无线传感器网络LEACH协议研究

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无线传感器网络LEACH协议研究

无线传感器网络LEACH协议的研究摘要：无线传感器网络因其在军事、经济、民生等方面广阔的应用前景成为21世纪的前沿热点研究领域[1]。

在传感器节点能量有限的情况下，提高路由效率，延长网络寿命成为无线传感器网络需考虑的问题。

由于采取分簇，数据融合的思想，LEACH协议有着较高的路由效率，但在实际应用，尤其是大规模网络中，仍存在负载不均衡等问题。

本文主要分析了LEACH协议的基本思想及优缺点，随后针对大规模的网络环境对其分簇算法进行改进。

前人提出一种有效的方法计算最优簇首个数，本文推算出适合本文中网络环境的公式并加以应用。

本文用NS2进行仿真，仿真后的结果表明，改进后的分簇算法更为有效，延长了网络寿命，增大了网络传送数据量。

关键词：无线传感器网络；路由协议；LEACH；分簇思想Research on Routing Protocol of LEACH in WSNShen Y uanyiDept. of Information and Telecommunication,NUPTABSTRACT：Nowadays, wireless sensor network has become a hot spot of 21st century because of its wide application on military, economy and human life. On the condition that the energy of a sensor node is limited, how to improve the routing efficiency and expand the network’s lifespan has been an important issue to consider. LEACH maintains quite high routing efficiency for its idea of clustering and data gathering. But in practical, it still has problems such as load unbalance especially in large scale network. The article mainly analyses the basic idea of LEACH, the benefits and drawbacks of it and later introduce an improvement on clustering algorithm according to large scale network.Key words：WSN；routing protocol; LEACH; clustering1LEACH协议介绍与分析1.1 LEACH算法思想算法基本思想[2]是：以循环的方式随机选择簇头节点，将整个网络的能量负载平均分配到每个传感器节点中，从而达到降低网络能源消耗、提高网络整体生存时间的目的。

分簇无线传感器网络中基于LEACH路由协议的跨层节能技术研究的开题报告

分簇无线传感器网络中基于LEACH路由协议的跨层节能技术研究的开题报告一、选题背景与意义随着无线传感器网络在环境监测、军事侦察、医疗健康等领域的广泛应用，其能耗问题逐渐引起了人们的关注。

在无线传感器网络中，大多数节点是由电池供电的，而更换电池需要耗费大量的人力、物力和财力，因此如何降低节点的能耗，延长网络寿命，成为了无线传感器网络中亟待解决的问题。

为了降低无线传感器网络的能耗，现有的研究往往是针对某一层进行优化，例如MAC层、网络层和应用层等。

然而，这些优化往往忽略了不同层之间的相互影响，导致了综合效果的不协调。

因此，跨层设计可以有效减少无线传感器网络的能耗，并提高网络的性能。

本研究将从无线传感器网络的分簇结构出发，采用LEACH路由协议作为基础，探索一种跨层节能技术，旨在通过合理设计不同层之间的交互，降低节点的能耗，延长网络寿命。

二、研究内容和方法本研究的主要内容和方法如下：1.研究无线传感器网络的分簇结构和LEACH路由协议的原理和应用。

2.分析无线传感器网络中不同层之间的交互，针对MAC层和应用层的特点和需求，设计一种跨层通信协议，实现能耗的有效管理。

3.在NS2仿真平台上，搭建分簇无线传感器网络模型，模拟网络运行过程，验证跨层节能技术的有效性和可行性。

4.通过对仿真实验数据的统计和分析，评估所提出的跨层节能技术的性能，在节点能耗、网络寿命等方面与传统技术进行比较。

三、研究进展和计划目前，本研究已经完成了无线传感器网络和分簇结构、LEACH路由协议以及分层设计的相关研究。

下一步，将在NS2仿真平台上，搭建分簇无线传感器网络模型，并进行仿真实验。

具体的研究计划如下：1.构建分簇无线传感器网络模型，实现跨层节能技术的仿真实验。

2.对仿真实验数据进行收集和统计，并对结果进行分析。

3.根据仿真实验结果，对跨层节能技术提出改进和优化方案。

4.撰写论文，并进行答辩。

预计研究周期为1年，具体进度如下：第一阶段（第1-3个月）：研究分簇无线传感器网络和LEACH路由协议的原理和应用。

基于LEACH的无线传感器网络路由协议研究的开题报告

基于LEACH的无线传感器网络路由协议研究的开题报告一、选题背景及意义随着无线传感器网络（WSN）技术的不断发展，WSN已经被广泛应用于智能交通、智能家居、环境监测、农业生产等领域。

WSN中的节点都是由消耗能量的设备组成的，为了延长网络寿命，如何合理地管理节点能量成为WSN路由协议设计的重要问题之一。

LEACH是一种经典的WSN路由协议，它采用了分簇的策略和轮流担任簇头的方式，通过降低节点能量消耗，延长了网络寿命。

在LEACH基础上，许多学者对其进行了改进和优化，如P-LEACH、SEP等。

本论文旨在对基于LEACH的WSN路由协议进行研究和分析，探究其优缺点及适用范围，并结合实验验证其性能。

二、研究内容1. 介绍无线传感器网络的基本概念及发展历程；2. 介绍LEACH协议的基本原理和运作方式，并分析其优缺点；3. 深入分析LEACH协议中的各种参数设置及对协议性能的影响；4. 对基于LEACH的改进协议进行分析，并探讨其优点及适用范围；5. 实验验证所研究的各种WSN路由协议的性能，包括能量消耗、延迟等指标；6. 结合实验结果，对所研究的各种WSN路由协议进行评价和总结。

三、研究方法和步骤1. 收集文献资料，了解无线传感器网络发展历程和LEACH协议的基本原理及发展过程；2. 初步了解LEACH协议中的各种参数设置，并进行分析；3. 分析LEACH协议的性能，探究其优缺点；4. 研究基于LEACH的WSN路由协议的改进及优化；5. 设计实验方案，验证各种WSN路由协议的性能；6. 分析实验数据，对实验结果进行评价和总结。

四、预期成果1. 深入掌握无线传感器网络的基本概念和LEACH协议的原理；2. 对LEACH协议中各个参数的调整方法有深入的了解和掌握；3. 对WSN路由协议设计有深入的理解和认识；4. 实验数据能够验证研究结论，评价和分析各种WSN路由协议的性能并做出总结。

无线传感器网络中的网络协议研究

无线传感器网络中的网络协议研究在当今数字化和智能化的时代，无线传感器网络（Wireless Sensor Network，WSN）正逐渐成为信息领域的重要组成部分。

它由大量分布在监测区域内的传感器节点组成，这些节点通过无线通信方式形成一个自组织的网络，能够实时感知、采集和处理各种环境信息，并将其传输到数据中心进行分析和决策。

而在无线传感器网络中，网络协议起着至关重要的作用，它决定了网络的性能、可靠性和能量效率等关键指标。

无线传感器网络的特点使得其对网络协议提出了独特的要求。

首先，传感器节点通常能量有限，而且很多情况下难以更换电池，因此网络协议必须具备低能耗的特性，以延长网络的生命周期。

其次，由于传感器节点的计算和存储能力相对较弱，协议的设计应该尽量简单高效，避免复杂的计算和大量的存储需求。

再者，网络中的节点分布密集，通信容易受到干扰和冲突，这就要求协议能够有效地解决信道竞争和冲突避免的问题。

此外，传感器网络的规模可能很大，节点可能会动态地加入或离开网络，协议需要具备良好的可扩展性和适应性，以应对网络拓扑的变化。

在无线传感器网络中，MAC（Medium Access Control）协议是决定节点如何共享无线信道资源的关键协议。

常见的MAC协议有基于竞争的协议和基于时分复用的协议。

基于竞争的MAC协议，如CSMA/CA （Carrier Sense Multiple Access with Collision Avoidance），节点在发送数据前先监听信道，如果信道空闲则发送数据，否则等待随机时间后再次尝试。

