无线多跳网络中低能耗后压路由算法

后压路由算法在投递数据时并不会考虑能耗对节点和网络的影响,这就导致后压路由算法的网络生存时间较短。为解决此问题,基于跨层优化方法建立能耗约束,一种低能耗的后压路由算法被提出。此算法能够自适应地选择能耗较低的下一跳节点投递数据,推迟了节点因能量耗尽而离开网络的时间。实验证明,所提算法与传统后压路由算法相比,能够有效地延长网络生存时间。     

基于无线信号不规则性的无线传感网层次型拓扑控制算法

构建层次型拓扑结构是延长网络生存时间的有效方法。该文将拓扑构建过程分为由簇成员组成的感知层和由簇头组成的平面数据转发层,建立了基于无线信号不规则性的网络能耗模型以及节点成簇稳定性模型,提出了基于无线信号不规则性的层次型拓扑控制(WSIBTC)算法。WSIBTC算法根据节点平均有效传输距离将监测区域划分为多个子区域,由成簇稳定性和节点在簇中的位置决定最终簇头,簇头间形成平面拓扑结构,延长网络生存时间。分析和仿真结果表明由WSIBTC算法得到的网络拓扑大幅度地提升了网络生存时间。     

基于最小费用最大流的无线多媒体传感网多路径路由算法

为了解决多媒体数据的实时传输和网络生存时间优化问题,提出基于最小费用最大流的无线多媒体传感网多路径路由算法(MRAMCMF).MRAMCMF分析了数据传输能耗、节点剩余能量和最小数据传输时延,提出了新的权值函数,并利用最小费用最大流算法构建多路径的数据路由方案.所有多媒体传感节点沿着多个传输路径将数据传输到sink节点.仿真结果表明,该算法能延长网络生存时间,降低和平衡节点能耗,降低数据传输时延,在一定的条件下,比Ratio_w算法和TPGF算法更优.     

一种主动均衡节点能耗的无线传感器网络拓扑控制算法

无线传感器网络中网络拓扑的动态调整对于提高路由协议和MAC协议的效率,延长网络的生存期,提高网络通信效率等方面具有重要的作用.本文在分析了一些拓扑控制算法的基础上,提出了一种新的层次型拓扑生成算法,该算法引入了时间门限值和节点剩余能量两个参数,在解决能耗不均衡问题上采取相对主动的方法.能够有效地均衡网络节点的能耗并延长网络的生存周期.     

三维水声传感器网络能量高效路由算法

针对三维水声传感器网络,在研究水声信道能耗特性的基础上,设计了一种能量高效的路由算法Hybrid LEACH。它基于经典的LEACH算法,而在簇间传输中使用一种树状路由取代簇头与汇聚节点直接通信,减少了簇头节点的能耗,达到延长网络生存时间的目的。仿真结果显示,Hybrid LEACH算法在网络规模较大的情况下可以有效延长网络生存时间。     

认知无线电网络自适应能量驱动双门限簇头轮换算法研究

 为了延长基于分簇结构的认知无线电网络的网络寿命,需要采用簇头轮换的方法来平衡认知无线电网络的能耗.通过分析网络中簇头节点与普通节点之间的能耗差异,建立能量消耗模型,并提出了一种基于簇头节点实时负载来估计其启动簇头轮换的双门限自适应簇头轮换算法(Double Thresholds Adaptive Cluster Rotation Algorithm,DTACRA).仿真结果表明,与现有算法LEACH和EDAC算法等比较,DTACRA算法可以有效提高节点能量利用效率,延长了网络生存时间.     

无线传感器网络路由协议与改进

详细介绍无线传感器网络(WSN)的两种代表性协议:信息协商传感器(SPIN)协议和低能量自适应分簇路由(LEACH)协议的概念、原理和优缺点.提出路由协议中需要进一步解决的问题.改进的WSN路由算法应尽可能降低节点能耗.以延长网络生存时间.     

基于生命期划分的无线传感器网络节能策略

提出一种基于生命期划分的节能数据传输策略.利用能耗判定建立多层簇,并在保证能耗均衡的基础上划分节点生命期,同时利用节点工作状态轮转,进一步延长了网络生存时间.仿真结果表明,本方法相比于其他一些能量有效的网络协议,拥有更长的网络生存时间及更均衡的网络能耗.     

