Mobile target tracking algorithm for wireless camera sensor networks with adjustable monitoring direction of nodes

Wireless camera sensor networks (WCSNs) possess a powerful physical environment monitoring capability. Camera nodes with adjustable monitoring directions further improve their flexibility. This study focuses on tracking multiple mobile targets to investigate the node scheduling and target location evaluation strategy of WCSNs on the basis of rotating nodes. By referring to existing research, this study improves the camera node monitoring and rotation model and proposes three network performance evaluation indicators. The proposed algorithm schedules nodes and their monitoring directions by using the unutilized energy of the nodes and the number of monitored targets. It also predicts the moving trends of the targets and selects active nodes by using the locations and linear speeds of the targets. Experimental results show that the proposed algorithm has a high target tracking accuracy. Compared with traditional target tracking algorithms, the proposed algorithm can effectively reduce the number of active nodes, balance the energy consumption between nodes, and prolong network lifetime.     

无线传感器网络自适应目标跟踪节点调度算法

在无线传感器网络中,设计合理的节点调度算法是提高网络感知能力、降低系统能耗的关键。在分析节点能耗模型的基础上,针对移动目标跟踪型网络应用,提出一种高能效的无线传感器网络自适应节点调度算法ANSTT。该算法根据节点对移动目标的感知能力,以及节点的相对剩余能量水平,自动调整节点工作模式。仿真实验表明,ANSTT算法在维持低感知延时、高目标感知率的同时,可有效降低系统能耗,延长网络寿命。     

基于格拉布斯准则和改进粒子滤波算法的水下传感网目标跟踪

水下无线传感网络(UWSN)执行目标跟踪时,因为各个传感器节点测量值对目标状态估计的贡献不一样以及节点能量有限,所以探索一种好的节点融合权重方法和节点规划机制能够获得更好的跟踪性能。针对上述问题,该文提出一种基于Grubbs准则和互信息熵加权融合的分布式粒子滤波(PF)目标跟踪算法(GMIEW)。首先利用Grubbs准则对传感器节点所获得的信息进行分析检验,去除干扰信息和错误信息。其次,在粒子滤波的重要性权值计算的过程中,引入动态加权因子,采用传感器节点的测量值与目标状态之间的互信息熵,来反映传感器节点提供的目标信息量,从而获得各个节点相应的加权因子。最后,采用3维场景下的簇-树型网络拓扑结构,跟踪监测区域内的目标。实验结果显示,该算法可有效提高水下传感器网络测量数据对目标跟踪预测的准确度,降低跟踪误差。     

二进制传感器网络加权目标跟踪算法研究

该文主要研究二进制传感器网络中加权目标跟踪算法的设计。针对已有算法中权值不能实时反映目标与感测节点之间距离关系的缺点,提出了距离加权和基于预测的距离加权目标跟踪算法。距离权值能够实时反映目标与各个感测节点间的距离关系,因此具有更高的跟踪精度。在距离加权算法中感测节点需要将感测信息和距离信息都传输到融合中心,这会增大感测节点的能量消耗。为了解决这个问题,文中提出一种基于预测的距离加权目标跟踪算法。该算法中感测节点不需要传输距离信息而只传输感测信息到融合中心从而减少了能耗。仿真结果表明,基于预测的距离加权算法比已有算法能够够精确地跟踪目标,在保证跟踪精度的同时减少了通信能耗。     

传感器组网新方法研究

提出了一种基于多目标蚁群优化算法的传感器组网节点快速选择方法，利用蚁群算法处理纯方位目标跟踪中需要同时满足目标定位精度和节点能量消耗这一多目标优化问题，计算机仿真表明，多目标蚁群节点选择方法所得到的跟踪精度和能量消耗比同条件下的最近邻法所得到的好。     

Distributed Energy Optimization for Target Tracking in Wireless Sensor Networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.     

监测无线传感器网络休眠调度算法

休眠调度设计是无线传感器网络一种重要的通信节能方法。针对监测典型应用,为了实现长时间的监测应用要求,充分利用冗余部署提供的能量资源,提出了一种能量相关的分布式自适应休眠调度算法。算法利用极大独立集构建思想,结合节点层次级别、实时的能量消耗、连通度等信息动态选择连通支配节点集作为网络骨干,使得网络活跃节点数量最小化。仿真试验分析表明,算法能够有效地利用冗余节点提供的能量资源,扩展了网络的生命周期。     

Information Content-Based Sensor Selection and Transmission Power Adjustment for Collaborative Target Tracking

For target tracking applications, wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration among themselves to improve the target localization and tracking accuracies. An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). A mutual-information-based sensor selection (MISS) algorithm is adopted for participation in the fusion process. MISS allows the sensor nodes with the highest mutual information about the target state to transmit data so that the energy consumption is reduced while the desired target position estimation accuracy is met. In addition, a novel approach to energy savings in WSNs is devised in the information-controlled transmission power (ICTP) adjustment, where nodes with more information use higher transmission powers than those that are less informative to share their target state information with the neighboring nodes. Simulations demonstrate the performance gains offered by MISS and ICTP in terms of power consumption and target localization accuracy.     