这种协议的优点是简单灵活，但容易产生冲突，导致能量浪费和传输延迟增加。

基于时分复用的MAC协议，如TDMA（Time Division Multiple Access），将时间划分为固定的时隙，每个节点在指定的时隙内发送数据。

这种协议能够有效地避免冲突，但需要严格的时间同步，实现起来相对复杂。

无线传感器网络中的路由协议比较分析

无线传感器网络中的路由协议比较分析无线传感器网络是由大量的分布式无线传感器节点组成的网络，这些节点能够感知和测量环境中的物理信息，并将这些信息传输到网络中。

在无线传感器网络中，路由协议负责决定数据在网络中的传输路径，以实现可靠、高效的数据传输。

本文将对常见的无线传感器网络中的路由协议进行比较分析。

首先，我们来看最常用的路由协议之一：LEACH（Low-Energy Adaptive Clustering Hierarchy）。

LEACH是一种分层的路由协议，通过将网络节点分成簇，然后选举一个簇首节点来管理每个簇的通信，从而降低网络的能量消耗。

LEACH协议的优点是简单且易于实现，能够有效减少网络节点所需的能量。

然而，LEACH的缺点是不能很好地处理节点的能量不平衡和节点的动态变化。

另一个常见的路由协议是PEGASIS（Power-Efficient Gathering in Sensor Information Systems）。

PEGASIS采用链状结构将节点连接起来，每个节点只与邻近的节点通信。

这种结构能够延长网络的生命周期，并减少能量的消耗。

PEGASIS的优点是能够充分利用网络节点之间的局部通信，从而降低了能量消耗。

然而，PEGASIS也存在一些问题，比如节点之间的链状结构可能会导致节点间的通信延迟增加，同时也容易出现消息丢失的情况。

还有一种常用的路由协议是SPIN（Sensor Protocols for Information via Negotiation）。

SPIN协议利用信息交换的方式来减少节点间的通信量，从而降低了能量的消耗。

SPIN的优点是具有较低的能量消耗和较高的网络稳定性。

然而，SPIN 也存在一些问题，比如在网络中产生的通信开销较大，以及对网络节点之间的同步要求较高。

除了以上三种常见的路由协议外，还有许多其他的无线传感器网络路由协议，比如TEEN、SEP等。

TEEN协议采用事件触发的方式进行数据传输，能够根据环境中的事件情况来调整数据传输策略，从而减少能量消耗。

leach协议

Leach协议简介Leach（Low Energy Adaptive Clustering Hierarchy）是一种无线传感器网络中常用的分簇协议。

该协议基于分簇的方式，使得无线传感器节点能够有效地将数据传输到基站，从而延长整个网络的生命周期。

本文将介绍Leach协议的工作原理、特点以及应用场景。

工作原理Leach协议采用分簇的方式组织无线传感器节点。

每个节点在每个轮次中以一定的概率成为簇头节点，并负责收集和聚合其他节点的数据，并将聚合后的数据传输给基站。

其工作原理如下：1.初始阶段：每个节点根据预设的概率成为簇头节点。

这个概率可以在每个轮次中动态调整，以保证所有节点都有机会成为簇头节点。

2.簇头选择：节点通过计算与其它节点的距离来决定自己是否成为簇头节点。

距离越小，成为簇头的概率越高。

这样可以保证簇头节点分布均匀，避免节点集中在某一区域。

3.簇头通信：簇头节点负责与其他节点进行通信，收集并聚合数据。

簇头节点通过多跳的方式将数据传输给基站。

这种多跳方式减小了节点到基站的距离，节约了能量。

4.簇头轮流变更：为了均衡网络中各个节点的能量消耗，每个节点在一个轮次中只能成为簇头一次。

通过轮流变更簇头节点，可以使得每个节点都有机会承担更多的能量负担。

特点Leach协议具有以下几个特点：1.能量均衡：通过每个节点轮流变更成为簇头节点，Leach协议可以使得网络中各个节点的能量消耗均衡。

避免了少数节点能量消耗过快导致网络寿命缩短的问题。

2.低能耗：Leach协议采用分簇的方式，只有簇头节点需要与基站进行通信，其余节点只需要将数据传输给簇头节点即可。

这种方式减小了节点的能量消耗，延长了网络的寿命。

3.自适应性：Leach协议中的簇头节点选择是基于节点之间的距离计算的，距离越小的节点成为簇头的概率越高。

这种自适应性使得网络能够适应节点的位置分布，提高了网络的覆盖范围。

4.扩展性：Leach协议支持大规模无线传感器网络。

无线传感器网络路由协议LEACH的研究与改进

网络生存期。
关键词：ＬＥＡＣＨ；分簇；距离；能量；无线传感器网络
中图分类号：ＴＰ３９３
文献标识码：Ａ
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０引言
无线传感器网络（ＷｉｒｅｌｅｓｓＳｅｎｓｏｒＮｅｔｗｏｒｋ，ＷＳＮ）作为物联网的感知层，主要是实现物联网的底层功能即连接物到网络。无线传感器网络由部署在监测区
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基于LEACH的无线传感器网络路由算法的分析与改进

基于LEACH的无线传感器网络路由算法的分析与改进基于LEACH的无线传感器网络路由算法的分析与改进一、引言随着无线传感器网络（Wireless Sensor Network, WSN）的发展，人们对于无线传感器网络路由算法的研究也日益增多。

在无线传感器网络中，路由算法对于网络的性能和能耗具有重要影响。

LEACH（Low Energy Adaptive Clustering Hierarchy）作为一种经典的无线传感器网络路由协议，具有较低的能耗和较好的性能。

本文将对LEACH算法进行分析，并提出一种改进方案。

二、LEACH算法的原理与优缺点分析1. LEACH算法原理LEACH算法是一种分簇式的路由算法，其基本思想是将网络中的节点划分为多个簇。

每个簇内有一个簇头节点负责管理簇内的通信，并将数据传输到基站。

LEACH算法主要包括两个阶段：簇头选择阶段和数据传输阶段。

在簇头选择阶段，每个节点根据阈值（摄取阈值）决定是否成为簇头节点。

节点通过计算能量消耗的阈值，来控制簇头节点的选择，以降低能耗。

簇头节点选定后，其他节点将成为其成员节点。

在数据传输阶段，节点将数据传输到簇头节点，簇头节点再将数据传输到基站。

为了减少能量消耗，簇头节点通常采取限制传输功率和路由选择的策略。

2. LEACH算法的优点与缺点LEACH算法具有以下优点：- 能量均衡性：通过轮流选取簇头节点和采用时分多路复用的方式，使得网络中的节点能量使用均匀，延长网络寿命；- 低延迟：数据通过簇头节点进行传输，减少了节点间的通信距离，缩短了数据传输的时间；- 无需全局信息：LEACH算法只需要节点之间的局部信息即可进行簇头节点的选择，无需全局信息的维护和通信。

然而，LEACH算法也存在以下缺点：- 随机性：簇头节点的选择过程采用随机算法，容易导致不同轮次簇头节点的能量不平衡；- 无线信号干扰：由于节点之间通信的无线信号干扰，导致网络性能下降。

无线传感器网络LEACH 协议的研究与改进

无线传感器网络LEACH 协议的研究与改进胡英武汉理工大学通信与信息系统系，武汉（430070）E-mail: whoing@摘要：LEACH （Low Energy Adaptive Clustering Hierarchy ）是一种经典的WSN 路由协议,得到了广泛的认可。

本文基于LEACH 算法提出了一个新的路由协议，综合考虑候选节点的剩余能量和簇首节点的分布位置以及簇首节点的个数，从而有效地降低了低能量与位置不佳的节点被选为簇首的可能性，进一步保证了网络节点能量负载的平衡性。

仿真结果表明，该算法能有效的平衡节点的能量消耗分布，延长节点与网络的寿命。

关键词：无线传感器网络；LEACH 协议；能量有效性；负载平衡1．引言无线传感器网络是由大量无处不在的、具有无线通信与计算能力的微小传感器节点构成的自组织(Ad-hoc)分布式网络系统, 是能根据环境自主完成指定任务的“智能”系统。

它以“数据为中心”, 具有有限的计算能力、有限的存储能力、有限的无线通信能力和有限的电源供应能力, 如何在这样有限的资源环境下获取尽可能多的、有效的感知对象的特征信息, 并传输到用户节点进行处理, 是目前研究的重点问题, 这些问题都可以归结为传感器网络的路由问题，即要有一个好的路由协议以尽量降低能耗、延长网络生存时间。

