Localized topology control for unicast and broadcast in wireless ad hoc networks

We propose a novel localized topology-control algorithm for each wireless node to locally select communication neighbors and adjust its transmission power accordingly such that all nodes together self-form a topology that is energy efficient simultaneously for both unicast and broadcast communications. We theoretically prove that the proposed topology is planar, which meets the requirement of certain localized routing methods to guarantee packet delivery; it is power-efficient for unicast - the energy needed to connect any pair of nodes is within a small constant factor of the minimum; it is also asymptotically optimum for broadcast - the energy consumption for broadcasting data on top of it is asymptotically the best among all structures constructed using only local information; it has a constant bounded logical degree, which will potentially save the cost of updating routing tables if used. We further prove that the expected average physical degree of all nodes is a small constant. To the best of our knowledge, this is the first localized topology-control strategy for all nodes to maintain a structure with all these desirable properties. Previously, only a centralized algorithm was reported. Moreover, by assuming that the node ID and its position can be represented in O(log n) bits for a wireless network of n nodes, the total number of messages by our methods is in the range of theoretical results are corroborated in the simulations.     

无线传感器网络中基于Voronoi覆盖及Delaunay三角剖分图的最小刚性拓扑控制算法

为同时满足覆盖与节能应用需求,本文提出了无线传感器网络中一种最小刚性拓扑控制算法MRTc（Minimal rigid topology control algorithm based on Voronoi coverage and Delaunay triangulation）.该算法基于Voronoi覆盖机制,准确控制节点工作状态,实现活动节点对目标区域的完全覆盖.在此基础上,MRTc利用Delaunay三角剖分图的特点,构建出适用于无线传感器网络的最小刚性拓扑结构.该结构有效约束了网络平均节点度,且同时具有容错性、覆盖性和稀疏性.此外,MRTc引入节点功率控制策略,在维持网络完全覆盖的基础上最小化节点能耗.仿真结果进一步验证了本文提出的MRTc算法的有效性.     

SNS:一种快速无偏的分层图抽样算法

抽样作为一种有效的统计分析方法,常被用于大规模图数据分析领域以提升性能。现有的图抽样算法大多存在高度节点或低度节点过度入样的问题,较大程度地影响了算法的性能。复杂网络具有无标度特性,即节点的度服从幂律分布,节点个体之间存在较大差异。在基于点选择策略的抽样方法的基础上,通过结合节点的近似度分布策略,设计并实现了高效无偏的分层图抽样算法SNS。在3个真实的图数据集上的实验结果表明,SNS算法比其他图抽样算法保留了更多的拓扑属性,且执行效率比FFS更高。SNS算法在度的无偏性、抽样结果拓扑属性近似性方面的表现均优于现有算法。     

WSN中基于可调感知半径的节点睡眠算法

讨论在无线传感器网络中节点的感知半径可调的情况下,如何合理设计节点的拓扑结构,实现网络的能量负载均衡、延长网络的生存周期。在保证覆盖和连通的前提下,分析当感知半径可调时节点的分布特征和拓扑结构,并对现有的冗余节点移除算法进行改进,提出一种自适应调节感知半径冗余节点睡眠算法(AASRS)。实验结果表明,该算法可以提高网络的能量负载均衡水平,并能最大化节点的感知覆盖区域,且使用的活动节点较少。     

Energy-aware scheduling for information fusion in wireless sensor network surveillance

Effective energy control while maintaining reliable monitoring performance becomes a key issue in wireless sensor networks (WSNs) based surveillance applications. While importance difference of surveillance zone, limited energy and dynamic network topology pose great challenges to surveillance performance. It is necessary to adjust sensor nodes' awakening frequency dynamically for information fusion. Thus an energy-aware scheduling with quality guarantee method named ESQG is proposed in this paper which considers sensor nodes' residual energy, different importance degrees of the surveillance zone and network topology comprehensively. It first uses a Voronoi diagram to determine the effective scope of each sensor node and then calculates node importance according to its residual energy and the importance degree of the effective scope. Then ESQG utilizes the importance of individual sensing scope and current forwarding costs to further compute node importance and awakening frequency for information fusion. In this way, ESQG can dynamically adapts each nodes awakening frequency to its dynamic network topology and importance degree of each individual sensing scope. The nodes are then turned on stochasticlly via the node awakening probability and node importance based information fusion is conducted for target detection. Besides, an adaptive process of perception factor C is proposed to match actual situation, and automatically change according to the detected data. Experiments results demonstrate that the proposed method ESQG can reduce the number of awakening nodes to a large extent while maintaining high reliability via information fusion.     

