基于前进跳距期望的异构WSNs非测距定位算法

无线传感网络(WSNs,wireless sensor networks)中传感节点的传输范围直接决定节点的通信区域,对定位精度有直接的影响.为此,针对异构WSNs,提出基于前进跳距期望的非测距定位算法.首先,分析传统推导前进跳距期望(EHP,expected hop progress)方法的不足,并证实了EHP值只依赖锚节点的传输范围是不准确的;然后,采用新方法推导了EHP,并结合泰勒级数展开以及加权最小二乘算法估计未知传感节点位置;最后,以降低误差为目的,迭代修正未知传感节点位置的估计值,从而提高定位精度.仿真结果表明,与传统的非测距定位算法相比,提出的算法的定位精度得到有效提升.     

一种基于跳数比的无线传感器网络定位算法

无线传感器网络节点定位至关重要,有着广泛的应用前景.在传统的DV-Hop定位算法的基础上,提出了一种基于跳数比值的定位改进算法.该算法用跳数比值替代距离比值,并在单跳距离中引入RSSI进一步精确跳数比值,根据节点间的几何关系估算节点位置,提高了定位精度,减少了定位过程中的能量消耗.仿真结果表明,该算法比DV-Hop定位算法拥有更好的定位精度和定位鲁棒性.     

An efficient range-free localization algorithm for wireless sensor networks

Node positioning is a fundamental problem in applications of wireless sensor networks (WSNs). In this paper, a new range-free algorithm, called spring swarm localization algorithm (SSLA), is proposed for positioning WSNs. To determine the locations of sensor nodes, the proposed algorithm uses network topology information and a small fraction of sensor nodes which know their locations. Numerical simulations show that high positioning accuracy can be obtained by using the algorithm. Some examples are given to...     

一种非测距节点定位算法

提出了一种较完善的基于非测距的无线传感网节点定位算法.算法中首先通过引入权重改进未知节点的平均跳距的计算方法;然后引入虚拟锚节点去提高网络覆盖率,接着引入共线性阈值NCD和跳数阈值THD选择合适锚节点组进行位置估计,最后通过质心算法得出最终的位置坐标.仿真结果表明:新算法在不需要任何额外硬件支持的条件下能提供更精确的位...     

基于Amorphous的无线传感器网络定位算法研究

无线传感器网络广泛应用于各个领域,节点位置信息起着至关重要的作用。在所有的经典定位算法中,Amorphous定位算法属于非测距算法,通过获得未知节点与信标节点之间的跳数,估算节点间距离,进而计算节点坐标。分析Amorphous定位算法的缺点并提出了对节点间跳数的修正。引用质心算法加权,提出改进的算法模型,经仿真验证:该算法可获得较为精确的定位结果。     

一种基于约束策略的无线传感器网络定位算法*

节点定位技术是无线传感器网络的关键支撑技术之一,对于无线传感器网络的基本理论方法和应用研究都具有重要意义。在深入研究分析距离无关定位算法的基础上,提出了基于约束策略的无线传感器网络定位算法。该算法无须测距,采用跳数估计节点间距离,并针对未知节点到锚节点距离计算中的不足,对锚节点的平均每跳距离作了修正;在估计未知节点坐标时,根据该未知节点通信范围内的锚节点对其所在位置进行约束。仿真结果表明,该算法具有较好的性能,比已有算法的定位精度有所提高。     

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Localization is stated as determination of physical co-ordinates of the sensor nodes that constitutes a WSN. In applications of sensor networks such as routing and target tracking, the data gathered by sensor nodes becomes meaningless without localization information. This work aims at determining the location of the sensor nodes with high precision. Initially this work is performed by localizing the sensor nodes using a range-free localization method namely, Mobile Anchor Positioning (MAP) which gives an approximate solution. To further minimize the location error, certain meta-heuristic approaches have been applied over the result given by MAP. Accordingly, Bat Optimization Algorithm with MAP (BOA-MAP), Modified Cuckoo Search with MAP (MCS-MAP) algorithm and Firefly Optimization Algorithm with MAP (FOA-MAP) have been proposed. Root mean square error (RMSE) is used as the evaluation metrics to compare the performance of the proposed approaches. The experimental results show that the proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches.     

