能量感知非均匀成簇路由优化算法

无线传感器网络(WSN)具有的能量有限,其能量利用效率的高低直接影响着网络的生命周期.为了提高无线传感器网络的能量利用效率,提出了一种能量感知非均匀成簇路由优化算法(Energy Awareness Unequal Clustering Routing Optimization Algorithm,EUCR).该算法通过节点在网络中所处的位置确定各节点的邻居节点,并以局部能量选举簇头,各簇头根据其邻居节点构建非均匀分簇网络.同时该算法在路由阶段考虑了簇头的剩余能量和转发代价.仿真结果表明,EUCR算法能有效提高网络的能量利用效率,并延长网络的生命周期.     

无线传感器网络模糊逻辑分簇路由协议

在无线传感器网络（Wireless Sensor Network,WSN）中,LEACH协议通过概率模型来选举簇头,由于没有考虑到传感器节点的分布情况和能量剩余等信息,可能会使得部分节点过早死亡.针对这一问题,提出基于模糊逻辑的分簇路由协议（DFLCP）.在预选簇头阶段,根据节点剩余能量等信息利用模糊逻辑计算出节点的竞争半径,使得簇头分布相对均匀;在簇头选举阶段,通过模糊逻辑确定节点成为簇头的概率.仿真结果表明：DFLCP协议可有效控制簇头节点的分布密度和簇的半径,均衡网络负载,延长节点平均生存时间.     

一种基于能量和距离的无线传感器网络分簇路由协议

在无线传感器网络的路由技术中,基于簇的路由算法在拓扑管理、能耗利用、数据融合等方面都有较强的优势.在分析EECS协议的基础上,提出了EECS协议存在的问题,并相应地修改了成簇算法,设计了ADEECS协议,增强了算法的能量均衡性能.在簇头选举阶段总是选择剩余能量最多的节点,在成簇阶段ADEECS协议同时考虑了节点的剩余能量和节点与基站的距离.仿真结果也证明了ADEECS协议相对于EECS很大地提高了网络的生命周期.     

基于半固定分区的无线传感器网络分簇算法

经典分簇路由算法在每轮的数据采集过程中均需要重新选举簇头和簇的划分,使得网络的拓扑结构极不稳定以及增加了不必要的网络开销,因此提出了一种基于半固定分区的无线传感器网络分簇算法.该算法在首轮,对传感器网路进行簇头的随机选举和簇的划分且其它轮不再重新分簇,然后在各个簇内依据节点的剩余能量和到汇聚节点的距离进行簇头的选举.实验结果表明,与传统分簇协议中的全网广播簇头选举机制相比,该算法不仅拥有稳定的簇结构,而且网络工作稳定期延长了约69.62%,有效地提高了无线传感器网络的可靠性.     

无线传感器网络能量异构分簇算法的研究

在无线传感器网络能量异构环境下对低功耗自适应的分簇算法(Low Energy Adaptive Clustering Hierarchy,LEACH)与稳定选举协议(Stable Election Protocol,SEP)算法进行了分析,针对其存在的不足提出了一种改进的方案。在簇头选举过程中提高了剩余能量高、距离基站较近节点当选为簇头的概率,同时对当选为簇头的节点设定能量阈值,避免能量过低的节点当选为簇头。仿真结果表明,改进后的算法较好地均衡了网络中节点的能量消耗,有效地提高了网络中能量的利用效率,并且极大地延长了网络正常工作的生命周期。     

一种多媒体传感器网络低能耗分簇协议

多媒体传感器网络面临的主要挑战是在能量受限的情况下传输大量数据。在经典分簇协议LEACH的基础上,提出一种考虑数据量的多媒体传感器网络低能耗分簇协议。在簇头选举阶段,选择剩余能量多和数据量大的节点作为簇头;在成簇阶段,同时考虑节点到簇头的通信距离和节点的数据量让节点加入簇。仿真结果表明,提出的协议能有效提高网络的生命周期。     

基于灰色关联度的Leach算法的改进

Leach算法是无线传感器网络中应用最为广泛的分簇路由协议之一,但是该算法的簇头是随机产生的,有可能导致节点过早死亡,从而使整个网络崩溃。针对这一问题,提出一种基于优选簇头的改进Leach算法——gc Leach算法。改进算法引入灰色关联度思想对簇头进行分区选举,兼顾考虑了簇头的剩余能量以及位置分布,有效地避免了簇头分布不合理,以及簇头剩余能量过低导致的节点过早死亡的情况。仿真结果表明,改进后的gc Leach算法能够有效地降低网络能耗,延长网络生命周期。     

一种传感器网络跨层能量优先成簇算法

文中提出CLEEC跨层能量优先成簇算法,基于节点剩余能量来选举簇头节点,使网络能量均匀消耗,延长网络的生存时间.模拟实验结果显示,与现有的典型成簇方案相比,新的成簇算法在传感器网络下提供了更长的网络生存时间和更大的网络吞吐量.     

