Extending the Lifetime of Wireless Sensor Networks Through Adaptive Sleep

In recent years, the use of wireless sensor networks for industrial applications has rapidly increased. However, energy consumption still remains one of the main limitations of this technology. As communication typically accounts for the major power consumption, the activity of the transceiver should be minimized, in order to prolong the network lifetime. To this end, this paper proposes an Adaptive Staggered sLEEp Protocol (ASLEEP) for efficient power management in wireless sensor networks targeted to periodic data acquisition. This protocol dynamically adjusts the sleep schedules of nodes to match the network demands, even in time-varying operating conditions. In addition, it does not require any a priori knowledge of the network topology or traffic pattern. ASLEEP has been extensively studied with simulation. The results obtained show that, under stationary conditions, the protocol effectively reduces the energy consumption of sensor nodes (by dynamically adjusting their duty-cycle to current needs) thus increasing significantly the network lifetime. With respect to similar nonadaptive solutions, it also reduces the average message latency and may increase the delivery ratio. Under time-varying conditions, the protocol is able to adapt the duty-cycle of single nodes to the new operating conditions, while keeping a consistent sleep schedule among sensor nodes. The results presented here are also confirmed by an experimental evaluation in a real testbed.      

Extending the Lifetime of Wireless Sensor Networks Through Mobile Relays

We investigate the benefits of a heterogeneous architecture for wireless sensor networks (WSNs) composed of a few resource rich mobile relay nodes and a large number of simple static nodes. The mobile relays have more energy than the static sensors. They can dynamically move around the network and help relieve sensors that are heavily burdened by high network traffic, thus extending the latter's lifetime. We first study the performance of a large dense network with one mobile relay and show that network lifetime improves over that of a purely static network by up to a factor of four. Also, the mobile relay needs to stay only within a two-hop radius of the sink. We then construct a joint mobility and routing algorithm which can yield a network lifetime close to the upper bound. The advantage of this algorithm is that it only requires a limited number of nodes in the network to be aware of the location of the mobile relay. Our simulation results show that one mobile relay can at least double the network lifetime in a randomly deployed WSN. By comparing the mobile relay approach with various static energy-provisioning methods, we demonstrate the importance of node mobility for resource provisioning in a WSN.      

On Prolonging the Lifetime for Wireless Video Sensor Networks

This paper investigates strategies for prolonging the system lifetime for wireless video sensor networks, by adopting a mobile sink and solar-powered video sensors. Issues of tracking moving objects in wireless video sensor networks are studied, and the effectiveness of adopting a mobile sink is evaluated. This paper applies a power-rate-distortion analysis framework, which provides a theoretical fundamental to quantify various properties of wireless video sensor networks. The performance of wireless video sensor networks is evaluated with a mobile sink versus a static sink, under different cluster sizes and number of sensors. Comparisons of network lifetime, tracking error, video distortion, are also covered in this paper. In addition, this paper also evaluates the performance of solar-powered video sensors under an unequal layered clustering topology.     

无线传感器网络MAC协议研究

本文首先从应用需求和业务特性两方面分析了无线传感器网络MAC协议设计面临的挑战.然后,结合现有典型协议,对无线传感器网络MAC协议的研究现状和趋势、节能策略及协议其他特点进行了分析与总结.最后,探讨了无线传感器网络MAC协议的待研究问题.     

