未知输入干扰下异构多传感器分布式两级信息滤波与偏差联合估计

针对目标跟踪过程中受未知输入影响的多传感器网络,提出一种局部单传感器抗干扰信息滤波算法并根据此算法实现分布式一致性多传感器融合滤波估计实现目标的精确跟踪。首先,建立包含未知输入的系统模型;其次,消除未知输入影响并设计局部单传感器两级信息滤波算法实现状态和广义偏差的同时估计;最后,根据提出的单传感器两级信息滤波算法进行分布式加权数据融合。仿真结果表明,该方法及其融合算法的系统偏差、状态估计误差和均方根误差均明显降低,目标跟踪精度有所提高,并且具有较低的运算量和较高的一致性。     

一种能量高效的水下传感器网络路由协议

水下传感器网络的应用特点,使得高效利用网络节点能量、延长网络生存期成为水下传感器网络路由协议的一个显著特征。在目的节点序列距离矢量DSDV(Destination-Sequenced Distance-Vector)路由协议和基于最小代价场路由协议的基础上,提出了一种延长网络寿命的路由算法。该协议通过均衡每个网络节点的能量消耗,来达到延长整个网络生存期的目的,并进行仿真论证,仿真结果表明提出的这个路由协议相对于DSDV,网络生存时间延长了大约10%。     

分布式传感器网络中基于条件后验克拉美-罗下界的被动声目标跟踪局部节点选择算法

针对分布式传感器网络下的被动声目标跟踪问题,提出了一种基于条件后验克拉美罗下界（Conditional Posterior Cramér-Rao Lower Bounds,CPCRLB）的局部传感器节点选择算法,基于被动声探测背景下的纯方位量测数据,采用粒子滤波器推导得到了CPCRLB的近似解析表达式,进而在该CPCRLB的基础上定义了节点效用贡献作为节点选择准则,结合分布式传感器网络的特点提出了一种局部节点选择方法,节点无需知道全网传感器节点的信息,而是仅利用局部节点信息来决定下一时刻节点的活动状态,从而在实现自治节点选择的同时大大减少网络通信量。通过仿真结果表明,该算法在跟踪精度、能量消耗和计算复杂度方面都表现出较好的性能。     

分布式传感器网络中基于条件后验克拉美-罗下界的被动声目标跟踪局部节点选择算法（英文）

针对分布式传感器网络下的被动声目标跟踪问题,提出了一种基于条件后验克拉美罗下界(Conditional Posterior Cramér-Rao Lower Bounds, CPCRLB)的局部传感器节点选择算法,基于被动声探测背景下的纯方位量测数据,采用粒子滤波器推导得到了CPCRLB的近似解析表达式,进而在该CPCRLB的基础上定义了节点效用贡献作为节点选择准则,结合分布式传感器网络的特点提出了一种局部节点选择方法,节点无需知道全网传感器节点的信息,而是仅利用局部节点信息来决定下一时刻节点的活动状态,从而在实现自治节点选择的同时大大减少网络通信量。通过仿真结果表明,该算法在跟踪精度、能量消耗和计算复杂度方面都表现出较好的性能。     

多传感器模糊融合跟踪算法

针对集中式融合结构跟踪系统,利用随机逼近算法分析了权值的最优分配原则,提出了一种基于模糊推理的多传感器融合跟踪算法。该算法采用协方差匹配技术,依据滤波新息,动态调整测量噪声方差,使融合系统的均方误差始终最小。同时利用双滤波器结构,根据系统方差,实现滤波器间的动态切换,提出了基于模糊推理的并行双Unscented卡尔曼滤波自适应跟踪算法,增强当前统计模型对弱机动目标的适应能力。针对机动和非机动飞行航路进行了算法仿真,结果表明,在时变测量噪声条件下,采用模糊融合跟踪算法前后的速度均方根误差分别为45.7m/s和36.2m/s, 18.7m/s和9.6m/s,提高了多传感器系统的稳健性和跟踪精度。     

一种基于区域联盟的传感器网络协同目标跟踪研究

针对传感器网络中的协同目标跟踪问题,提出一种基于区域联盟的传感器网络协同目标跟踪框架,给出一个简单的、节能的面向目标跟踪的无线传感器网络体系结构。并在YangH.和SikdarB提出的事件驱动的分布式预测算法的基础上进行改进提出适合于区域联盟的传感器网络协同目标跟踪的算法,使其既能满足目标跟踪,又能更好地节约能量。     

认知网络能效性协作功率分配算法

当前认知无线电(cognitive radio,CR)协作策略的目的主要是为了提高认知系统的容量、中断概率或带宽利用率,而对于能量有限认知网络,如传感器网络,协作传输减少传输能量消耗和延长网络生命周期才是最关键的问题.协作提高CR系统能效性策略和现在的研究相比,中继节点选择及功率分配有所不同.本文提出了一种基于共存式频谱共享情况下的CR协作功率分配策略,目的是最小化CR系统的能量消耗.     