WSN中一种改进的目标跟踪方法

目标跟踪作为无线传感器网络(WSN)的一项基本应用,已得到广泛研究。提出了一种改进的目标跟踪方法,该方法主要分为邻域检测、目标跟踪和目标修正3个阶段。节点通过获取对于目标的感知信息收益值来实现邻域检测。每个节点通过计算自己的节点权值来决定是否参与目标的跟踪。基于目标的运动趋势,通过发送数据报告来自适应地对目标进行修正。仿真实验表明,该算法减少了参与目标跟踪的节点数,节省了能量,与PM算法相比,该算法提高了目标跟踪的准确率。     

A Novel Energy-Aware Target Tracking Method by Reducing Active Nodes in Wireless Sensor Networks

Energy consumption is one of the main challenges in wireless sensor networks. Additionally, in target tracking algorithms, it is expected to have a longer lifetime for the network, when a better prediction algorithm is employed, since it activates fewer sensors in the network. Most target tracking methods activate a large number of nodes in sensor networks. This paper proposes a new tracking algorithm reducing the number of active nodes in both positioning and tracking by predicting the target deployment area in the next time interval according to some factors including the previous location of the target, the current speed and acceleration of the target without reducing the tracking performance. The proposed algorithm activates the sensor nodes available in the target area by predicting the target position in the next time interval. The problem of target loss is also considered and solved in the proposed tracking algorithm. In the numerical analysis, the number of nodes involved in target tracking, energy consumption and the network lifetime are compared with other tracking algorithms to show superiority of the proposed algorithm.     

交通路灯监控系统的无线传感网链状路由算法

在交通路灯监控系统中为节省网络节点能耗和降低数据传输时延,提出一种无线传感网链状路由算法（CRASMS）。该算法根据节点和监控区域的信息将监控区域分成若干个簇区域,在每一个簇区域中依次循环选择某个节点为簇头节点,通过簇头节点和传感节点的通信建立簇内星型网络,最终簇头节点接收传感节点数据,采用数据融合算法降低数据冗余,通过簇头节点间的多跳路由将数据传输到Sink节点并将用户端的指令传输到被控节点。仿真结果表明：CRASMS算法保持了PEGASIS算法在节点能耗方面和LEACH算法在传输时延方面的优点,克服了PEGASIS 算法在传输时延方面和LEACH算法在节点能耗方面的不足,将网络平均节点能耗和平均数据传输时延保持在较低水平。在一定的条件下,CRASMS算法比LEACH和PEGASIS算法更优。     

Adaptive Low Power Listening for Wireless Sensor Networks

Most sensor networks require application-specific network-wide performance guarantees, suggesting the need for global and flexible network optimization. The dynamic and nonuniform local states of individual nodes in sensor networks complicate global optimization. Here, we present a cross-layer framework for optimizing global power consumption and balancing the load in sensor networks through greedy local decisions. Our framework enables each node to use its local and neighborhood state information to adapt its routing and MAC layer behavior. The framework employs a flexible cost function at the routing layer and adaptive duty cycles at the MAC layer in order to adapt a node's behavior to its local state. We identify three state aspects that impact energy consumption: 1) number of descendants in the routing tree, 2) radio duty cycle, and 3) role. We conduct experiments on a test-bed of 14 mica2 sensor nodes to compare the state representations and to evaluate the framework's energy benefits. The experiments show that the degree of load balancing increases for expanded state representations. The experiments also reveal that all state representations in our framework reduce global power consumption in the range of one-third for a time-driven monitoring network and in the range of one-fifth for an event-driven target tracking network.     

一种新的无线传感器网络定位算法研究

针对传统无线传感器网络定位算法平均误差大、节点能耗过高、定位精度不够理想等缺陷,提出了一种新的无线传感器网络定位算法IMDV-Hop.该算法引进了局部跳数Si和修正因子δ-i,用修正因子-δi对局部跳数进行修正,使待定位节点到锚节点的平均跳数更加符合实际情况;通过权衡定位精度和能耗,分三种情况计算了平均每跳间距,使得平均每跳间距更接近于真实值.仿真实验结果表明IMDV-Hop算法平均定位误差低,具有较小的通信开销,在非规则网络中可达到较好的定位精度.     

Distributed Mobility Management for Target Tracking in Mobile Sensor Networks

Mobility management is a major challenge in mobile ad hoc networks (MANETs) due in part to the dynamically changing network topologies. For mobile sensor networks that are deployed for surveillance applications, it is important to use a mobility management scheme that can empower nodes to make better decisions regarding their positions such that strategic tasks such as target tracking can benefit from node movement. In this paper, we describe a distributed mobility management scheme for mobile sensor networks. The proposed scheme considers node movement decisions as part of a distributed optimization problem which integrates mobility-enhanced improvement in the quality of target tracking data with the associated negative consequences of increased energy consumption due to locomotion, potential loss of network connectivity, and loss of sensing coverage.     