基于移动Agent无线传感器网络节点自定位算法

目前提出的无线传感器网络自身定位技术有基于测距和不基于测距两类方法,在无线传感器网络应用中,它们各自有其局限性,而移动Agent技术可以较好地弥补这些缺陷。提出了一种基于移动Agent的无线传感器网络自身定位算法,介绍了算法的基本原理和实现方法。该算法不需要额外的硬件支持,减少了无线传感器网络自身定位的通信和计算开销,提高了定位精度。     

基于锚节点连通性的移动WSN定位优化算法

为提高传统移动无线传感网络非测距方式定位算法的节点定位精度、降低算法对锚节点密度的要求,提出一种基于网络中锚节点连通性的蒙特卡洛优化定位算法,并分析了其节点定位性能.算法首先引入平均锚节点连通度的概念来评价网络锚节点连通性,然后提出根据节点实时分布情况进行采样区域划分,并实时控制移动锚节点分布,提升网络的整体定位精度.仿真结果表明,相较于传统的移动无线传感网络中基于蒙特卡洛方法的节点定位算法,所提出的算法有效提升了整体的定位精度,并有效降低了算法对于锚节点密度的要求,提升了算法节点定位性能.     

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.     

Efficient and accurate sensor network localization

Wireless sensor networks (WSN) have great potential in ubiquitous computing. However, the severe resource constraints of WSN rule out the use of many existing networking protocols and require careful design of systems that prioritizes energy conservation over performance optimization. A key infrastructural problem in WSN is localization—the problem of determining the geographical locations of nodes. WSN typically have some nodes called seeds that know their locations using global positioning systems or other means. Non-seed nodes compute their locations by exchanging messages with nodes within their radio range. Several algorithms have been proposed for localization in different scenarios. Algorithms have been designed for networks in which each node has ranging capabilities, i.e., can estimate distances to its neighbours. Other algorithms have been proposed for networks in which no node has such capabilities. Some algorithms only work when nodes are static. Some other algorithms are designed specifically for networks in which all nodes are mobile. We propose a very general, fully distributed localization algorithm called range-based Monte Carlo boxed (RMCB) for WSN. RMCB allows nodes to be static or mobile and that can work with nodes that can perform ranging as well as with nodes that lack ranging capabilities. RMCB uses a small fraction of seeds. It makes use of the received signal strength measurements that are available from the sensor hardware. We use RMCB to investigate the question: “When does range-based localization work better than range-free localization?” We demonstrate using empirical signal strength data from sensor hardware (Texas Instruments EZ430-RF2500) and simulations that RMCB outperforms a very good range-free algorithm called weighted Monte Carlo localization (WMCL) in terms of localization error in a number of scenarios and has a similar computational complexity to WMCL. We also implement WMCL and RMCB on sensor hardware and demonstrate that it outperforms WMCL. The performance of RMCB depends critically on the quality of range estimation. We describe the limitations of our range estimation approach and provide guidelines on when range-based localization is preferable.     

An agent-assisted QoS-based routing algorithm for wireless sensor networks

Existing routing algorithms are not effective in supporting the dynamic characteristics of wireless sensor networks (WSNs) and cannot ensure sufficient quality of service in WSN applications. This paper proposes a novel agent-assisted QoS-based routing algorithm for wireless sensor networks. In the proposed algorithm, the synthetic QoS of WSNs is chosen as the adaptive value of a Particle Swarm Optimization algorithm to improve the overall performance of network. Intelligent software agents are used to monitor changes in network topology, network communication flow, and each node's routing state. These agents can then participate in network routing and network maintenance. Experiment results show that the proposed algorithm can ensure better quality of service in wireless sensor networks compared with traditional algorithms.     