无线传感器网络的路由协议【1】可以分成平面路由协议和分层路由协议两种。

由于平面路由协议需要维持较大的路由表, 占据较多的存储空间, 因而并不适合在大规模网络中采用分层路由算法可以在一定程度上解决这个问题。

LEACH 算法是比较成熟经典且常用具有代表性的分层路由算法。

因此本文主要研究LEACH 算法, 并针对其不足进行了改进。

2．LEACH 路由算法2.1 算法描述LEACH 是MIT 的Chandrakasan 等人为无线传感网设计的低功耗自适应分层路由算法。

它的基本思想是以循环的方式随机选择簇首节点，将整个网络的能量负载平均分配到每个传感器节点中，从而达到降低网络能源消耗、提高网络整体生存时间的目的。

基于leach的无线传感器网络路由协议分析与改进

AbstractAbstractWireless sensor networks(WSNs) is a hot new research field of computer science. Its basic unit is micro sensor node. A large amount of micro sensor nodes randomly deployed in monitoring area, perceive and collect data collaboratively, then send to terminal user with wireless communication mode. The energy of sensor nodes is limited and the number of nodes is large. There is a need for research a simple and efficient routing mechanism to reduce the energy consumption of nodes, balance the energy consumption of the network and improve the lifetime of WSNs.This paper analyses the classical clustering routing protocol LEACH which is acted as our research object. For LEACH, randomization method used to select cluster heads(CHs) results in a uneven distribution of CHs and a wide fluctuation range of the number of CHs, producing a unbalanced energy consumption of the whole network; And that one-hop communication mechanism applied to data transmission between CHs and the base station(BS) produces huge energy depletion, which is adverse to improving the network lifetime. To overcome these drawbacks, we propose an enhanced multi-hop LEACH protocol based on optimal cluster-heads and region division(called LEACH-OCHRM for short). It makes improvements mainly in two aspects: on one hand, improvement in clustering. Calculate the optimal number of CHs as k, then divide the network into k sub-region each of which contains the same number of nodes. For each sub-region, calculate the coordinate of center of mass. Finally, the high-energy node that is close to center of mass is selected as CH in each sub-region. On the other hand, improvement in communication mechanism, the communication between CHs and Sink is no longer mode of single-hop, but a multi-hop communication link established according to the weight of energy of CH and distance between CH and BS. In this way, the CH far from BS will choose a higher-weight farward CH as the relay node to forward data then to BS finally. All of CHs transmit data to Sink via the multi-hop chain.Take LEACH、LEACH-EE and LEACH-OCHRM as simulate project and simulate performance of these three protocols by Matlab, mainly contrast theIIAbstractperformance figure such as topology structure, the number of CHs, the total energy consumption and lifetime of network. The simulation results indicate that the improved LEACH-OCHRM algorithm produces an evener distribution of CHs, balances the energy consumption of the network, greatly improving the performance and lifetime of the network as a result.Keywords: wireless sensor networks; LEACH; optimal number of CHs; same number of nodes; region distribution; multi-hopIII目录目录第1章绪论 (1)1.1 背景与意义 (1)1.2 无线传感器网络的应用 (2)1.3 无线传感器网络路由协议的研究现状 (4)1.4 研究内容与论文组织架构 (5)第2章无线传感器网络路由协议分析 (8)2.1 无线传感器网络路由协议概述 (8)2.2 无线传感器网络路由协议分类 (9)2.3平面型路由协议 (9)2.3.1Flooding (9)2.3.2Gossip (10)2.3.3SPIN (11)2.3.4DD (12)2.4 层次型路由协议 (13)2.4.1LEACH (14)2.4.2SEP (15)2.4.3PEGASIS (16)2.4.4TEEN (17)2.4.5LEACH-C和LEACH-F (18)2.4.6LEACH-EE (19)2.5 无线传感器网络路由协议的性能比较 (20)第3章 LEACH路由协议的研究与改进 (21)3.1LEACH协议描述 (21)3.2LEACH协议的能量模型 (23)3.3LEACH协议的优缺点分析 (25)3.4LEACH-OCHRM协议 (27)3.4.1 最优簇首数目计算 (27)IV目录3.4.2 基于最优簇首数目的区域划分 (29)3.4.3 簇首选举 (30)3.4.4 建立多跳路由 (32)第4章仿真结果与分析 (36)4.1 仿真环境 (36)4.2 仿真结果与分析 (37)4.2.1 拓扑结构 (37)4.2.2 簇首数目 (39)4.2.3 簇负载平衡度 (40)4.2.4 能量消耗 (41)4.2.5 生命周期 (42)4.2.6 稳定期与不稳定期 (43)4.2.7 吞吐量 (45)第5章总结与展望 (49)5.1 论文总结 (49)5.2 展望 (50)致谢 (51)参考文献 (53)攻读学位期间的研究成果 (56)V缩略词说明缩略词说明WSN Wireless Sensor Network SNV Sensor Node Value CH Cluster Head BS Base Station DSSS Direct Sequence Spread Spectrum non-CH non-Cluster head CM Cluster Member FCH Farward Cluster Head LBF Load Balance Factor FND First Death of Nodes LND Last Death of NodesVI第1章绪论1第1章绪论1.1 背景与意义在移动互联网持续高速发展的今天，移动终端得到大面积的普及，这也使得人们更加迫切地追求获取移动数据信息的实时性和有效性[1]。

leach(研究无线传感器网络协议的文章)