基于改进LEACH的多簇头分簇路由算法

为了降低无线传感器网络(WSN)的能耗,延长网络的生存周期,提出一种多簇头双工作模式的分簇路由算法.算法对低功耗自适应集簇分层(LEACH)协议作了以下改进:采用多簇头双工作模式来分担单簇头的负荷,以解决单簇头因能耗较大而过早消亡的问题;选举簇头时充分考虑节点位置和节点剩余能量,并应用粒子群优化(PSO)算法优化簇头的选举,以均衡网络内各节点的能耗;建立簇与簇之间的数据传输路由,以减少簇间通信的能耗.仿真结果表明,算法有效降低了网络的能耗,延长了网络的生存周期.     

一种基于LEACH路由协议的改进算法

无线传感器网络的生存时间受传感器节点软硬件条件的限制,改进传感器网络路由协议是延长网络生存时间的有效途径。LEACH协议是最早提出的经典分层路由协议,文中基于LEACH协议提出改进,应用K-medoids算法改进LEACH协议的簇首分簇机制,并通过Matlab仿真实验,证实了改进后的LEACH算法在均衡化网络能耗,延长网络的生命周期方面具有优越性。     

基于遗传聚类的无线传感器负载均衡路由算法

通常的无线传感器分簇网络存在节点负载不均衡的问题。为均衡各节点能量消耗,延长网络生存周期,将K均值算法与遗传算法相结合,提出一种负载均衡的无线传感器网络路由算法,算法利用遗传算法的全局寻优能力以克服传统K均值算法的局部性和对初始中心的敏感性,实现了传感器网络节点自适应成簇与各节点负载均衡。仿真实验表明,该算法显著延长了网络寿命,相对于其他分簇路由算法,其网络生存时间延长了约43%。     

A Tabu search based routing algorithm for wireless sensor networks

In this paper, a Tabu search based routing algorithm is proposed to efficiently determine an optimal path from a source to a destination in wireless sensor networks (WSNs). There have been several methods proposed for routing algorithms in wireless sensor networks. In this paper, the Tabu search method is exploited for routing in WSNs from a new point of view. In this algorithm (TSRA), a new move and neighborhood search method is designed to integrate energy consumption and hop counts into routing choice. The proposed algorithm is compared with some of the ant colony optimization based routing algorithms, such as traditional ant colony algorithm, ant colony optimization-based location-aware routing for wireless sensor networks, and energy and path aware ant colony algorithm for routing of wireless sensor networks, in term of routing cost, energy consumption and network lifetime. Simulation results, for various random generated networks, demonstrate that the TSRA, obtains more balanced transmission among the node, reduces the energy consumption and cost of the routing, and extends the network lifetime.     

无线传感器网络中的分簇算法

分簇算法是无线传感器网络中实施分层路由所采用的重要方法,尤其是传感器节点受到能量和带宽的严重制约。如何合理、有效地利用分簇算法使得网络中节点的能量分布趋于一致,从而延长网络的生命周期,提高系统的整体性能指标,成为当前无线传感器网络研究领域内的热点问题之一。本文从能量有效性的角度出发,对最新的分簇算法进行研究与分析,指出如何根据无线传感器网络不同的分布区域、应用场景和多样的无线环境,生成性能优越的分簇算法。     

Topology estimation method for telecommunication networks

In order to resolve the traditional limited lifetime problem, energy harvesting technology has been introduced into wireless sensor network (WSN) in recent years, engendering a new kind of network which is called energy harvesting wireless sensor network (EHWSN). In EHWSNs, besides the traditional issues, such as energy consumption, energy equilibrium, transmission efficiency, etc., there are still new challenges, such as how to utilize harvested energy efficiently and how to make more sensor nodes so as to achieve unlimited lifetime under actual situation. In this paper, inspired by slime mold Physarum polycephalum, a novel bionic routing protocol, abbreviated as EHPRP, is proposed for EHWSNs to address above problems without predicting harvestable energy value. Three distributed routing algorithms with low algorithm complexity are proposed which would prominently reduce the processing delay and conserve energy. Furthermore, the mathematic theoretical analysis is made to prove the stability of EHPRP routing strategy. Finally, simulation results present that, compared with other typical algorithms, EHPRP consumes less energy, always making the whole network obtain an unlimited lifetime, and displaying more uniform network energy distribution under different workload conditions.     

Lifetime Maximization Based on Coverage and Connectivity in Wireless Sensor Networks

We consider information retrieval in a wireless sensor network deployed to monitor a spatially correlated random field. We address optimal sensor scheduling and information routing under the performance measure of network lifetime. Both single-hop and multi-hop transmissions from sensors to an access point are considered. For both cases, we formulate the problems as integer programming based on the theories of coverage and connectivity in sensor networks. We derive upper bounds for the network lifetime that provide performance benchmarks for suboptimal solutions. Suboptimal sensor scheduling and data routing algorithms are proposed to approach the lifetime upper bounds with reduced complexity. In the proposed algorithms, we consider the impact of both the network geometry and the energy consumption in communications and relaying on the network lifetime. Simulation examples are used to demonstrate the performance of the proposed algorithms as compared to the lifetime upper bounds.     

