基于动态集群和DKF的协作式目标跟踪

针对具有有限感知范围的无线传感器网络中的动态目标跟踪问题,提出了一种将卡尔曼一致滤波和动态集群自组织相结合的协作式动态目标跟踪算法。首先,算法采用一个由群头挑选阶段和集群重新配置阶段构成的动态集群协议来限制参与目标状态估计过程中节点间的信息交换,然后用一个分布式加权估计预测算法即卡尔曼一致滤波来估计目标状态并预测其下一个位置,这样有助于唤醒最合适的节点来进行目标跟踪并最恰当地组织网络通信,而其他节点保持在睡眠状态。仿真结果表明,提出的算法相比于集中式和其他2种常用的分布式动态目标跟踪算法,不仅能够降低网络的平均能耗,而且能够明显提高跟踪过程中的误差估计质量。     

Energy Efficient Target Tracking with Collision Avoidance in WSNs

Wireless sensor network (WSN) is one of the most evolving technologies. WSN involves collecting, processing, transferring and storing information about objects with the help of sensor nodes. Tracking and detection of targets is one of the most attractive applications of WSN in surveillance systems. To resolve the problem of target tracking, it is essential to deploy a system model. It has been observed that clustering algorithms play an important role in cluster head selection, but they consume significant amount of energy. In this paper an energy efficient system model is deployed with a novel target tracking algorithm to track the target around the vicinity of the WSN. As there is more possibility of collision proximate to the base station, a new collision avoidance method is introduced. The lifetime of the network on the basis of congestion around the sink node, packet density and path loss are also measured efficiently.

     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.     

异构传感器网络中基于闲时能量开销优化的密度控制算法研究

基于闲时能量开销优化目标提出了一种适用于异构传感器网络的密度控制算法(DCA),DCA能寻找到一个闲时能量开销近似最小化的连通覆盖集合,该集合最终映射为活跃节点集合.理论分析和实验数据表明,DCA所生成的拓扑能有效降低网络闲时能量开销,延长了网络的生命剧期.     

Position and hop-count assisted full coverage control in dense sensor networks

In recent years, wireless sensor networks (WSN’s) have gained much attention due to its various applications in military, environmental monitoring, industries and in many others. All these applications require some target field to be monitored by a group of sensor nodes. Hence, coverage becomes an important issue in WSN’s. This paper focuses on full coverage issue of WSN’s. Based on the idea of some existing and derived theorems, Position and Hop-count Assisted (PHA) algorithm is proposed. This algorithm provides full coverage of the target field, maintains network connectivity and tries to minimize the number of working sensor nodes. Algorithm works for communication range less than root three times of sensing range and it can be extended for arbitrary relation between communication range and sensing range. By using hop-count value, three-connectivity in the network is maintained. Also, neighbors information is used to create logical tree structure which can be utilized in routing, redundant data removal and in other areas. Simulation results show that PHA algorithm outperforms layered diffusion-based coverage control algorithm by providing better area coverage and activating fewer nodes.     

Multi mobile agent itinerary planning based on network coverage and multi-objective discrete social spider optimization algorithm

The multi mobile agent collaboration planning model was constructed based on the mobile agent load balancing and total network energy consumption index.In order to prolong the network lifetime,the network node dormancy mechanism based on WSN network coverage was put forward,using fewer worked nodes to meet the requirements of network coverage.According to the multi mobile agent collaborative planning technical features,the multi-objective discrete social spider optimization algorithm (MDSSO) with Pareto optimal solutions was designed.The interpolation learning and exchange variations particle updating strategy was redefined,and the optimal set size was adjusted dynamically,which helps to improve the accuracy of MDSSO.Simulation results show that the proposed algorithm can quickly give the WSN multi mobile agent path planning scheme,and compared with other schemes,the network total energy consumption has reduced by 15%,and the network lifetime has increased by 23%.     