Copyright2000IEEE.Published in the Proceedings of the Hawaii International Conference on System Sciences,January4-7,2000,Maui,Hawaii. Energy-Efﬁcient Communication Protocol for Wireless Microsensor Networks Wendi Rabiner Heinzelman,Anantha Chandrakasan,and Hari BalakrishnanMassachusetts Institute of TechnologyCambridge,MA02139wendi,anantha,hari@AbstractWireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications.In this paper,we look at communication protocols,which can have signiﬁcant im-pact on the overall energy dissipation of these networks. Based on ourﬁndings that the conventional protocols of direct transmission,minimum-transmission-energy,multi-hop routing,and static clustering may not be optimal for sensor networks,we propose LEACH(Low-Energy Adap-tive Clustering Hierarchy),a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads)to evenly distribute the energy load among the sensors in the network.LEACH uses localized coordi-nation to enable scalability and robustness for dynamic net-works,and incorporates data fusion into the routing proto-col to reduce the amount of information that must be trans-mitted to the base station.Simulations show that LEACH can achieve as much as a factor of8reduction in energy dissipation compared with conventional routing protocols. In addition,LEACH is able to distribute energy dissipation evenly throughout the sensors,doubling the useful system lifetime for the networks we simulated.1.IntroductionRecent advances in MEMS-based sensor technology, low-power analog and digital electronics,and low-power RF design have enabled the development of relatively in-expensive and low-power wireless microsensors[2,3,4]. These sensors are not as reliable or as accurate as their ex-pensive macrosensor counterparts,but their size and cost enable applications to network hundreds or thousands of these microsensors in order to achieve high quality,fault-tolerant sensing networks.Reliable environment monitor-ing is important in a variety of commercial and military applications.For example,for a security system,acoustic,seismic,and video sensors can be used to form an ad hoc network to detect intrusions.Microsensors can also be used to monitor machines for fault detection and diagnosis.Microsensor networks can contain hundreds or thou-sands of sensing nodes.It is desirable to make these nodes as cheap and energy-efﬁcient as possible and rely on their large numbers to obtain high quality work pro-tocols must be designed to achieve fault tolerance in the presence of individual node failure while minimizing en-ergy consumption.In addition,since the limited wireless channel bandwidth must be shared among all the sensors in the network,routing protocols for these networks should be able to perform local collaboration to reduce bandwidth requirements.Eventually,the data being sensed by the nodes in the net-work must be transmitted to a control center or base station, where the end-user can access the data.There are many pos-sible models for these microsensor networks.In this work, we consider microsensor networks where:The base station isﬁxed and located far from the sen-sors.All nodes in the network are homogeneous and energy-constrained.Thus,communication between the sensor nodes and the base station is expensive,and there are no“high-energy”nodes through which communication can proceed.This is the framework for MIT’s-AMPS project,which focuses on innovative energy-optimized solutions at all levels of the system hierarchy,from the physical layer and communica-tion protocols up to the application layer and efﬁcient DSP design for microsensor nodes.Sensor networks contain too much data for an end-user to process.Therefore,automated methods of combining or aggregating the data into a small set of meaningful informa-tion is required[7,8].In addition to helping avoid informa-tion overload,data aggregation,also known as data fusion, can combine several unreliable data measurements to pro-duce a more accurate signal by enhancing the common sig-nal and reducing the uncorrelated noise.The classiﬁcation performed on the aggregated data might be performed by a human operator or automatically.Both the method of per-forming data aggregation and the classiﬁcation algorithm are application-speciﬁc.For example,acoustic signals are often combined using a beamforming algorithm[5,17]to reduce several signals into a single signal that contains the relevant information of all the individual rge en-ergy gains can be achieved by performing the data fusion or classiﬁcation algorithm locally,thereby requiring much less data to be transmitted to the base station.By analyzing the advantages and disadvantages of con-ventional routing protocols using our model of sensor net-works,we have developed LEACH(Low-Energy Adaptive Clustering Hierarchy),a clustering-based protocol that min-imizes energy dissipation in sensor networks.The key fea-tures of LEACH are:Localized coordination and control for cluster set-up and operation.Randomized rotation of the cluster“base stations”or “cluster-heads”and the corresponding clusters.Local compression to reduce global communication. The use of clusters for transmitting data to the base sta-tion leverages the advantages of small transmit distances for most nodes,requiring only a few nodes to transmit far distances to the base station.However,LEACH out-performs classical clustering algorithms by using adaptive clusters and rotating cluster-heads,allowing the energy re-quirements of the system to be distributed among all the sensors.In addition,LEACH is able to perform local com-putation in each cluster to reduce the amount of data that must be transmitted to the base station.This achieves a large reduction in the energy dissipation,as computation is much cheaper than communication.2.First Order Radio ModelCurrently,there is a great deal of research in the area of low-energy radios.Different assumptions about the radio characteristics,including energy dissipation in the transmit and receive modes,will change the advantages of different protocols.In our work,we assume a simple model where the radio dissipates nJ/bit to run the transmit-ter or receiver circuitry and pJ/bit/m for the transmit ampliﬁer to achieve an acceptableFor example,the Bluetooth initiative[1]speciﬁes700Kbps radios that operate at2.7V and30mA,or115nJ/bit.Operationenergy loss due to channel transmission.Thus,to trans-mit a-bit message a distance using our radio model,the radio expends:(1) and to receive this message,the radio expends:(2)For these parameter values,receiving a message is not a low cost operation;the protocols should thus try to minimize not only the transmit distances but also the number of transmit and receive operations for each message.We make the assumption that the radio channel is sym-metric such that the energy required to transmit a message from node A to node B is the same as the energy required to transmit a message from node B to node A for a given SNR.For our experiments,we also assume that all sensors are sensing the environment at aﬁxed rate and thus always have data to send to the end-user.For future versions of our protocol,we will implement an”event-driven”simulation, where sensors only transmit data if some event occurs in the environment.3.Energy Analysis of Routing ProtocolsThere have been several network routing protocols pro-posed for wireless networks that can be examined in thecontext of wireless sensor networks.We examine two such protocols,namely direct communication with the base sta-tion and minimum-energy multi-hop routing using our sen-sor network and radio models.In addition,we discuss a conventional clustering approach to routing and the draw-backs of using such an approach when the nodes are all energy-constrained.Using a direct communication protocol,each sensorsends its data directly to the base station.If the base sta-tion is far away from the nodes,direct communication will require a large amount of transmit power from each node(since in Equation1is large).This will quickly drain the battery of the nodes and reduce the system lifetime.How-ever,the only receptions in this protocol occur at the base station,so if either the base station is close to the nodes,or the energy required to receive data is large,this may be an acceptable(and possibly optimal)method of communica-tion.The second conventional approach we consider is a“minimum-energy”routing protocol.There are several power-aware routing protocols discussed in the literature[6, 9,10,14,15].In these protocols,nodes route data des-tined ultimately for the base station through intermediate nodes.Thus nodes act as routers for other nodes’data in addition to sensing the environment.These protocols dif-fer in the way the routes are chosen.Some of these proto-cols[6,10,14],only consider the energy of the transmitter and neglect the energy dissipation of the receivers in de-termining the routes.In this case,the intermediate nodes are chosen such that the transmit ampliﬁer energy(e.g.,)is minimized;thus node A would transmit to node C through node B if and only if:(3) or(4) However,for this minimum-transmission-energy(MTE) routing protocol,rather than just one(high-energy)trans-mit of the data,each data message must go through(low-energy)transmits and receives.Depending on the rela-tive costs of the transmit ampliﬁer and the radio electronics, the total energy expended in the system might actually be greater using MTE routing than direct transmission to the base station.To illustrate this point,consider the linear network shown in Figure2,where the distance between the nodes is.If we consider the energy expended transmitting a sin-gle-bit message from a node located a distance froma(6) Therefore,direct communication requires less energy than MTE routing if:(7)Using Equations1-6and the random100-node network shown in Figure3,we simulated transmission of data from every node to the base station(located100m from the clos-est sensor node,at(x=0,y=-100))using MATLAB.Figure4 shows the total energy expended in the system as the net-work diameter increases from10m10m to100m100 m and the energy expended in the radio electronics(i.e.,)increases from10nJ/bit to100nJ/bit,for the sce-nario where each node has a2000-bit data packet to send to the base station.This shows that,as predicted by our anal-ysis above,when transmission energy is on the same order as receive energy,which occurs when transmission distance is short and/or the radio electronics energy is high,direct transmission is more energy-efﬁcient on a global scale than MTE routing.Thus the most energy-efﬁcient protocol to use depends on the network topology and radio parameters of the system.