一种能量有效的无线传感器网络路由算法

无线传感器网络中传感器节点能量有限,为了提高能量利用率,针对现有算法随机选择簇首、簇结构不合理等缺陷提出了一种新的能量有效的分簇路由算法EERA.EERA采用新的簇首选举、成簇,以及构建簇间路由算法,基于节点剩余能量与节点的相对位置选择簇首、成簇,使剩余能量较多的节点优先成为簇首并且各簇首能较均匀的分布在网络区域内;构建簇间路由时将最小跳数路由算法与改进的MTE算法结合起来,在簇间形成最小跳数、最小能耗路径.仿真结果表明,EERA算法可以均衡全网能量消耗,延长网络的生命周期.     

LEACH协议簇头选择算法的改进

LEACH协议存在簇头节点个数和位置分布不稳定的现象。在改进的LEACH-H协议在簇头节点的选举过程中,充分考虑了簇头节点剩余能量因素,设定了簇头的能量阀值,防止了低能量的节点成为簇头。在此基础上引进簇头调整过程,该过程通过排除紧密邻居簇头和增加必要的簇头,在一定程度上解决了LEACH协议存在的问题,从而达到均衡网络能量消耗,延长生存期的目的。网络仿真证明了新算法的可行性。     

Design and Implementation of Routing Algorithm to Enhance Network Lifetime in WBAN

Today’s era is the era of smart and remote applications exploiting advancement in sensors, cloud, Internet of things etc. Major application is in healthcare monitoring and support using wireless body area network (WBAN) in which sensor nodes sense vital physiological parameters and send to server through sink i.e. smart phone nowadays for seamless monitoring. The most significant issue in such applications is energy efficiency which leads to enhanced network life time that ensures uninterrupted seamless services. From source to sink data transmission may occur considering three different scenarios: source to sink single hop direct data transmission irrespective of in-between node distance, source to sink multi hop data transmission in which transmission range of source node is fixed at a threshold to find next forwarder node and transmission range of source node is incremented by affixed value until data gets transmitted to sink. In this work WBAN having different network configurations based on fixed or random positions of nodes have been simulated. Different scenarios with fixed and varying number of nodes are framed and simulated using MATLAB 2020a for performance evaluation of proposed algorithm in terms of energy consumption, network lifetime, path loss etc. due to data transmission from source to sink. Experimental results show that incremental approach is better than direct one in terms of energy consumption, path loss and network lifetime. While selecting transmission range of a source node, it is considered to keep Specific Absorption Rate (SAR) lower to reduce impact on human tissue.

     

基于改进蚁群算法的高能效WSN协作传输策略*

针对传统传感器网络分簇不均匀,数据传输能耗相对较高的问题,提出了I-CoopACO(Cooperative transmission scheme based improved Ant Colony Optimal algorithm)算法.该算法在协作LEACH (Low Energy Adaptive Clustering Hierar-chy)的技术基础上,改进了成簇过程,使得分簇规模更加均匀;在稳定传输阶段,利用节点剩余能量和传输功耗构建启发因子,通过改进的蚁群算法搜索下一跳中继节点获得最优节点,使得传输功耗更低,能耗更均衡.仿真结果表明,在随机分布的感知网络中,I-CoopACO算法减少了传输能耗,均衡了网络负载,延长了网络工作寿命,比协作LEACH算法延长了64.93％的工作寿命.     

低能耗节点位置未知无线传感器网络控制方案

介绍了一种低能耗节点位置未知的网络控制方案,根据不同的网络运行轮数设定网络节点的通信半径,使网络具有良好的能量有效性.网络中基站经过构建阶段的启动过程、节点信息收集过程和节点信息上报过程,获得了整个网络节点的相对位置分布,然后整合节点-节点信息支路,得到具有回路链接的簇首节点集,其他节点根据自己邻居信息选择簇首节点,实现网络近似最小能耗拓扑的构建.通过仿真与同类典型算法LEACH-C、MCLB进行比较,结果显示该方案应用于网络运行时具有更长的网络生命周期、更少的信息总数和更低的网络构建代价.     

一种用于水声传感网的MAC层协议

针对分簇的水声传感网,提出了一种基于时分多址（TDMA）的MAC层协议——Cluster-TDMA。该协议主要由规划阶段和传输阶段组成。规划阶段,首先由网关节点规划能造成簇间干扰的子节点的传输,其次由各簇头节点分别规划本簇内其他子节点的传输;传输阶段,子节点根据规划表周期性地向簇头节点发送数据,这些数据最终汇聚到网关节点。该协议简单有效地解决了引起簇间干扰子结点的传输规划问题。C++仿真实验表明,该协议具有良好的吞吐率和能量效率性能。     

基于TinyOS的无线传感器网络LEACH算法的改进

LEACH协议是无线传感器网络中最流行的分簇路由协议之一.针对LEACH算法簇分布不均匀以及网络能耗不均衡等问题提出了一种高效节能多跳路由算法.在簇建立阶段,新算法根据网络模型计算出最优簇头间距值,调整节点通信半径以控制簇的大小,形成合理网络拓扑结构;在数据传输阶段,簇头与基站之间采用多跳的通信方式,降低了节点能耗.在TinyOS操作系统下,使用nesC语言设计实现了LEACH-EEMH算法.基于TOSSIM平台的仿真结果表明,新算法较LEACH算法在均衡网络能耗、延长网络寿命方面具有显著优势.     