An energy efficient hole repair node scheduling algorithm for WSN

A sensor node in the wireless sensor network has limited energy and it normally cannot be replaced due to the random deployment, so how to prolong the network life time with limited energy while satisfying the coverage quality simultaneously becomes a crucial problem to solve for wireless sensor networks (WSN). In this work, we propose an energy efficient algorithm based on the sentinel scheme to reduce the sleeping node detection density by defining a new deep sleeping state for each sensor node. The average energy consumed by probing neighboring nodes is introduced as a factor to calculate the detection rate. In addition, after some theoretical analysis of the existence of coverage holes in WSN, a triangle coverage repair procedure is defined to repair coverage holes. Simulation results show that our proposed algorithm obtained better performance in terms of the coverage quality and network life time compared with some existing algorithms in the literature.     

Potential position node placement approach via oppositional gravitational search for fulfill coverage and connectivity in target based wireless sensor networks

Wireless Sensor Network (WSN) has appeared as a powerful technological platform with tremendous and novel applications. Now-a-days, monitoring and target tracking are the most major application in WSNs. In target based WSN, coverage and connectivity are the two most important issues for definite data forwarding from every target to a remote base station. An NP entire issue is to find least number of potential or possible locations to set sensor nodes gratifying both coverage and connectivity from a given a group of target points. In this article, we propose an Oppositional Gravitational Search algorithm (OGSA) based approach to solve this problem. This approach helps that the sensor nodes are prone to failure, the proposed system provides l-coverage to all targets and n-connectivity to each sensor node. This OGSA based system is presented with agent representation, derivation of efficient fitness function along with the usual Gravitational Search algorithm operators. The approach is simulated broadly with various scenarios of Wireless Sensor Network. The experimentation results are compared with some relevant existing algorithms to demonstrate the efficiency of the proposed approach.     

Optimized mobile sink based grid coverage-aware sensor deployment and link quality based routing in wireless sensor networks

Mobile sink (MS) has been used in wireless sensor networks (WSN) to increase the network lifetime by changing the location over time. The major quality of service given by WSN is coverage energy consumption (EC) and network lifetime. There are many methods implemented for enhance the coverage hole restoration and reduce the EC. We propose a novel MSCOLER (MS based Coverage Optimization and Link-stability Estimation Routing) protocol for Optimal Coverage restoration and Link stability Estimation. An optimization algorithm is used to optimize the coverage hole and move the redundant node besides the hole. During the routing process, link quality based routing is used to discover the relay nodes with the estimation of link stability to enhance the entire network lifetime and practically make the perfect transmission distance for energy saving. Experimental results demonstrate that proposed protocol can solve the coverage restoration problem, decrease the EC and reduce the network lifetime. The performance is evaluated regarding Average of residual energy (ARE), Receiving packets ratio (RPR), Moving energy consumption (MEC), Network lifetime (NL), Percentage of coverage (%C) and Average Energy Consumption (AEC).     

Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization

3D wireless sensor network (3D-WSN) has attracted significant interests in recent years due to its applications in various disciplinary fields such as target detection, object tracking, and security surveillance. An important problem in 3D WSN is the sensor energy optimization which determines a topology of sensors to prolong the network lifetime and energy expenditure. The existing methods for dealing with this matter namely low energy adaptive clustering hierarchy, LEACH-centralized, K-Means, single hop clustering and energy efficient protocol, hybrid-LEACH and fuzzy C-means organize the networks into clusters where non-cluster head nodes mainly carry out sensing tasks and send the information to the cluster head, while cluster head collect data from other nodes and send to the base station (BS). Although these algorithms reduce the total energy consumption of the network, they also create a large number of network disconnect which refers to the number of sensors that cannot connect to its cluster head and the number of cluster heads that cannot connect to the BS. In this paper, we propose a method based on fuzzy clustering and particle swarm optimization to handle this problem. Experimental validation on real 3D datasets indicates that the proposed method is better than the existing methods.     

Adaptive directed evolved NSGA2 based node placement optimization for wireless sensor networks

Wireless sensor network (WSN) is a wireless network composed of a large number of static or mobile sensors in a self-organizing and multi-hop manner. In WSN research, node placement is one of the basic problems. In view of the coverage, energy consumption and the distance of node movement, an improved multi-objective optimization algorithm based on NSGA2 is proposed in this paper. The proposed algorithm is used to optimize the node placement of WSN. The proposed algorithm can optimize both the node coverage and lifetime of WSN while also considering the moving distance of nodes, so as to optimize the node placement of WSN. The experiments show that the improved NSGA2 has improvements in both searching performance and convergence speed when solving the node placement problem.