−25−20−15−10−5051015202505101520253035404550Figure 3.100-node random network.Figure 4.Total energy dissipated in the 100-node random network using direct commu-nication and MTE routing (i.e.,and).pJ/bit/m ,and the mes-sages are 2000bits.Figure 5.System lifetime using direct trans-mission and MTE routing with 0.5J/node.It is clear that in MTE routing,the nodes closest to the base station will be used to route a large number of data messages to the base station.Thus these nodes will die out quickly,causing the energy required to get the remaining data to the base station to increase and more nodes to die.This will create a cascading effect that will shorten system lifetime.In addition,as nodes close to the base station die,that area of the environment is no longer being monitored.To prove this point,we ran simulations using the random 100-node network shown in Figure 3and had each sensor send a 2000-bit data packet to the base station during each time step or “round”of the simulation.After the energy dissipated in a given node reached a set threshold,that node was considered dead for the remainder of the simulation.Figure 5shows the number of sensors that remain alive after each round for direct transmission and MTE routing with each node initially given 0.5J of energy.This plot shows that nodes die out quicker using MTE routing than direct transmission.Figure 6shows that nodes closest to the base station are the ones to die out ﬁrst for MTE routing,whereas nodes furthest from the base station are the ones to die out ﬁrst for direct transmission.This is as expected,since the nodes close to the base station are the ones most used as “routers”for other sensors’data in MTE routing,and the nodes furthest from the base station have the largest transmit energy in direct communication.A ﬁnal conventional protocol for wireless networks is clustering,where nodes are organized into clusters that communicate with a local base station,and these local base stations transmit the data to the global base station,where it is accessed by the end-user.This greatly reduces the dis-tance nodes need to transmit their data,as typically the local base station is close to all the nodes in the cluster.−25−20−15−10−5051015202505101520253035404550X−coordinateY −c o o r d i n a t eFigure 6.Sensors that remain alive (circles)and those that are dead (dots)after 180rounds with 0.5J/node for (a)direct trans-mission and (b)MTE routing.Thus,clustering appears to be an energy-efﬁcient commu-nication protocol.However,the local base station is as-sumed to be a high-energy node;if the base station is an energy-constrained node,it would die quickly,as it is be-ing heavily utilized.Thus,conventional clustering would perform poorly for our model of microsensor networks.The Near Term Digital Radio (NTDR)project [12,16],an army-sponsored program,employs an adaptive clustering approach,similar to our work discussed here.In this work,cluster-heads change as nodes move in order to keep the network fully connected.However,the NTDR protocol is designed for long-range communication,on the order of 10s of kilometers,and consumes large amounts of power,on the order of 10s of Watts.Therefore,this protocol also does not ﬁt our model of sensor networks.4.LEACH:Low-Energy Adaptive Clustering HierarchyLEACH is a self-organizing,adaptive clustering protocol that uses randomization to distribute the energy load evenly among the sensors in the network.In LEACH,the nodes organize themselves into local clusters,with one node act-ing as the local base station or cluster-head .If the cluster-heads were chosen a priori and ﬁxed throughout the system lifetime,as in conventional clustering algorithms,it is easy to see that the unlucky sensors chosen to be cluster-heads would die quickly,ending the useful lifetime of all nodes belonging to those clusters.Thus LEACH includes random-ized rotation of the high-energy cluster-head position such that it rotates among the various sensors in order to not drain the battery of a single sensor.In addition,LEACH performs local data fusion to “compress”the amount of data being sent from the clusters to the base station,further reducing energy dissipation and enhancing system lifetime.Sensors elect themselves to be local cluster-heads at any given time with a certain probability.These cluster-head nodes broadcast their status to the other sensors in the network.Each sensor node determines to which clus-ter it wants to belong by choosing the cluster-head that re-quires the minimum communication energy .Once all the nodes are organized into clusters,each cluster-head creates a schedule for the nodes in its cluster.This allows the radio components of each non-cluster-head node to be turned off at all times except during its transmit time,thus minimizing the energy dissipated in the individual sensors.Once the cluster-head has all the data from the nodes in its cluster,the cluster-head node aggregates the data and then transmits the compressed data to the base station.Since the base station is far away in the scenario we are examining,this is a high energy transmission.However,since there are only a few cluster-heads,this only affects a small number of nodes.As discussed previously,being a cluster-head drains the battery of that node.In order to spread this energy usage over multiple nodes,the cluster-head nodes are not ﬁxed;rather,this position is self-elected at different time intervals.Thus a set of nodes might elect themselves cluster-heads at time ,but at time a new set of nodes elect themselves as cluster-heads,as shown in Figure 7.The de-cision to become a cluster-head depends on the amount of energy left at the node.In this way,nodes with more en-ergy remaining will perform the energy-intensive functions of the network.Each node makes its decision about whether to be a cluster-head independently of the other nodes in the05101520253035404550Figure 7.Dynamic clusters:(a)cluster-head nodes =at time (b)cluster-head nodes=at time .All nodes marked with a given symbol belong to the same cluster,and the cluster-head nodes are marked with a .network and thus no extra negotiation is required to deter-mine the cluster-heads.The system can determine,a priori,the optimal number of clusters to have in the system.This will depend on sev-eral parameters,such as the network topology and the rela-tive costs of computation versus communication.We sim-ulated the LEACH protocol for the random network shown in Figure 3using the radio parameters in Table 1and a com-putation cost of 5nJ/bit/message to fuse 2000-bit messages while varying the percentage of total nodes that are cluster-heads.Figure 8shows how the energy dissipation in the system varies as the percent of nodes that are cluster-heads is changed.Note that 0cluster-heads and 100%cluster-heads is the same as direct communication.From this plot,we ﬁnd that there exists an optimal percent of nodes that should be cluster-heads.If there are fewer than cluster-heads,some nodes in the network have to transmit their data very far to reach the cluster-head,causing the global energyFigure 8.Normalized total system energy dis-sipated versus the percent of nodes that are cluster-heads.Note that direct transmission is equivalent to 0nodes being cluster-heads or all the nodes being cluster-heads.in the system to be large.If there are more than cluster-heads,the distance nodes have to transmit to reach the near-est cluster-head does not reduce substantially,yet there are more cluster-heads that have to transmit data the long-haul distances to the base station,and there is less compression being performed locally.For our system parameters and topology,%.Figure 8also shows that LEACH can achieve over a fac-tor of 7reduction in energy dissipation compared to direct communication with the base station,when using the opti-mal number of cluster-heads.The main energy savings of the LEACH protocol is due to combining lossy compression with the data routing.There is clearly a trade-off between the quality of the output and the amount of compression achieved.In this case,some data from the individual sig-nals is lost,but this results in a substantial reduction of the overall energy dissipation of the system.We simulated LEACH (with 5%of the nodes being cluster-heads)using MATLAB with the random network shown in Figure 3.Figure 9shows how these algorithms compare using nJ/bit as the diameter of the net-work is increased.This plot shows that LEACH achieves between 7x and 8x reduction in energy compared with di-rect communication and between 4x and 8x reduction in energy compared with MTE routing.Figure 10shows the amount of energy dissipated using LEACH versus using di-rect communication and LEACH versus MTE routing as the network diameter is increased and the electronics energy varies.This ﬁgure shows the large energy savings achieved using LEACH for most of the parameter space.Network diameter (m)T o t a l e n e r g y d i s s i p a t e d i n s y s t e m (J o u l e s )Figure 9.Total system energy dissipated us-ing direct communication,MTE routing and LEACH for the 100-node random network shown in Figure 3.nJ/bit,pJ/bit/m ,and the messages are 2000bits.In addition to reducing energy dissipation,LEACH suc-cessfully distributes energy-usage among the nodes in thenetwork such that the nodes die randomly and at essentially the same rate.Figure 11shows a comparison of system lifetime using LEACH versus direct communication,MTE routing,and a conventional static clustering protocol,where the cluster-heads and associated clusters are chosen initially and remain ﬁxed and data fusion is performed at the cluster-heads,for the network shown in Figure 3.For this exper-iment,each node was initially given 0.5J of energy.Fig-ure 11shows that LEACH more than doubles the useful sys-tem lifetime compared with the alternative approaches.We ran similar experiments with different energy thresholds and found that no matter how much energy each node is given,it takes approximately 8times longer for the ﬁrst node to die and approximately 3times longer for the last node to die in LEACH as it does in any of the other protocols.The data from these experiments is shown in Table 2.The ad-vantage of using dynamic clustering (LEACH)versus static clustering can be clearly seen in Figure ing a static clustering