基于OPNET的自组网拓扑控制有效性的研究

拓扑控制通过调节节点的发送功率,减少信号干扰,降低节点能量消耗,提高网络传输能力。该文通过考察自组网中几种经典的拓扑控制算法,研究了拓扑图能保证连通具有较小的节点度和传输半径。OPNET网络仿真平台仿真发现,拓扑控制的效能受网络中每秒的发包个数影响,当每秒发包个数大于一定值时,拓扑控制将提高网络吞吐量。     

基于移动Agent的无线传感器网络拓扑发现机制

对监测区域中部署的传感器节点的拓扑发现是传感器网络应用的前提,它反映了传感器网络的监测能力。考虑目前拓扑发现算法中能量消耗过多、网络连通性不强等问题,文中结合移动Agent的特点,提出了一种基于移动Agent的无线传感器网络拓扑发现机制,通过建立数学模型,利用相关邻近图（relative neighborhood graph）理论生成网络拓扑。实验结果表明,基于移动Agent的拓扑发现机制相对于当前存在的拓扑发现算法具有很好的稳定性和良好的节能效果,该算法可以解决节点拓扑请求信息讨多导致过多能量消耗的问颢．     

基于自适应波束天线的局部优化拓扑控制算法

拓扑控制有助于提高ad hoc网络的性能,采用定向天线的自组网拓扑控制比全向天线网络更为复杂。基于自适应波束定向天线模型提出一种局部区域优化的拓扑控制算法。该算法利用分簇的思想将网络划分为可重叠的多个区域,区域内节点采用最小生成树（MST）的思想确定邻居关系,通过调整节点发射功率,改变天线波束的朝向、宽度和增益来构建拓扑。算法减小了节点的平均度数,降低了节点的发射功率,从而降低节点能耗,减少了节点间干扰,提高了网络吞吐量,仿真结果表明,算法显著提高了网络性能。     

Energy-aware self-organisation algorithms with heterogeneous connectivity in wireless sensor networks

Heterogeneity of node energy is a common phenomenon in wireless sensor networks. In such node energy heterogeneous sensor network, how to balance the energy consumption is the key problem on extending the lifetime of the sensor network system. An energy-efficient self-organisation algorithm with heterogeneous connectivity based on energy-awareness is proposed. Each sensor node in the network adjusts its own transmission radius based on the local energy information during the constructing and operating phase. Thus heterogeneous network topology, in which the nodes can choose different transmission radius, is formed. In contrast to the homogeneous network model, in which the node carries the same radius, simulation and analysis are conducted to explore the topology characteristics and robustness with different node energy distribution. The degree distribution shows the scale-free property in the heterogeneous model. The proposed network model enjoys higher efficiency of transmitting data, less clustering, higher robustness under node random failures and longer network lifetime than those in the homogeneous ones.     

自组网中基于自适应波束天线的拓扑控制算法

定向天线自组网拓扑的构建问题比全向天线网络复杂.基于自适应波束定向天线模型提出一种分布式拓扑控制算法,通过调整节点发射功率,改变天线波束的朝向、宽度和增益来构建拓扑.网络中每个节点收集其邻居节点信息,采用功率控制调度策略选择最优相邻节点,并选取覆盖所有最优相邻节点的最小发射功率为此节点的发射功率.算法在保证网络连通性与无向性的同时,降低了节点的发射功率,减小了节点的平均度数,从而降低节点能耗,减少了节点间干扰,提高了网络吞吐量.仿真结果表明,算法显著提高了网络性能.     

无线传感器网络的一种可调节的拓扑控制算法

在无线传感器网络的拓扑控制问题中,保持节点能耗最低路径和低节点度之间存在一种平衡.最佳的平衡点与具体的应用和网络状态有关.文中提出一种新的拓扑控制算法,使所构造的拓扑能在这两个不一致的目标之间进行调节.该算法所构造的拓扑结构在一极能保持所有能耗最低路径,另一极能使平均节点度逼近理论最小值.仿真结果证实新算法在比已有方案更真实的能量消耗模型下可以保持所有能耗最低路径,同时也显示新算法对节点度有更大的调节范围.     

Energy-aware weighted graph based dynamic topology control algorithm

In this paper a new energy-aware weighted dynamic topology control (WDTC) algorithm is proposed to extend the lifetime of wireless network and balance the nodes’ energy consumption. The idea is that each node builds its local minimum spanning tree (MST) based on the energy-aware weighted graph and the network topology is adjusted accordingly. It was proved theoretically that the topology under WDTC algorithm could preserve the network connectivity and a sufficient condition for the degree of no more than 6 was also given. Simulation shows that WDTC algorithm can effectively prolong the network lifetime and has good topological features.     

一种基于能耗均衡的分区节点部署算法

鉴于能耗问题是无线传感器网络研究的重要问题, 首先建立一种非均匀部署网络拓扑模型, 该模型中越靠近sink 的区域节点部署越密集, 节点通信距离越小; 然后针对节点初始随机部署情况, 提出一种基于节点能耗均衡的分区域节点重部署算法, 该算法利用分区域的节点移动, 减少节点移动距离, 降低移动能耗, 提高算法收敛速度; 最后通过仿真表明, 所提出的算法可以用较少数量的节点覆盖监测区域, 保证网络中各节点能量均衡消耗, 提高网络生存周期.