基于DV-hop算法的楼宇内三维投影质心定位算法研究

针对DV-hop定位算法在无线传感器网络中非测距定位时存在的精度不高的缺点,提出将其与投影技术及加权质心算法相结合的算法.该算法利用投影测距技术将楼宇三维空间转化为二维,将平均跳距算法改进为全网平均节点跳距算法,并利用数学方法中均分的思想将计算公式进行优化.在DV-hop算法的最后阶段利用三边加权质心算法建立目标函数.仿真结果表明,改进的楼宇三维投影质心定位算法在不增加硬件开销的前提下,减小了定位误差及不可定位节点比例,提高了定位精度.     

无线传感器网络中非测距混合定位算法

节点定位是无线传感器网络中的关键技术。针对质心定位算法和DV-Hop算法的不足,提出了一种非测距混合定位算法,采用DV-Hop算法得到节点之间的距离和粗略的未知节点估计坐标以作为质心定位算法的权重,经过两次加权质心定位算法得到未知节点更为精确的坐标。仿真实验结果表明：在不同情况下,该混合算法均能获得比两种原始算法更高的定位精度;与其他混合定位算法相比,计算量小,耗能更低。     

无线传感器网络中DV-Hop定位算法的改进

针对无线传感器网络非基于测距的DV-Hop定位算法中,信标节点与未知节点之间平均跳距估计的不足以及三边定位过程中信标节点的选择对定位误差的影响,提出一种改进的DV-Hop定位算法.在改进策略中,对平均跳距采用加权处理进行修正,并有选择性的选取信标节点参与最后的三边定位.仿真结果表明,改进后的DV-Hop算法能够更准确地对平均跳距进行估计,并且有效地降低了未知节点的定位误差.     

Sleep–wake up scheduling with probabilistic coverage model in sensor networks1

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work.     

Divide-and-conquer architecture based collaborative sensing for target monitoring in wireless sensor networks

Most surveillance applications in wireless sensor network (WSN) have stringent accuracy requirements in targets surveillance with maximized system lifetime, while large amount of continuous sensing data and limited resource in WSNs pose great challenges. So it is necessary to select appropriate sensors that can collaboratively work with each other in order to obtain balance between accuracy and system lifetime. However, because of sensing diversity and big data from WSN, most existing methods can not select appropriate sensors to cover all critical monitoring locations in large scale real deployments. Accordingly, an AdaBoost based algorithm is first proposed to identify valid sensors with contribution towards accuracy improvement, which can reduce computation and communication overhead by excluding invalid sensors. The valid sensors are combined and work in a collaborative way, which can obtain better performance than other ways. Then, because of independence of each monitoring location, a divide-and-conquer architecture based method (EasiSS) is proposed to select the most informative sensor clusters from the valid sensors for critical monitoring locations. EasiSS can obtain higher classification accuracy at different user requirement. Finally, according to the experiment on real data, we demonstrate that our proposed method can get a better performance of sensor selection, comparing with traditional methods.     

无线传感器网络Range-Free自身定位机制与算法

无线传感器网络是一种全新的信息获取和处理技术,能够实时监测、感知和采集各种环境或监测对象的信息。而网络自身定位是其大多数应用的基础。在综合分析大量无线传感器网络定位算法的技术文献和最新研究结果的基础上,从测距技术和算法两方面阐述了range-based定位机制的局限性,着重论述和比较了现有的六种range-free定位算法,指出无线传感器网络自身定位问题的研究方向。     

基于SVM分类区域的传感器网络节点自定位算法

针对无线传感器网络(WSN)低成本、低功耗的要求,提出了一种基于支持向量机(SVM)分类区域的距离无关的定位算法,首先SVM通过对训练数据的学习构造一个二叉决策树分类器,然后该分类器将未知节点的位置确定在某一分类区域中,最后取这一区域的中心作为节点的估计位置。该算法仅仅需要网络连通性信息（即跳数）,降低了网络成本和通信负荷。仿真表明该算法在保证一定的定位精度下,有效减轻了覆盖漏洞和边缘问题。     

Editorial

Wireless sensor network(WSN)is characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon.The main advantages of WSN include its low cost,rapid deployment,self-organization,and fault tolerance.WSN has received tremendous interests of various research communities,and significant progresses have been made in various aspects including sensor platform development,wireless communication and networking,signal and information processing,as well as network performance eva...