algorithm,as soon as the cluster-head node dies,all nodes from that cluster effectively die since there is no way to get their data to the base station.While these simu-lations do not account for the setup time to conﬁgure the dynamic clusters (nor do they account for any necessary routing start-up costs or updates as nodes die),they give a good ﬁrst order approximation of the lifetime extension we can achieve using LEACH.Another important advantage of LEACH,illustrated in Figure 12,is the fact that nodes die in essentially a “ran-Figure 10.Total system energy dissipated using (a)direct communication and LEACH and (b)MTE routing and LEACH for the ran-dom network shown in Figure 3.pJ/bit/m ,and the messages are 2000bits.Figure 11.System lifetime using direct trans-mission,MTE routing,static clustering,and LEACH with 0.5J/node.Table2.Lifetimes using different amounts ofinitial energy for the sensors.Energy Protocol Round last(J/node)node diesDirect1175Static Clustering673941090.5MTE42980LEACH1312Direct46815Static Clustering2401848-ing this threshold,each node will be a cluster-head at somepoint withinrounds.Thus the probability that the remainingnodes are cluster-heads must be increased,since there arefewer nodes that are eligible to become cluster-heads.Af-terrounds,all nodes are onceagain eligible to become cluster-heads.Future versions ofthis work will include an energy-based threshold to accountfor non-uniform energy nodes.In this case,we are assum-ing that all nodes begin with the same amount of energyand being a cluster-head removes approximately the sameamount of energy for each node.Each node that has elected itself a cluster-head for thecurrent round broadcasts an advertisement message to therest of the nodes.For this“cluster-head-advertisement”phase,the cluster-heads use a CSMA MAC protocol,and allcluster-heads transmit their advertisement using the sametransmit energy.The non-cluster-head nodes must keeptheir receivers on during this phase of set-up to hear the ad-vertisements of all the cluster-head nodes.After this phaseis complete,each non-cluster-head node decides the clusterto which it will belong for this round.This decision is basedon the received signal strength of the advertisement.As-suming symmetric propagation channels,the cluster-headadvertisement heard with the largest signal strength is thecluster-head to whom the minimum amount of transmittedenergy is needed for communication.In the case of ties,a random cluster-head is chosen.5.2Cluster Set-Up PhaseAfter each node has decided to which cluster it belongs, it must inform the cluster-head node that it will be a member of the cluster.Each node transmits this information back to the cluster-head again using a CSMA MAC protocol.Dur-ing this phase,all cluster-head nodes must keep their re-ceivers on.5.3Schedule CreationThe cluster-head node receives all the messages for nodes that would like to be included in the cluster.Based on the number of nodes in the cluster,the cluster-head node creates a TDMA schedule telling each node when it can transmit.This schedule is broadcast back to the nodes in the cluster.5.4Data TransmissionOnce the clusters are created and the TDMA schedule isﬁxed,data transmission can begin.Assuming nodes al-ways have data to send,they send it during their allocated transmission time to the cluster head.This transmission uses a minimal amount of energy(chosen based on the received strength of the cluster-head advertisement).The radio of each non-cluster-head node can be turned off un-til the node’s allocated transmission time,thus minimizing energy dissipation in these nodes.The cluster-head node must keep its receiver on to receive all the data from the nodes in the cluster.When all the data has been received, the cluster head node performs signal processing functions to compress the data into a single signal.For example,if the data are audio or seismic signals,the cluster-head node can beamform the individual signals to generate a compos-ite signal.This composite signal is sent to the base station. Since the base station is far away,this is a high-energy trans-mission.This is the steady-state operation of LEACH networks. After a certain time,which is determined a priori,the next round begins with each node determining if it should be a cluster-head for this round and advertising this information, as described in Section5.1.5.5.Multiple ClustersThe preceding discussion describes how the individual clusters communicate among nodes in that cluster.How-ever,radio is inherently a broadcast medium.As such, transmission in one cluster will affect(and hence degrade)toure13A’s transmission,while intended for Node B,corrupts any transmission to Node C.To reduce this type of interference, each cluster communicates using different CDMA codes. Thus,when a node decides to become a cluster-head,it chooses randomly from a list of spreading codes.It informs all the nodes in the cluster to transmit using this spreading code.The cluster-head thenﬁlters all received energy using the given spreading code.Thus neighboring clusters’radio signals will beﬁltered out and not corrupt the transmission of nodes in the cluster.Efﬁcient channel assignment is a difﬁcult problem,even when there is a central control center that can perform the necessary ing CDMA codes,while not nec-essarily the most bandwidth efﬁcient solution,does solves the problem of multiple-access in a distributed manner.5.6.Hierarchical ClusteringThe version of LEACH described in this paper can be extended to form hierarchical clusters.In this scenario,the cluster-head nodes would communicate with“super-cluster-head”nodes and so on until the top layer of the hierarchy, at which point the data would be sent to the base station. For larger networks,this hierarchy could save a tremendous amount of energy.In future studies,we will explore the de-tails of implementing this protocol without using any sup-port from the base station,and determine,via simulation, exactly how much energy can be saved.6.ConclusionsIn this paper,we described LEACH,a clustering-based routing protocol that minimizes global energy usage by dis-tributing the load to all the nodes at different points in time. LEACH outperforms static clustering algorithms by requir-ing nodes to volunteer to be high-energy cluster-heads and adapting the corresponding clusters based on the nodes that choose to be cluster-heads at a given time.At different times,each node has the burden of acquiring data from the nodes in the cluster,fusing the data to obtain an aggregate signal,and transmitting this aggregate signal to the base sta-tion.LEACH is completely distributed,requiring no control information from the base station,and the nodes do not re-quire knowledge of the global network in order for LEACH to operate.Distributing the energy among the nodes in the network is effective in reducing energy dissipation from a global per-spective and enhancing system lifetime.Speciﬁcally,our simulations show that:LEACH reduces communication energy by as much as 8x compared with direct transmission and minimum-transmission-energy routing.Theﬁrst node death in LEACH occurs over8times later than theﬁrst node death in direct transmission, minimum-transmission-energy routing,and a static clustering protocol,and the last node death in LEACH occurs over3times later than the last node death in the other protocols.In order to verify our assumptions about LEACH,we are currently extending the network simulator ns[11]to simulate LEACH,direct communication,and minimum-transmission-energy routing.This will verify our assump-tions and give us a more accurate picture of the advantages and disadvantages of the different protocols.Based on our MATLAB simulations described above,we are conﬁdent that LEACH will outperform conventional communication protocols,in terms of energy dissipation,ease of conﬁgura-tion,and system lifetime/quality of the network.Providing such a low-energy,ad hoc,distributed protocol will help pave the way for future microsensor networks. AcknowledgmentsThe authors would like to thank the anonymous review-ers for the helpful comments and suggestions.W.Heinzel-man is supported by a Kodak Fellowship.This work was funded in part by DARPA.References[1]Bluetooth Project.,1999.[2]Chandrakasan,Amirtharajah,Cho,Goodman,Konduri,Ku-lik,Rabiner,and Wang.Design Considerations for Dis-tributed Microsensor Systems.In IEEE1999Custom In-tegrated Circuits Conference(CICC),pages279–286,May 1999.[3]Clare,Pottie,and Agre.Self-Organizing Distributed Sen-sor Networks.In SPIE Conference on Unattended Ground Sensor Technologies and Applications,pages229–237,Apr.1999.[4]M.Dong,K.Yung,and W.Kaiser.Low Power SignalProcessing Architectures for Network Microsensors.In Proceedings1997International Symposium on Low Power Electronics and Design,pages173–177,Aug.1997.[5] D.Dudgeon and R.Mersereau.Multidimensional DigitalSignal Processing,chapter6.Prentice-Hall,Inc.,1984. [6]M.Ettus.System Capacity,Latency,and Power Consump-tion in Multihop-routed SS-CDMA Wireless Networks.In Radio and Wireless Conference(RAWCON’98),pages55–58,Aug.1998.[7] D.Hall.Mathematical Techniques in Multisensor Data Fu-sion.Artech House,Boston,MA,1992.[8]L.Klein.Sensor and Data Fusion Concepts and Applica-tions.SPIE Optical Engr Press,W A,1993.[9]X.Lin and I.Stojmenovic.Power-Aware Routing in Ad HocWireless Networks.In SITE,University of Ottawa,TR-98-11,Dec.1998.[10]T.Meng and R.V olkan.Distributed Network Protocolsfor Wireless Communication.In Proc.IEEEE ISCAS,May 1998.[11]UCB/LBNL/VINT Network Simulator-ns(Version2)./ns/,1998.[12]R.Ruppe,S.Griswald,P.Walsh,and R.Martin.Near TermDigital Radio(NTDR)System.In Proceedings MILCOM ’97,pages1282–1287,Nov.1997.[13]K.Scott and N.Bambos.Routing and Channel Assignmentfor Low Power Transmission in PCS.In5th IEEE Int.Conf.on Universal Personal Communications,volume2,pages 498–502,Sept.1996.[14]T.Shepard.A Channel Access Scheme for Large DensePacket Radio Networks.In Proc.ACM SIGCOMM,pages 219–230,Aug.1996.[15]S.Singh,M.Woo,and C.Raghavendra.Power-Aware Rout-ing in Mobile Ad Hoc Networks.In Proceedings of the Fourth Annual ACM/IEEE International Conference on Mo-bile Computing and Networking(MobiCom’98),Oct.1998.[16]L.Williams and L.Emergy.Near Term Digital Radio-a FirstLook.In Proceedings of the1996Tactical Communications Conference,pages423–425,Apr.1996.[17]K.Yao,R.Hudson,C.Reed,D.Chen,and F.Lorenzelli.Blind Beamforming on a Randomly Distributed Sensor Ar-ray System.Proceedings of SiPS,Oct.1998.。