无线传感器网络系统综述

介绍了无线传感器网络的概念、几种主要的无线传感器网络协议，尤其是MAC层协议的研究情况，给出了一些比较经典的无线传感器网络MAC协议的思想。     

Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks

This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered to a base station. A fast approximate algorithm is presented, which is able to compute (1−ε)-approximation to the optimal lifetime for any ε > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm, which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical analysis and extensive simulation results. Yuan Xue received her B.S. in Computer Science from Harbin Institute of Technology, China in 1994 and her M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002, and 2005. Currently she is an assistant professor at the Department of Electrical Engineering and Computer Science of Vanderbilt University. Her research interests include wireless and sensor networks, mobile systems, and network security. Yi Cui received his B.S. and M.S. degrees in 1997 and 1999, from Department of Computer Science, Tsinghua University, China, and his Ph.D. degree in 2005 from the Department of Computer Science, University of Illinois at Urbana-Champaign. Since then, he has been with the Department of Electrical Engineering and Computer Science at Vanderbilt University, where he is currently an assistant professor. His research interests include overlay network, peer-to-peer system, multimedia system, and wireless sensor network. Klara Nahrstedt (M ' 94) received her A.B., M.Sc degrees in mathematics from the Humboldt University, Berlin, Germany, and Ph.D in computer science from the University of Pennsylvania. She is an associate professor at the University of Illinois at Urbana-Champaign, Computer Science Department where she does research on Quality of Service(QoS)-aware systems with emphasis on end-to-end resource management, routing and middleware issues for distributed multimedia systems. She is the coauthor of the widely used multimedia book ‘Multimedia:Computing, Communications and Applications’ published by Prentice Hall, and the recipient of the Early NSF Career Award, the Junior Xerox Award and the IEEE Communication Society Leonard Abraham Award for Research Achievements, and the Ralph and Catherine Fisher Professorship Chair. Since June 2001 she serves as the editor-in-chief of the ACM/Springer Multimedia System Journal. An erratum to this article is available at .     

无线传感器网络生命周期最大化研究

无线传感器网络中节点很容易因能量的缺失、环境的影响、遭受意外破坏等原因而停止工作,影响整个网络的生命周期,因此网络生命周期最大化成为传感器网络设计的关键.从节点和网络两个方面阐述了延长网络生命周期的一些策略.     

一种在无线自组织网络中延长网络生存时间的累积广播算法

文章考虑一个无线自组织网络中的广播问题,分析研究了传统累积广播算法及其性能表现,提出了一种新颖的算法,延长生存时间累积广播算法(ELAB算法)。然后通过一个4节点网络模型详细阐述了这一算法,并将它扩展到N节点无线自组织网络中。理论分析和仿真结果证明,相比于传统累积广播算法,对于不同的网络结构和节点数量,ELAB算法可延长网络的生存时间至少50%。     

Maximum Lifetime Target Coverage in Wireless Sensor Networks

Wireless Personal Communications - The challenge in the deployment of wireless sensor networks is to ensure the coverage of targets with high energy efficiency, particularly when coverage and...     

基于网格的移动无线传感网生存时间优化算法

为克服陆地静态无线传感网和水下无线传感网因节点能耗分布不均衡而出现的能量空穴问题,和具有单一移动Sink节点的无线传感网数据收集时延过长问题,该文提出基于网格的移动无线传感网生存时间优化算法(Grid-based Lifetime Optimization Algorithm,GLOA)。GLOA算法考虑多个Sink节点的移动,将监测区域分成多个大小相同的网格。根据网格潜能值确定Sink节点移动的锚点,将锚点分配给不同的Sink节点,建立路径选择优化模型并获得Sink节点的最短移动路径,采用移动收集方法或静态收集方法循环收集数据。仿真结果表明:与Ratio_w或TPGF算法相比,GLOA算法能延长网络生存时间,降低和均衡节点能耗。与LOA_SMSN算法相比,GLOA算法能降低数据收集时延。在一定的条件下,比Ratio_w,TPGF和LOA_SMSN算法更优。     