     

A distributed activity scheduling algorithm for wireless sensor networks with partial coverage

One of the most important design objectives in wireless sensor networks (WSN) is minimizing the energy consumption since these networks are expected to operate in harsh conditions where the recharging of batteries is impractical, if not impossible. The sleep scheduling mechanism allows sensors to sleep intermittently in order to reduce energy consumption and extend network lifetime. In applications where 100% coverage of the network field is not crucial, allowing the coverage to drop below full coverage while keeping above a predetermined threshold, i.e., partial coverage, can further increase the network lifetime. In this paper, we develop the distributed adaptive sleep scheduling algorithm (DASSA) for WSNs with partial coverage. DASSA does not require location information of sensors while maintaining connectivity and satisfying a user defined coverage target. In DASSA, nodes use the residual energy levels and feedback from the sink for scheduling the activity of their neighbors. This feedback mechanism reduces the randomness in scheduling that would otherwise occur due to the absence of location information. The performance of DASSA is compared with an integer linear programming (ILP) based centralized sleep scheduling algorithm (CSSA), which is devised to find the maximum number of rounds the network can survive assuming that the location information of all sensors is available. DASSA is also compared with the decentralized DGT algorithm. DASSA attains network lifetimes up to 92% of the centralized solution and it achieves significantly longer lifetimes compared with the DGT algorithm.     

A pragmatic approach to area coverage in hybrid wireless sensor networks

Success of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. This paper seeks to address the problem of determining the current coverage achieved by the non‐deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. We identify three key elements that are critical for ensuring effective area coverage in Hybrid WSN: (i) determining the boundary of the target region and evaluating the area coverage (ii) locating coverage holes and maneuvering mobile nodes to fill these voids, and (iii) maintaining the desired coverage over the entire operational lifetime of the network. We propose a comprehensive solution that addresses all of the aforementioned aspects of the area coverage, called MAPC (mobility assisted probabilistic coverage). MAPC is a distributed protocol that operates in three distinct phases. The first phase identifies the boundary nodes using the geometric right‐hand rule. Next, the static nodes calculate the area coverage and identify coverage holes using a novel probabilistic coverage algorithm (PCA). PCA incorporates realistic sensing coverage model for range‐based sensors. The second phase of MAPC is responsible for navigating the mobile nodes to plug the coverage holes. We propose a set of coverage and energy‐aware variants of the basic virtual force algorithm (VFA). Finally, the third phase addresses the problem of coverage loss due to faulty and energy depleted nodes. We formulate this problem as an Integer Linear Program (ILP) and propose practical heuristic solutions that achieve similar performance as that of the optimal ILP solution. A guiding principle in our design process has been to ensure that the MAPC can be readily implemented in real‐world applications. We implemented the boundary detection and PCA algorithm (i.e., Phase I) of the MAPC protocol on off‐the‐shelf sensor nodes and results show that the MAPC can successfully identify boundary nodes and accurately determine the area coverage in the presence of real radio irregularities observed during the experiments. Extensive simulations were carried out to evaluate the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage, while reducing the total energy consumption by up to 70% as compared with the basic VFA. Copyright © 2010 John Wiley & Sons, Ltd.     

Distributed Energy Efficient Tracking in Hybrid wireless sensor network (DEETH)

In wireless sensor network (WSN), it is a complex task to track the target when it is moving randomly in an unknown environment. It also becomes difficult to cover a complete searching area because of the limited searching range and energy of sensor nodes as they are few in number. The author proposes a distributed energy efficient tracking in a hybrid WSN (DEETH) to track a randomly moving target in an unknown searching. Hybrid WSN that is proposed has both static sensor nodes (SSNs) and mobile sensor nodes (MSNs), which are deployed in the searching area. The MSNs move collectively using particle swarm techniques to search a target. The SSNs are deployed for tracking the presence of a target and giving this information to the base station. As per the information given by SSN, MSNs travel to the target and track it. Simulation results prove that proposed technique successfully tracks the target using less number of nodes and also less amount of energy.     

