基于能量因素的无线传感器网络关键节点判定算法

无线传感器网络中关键节点的判定对网络抗毁性研究具有重要作用。考虑到节点能量受限,该文综合节点剩余生命期和节点移除导致的网络能耗值增加,提出一种将能量因素作为衡量节点重要程度的关键节点判定算法(CNDBE),解决了能量受限的无线传感器网络关键节点判定问题。实验结果表明,在对基于CNDBE,最短路径树算法(SPT)和能量感知的关键节点生成树算法(ENCAST)判定得到的关键节点进行保护时,CNDBE具有更强的网络抗毁性和更长的网络生命期。     

无线传感器网络

无线传感器网络具有广泛的应用前景．将引起人们越来越多的关注。本文介绍了无线传感器网络的基本组成．网络的特点．并分析了当前无线传感器网络的研究应用现状及一些研究成果。     

用无线传感器组成无线传感器网络

物联网的愿景之一是能够测量以前从未测量过的变量。无论应用是监视基础设施老化(例如桥梁、隧道或电力传输线的老化),还是实时提供停车及交通信息,都需要无线传感器网络(WSN)提供与有线网络类似的性能,而且适合实际部署。传感器网络要能够扩展至包含大量无线节点,而且在很多情况下,需要跨越很长的距离。     

无线传感器网络研究概述

集中传感器、计算机以及通信技术的无线传感器网络技术由于其良好的性能以及广阔的运用空间,得到了广大学者的关注和研究。基于此介绍了无线传感器网络的基本概念、属性,讨论了无线传感器网络的关键技术,并就使用领域进行了阐述。     

无线磁场传感器网络

无线磁场传感器网络是一种多学科多技术结合的产物,其可以对进入网络的目标进行实时检测。本文采用巨磁阻抗传感器为网络节点组建无线磁场传感器网络,对移动目标进行了测量,并对结果进行了分析。     

无线传感器网络LEACH协议分析

在无线传感器网络中,基于簇的层次结构能够优化网络带宽的应用,提高共享信道的利用率,减少路由维护的代价以及提高应用的可扩展性,在路由、安全、网络管理等方面具有广泛的应用。基于此,分析研究了典型的基于簇的LEACH协议,并提出了LEACH协议几个可能的研究方向。     

无线传感器网络

无线传感器网络(WSN)被认为是21世纪最重要的技术之一,它将会对人类未来的生活方式产生深远影响。本文介绍了传感器网络的概念和特点,探讨了传感器网络与现有的无线通信网络相结合的业务。     

基于扩散机制的无线传感器网络时间同步协议

针对当前无线传感器网络时间同步协议普遍存在抗毁能力不足的缺陷,提出了一种基于扩散机制的无线传感器网络时间同步协议,全局时间通过邻居节点间定时随机交换时戳信息维护,取消同步发起节点在同步网络中可能带来的不安全隐患,实现同步网络拓扑最优.同时利用容错、时分等策略进一步提高同步网络的抗毁性能.通过对典型无线传感器节点的实际测试表明,该协议在抗毁能力、同步精度、可扩展能力等方面均取得了较好结果.     

无线传感器网络节点机的研制

分析了无线传感器网络节点的体系结构,设计了节点机的硬件电路.以此为平台,实现各节点间的多跳通信.节点机采用支持IEEE 802.15.4的CC2420芯片作为无线通信模块,8位AVR单片机ATmega128L作为处理器,结合外围传感器,对环境进行监测.实验表明:该型节点机具有扩展方便、低功耗、可靠性高的特点.     