无线传感网络中能量和距离改良的LEACH分簇算法

LEACH算法作为经典分簇算法在无线传感器网络中有着广泛应用,但由于没有考虑簇头数量及监测区域等因素,使得网络消耗巨大,大大缩减了网络的生命周期.针对这一缺陷,在Warneke的最优覆盖定理的基础上,提出CDE-LEACH算法,通过在基站中预构建“数据表”存储最优覆盖理想簇头位置坐标,结合保证网络能量消耗最小这一目标来选取最优的簇头,改善LEACH算法随机选择簇头的弊端.在Matlab 7.0实验仿真平台下对提出的CDELEACH算法进行仿真,与LEACH算法结果对比发现,网络能量消耗大大减少,并且延长了网络生命周期.     

基于距离分区的多级异构无线传感器网络成簇算法

为了防止无线传感器网络(WSN)节点因为通信距离过长而过早死亡,有效延长网络生命周期,提出了一种基于距离分区的高能效的多级异构无线传感器网络成簇算法(MHCADP)。此算法将监测区域分为三部分,并根据不同监测区域和基站的距离部署能量不同的三类节点,按照节点剩余能量与网络平均能量的比例来选举簇头节点,让较高初始能量和剩余能量的节点拥有更多的机会成为簇头。另外,在数据传输时,考虑节点和基站的距离以及自身剩余能量,选择单跳或多跳的传输方式。仿真实验结果表明,与现有的重要成簇算法——低能耗自适应分簇分层(LEACH)算法和稳定选举协议(SEP)算法相比,MHCADP算法能够有效减少网络能量消耗和平衡网络负载,使网络稳定周期和生命周期延长50%以上。     

事件驱动型传感器网络能量有效数据融合算法

从降低网络能耗和平衡网络负载的角度,提出了网络的一种能量有效的数据融合算法EFDAA,可应用于节点数量及覆盖度均较大的事件驱动型无线传感器网络.该算法采用正六边形网格划分方法,基于全网能量消耗模型计算所需的融合节点数,解决由于无规则选取融合节点数量而造成的网络能耗增加问题,并且能够优化融合节点的分布;为平衡网格内节点负载,以节点剩余能量、邻节点度和移动性作为选取融合节点的权重因子,基于距离信息自适应调整网格内节点间的单跳通信级别.仿真实验结果表明,融合节点数量的优选,降低了网络总的能量消耗;相比较于HEED算法,EFDAA有效延长了网络生命期.     

基于能耗均衡的水下传感器网络分簇路由算法

针对水下传感器网络能耗不均衡问题,提出一种能耗均衡的多跳非均匀分簇路由算法。算法在水下传感器网络非均匀分簇的基础上,通过改进节点簇头竞选的阈值计算方式,解决了网络后期簇头竞选阈值低导致的网络能耗激增;通过引入多跳路由选择公式,综合考虑节点剩余能量和链路能耗,延长网络生命周期。仿真表明,提出的算法生成簇头数目稳定,能耗较低,并且能有效延长水下传感器网络的生命周期。     

时间异步无线传感器网络的目标跟踪动态成簇算法

为了解决时间异步无线传感器网络在目标跟踪时的节点协作管理和跟踪时间配准问题,提出了一种适用于时间异步条件下目标跟踪的动态成簇算法。该方法通过分析目标的无线信号强度和各节点至目标的距离来动态组建跟踪簇,然后依据目标及簇头的通信距离对簇头射频信号的覆盖区域进行功能划分,实现节点对目标的协作跟踪,同时以簇为跟踪时间的计算单元,通过簇内计时和簇间贯序传递的方法实现跟踪时间的配准。仿真实验表明,该算法进行目标跟踪时能有效均衡网络能耗,且具有较好的跟踪精度和系统鲁棒性。     

HREF: a highest remaining energy first adaptive clustering algorithm for wireless sensor/mesh networks