传感器网络中一种基于一元线性回归模型的空时数据压缩算法

针对传感器网络中节点采样数据的空间和时间冗余特点以及节能要求,该文提出了一种基于一元线性回归模型的空时数据压缩算法ODLRST。ODLRST先在每个节点内进行消除时间冗余的数据压缩,再在节点汇集处对来自不同节点的数据消除空间冗余以进一步压缩数据。仿真实验证明,ODLRST能够极大地减少节点发送的数据量和网络中的通信流量,节省并平衡网络中的能量消耗。     

基于稀疏密集阵传输机制的WSN数据传输汇聚算法

为了改善无线传感网(WSN)的数据传输汇聚能力,提出了基于稀疏密集阵传输机制的WSN数据传输汇聚算法。引入核生成函数,设计了一种新的传输矩阵,将簇头节点与sink节点之间连通程度及负载程度进行量化,以提升簇头节点传输效果的评估能力;采用特征向量按列排序并结合卷积算法降低簇头节点传输值,以有效减少簇头节点负载;采用树分解模式搜寻可用哈密尔顿回路,构建了基于路径分解优化机制的汇聚稳定方法;通过使用哈密尔顿寻址来优化叶子节点与根节点之间的数据链路,以增强簇头节点覆盖能力与提高数据传输过程的稳定性能。仿真实验表明,与当前常用的基于阈值筛选模糊分簇的WSN数据稳定汇聚算法和面向医疗应用的无线传感器网络多径数据传输方法相比,所提算法具有更为集中的传输报文集中度,以及更高的传输链路抖动控制能力和网络汇聚带宽。     

Energy-efficient Tree-based Message Ferrying Routing Schemes for Wireless Sensor Networks

Wireless sensor networks (WSNs) are prone to partitioning due to limited energy in sensor nodes and unreliable radio communications between them. Message ferrying (MF) has been proposed as an effective means to deliver data between disjoint parts of a partitioned WSN. In this paper, we propose a tree-based MF algorithm (TMFA) with least ratio tree (LRT) construction in order to prolong the lifetime and reduce energy usage in a WSN employing MF routing. LRT constructs a spanning tree from the topology graph of each partition of the WSN by setting the weight of each edge in the graph as the ratio between the energy cost to deliver a packet over the corresponding wireless link and a linear combination of the residual energy of the transmitting and receiving nodes connected by the link. In addition, the root of the spanning tree is randomly chosen among the nodes in the partition with residual energy equal to or larger than the mean residual energy of all nodes in the partition, so that the energy of nodes are expended evenly. Experimental results show that, compared with the previously proposed Least Energy Tree (LET) and Minimum Spanning Tree (MST) construction methods for TMFA, LRT construction outperforms both the LET and the MST construction in network lifetime and in the ratio of the number of packets reaching the sink to the total energy expended by all the nodes.     

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。     

一种基于无交集节点分组的无线传感器网络覆盖算法

无线传感器网络是一种无线自组织网络,它由大量能量有限的传感器节点组成.能量消耗和网络覆盖是无线传感器网络的两个核心问题,网络覆盖决定了无线传感器网络对物理世界的监测能力,能量消耗则决定了无线传感器网络的生存时间.本文研究了一种改进的基于无交集节点分组算法,针对随机选取节点实现无交集节点分组方式获得的分组个数少且节点通信...     

应用消息传递和卡尔曼模型的无线传感网定位跟踪算法

针对无线传感网系统中传感节点的定位跟踪问题,提出了一种基于消息传递和卡尔曼模型的定位跟踪算法。将目标物与多个锚节点之间的测距整理为线性混合模型,通过贝叶斯公式进行因式分解,建立因子图。假定目标物位置随时间连续移动,相邻的定位坐标之间存在潜在的缓变关系,并利用卡尔曼模型挖掘该关系,即将测距、定位和卡尔曼模型整体进行优化。在因子图中选择合适的消息更新规则,对每条边上的消息进行更新计算,形成定位跟踪算法。最后,建立虚拟和实测环境对所提算法进行数值验证。由于将测距、跟踪和卡尔曼模型融合为一个整体进行优化,相比文献中已有方法,所提定位跟踪算法在仿真环境中表现出超过2 dB的性能优势,在实测场景下也具有接近1 dB的性能增益,证明了其有效性。