浅谈无线传感器网络LEACH协议的研究与改进

一
技术市场
浅谈无线传感器网络ＬＡ协议的ＣＥＨ
研究与改进
程彦智
（酒钢宏兴股份公司检修工程部，肃甘嘉峪关７５０）３１０
【要】摘文章在分析了无线传感器网络中分层路由ＬＡＨ算法的基础上，出了一种新型选择簇首节点的方法，ＥＣ提并通过ｍａａ对改进后的ＬＡＨ算法进行了仿真，仿真结果显示改进后的ＬＡＨ算法在网络生命周期和网络能量消耗两方面比ｔｂｌＥＣＥＣ
１任东陈伟，自雪峰．《美国宪政历程：响美国的２个司法大影５的犯罪分子大得多。如果政府无视正当法律程序，执法犯法，那【］来，
案》．中国法制出版社，０４ｌ２５２０，（９）
［胡健泼．北京大学．沉默权若干问题研究．２０（２］０１４）
民大学出版社，０３４２２０（１）
３陈瑞华．《问题与主义之问——刑事诉讼基本问题研究》．中国人法律必须尊重每个人的自由和尊严，在诉讼程序中要求将［］
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权利，明罪犯的责任由控诉方承担。沉默与自由是当事人的告人提供了一种在保持沉默与作出供述之间进行选择的权利证而不是把如实回答的义务加在嫌疑人、被告人身上的权利，否则口供归于无效沉默权对防止政府官员执法犯法、滥的自由，用权力，防止刑讯逼供有很重要的作用。在其实际意义中对警强制。沉默权是保护了嫌疑人的人权，而刑事诉讼肩负的是惩保护受害者人权的责任。因此，行使沉默权的嫌疑人也察为了及时破案、功请赏，邀而对犯罪嫌疑人进行的的疲劳审处犯罪，讯、刑讯逼供和精神恐吓的现象得到了遏制。会陷入对自己不利的地位。因为坚持缄默的同时也丧失了为自司法当局就会依据其他证据做出不利于在程序上保障了其正义性，程序公正包括立法公正和执法己辩护的权力和机会，公正，其核心内容是程序的合理性、公开性、平等性和效率性。沉默者的推定和判决。正义的程序作为刑事诉讼制度追求的基本的价值目标，要求国对于沉默权问题，果以纯粹对策式的法学研究方法来研如家司法机关在追究犯罪、罚犯罪的刑事诉讼活动中必须以依究沉默权将很难走出用西方理论研究和论证西方沉默权的怪惩从而难以从我国的司法体制、诉讼制度甚至整个社会环境照程序和步骤办事，保证其程序的正当性、合理性和统一性。其圈，主旨在于防止权力的滥用，进而保护涉讼公民的人身权和身体的高度来把握我国的沉默权问题。英美国家在沉默权问题上走之字型的曲折道路，它给我们提供了许多有益的启权。程序公正价值要求司法侦讯人员在刑事诉讼中对犯罪嫌疑了一段“ ” 应人和被告人告知其享有沉默的权利。程序平等价值要求保障诉示。我们在研究到底是否应当引进沉默权制度时，当认真地讼参与人在诉讼活动中能够平等的参与活动，受到平等的待回顾这一段反复变化的历史。遇，进而保障程序参与者的人格主体性，由此实现程序的平等。

无线传感器网络路由协议研究

无线传感器网络路由协议研究摘要：对无线传感器网络经典的LEACH 路由算法作了分析，提出了一种改进型算法。

通过仿真，改进后的LEACH 算法提高了传感器网络的能效，延长了系统的工作寿命。

关键词：无线传感器；路由协议；LEACH0 引言传感器网络通常由覆盖一个地区的若干传感器节点组成。

每个传感器节点独立进行数据收集及处理，并将得到的数据通过无线连接传送到网关节点，再由网关节点向互联网发送。

对于传感器网络，路由协议设计是很具挑战性的。

首先，节点没有全球唯一的标识符，传统的互联网路由协议无法应用在传感器网络中；第二，传感器网络中的所有节点都是源节点，向唯一的目的节点Sink 发送数据；第三，由于在被测对象内部或附近部署了大量的节点，它们采集到的数据是相同或相近的。

这就需要路由协议具有数据融合力，以节约电能，提高带宽利用率；第四，节点具备处理能力。

节点的电能存储能力是很有限的，需要强大的资源管理和任务调度能力。

因此，传感器网络的路由协议是与传统网络截然不同的。

1 LEACH 协议簇的建立和簇头特定任务的分配对于整个系统的可扩展性、寿命和能量效率起着非常大的作用。

聚类路由是降低簇中能量消耗的一种有效方式。

LEACH(Low-Energy Adaptive Cluster-based Hierarchy)算法是最早的比较成熟的聚类路由算法。

LEACH 协议的随机簇头选择分布不均匀，而且LEACH 协议是根据节点曾经担当簇头的次数来决定是否担任簇头而没有考虑节点的剩余能量；同时，LEACH 网络协议在节点数量大的无线传感器网络中使用时会采集大量的冗余数据，这样会使网络由于处理大量的冗余数据而使网络能耗大大增加，缩短了网络的生存周期。

LEACH-C(LEACH-centralized)是集中式的簇头产生算法，由基站负责挑选簇头。

因为无线传感器网络中使用节点数量大，节点覆盖密度也大，这样无法避免地使单个节点采集的数据与整个。

无线传感器网络路由协议研究

无线传感器网络路由协议研究[摘要] 无线传感器网络是由传感器技术、计算机网络技术和微机电系统(MEMS)综合发展而来的一门新型信息采集和处理的新兴技术产物,其广泛应用于环境科学、国防军事、医疗健康等领域,日益受到国内外的关注。

本文以典型的分簇式路由协议--LEACH协议作为研究对象,分析了LEACH协议的特点,并与异构分簇协议DEEC协议进行了比较。

模拟实验结果显示,与LEACH分簇协议相比,此分簇算法在异构网络下提供了更长的网络生存时间和更大的网络有效吞吐量。

[关键词] 无线传感器网络路由协议LEACH DEEC 能量有效1.研究背景和意义无线传感器网络涉及了诸多学科高度交叉和众多知识高度集成的一门新兴技术,是当今研究的热点前沿领域无线传感器网络(Wireless Sensor Network)是由随机部署在所监测区域内大量微型传感器节点通过无线通信技术组成的自组织网络,各传感器节点协作地感知、采集和处理网络覆盖区域中感知对象的信息,并发送给观测者。

无线传感器网络集数据的采集、传输及融合分析于一体,是信息技术的一个新热点研究领域,在国防军事、环境监测、工农业、城市管理、生物医疗、抢险救灾、远程监控等领域具有广阔的应用前景及巨大潜在的应用价值,已经被认为对21世纪产生巨大影响的重大技术之一,成为一个公认的新兴热点研究领域。

无线传感器网络路由协议研究设计的重要目标是降低节点能源损耗,提高网络生存时间,这一重要目标已经成为无线传感器网络研究中的热点。

2.无线传感器网络的结构无线传感器网络是由众多传感器节点以AdHoc(移动自组织、多跳网络)方式构成的无线网络。

其目的是感知、采集和转发网络覆盖的地理区域中感知对象的各种信息,并发送给观测者。

依据以上的定义,传感器节点、感知对象和观测者是无线传感器网络的三个组成部分;无线网络是传感器之间、传感器节点与观测者之间进行数据传送的通信基础,用于在传感器与观测者之间建立通信路径;协作地感知、采集、处理、发送感知信息是无线传感器网络的基本功能。

无线传感器网络LEACH路由协议研究与改进

无线传感器网络LEACH路由协议研究与改进王开通;熊庆宇;王小刚;齐洋洋;于海存【期刊名称】《计算机工程与应用》【年(卷),期】2015(000)010【摘要】In view of the LEACH algorithm randomly selects a cluster head, without considering the residual energy of the nodes and single hop communication with sink nodes bring about the problem of excessive energy loss. A waiting time for cluster head competition mode, introduced the residual energy and the number of neighbor nodes asweighting factors to selecting cluster head is proposed. And it proposes to combine the LEACH and ant colony algorithm to establish the inter-cluster routing mechanism, updating the local pheromone and synthesizing the residual energy, distance nodes of cluster head and cluster head node for communication with sink based multi-hop routing, to reach the purpose of reducing the cluster head node energy consumption too fast. The simulation experimental results show that the improved algorithm in reducing energy consumption and prolonging the network life cycle has a great improvement compared with LEACH algorithm.%针对LEACH算法随机选取簇头，未考虑节点剩余能量及单跳与sink节点通信造成能量损耗过快的问题。