无线传感器网络中丢包现象的研究

无线传感器网络(Wireless Sensor Network,WSN)中通信链路质量的好坏影响通信的效率与可靠性,因而研究影响WSN中包发送成功与否的原因具有现实意义。在一定范围内,节点间的距离和包的接收没有必然的关系。同时,由于应用程序设计的原因,只要节点在某一时刻没有收到包,就会在下一时刻出现丢包的情况。最后,来自无线局域网(Wireless Local AreaNetwork,WLAN)的干扰对WSN通信具有较大影响。     

DR-NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time-series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR-NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)-based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR-NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA-based clustered routing and clustering scheme using Canada Geese Migration Principle (CS-CGMP) for routing, respectively.     

Relay sensor placement in wireless sensor networks

This paper addresses the following relay sensor placement problem: given the set of duty sensors in the plane and the upper bound of the transmission range, compute the minimum number of relay sensors such that the induced topology by all sensors is globally connected. This problem is motivated by practically considering the tradeoff among performance, lifetime, and cost when designing sensor networks. In our study, this problem is modelled by a NP-hard network optimization problem named Steiner Minimum Tree with Minimum number of Steiner Points and bounded edge length (SMT-MSP). In this paper, we propose two approximate algorithms, and conduct detailed performance analysis. The first algorithm has a performance ratio of 3 and the second has a performance ratio of 2.5. Xiuzhen Cheng is an Assistant Professor in the Department of Computer Science at the George Washington University. She received her MS and PhD degrees in Computer Science from the University of Minnesota - Twin Cities in 2000 and 2002, respectively. Her current research interests include Wireless and Mobile Computing, Sensor Networks, Wireless Security, Statistical Pattern Recognition, Approximation Algorithm Design and Analysis, and Computational Medicine. She is an editor for the International Journal on Ad Hoc and Ubiquitous Computing and the International Journal of Sensor Networks. Dr. Cheng is a member of IEEE and ACM. She received the National Science Foundation CAREER Award in 2004. Ding-Zhu Du received his M.S. degree in 1982 from Institute of Applied Mathematics, Chinese Academy of Sciences, and his Ph.D. degree in 1985 from the University of California at Santa Barbara. He worked at Mathematical Sciences Research Institutea, Berkeley in 1985-86, at MIT in 1986-87, and at Princeton University in 1990-91. He was an associate-professor/professor at Department of Computer Science and Engineering, University of Minnesota in 1991-2005, a professor at City University of Hong Kong in 1998-1999, a research professor at Institute of Applied Mathematics, Chinese Academy of Sciences in 1987-2002, and a Program Director at National Science Foundation of USA in 2002-2005. Currently, he is a professor at Department of Computer Science, University of Texas at Dallas and the Dean of Science at Xi’an Jiaotong University. His research interests include design and analysis of algorithms for combinatorial optimization problems in communication networks and bioinformatics. He has published more than 140 journal papers and 10 written books. He is the editor-in-chief of Journal of Combinatorial Optimization and book series on Network Theory and Applications. He is also in editorial boards of more than 15 journals. Lusheng Wang received his PhD degree from McMaster University in 1995. He is an associate professor at City University of Hong Kong. His research interests include networks, algorithms and Bioinformatics. He is a member of IEEE and IEEE Computer Society. Baogang Xu received his PhD degree from Shandong University in 1997. He is a professor at Nanjing Normal University. His research interests include graph theory and algorithms on graphs.     

基于局域世界的无线传感器网络分簇演化模型

当前无线传感器网络无标度演化模型研究往往将网络视为同质网络,且未充分考虑网络在真实情形下的演化特征,导致所获网络拓扑与实际网络差异明显。因此,基于局域世界理论,考虑无线传感器网络典型分簇结构、能耗敏感与真实网络中普遍存在的节点与链路退出的动态性行为等特征,提出无线传感器网络分簇演化模型。该模型与同类研究成果相比,更为接近真实网络情形。利用平均场理论推导出此模型具有无标度特征。通过研究拓扑生长对网络容错性能影响,发现扩大局域世界规模与提升饱和度约束上限可有效提升网络容错性能。与之相反,簇头比例与节点删除概率的上升将导致网络容错性能的下降。     