On Node Lifetime Problem for Energy-Constrained Wireless Sensor Networks

A fundamental problem in wireless sensor networks is to maximize network lifetime under given energy constraints. In this paper, we study the network lifetime problem by considering not only maximizing the time until the first node fails, but also maximizing the lifetimes for all the nodes in the network, which we define as the Lexicographic Max-Min (LMM) node lifetime problem. The main contributions of this paper are two-fold. First, we develop a polynomial-time algorithm to derive the LMM-optimal node lifetime vector, which effectively circumvents the computational complexity problem associated with an existing state-of-the-art approach, which is exponential. The main ideas in our approach include: (1) a link-based problem formulation, which significantly reduces the problem size in comparison with a flow-based formulation, and (2) an intelligent exploitation of parametric analysis technique, which in most cases determines the minimum set of nodes that use up their energy at each stage using very simple computations. Second, we present a simple (also polynomial-time) algorithm to calculate the flow routing schedule such that the LMM-optimal node lifetime vector can be achieved. Our results in this paper advance the state-of-the-art algorithmic design for network-wide node lifetime problem and facilitate future studies of the network lifetime problem in energy-constrained wireless sensor networks. Y. Thomas Hou obtained his B.E. degree from the City College of New York in 1991, the M.S. degree from Columbia University in 1993, and the Ph.D. degree from Polytechnic University, Brooklyn, New York, in 1998, all in Electrical Engineering. From 1997 to 2002, Dr. Hou was a research scientist and project leader at Fujitsu Laboratories of America, IP Networking Research Department, Sunnyvale, California(Silicon Valley). Since Fall 2002, he has been an Assistant Professor at Virginia Tech, the Bradley Department of Electrical and Computer Engineering, Blacksburg, Virginia. Dr. Hou's research interests are in the algorithmic design and optimization for network systems. His current research focuses on wireless sensor networks and multimedia over wireless ad hoc networks. In recent years, he has worked on scalable architectures, protocols, and implementations for differentiated services Internet; service overlay networking; multimedia streaming over the Internet; and network bandwidth allocation policies and distributed flow control algorithms. He has published extensively in the above areas and is a co-recipient of the 2002 IEEE International Conference on Network Protocols (ICNP) Best Paper Award and the 2001 IEEE Transactions on Circuits and Systems for Video Technology (CSVT) Best Paper Award. He is a member of ACM and a senior member of IEEE. Yi Shi received his B.S. degree from University of Science and Technology of China, Hefei, China, in 1998, a M.S. degree from Institute of Software, Chinese Academy of Science, Beijing, China, in 2001, and a second M.S. degree from Virginia Tech, Blacksburg, VA, in 2003, all in computer science. He is currently working toward his Ph.D. degree in electrical and computer engineering at Virginia Tech. While in undergraduate, he was a recipient of Meritorious Award in International Mathematical Contest in Modeling and 1997 and 1998, respectively. Yi's current research focuses on algorithms and optimization for wireless sensor networks and wireless ad hoc networks. His work has appeared in highly selective international conferences (e.g., ACM MobiCom and MobiHoc). Hanif D. Sherali is the W. Thomas Rice Endowed Chaired Professor of Engineering in the Industrial and Systems Engineering Department at Virginia Polytechnic Institute and State University. His area of research interest is in discrete and continuous optimization, with applications to location, transportation, and engineering design problems. He has published about 200 papers in Operations Research journals, has co-authored four books in this area, and serves on the editorial board of eight journals. He is a member of the National Academy of Engineering.     

无线传感器网络在国民经济中的应用

以数据为中心的面向应用的无线传感器网络是一种新型网络。其网络的特点决定了在军事和民用领域的应用很大机会。无线传感器网络特别适用于设备成本较低、传输数据量较少、使用电池供电并且要求工作时间较长的应用场合。随着关键问题的不断解决,无线传感器网络对未来社会进步将发挥巨大的作用。     

Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks

We consider the joint optimal design of the physical, medium access control (MAC), and routing layers to maximize the lifetime of energy-constrained wireless sensor networks. The problem of computing lifetime-optimal routing flow, link schedule, and link transmission powers for all active time slots is formulated as a non-linear optimization problem. We first restrict the link schedules to the class of interference-free time division multiple access (TDMA) schedules. In this special case, we formulate the optimization problem as a mixed integerconvex program, which can be solved using standard techniques. Moreover, when the slots lengths are variable, the optimization problem is convex and can be solved efficiently and exactly using interior point methods. For general non-orthogonal link schedules, we propose an iterative algorithm that alternates between adaptive link scheduling and computation of optimal link rates and transmission powers for a fixed link schedule. The performance of this algorithm is compared to other design approaches for several network topologies. The results illustrate the advantages of load balancing, multihop routing, frequency reuse, and interference mitigation in increasing the lifetime of energy-constrained networks. We also briefly discuss computational approaches to extend this algorithm to large networks     