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无线传感器网络LEACH协议的研究摘要：无线传感器网络因其在军事、经济、民生等方面广阔的应用前景成为21世纪的前沿热点研究领域[1]。

在传感器节点能量有限的情况下，提高路由效率，延长网络寿命成为无线传感器网络需考虑的问题。

由于采取分簇，数据融合的思想，LEACH协议有着较高的路由效率，但在实际应用，尤其是大规模网络中，仍存在负载不均衡等问题。

本文主要分析了LEACH协议的基本思想及优缺点，随后针对大规模的网络环境对其分簇算法进行改进。

前人提出一种有效的方法计算最优簇首个数，本文推算出适合本文中网络环境的公式并加以应用。

本文用NS2进行仿真，仿真后的结果表明，改进后的分簇算法更为有效，延长了网络寿命，增大了网络传送数据量。

LEACH在运行过程中不断的循环执行簇的重构过程，每个簇重构过程可以用回合的概念来描述[3]。

每个回合可以分成两个阶段：簇的建立阶段和传输数据的稳定阶段。

1.2 LEACH算法的分析LEACH协议的优点[4]有: （1）LEACH 通过减少参与路由计算的节点数目，减少了路由表尺寸。

（2）LEACH协议是一种分簇路由协议，降低了非簇首节点的任务复杂度，不必对通信路由进行维护。

（3）协议不需要周期性的传输数据。

（4）在给定的时间间隔后，协议重新选举簇首节点，以保证无线传感器网络获取同意的能量分布。

由于LEACH算法是建立在一些假设上，所以在实际应用中LEACH协议存在一些问题：（1）在LEACH协议中，簇头的选举是随机产生的，这样的随机性可能会导致簇头分布不均。

簇头节点可能会集中分布在某一区域，分簇就失去了意义。

（2）在小规模的网络中，LEACH的表现较为理想，而在大规模的网络中，距离基站较远的节点总要比距离基站较近的节点消耗更多的能量，因而会较早的死亡，使整个网络负载不均衡。

（3）簇首选择的时候没有考虑节点的剩余能量，如果某个节点的剩余能量比较小，而它又恰巧被选为簇首节点，由于簇首的能量消耗比较大，那么一旦簇首的能量耗尽，那么该簇所收集的信息将不能传回汇聚节点，从而影响网络的生命周期。

2 LEACH协议的改进2.1 门限的改进LEACH在小规模的网络中性能表现较佳，但在大规模的网络环境中，就会出现能量负载不均，性能明显下降的情况。

进一步分析可以发现，在大规模网络中，远距离的节点距离基站的距离较远，无论如何分簇，传输数据要消耗更多的能量。

因此在网络中的边缘节点总是较快的耗尽能量。

而靠近基站较近的节点，相反的，因为传输数据所要消耗的能量较小，所以通常是最后死亡。

结合前人实验的结果，可以得到LEACH协议节点大致的死亡时间分布，见图，最外围的节点死亡的概率最大，次外圈的其次，而最里圈的死亡概率最小。

由于LEACH算法中，在每一轮中，簇首节点负责数据融合和与基站通信，比非簇首节点需要消耗更多的能量。

网络中的边缘簇首节点与基站通信本身就要消耗大量的能量，再加上进行数据融合，会很快死亡，甚至有可能在与基站通信时能量就消耗殆尽，造成数据的丢失。

可以看出，在大规模的网络环境中，对边缘节点进行分簇，反而会加速边缘节点的死亡，分簇失去了它的意义。

因此本文LEACH的改进中，考虑在边缘区域内，不进行分簇，即边缘区域的门限设为0。

而为了使整个网络簇首节点的期望值保持不变，相应的，增大边缘区域以外节点的门限值。

为了便于描述，假设d为网络覆盖区域边长的一半，如图2，为网络覆盖区域内到基站最d表示基站到边缘线的距离，距基站大于(1α-的范围就是边缘区域；γ为门限值增大的系数。

2.2 最优簇首节点个数层次路由协议的第一步就是分簇、选择簇首，簇的个数影响着整个网络的性能和生存周期。

若簇首数目过少，每个簇所覆盖的区域过大，成员节点到簇首节点的距离就会图1节点死亡概率分布图较远，传输数据造成的能量消耗就会增大，而且簇首节点的通信负担过重，会很快死亡，这样不利于网络的能耗平衡。

若簇首数目过多，簇首节点所消耗的能量要远远大于非簇首节点，整个网络在每轮中能量消耗增大。

另外，簇首数目过多导致数据融合的效率降低，产生过多不必要的通信能耗。

因此，一定存在一个最佳的簇首概率值[5]，使得网络在每一轮中的能耗最小，尽可能地延长整个网络的生命周期。

本文中将簇头节点的优化方案融入改进的协议，并且在本文实验的条件下对其进一步精确化。

簇头节点如下公式：。

其中，N 为无线传感器网络中的总节点个数；为自由信道传输放大器的能耗系数，单位为J/bit/m2;为多径信道传输放大器的能耗系数，单位为J/bit/m2;M 为无线传感器网络覆盖区域长度；为网络中节点到基站的距离。

3. 实验分析与改进结果3.1 仿真参数本实验针对的无线传感网络为一个正方形网络，边长为M 。

网络节点数为N 。

节点随机分布且所有节点初始能量都相同。

以轮作为运行时间的参考，即节点的存活时间等参数以运转的轮数衡量其长短。

其余参数见表格1：3.2 实验结果与分析利用awk 语言取得节点的坐标并算出节点距基站的距离，编辑关于节点的距离与节点死亡时间的文件，可得出节点存活时间与节点距基站距离的关系图。

选取实验中结果最好的一组数据和LEACH 协议的数据，用gnuplot 软件画出节点存活时间与节点距基站距离的关系图，如图2，对比其差异。

改进后的LEACH 协议比之前有较大的提高，见图3。

改进后，网络节点的存活数量在相同时间段内有明显的增加。

改进后的协议中，改变，的大小所得结果的情况为：（=0.8，=1.15）优于（α=0.8，=1.1）优于（=0.77，=1.1）。

在开始opt k =fsεm pεtoB Sd αγαγγαγ表格1 实验参数图 2改进前后LEACH 节点存活时间与节点距基站距离的关系图阶段，（=0.77，=1.1）的节点剩余最多，但是中间阶段节点存活数量下降得很快，尤其在120轮到130间，节点死亡数目比LEACH协议多了很多，所以总体性能只是较LEACH稍有提高。

而（=0.8，=1.15）虽然在开始阶段，与LEACH相比并没有太大改善，但总体节点数目较为平缓的减少，尤其在最后阶段，几个节点运行了很久才能量耗尽，使网络生存时间大大提高。

对比（=0.77，=1.1）和（α=0.8，=1.1）的曲线，即图中的绿线和蓝线，可以得出α=0.8较为合理。

=0.77范围稍偏大，以其值确定的边缘范围中，对一部分节点进行分簇网络效率更高。

对比（α=0.8，=1.1）和（=0.8，=1.15），即图中的蓝线和紫线，可以得出，在α的较优值确定的情况下，适当增大簇头的门限值，即使簇首节点数目增多，网络的效率更高。

当门限值适当增大时，曲线下降得较为平缓，说明能量分布的较为均匀。

因而网络能更久的维持。

参考文献[1] 任丰原,黄海宁,林闯. 无线传感器网络.软件学报,2003,Vol.14, No.7[2] A. Nayebi,H.Sarbazi-Azad, Performance modeling of the LEACH protocol for mobile wireless sensor networks,J.Parallelput.(2011),doi:10.116/j.jp dc.2011.02.004[3] Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” Proc. 33rd Hawaii Int’l. Conf. Sys. Sci., Jan. 2000.[4] 刘昌鑫,夏春和. 无线传感器网络路由协议比较研究. 微计算机信息 2006,22(25)[5] Yang Tao, Yaling Zheng. The Combination of the Optimal Number of Cluster-Heads and Energy Adaptive Cluster-Head Selection Algorithm in Wireless Sensor Networks. WiC0M 2006 InternationalConference.Wuhan.China.2006:1.4.αγαγαγγαγαγ图 3 LEACH改进前后网络存活节点数与运行时间关系图。