性,更有效地控制网络成本。     

基于ZigBee的低功耗无线传感器网络改进协议

如何降低无线传感器网络(WSNs)节点的能耗来延长网络寿命是非常重要的,无线网路的性能主要取决于MAC协议,若要降低节点能耗,合理的设计与改进MAC协议就成为一个关键性问题.主要介绍了无线传感器网络中的ZigBee技术发展与应用,针对相关能耗问题,将延迟测量时间同步(DMTS)算法融入到ZigBee网络中,同时引入了基于S-MAC协议机制的周期性侦听/睡眠、碰撞避免等措施对协议进行改进,通过仿真与基本协议进行比较.仿真结果表明:改进的协议能够有效降低网络节点能耗.     

一种用于室内人员定位的RSSI定位算法

采用基于比较接收信号强度（RSSI）的测距技术,利用中值策略从锚节点多组测量值中选取最合适的RSSI值用于定位计算。针对算法用于室内人员定位的具体需求,采用室内环境下的无线信号传播模型,提出目标节点空间补偿模型,解决了因目标节点与锚节点处于不同平面导致定位误差较大的问题。最后验证改进算法的定位性能有较大提高。     

Existence of dumb nodes in stationary wireless sensor networks

Wireless sensor networks (WSNs), which are typically autonomous and unattended, require energy-efficient and fault-tolerant protocols to maximize the network lifetime and operations. In this work, we consider a previously unexplored aspect of the sensing nodes – dumb behavior. A sensor node is termed as “dumb”, when it can sense its surroundings, but cannot communicate with its neighbors due to shrinkage in communication range attributed to adverse environmental effects and can behave normally in the presence of favorable environment. As a result of this temporary behavior, a node may get isolated from the network when adverse environmental effects are present, but re-connects with the network with the resumption of favorable environmental conditions. We consider the effects of dumb nodes on the, otherwise, energy-efficient stationary WSNs having complete network coverage achieved using sufficient number of activated sensor nodes. While the presence of redundancy in the deployment of nodes, or the number of active nodes can guarantee communication opportunities, such deployment is not necessarily energy-efficient and cost-effective. The dumb behavior of nodes results in wastage of power, thereby reducing the lifetime of a network. Such effects can be detrimental to the performance of WSN applications. The simulation results exhibit that the network performance degrades in the presence of dumb nodes in stationary WSNs.     

无线传感器网络中多Sink节点位置部署研究

通过对随机分布的无线传感器网络节点密度和能量消耗的关系的分析,提出了无线传感器网络中多异构节点位置部署的区域密度优先(RDF)算法。此算法采用密度优先原则来决定Sink节点的放置位置,通过栅格和异构节点通信范围对网络进行区域划分。该算法比递归算法的异构节点放置位置优越,虽然在网络寿命上相接近,但远大于随机分布策略的寿命,且RDF更适合实际应用。通过仿真验证:该算法能够有效延长网络寿命和快速实现部署。     

基于人工势场方法的WSN传感覆盖率提高算法

感覆盖率是WSN系统中重要的性能指标之一。Sink节点的移动性能提高系统的传感覆盖率,人工势场能描述普通节点与Sink节点之间的关系。提出了一种利用人工势场方法有效的Sink节点移动控制策略,通过Sink节点的移动能够提高系统的传感覆盖率。普通节点搜集的数据能通过多跳的方式传送给Sink节点。在最初的随机部署之后,由人工势场产生的力迫使移动Sink节点移动到整个环境中的平衡点以获得更高的传感覆盖率。在整个算法执行过程中无需事先了解环境的情况,此方法能在实时的环境中采用。给出了具体的算法步骤,最后利用一个仿真实例对控制效果进行了说明。