无线传感器网络研究

在信息化时代,信息的接收、处理及分析对用户而言至关重要。特别是信息的接收环节,若收集到的信息不全、有误或者接收不到,后续工作都将无法开展。无线传感器是专门用于接收信息的一类工具,通过网络操作,使人们收集到自己想要的信息。无线传感器网络节点的特点是智能化、功率损耗小、能接收大量信息,传感器的工作运行离不开网络。文章对无线传感器目前的发展现状、技术特点以及应用前景等方面进行了阐述。     

Security in wireless sensor networks

Recent advances in electronics and wireless communication technologies have enabled the development of large-scale wireless sensor networks that consist of many low-power, low-cost, and small-size sensor nodes. Sensor networks hold the promise of facilitating large-scale and real-time data processing in complex environments. Security is critical for many sensor network applications, such as military target tracking and security monitoring. To provide security and privacy to small sensor nodes is challenging, due to the limited capabilities of sensor nodes in terms of computation, communication, memory/storage, and energy supply. In this article we survey the state of the art in research on sensor network security.     

一种节能无线传感器网络

针对无线传感器网络节点能量受限制问题,提出一种节能网络,把监测环境中通电电缆的磁能转化为电能,实现节点能量的自给。采集的能量采用双边调谐阻抗网络传输,通过设计原副边回路参数,令其回路发生谐振来降低自身损耗;组网模式上,用节能静态网络模型代替能耗大的动态组网方式,结合需要,通过切换节点的工作方式来降低能耗。实验证明,能量采集模块一个工作周期采集的能量为10.93 mJ,能够满足传感网络节点工作周期的最大能耗4.68 mJ,验证了节能传感网络可以实现能量自給。     

Ultrawideband wireless body-area sensor networks

The operation principles of wireless body-area networks (WBANs) and requirements to their infrastructure are described. The new IEEE 802.15.6 WBAN standard, in which an ultrawideband (UWB) signal is used as an information carrier, is discussed. The general properties of the given standard and its distinctions from the existing wireless personal communications standards are analyzed. It is demonstrated that UWB direct chaotic transceivers are promising for use in the wireless sensor networks based on the new standard.     

Security in wireless sensor networks

With sensor networks on the verge of deployment, security issues pertaining to the sensor networks are in the limelight. Though the security in sensor networks share many characteristics with wireless ad hoc networks, the two fields are rapidly diverging due to the fundamental differences between the make‐up and goals of the two types of networks. Perhaps the greatest dividing difference is the energy and computational abilities. Sensor nodes are typically smaller, less powerful, and more prone to failure than nodes in an ad hoc network. These differences indicate that protocols that are valid in the context of ad‐hoc networks may not be directly applicable for sensor networks. In this paper, we survey the state of art in securing wireless sensor networks. We review several protocols that provide security in sensor networks, with an emphasis on authentication, key management and distribution, secure routing, and methods for intrusion detection. Copyright © 2006 John Wiley & Sons, Ltd.     

Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes

Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes.     

Non-intrusive aggregation in wireless sensor networks

Since energy is scarce in sensor nodes, wireless sensor networks aim to transmit as few packets as possible. To achieve this goal, sensor protocols often aggregate measured data from multiple sensor nodes into a single packet. In this paper, a survey of aggregation techniques and methods is given. Based on this survey, it is concluded that there are currently several dependencies between the aggregation method and the behavior of the other network layers. As a result, existing aggregation methods can often not be combined with different routing protocols. To remedy this shortcoming, the paper introduces a new ‘non-intrusive’ aggregation approach which is independent of the routing protocol. The proposed aggregation method is evaluated and compared to traditional aggregation approaches using a large-scale sensor testbed of 200 TMoteSky sensor nodes. Our experimental results indicate that existing aggregation approaches are only suited for a limited set of network scenarios. In addition, it is shown both mathematically and experimentally that our approach outperforms existing non-intrusive techniques in a wide range of scenarios.