     

基于LS-RTS机制和功率可调路由的跨层协议

为了降低线型无线传感网络能耗和传输延时,在改进CLM协议跨层方法的基础上融合其他经典机制,提出了一种适应线型拓扑结构的跨层协议。协议删除中继路由判决以优化原通信判决门限;对节点统一标记链编号形成新LS-RTS机制以提高链路传输率;精简时隙单元和多跳传输时序降低数据传输时延;并引入功率可调路由减少节点路由计算负担以此降低节点能耗。仿真结果表明,相较于CLM和CLR-DCE等两种跨层协议,本文提出的I-CLM跨层协议在降低传输时延的同时整体能耗至少降低了18.75%和7.32%,一定程度上延长了网络生存时间。     

一种无线传感器网络能耗均衡的自适应拓扑博弈算法

针对无线传感器网络节点能量有限与能耗不均衡导致网络生命周期提前结束的问题,运用势博弈理论将节点的平均寿命、节点最短寿命、网络的连通性以及覆盖性应用到效益函数的设计中,建立一种基于序数势博弈的能耗均衡的拓扑控制模型,以证明博弈模型是序数势博弈.基于该势博弈模型,提出一种能耗均衡的自适应拓扑博弈算法.该算法根据节点平均寿命调整自身的功率,帮助最短寿命节点降低功率,延长整个网络的生存时间.仿真实验及对比分析表明,所提出的算法相比于其他基于博弈论的拓扑控制算法,能够改善网络能量的均衡性,提高网络能量效率,保证网络拓扑的健壮性,增强网络拓扑的自适应性.     

基于WSN目标跟踪的移动Agent路由算法

设计一种基于无线传感器网络目标跟踪的移动Agent路由算法OSER。基于传感节点对目标信息的感知强度和节点的剩余能量确定Agent的目标跟踪路径,在有效相邻节点间建立局部网路拓扑,选择能量度最大的有效邻节点作为Agent的下一迁移节点,依次形成最优路由。实验结果表明,OSER能量消耗和传输延迟较小,有利于维持网络的稳定性和延长网络的生命期,提高目标跟踪效率。     

无线传感器网络中一种负载均衡的跳跃式路由协议

如何实现能量的高效利用,延长网络的生命周期一直是无线传感器网络研究的热点.在传统的路由协议中,靠近基站的节点由于要转发其它节点的数据容易过早耗尽能量而失效.本文基于传感器节点传输功率可调这一假设,提出了一种负载均衡的跳跃式路由协议(LBR).协议中每个节点维护K跳的路由表.在需要发送数据时,剩余能量较少的节点将数据发送给下一跳节点,而剩余能量较多的节点可以跨越多跳进行数据传输.从而均衡了节点的能量消耗.模拟结果表明,采用该协议建立的路由,不仅具有延时小,数据传送的路径长度短,维护简单等优点,并且能够改善网络的负载均衡,极大地延长网络的生命周期.     

带状无线传感器网络能量高效的节点部署策略

带状无线传感器网络具有特殊的拓扑结构,满足很多重要领域的应用需求。针对带状网络中的能量消耗不均衡问题,分析了带状无线传感器网络在多跳通信时的拓扑结构和能耗模型,在对带状网络进行分簇的基础上,提出一种非均匀的节点部署策略,该策略量化了带状传感器网络中簇内节点的数目关系,并设计了相应的路由协议。仿真结果表明,非均匀的节点部署策略可以缓解带状网络的能量空洞问题,使网络能耗趋于均衡,延长整个网络的生命周期。     

基于最优刚性图的链路质量与能量的拓扑控制算法

针对目前无线传感器网络研究中网络能量利用率低和通信链路不可靠等问题, 提出一种基于最优刚性图的网络拓扑优化算法. 该算法通过建立包含链路质量和能量两方面内容的链路权值函数来构建链路可靠性强、能量利用率高的网络拓扑结构. 研究结果表明, 所构建的拓扑具有平均节点度低和链路性能好等优越特性. 仿真结果表明, 与现有拓扑控制算法相比, 所提出的算法能够更有效地减少能量消耗, 从而延长网络寿命.

     

应用Delaunay图的拓扑控制

无线传感器网络拓扑控制的主要任务是减少节点的能量消耗,从而延长整个网络的生存时间。而无线传感器网络的能量消耗主要集中在无线通信模块上,因此,通过降低无线通信模块的能量消耗和控制邻居节点集,减少通信链路,把通信限制在重要链路中,可以减少节点的能量消耗。基于以上因素,将MG模型与Delaunay图结合,在Delaunay图中限制通信链路并保留最优能耗路,得到MEDel算法。该算法具有强连通性、对称性和平均度有界的优点。