Distributed Mobility Management for Target Tracking in Mobile Sensor Networks

Mobility management is a major challenge in mobile ad hoc networks (MANETs) due in part to the dynamically changing network topologies. For mobile sensor networks that are deployed for surveillance applications, it is important to use a mobility management scheme that can empower nodes to make better decisions regarding their positions such that strategic tasks such as target tracking can benefit from node movement. In this paper, we describe a distributed mobility management scheme for mobile sensor networks. The proposed scheme considers node movement decisions as part of a distributed optimization problem which integrates mobility-enhanced improvement in the quality of target tracking data with the associated negative consequences of increased energy consumption due to locomotion, potential loss of network connectivity, and loss of sensing coverage.     

基于PSO优化模糊C均值的WSN分簇路由算法

针对无线传感器网络节点能量有限、负载不均衡的问题,提出了一种基于粒子群优化模糊C均值的分簇路由算法POFCA。POFCA分别从成簇阶段和数据传输阶段进行了优化。成簇阶段,首先使用粒子群优化算法优化模糊C均值算法,克服了模糊C均值对初始聚类中心的敏感,并根据节点剩余能量和相对距离动态更新簇首,平衡簇内负载。数据传输阶段,基于距离因子、能量因子和节点负载设计了路径评价函数,并使用猫群优化算法为簇首搜寻最优路由路径,在平衡簇首负载的同时又不会加剧中继节点负载。仿真结果表明,与LEACH和LEACH-improved算法相比,POFCA能有效地平衡网络负载,降低网络能耗,延长网络生命周期。     

Adaptive concentric chains protocol for energy efficient routing in wireless sensor networks

This paper addresses the energy efficiency of data collection based on a concentric chain clustering topology for wireless sensor networks (WSNs). To conserve the energy dissipation of nodes spent in data routing, the paper attempts to take advantage of the two opportunities: (a) the impact of the relative positions of wireless nodes to the base station on the energy efficiency of the routing chain within each cluster; (b) the effect of the varying‐sized chains on the selection rule of cluster heads (CHs). To establish an energy‐efficient chain to connect all the nodes in a cluster, the paper proposes a principal vector projection approach, which takes into account both the position of each node and that of the base station, to determine the order to which a node can be linked into the chain in order to reduce the energy requirement of the chain. Since the CH selection rules in the concentric chains are mutually independent, solely based on their self‐cluster sizes, the multi‐hop path passing through all the CHs will consist of longer links and thus consume a significant fraction of the total energy. Thus, in order to suppress the effect of the unequal cluster sizes on decreasing the energy efficiency of the multi‐hop path of CHs, the paper offers an average‐cluster‐size‐based rule (ACSB) for each cluster in order to adapt the CH selection with both the number of active nodes in the current cluster and the average value of all cluster sizes. With these two proposed schemes, an adaptive concentric chain‐based routing algorithm is proposed which enables nodes to collaboratively reduce the energy dissipation incurred in gathering sensory data. By computer simulation, the results demonstrate that the proposed algorithm performs better than other similar protocols in terms of energy saved and lifetime increased capabilities for WSNs which deploy random sensor nodes. Copyright © 2010 John Wiley & Sons, Ltd.     

An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks

In this paper we investigate the expected lifetime and information capacity, defined as the maximum amount of data (bits) transferred before the first sensor node death due to energy depletion, of a data-gathering wireless sensor network. We develop a fluid-flow based computational framework that extends the existing approach, which requires precise knowledge of the layout/deployment of the network, i.e., exact sensor positions. Our method, on the other hand, views a specific network deployment as a particular instance (sample path) from an underlying distribution of sensor node layouts and sensor data rates. To compute the expected information capacity under this distribution-based viewpoint, we model parameters such as the node density, the energy density and the sensed data rate as continuous spatial functions. This continuous-space flow model is then discretized into grids and solved using a linear programming approach. Numerical studies show that this model produces very accurate results, compared to averaging over results from random instances of deployment, with significantly less computation. Moreover, we develop a robust version of the linear program, which generates robust solutions that apply not just to a specific deployment, but also to topologies that are appropriately perturbed versions. This is especially important for a network designer studying the fundamental lifetime limit of a family of network layouts, since the lifetime of specific network deployment instances may differ appreciably. As an example of this model's use, we determine the optimal node distribution for a linear network and study the properties of optimal routing that maximizes the lifetime of the network.