An energy efficient clustering routing algorithm for wireless sensor networks

~~An energy efficient clustering routing algorithm for wireless sensor networks1. Mainwaring A, Polastre J, Szewczyk R, et al. Wireless sensor networks for habitat monitoring. Proceedings of the ACM International Workshop on Wireless Sensor Networks and A…     

Joint optimization of energy allocation and routing problems in wireless sensor networks

Energy allocation problems and routing problems are both important research issues in the wireless sensor network (WSN) field. The former usually aims at considering how to allocate a certain number of sensor devices in a sensing region to form a WSN so that the objective function value (e.g., the network connectivity or the network lifetime) of the constructed network is optimized. For the message routing problem in WSNs, researchers tend to consider how to find an energy conservable message transmission routing scheme for notifying the supervisor of the WSN when an event occurs. Till now, many solutions have been proposed for the above two categories of optimization problems. However, unifying the above two network optimization problems to maximize the network lifetime, to the best of our knowledge, still lacks related research. This paper considers a joint optimization problem for energy allocation and energy‐aware routing called the joint optimization of energy allocation and routing problem (JOEARP) for a hierarchical cluster‐based WSN. We propose an exact algorithm to provide the optimum solution for the JOEARP. The simulation results show that this solution performed better in prolonging the network lifetime of a WSN in a real situation, compared to other compositions of conventional energy allocation schemes with some known routing algorithms. Copyright © 2009 John Wiley & Sons, Ltd.     

Betweenness centrality based connectivity aware routing algorithm for prolonging network lifetime in wireless sensor networks

Network lifetime is the key design parameter for wireless sensor network protocols. In recent years, based on energy efficient routing techniques numerous methods have been proposed for enhancing network lifetime. These methods have mainly considered residual energy, number of hops and communication cost as route selection metrics. This paper introduces a method for further improvement in the network lifetime by considering network connectivity along with energy efficiency for the selection of data transmission routes. The network lifetime is enhanced by preserving highly connected nodes at initial rounds of data communication to ensure network connectivity during later rounds. Bassed on the above mentioned concept, a connectivity aware routing algorithm: CARA has been proposed. In the proposed algorithm, connectivity factor of a node is calculated on the basis of Betweenness centrality of a node and energy efficient routes are found by using fuzzy logic and ant colony optimization. The simulation results show that the proposed algorithm CARA performs better than other related state-of-the-art energy efficient routing algorithms viz. FML, EEABR and FACOR in terms of network lifetime, connectivity, energy dissipation, load balancing and packet delivery ratio.     

基于跨层网络编码感知的无线传感器网络节能路由算法研究

编码感知路由可以发现路由中的网络编码机会,减少数据传输次数,提高网络吞吐量,是近年来路由算法研究的一个热点.当前编码感知路由存在编码条件失效、未考虑节点能量的问题,不适合直接应用于无线传感器网络.本文提出基于跨层网络编码感知的无线传感器网络节能路由算法CAER (Cross layer coding Aware Energy efficient Routing).提出并证明了修正后的网络编码条件,以解决编码条件失效问题.基于跨层思想,将网络编码感知机制与拓扑控制、覆盖控制结合,挖掘潜在编码机会.提出综合考虑节点编码机会、节点能量的跨层综合路由度量CCRM (Cross layer Coding aware Routing Metric).仿真结果表明,相比现有编码感知路由,CAER能够提高网络编码感知准确性,增加网络编码机会数量5%~15%,延长网络生存时间8%~12%.     

基于ERPMT改进启发式方法的WSN寿命最大化算法

针对传感器节点的电池容量限制导致无线传感网络寿命低的问题,基于容量最大化(CMAX)、线上最大化寿命(OML)两种启发式方法以及高效路由能量管理技术(ERPMT),提出了基于ERPMT改进启发式方法的无线传感网络寿命最大化算法。首先,通过启发式方法初始化每个传感器节点,将节点能量划分为传感器节点起源数据和其它节点数据延迟;然后利用加入的一种优先度量延迟一跳节点的能量消耗;最后,根据路径平均能量为每个路由分配一个优先级,并通过ERPMT实现最终的无线传感网络优化。针对不同分布类型网络寿命的实验验证了本文算法的有效性及可靠性,实验结果表明,相比较为先进的启发式方法CMAX及OML,本文算法明显增大了无线传感网络的覆盖范围,并且大大地延长了网络的寿命。