无线传感器网络定位算法综述

无线传感器网络(WSN)是一个多学科的研究领域,具有很广泛的应用前景,其中,WSN的定位是非常重要的研究方向。介绍了国内外研究机构在WSN定位方面的研究进展,并对这些工作进行了归纳和总结。将每种定位算法按照需不需要测距分为两大类,而且在具体算法中讨论了其以下几个特征,包括:需要/不需要锚节点、集中式/分布式、固定/移动等。     

Lifetime Maximization for Connected Target Coverage in Wireless Sensor Networks

In this paper, we consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity among all the active sensors and the sink. We model the CTC problem as a maximum cover tree (MCT) problem and prove that the MCT problem is NP-Complete. We determine an upper bound on the network lifetime for the MCT problem and then develop a $(1+w)H(mathhat{M})$ approximation algorithm to solve it, where $w$ is an arbitrarily small number, $H(mathhat{M})=sum_{1leq ileqmathhat{M}}(1/i)$ and $mathhat{M}$ is the maximum number of targets in the sensing area of any sensor. As the protocol cost of the approximation algorithm may be high in practice, we develop a faster heuristic algorithm based on the approximation algorithm called Communication Weighted Greedy Cover (CWGC) algorithm and present a distributed implementation of the heuristic algorithm. We study the performance of the approximation algorithm and CWGC algorithm by comparing them with the lifetime upper bound and other basic algorithms that consider the coverage and connectivity problems independently. Simulation results show that the approximation algorithm and CWGC algorithm perform much better than others in terms of the network lifetime and the performance improvement can be up to 45% than the best-known basic algorithm. The lifetime obtained by our algorithms is close to the upper bound. Compared with the approximation algorithm, the CWGC algorithm can achieve a similar performance in terms of the network lifetime with a lower protocol cost.      

Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.     

Energy Minimization for Real-Time Data Gathering in Wireless Sensor Networks

This paper studies the challenging problem of energy minimization for data gathering over a multiple-sources single-sink communication substrate in wireless sensor networks by exploring the energy-latency tradeoffs using rate adaptation techniques. We consider a real-time scenario for mission-critical applications, where the data gathering must be performed within a specified latency constraint. We first propose an offline numerical optimization algorithm with performance analysis for a special case with a complete binary data gathering tree. Then, by discretizing the transmission time, we present a simple, distributed on-line protocol that relies only on the local information available at each sensor node. Extensive simulations were conducted for both long and short-range communication scenarios using two different source placement models. We used the baseline of transmitting all packets at the highest speed and shutting down the radios afterwards. Our simulation results show that compared with this baseline, up to 90% energy savings can be achieved by our techniques (both off-line and on-line), under different settings of several key system parameters     

无线传感器网络边界节点识别研究进展综述

边界节点的识别是无线传感器网络研究中的一个基本问题,是识别覆盖空洞的关键,也是优化网络覆盖的基础和数据可靠传输的保障.在综述边界节点识别算法的基础上,提出了基于节点可用信息和技术策略的算法分类方法,在归纳各类算法技术特点的基础上对比分析了不同算法的优点及存在的问题,并对未来的研究方向进行了总结与展望.     

Rate Allocation and Network Lifetime Problems for Wireless Sensor Networks

An important performance consideration for wireless sensor networks is the amount of information collected by all the nodes in the network over the course of network lifetime. Since the objective of maximizing the sum of rates of all the nodes in the network can lead to a severe bias in rate allocation among the nodes, we advocate the use of lexicographical max-min (LMM) rate allocation. To calculate the LMM rate allocation vector, we develop a polynomial-time algorithm by exploiting the parametric analysis (PA) technique from linear program (LP), which we call serial LP with parametric analysis (SLP-PA). We show that the SLP-PA can be also employed to address the LMM node lifetime problem much more efficiently than a state-of-the-art algorithm proposed in the literature. More important, we show that there exists an elegant duality relationship between the LMM rate allocation problem and the LMM node lifetime problem. Therefore, it is sufficient to solve only one of the two problems. Important insights can be obtained by inferring duality results for the other problem.     

Lifetime Increase for Wireless Sensor Networks Using Cellular Learning Automata

Wireless Personal Communications - With the advent of automation, more and more information is being generated. Thereby, increasingly sensors are being used, featuring increasingly dense networks....