无线传感器网络中DV-Hop定位算法的改进

无线传感器网络中基于无需测距的节点定位算法定位精度不高,一般应用在粗精度定位中。为了提高基于无需测距的DV-Hop算法定位精度,利用最小均方差准则改进算法,通过修改指数值精化平均每一跳距离,提出不同通信半径、不同锚节点覆盖率下的最佳指数值概念,并应用在一种锚节点均匀分布环境中,进一步提高定位精度。MTLAB仿真结果表明,在最佳指数值下,改进的算法在不同锚节点覆盖率、不同通信半径下能提高定位精度,同时不会增加节点能量消耗与硬件成本。     

无线传感器网络中用于目标跟踪的节点规划算法

利用无线传感器网络进行目标跟踪时,由于各传感器节点的能量有限,数据蕴含的有效信息又各不相同,因此有必要规划参与目标跟踪的节点集和参与方式,以降低系统开销。本文提出了一种新的基于领导节点的节点规划算法,综合考虑收集数据和领导节点迁移过程中的通信开销,以最大化目标跟踪的性能。求解中以跟踪过程中的误差矩阵作为目标度量,采用高斯-赛德尔（Gauss-Seidel）和凸松弛等方法,使得复杂的带约束优化问题能够在接近O（N3）的时间复杂度内得到求解。仿真结果表明,与对比算法相比,本算法在相同的通信能量约束下能够达到更好的跟踪性能。     

移动无线传感网的生存时间优化算法研究

当sink节点位置固定不变时,分布在sink 节点周围的传感节点很容易成为枢纽节点,因转发较多的数据而过早失效。为解决上述问题,提出移动无线传感网的生存时间优化算法（LOAMWSN）。LOAMWSN算法考虑sink节点的移动,采用减聚类算法确定sink节点移动的锚点,采用最近邻插值法寻找能遍历所有锚点的最短路径近似解,采用分布式非同步Bellman-Ford算法构建sink节点k跳通信范围内的最短路径树。最终,传感节点沿着最短路径树将数据发送给sink节点。仿真结果表明：在节点均匀分布和非均匀分布的无线传感网中,LOAMWSN算法都可以延长网络生存时间、平衡节点能耗,将平均节点能耗保持在较低水平。在一定的条件下,比Ratio_w、TPGF算法更优。     

Energy-Efficient Scheduling for Wireless Sensor Networks

We consider the problem of minimizing the energy needed for data fusion in a sensor network by varying the transmission times assigned to different sensor nodes. The optimal scheduling protocol is derived, based on which we develop a low-complexity inverse-log scheduling (ILS) algorithm that achieves near-optimal energy efficiency. To eliminate the communication overhead required by centralized scheduling protocols, we further derive a distributed inverse-log protocol that is applicable to networks with a large number of nodes. Focusing on large-scale networks with high total data rates, we analyze the energy consumption of the ILS. Our analysis reveals how its energy gain over traditional time-division multiple access depends on the channel and the data-length variations among different nodes.     

An adaptive target tracking method for 3D underwater wireless sensor networks

Today, underwater target tracking using underwater wireless sensor networks (UWSNs) is an essential part in many military and non-military applications. Most of moving target tracking studies in UWSNs are considered in two-dimensional space. However, most practical applications require to be implemented in three-dimensional space. In this paper an adaptive method based on Kalman filter for moving target tracking in three dimensional space using UWSNs is proposed. Since, energy protection is a vital task in UWSNs; the proposed method reduces the energy consumption of the entire network by a sleep/wake plan. In this plan only 60% of the closer nodes along the path of the moving target will be waked up using a sink activation message and participate in the tracking, while the other nodes remain in sleep state. At each stage of tracking, the location of the target is estimated using a 3D underwater target tracking algorithm with the trilateration method. Subsequently, the estimations and target tracking results are inserted into the Kalman filter as measuring model to produce the final result. Performance evaluation and simulations results indicated that the proposed method improves the average location error by 45%, average estimated velocity by 86%, and average energy consumption by 33% in comparison to the trilateration method. However, computation time is increased as a result of improving tracking accuracy; and tracking accuracy is lost about 20% due to saving energy. It was shown that the proposed method has been able to adaptively achieve a trade-off between tracking accuracy and energy consumption based on real-time user requirements. Such adaption can be controlled trough the sink node based on real-time requirements.     

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Prolonging network lifetime is a fundamental requirement in wireless sensor network (WSN). Existing charging scheduling algorithms suffer from high node redundancy and energy consumption issues. In this paper, we study WSN charging problem from the perspectives of energy conservation combined with energy replenishment scheduling. Firstly, we detect the redundant nodes whose energy is wasted in the network functionality and develop a K‐covering redundant nodes sleeping scheduling algorithm (KRSS) for reducing energy. Secondly, we employed multiple wireless charging vehicles (WCVs) for energy replenishment and optimize the charging scheduling algorithm to prevent any exhaustion of nodes, and we proposed a distance and energy–oriented charging scheduling algorithm (DECS) with multiple WCVs. Simulation experiments are conducted to show the advantages of the proposed KRSS+DECS, confirming that our scheme is capable of removing redundant nodes, lowering node failures, and prolonging network lifetime.