A simple mechanism to prolong the life cycle of the network by balancing nodes’ energy consumption is to rotate the active dominating set (DS) through a set of legitimate DSs. This paper proposes a novel adaptive clustering algorithm named HREF (Highest Remaining Energy First). In the HREF algorithm, cluster formation is performed cyclically and each node can declare itself as a cluster head autonomously if it has the largest residual energy among all its adjacent nodes. The performance effectiveness of the HREF algorithm is investigated and compared to the D-WCDS (Disjoint Weakly Connected Dominating Set) algorithm. In this paper, we assume the network topology is fixed and does not require sensor mobility. This allows us to focus on the impact of clustering algorithms on communication between network nodes rather than with the base station. Simulation results show that in the D-WCDS algorithm energy depletion is more severe and the variance of the node residual energy is also much larger than that in the HREF algorithm. That is, nodes’ energy consumption in the HREF algorithm is in general more evenly distributed among all network nodes. This may be regarded as the main advantage of the HREF adaptive clustering algorithm.     

ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN

Wireless sensor network (WSN) has been widely used due to its vast range of applications. The energy problem is one of the important problems influencing the complete application. Sensor nodes use very small batteries as a power source and replacing them is not an easy task. With this restriction, the sensor nodes must conserve their energy and extend the network lifetime as long as possible. Also, these limits motivate much of the research to suggest solutions in all layers of the protocol stack to save energy. So, energy management efficiency becomes a key requirement in WSN design. The efficiency of these networks is highly dependent on routing protocols directly affecting the network lifetime. Clustering is one of the most popular techniques preferred in routing operations. In this work we propose a novel energy-efficient protocol for WSN based on a bat algorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithm for WSN) to prolong the network lifetime. We use an objective function that generates an optimal number of sensor clusters with cluster heads (CH) to minimize energy consumption. The performance of the proposed approach is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy Efficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interesting in terms of energy-saving and prolongation of the network lifetime.     

Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks

Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.      

低时延能耗均衡的水声传感器网络簇间路由算法

针对水声传感器网络的簇间路由选择问题,提出了一种基于前向网关的低时延能耗均衡路由算法,该算法采用最优方向角原则和能耗均衡原则选择中继簇头和中继网关,以减小长延迟和高能耗对水声通信的影响。仿真结果表明该算法在网络平均能耗、端到端时延和网络生命周期等方面具有较好的性能。     

A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm

In a large-scale wireless sensor network (WSN), densely distributed sensor nodes process a large amount of data. The aggregation of data in a network can consume a great amount of energy. To balance and reduce the energy consumption of nodes in a WSN and extend the network life, this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm. The algorithm uses a clustering method to form and optimize clusters, and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN. To ensure that the cluster head (CH) selection in the network is fair and that the location of the selected CH is not concentrated within a certain range, we chose the appropriate CH competition radius. Simulation results show that, compared with LEACH, LEACH-C, and the DEEC clustering algorithm, this algorithm can effectively balance the energy consumption of the CH and extend the network life.     

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption     

基于无线传感器网络的社区保健监测系统

以躯域传感网络技术为载体,结合无线传感器网络在社区的合理安置,研究人体生理参数的无创连续监测技术以及穿戴式医疗仪器开发,设计了一个适用于慢性病人(如糖尿病人)的社区无线医疗保健监测网络系统;同时设计出一种新的无线传感器网络低能量数据汇聚模型——集合汇聚模型,把此模型应用于本系统以期达到降低能耗延长网络生命周期目的.最后,提出了在人体生理数据处理过程中需考虑的数据分级处理和安全问题.     

Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks

In the past few decades, Energy Efficiency (EE) has been a significant challenge in Wireless Sensor Networks (WSNs). WSN requires reduced transmission delay and higher throughput with high quality services, it further pays much attention in increased energy consumption to improve the network lifetime. To collect and transmit data Clustering based routing algorithm is considered as an effective way. Cluster Head (CH) acts as an essential role in network connectivity and perform data transmission and data aggregation, where the energy consumption is superior to non-CH nodes. Conventional clustering approaches attempts to cluster nodes of same size. Moreover, owing to randomly distributed node distribution, a cluster with equal nodes is not an obvious possibility to reduce the energy consumption. To resolve this issue, this paper provides a novel, Balanced-Imbalanced Cluster Algorithm (B-IBCA) with a Stabilized Boltzmann Approach (SBA) that attempts to balance the energy dissipation across uneven clusters in WSNs. BIBCA utilizes stabilizing logic to maintain the consistency of energy consumption among sensor nodes’. So as to handle the changing topological characteristics of sensor nodes, this stability based Boltzmann estimation algorithm allocates proper radius amongst the sensor nodes. The simulation shows that the proposed B-IBCA outperforms effectually over other approaches in terms of energy efficiency, lifetime, network stability, average residual energy and so on.