基于能量均衡的最大化覆盖目标算法

目标覆盖问题是无线传感网络WSNs(Wireless sensor networks)最重要的问题之一.每个目标至少被一个传感节点覆盖,为此提出基于能量均衡的最大化覆盖目标EMNL(Energy-balance-based Maximizing Network Lifetime)算法.EMNL算法将所有传感节点划分不同的传感节点覆盖区SC(Sensor Cover),致使每个SC能够维持对所有目标监测一个固定时间.通过有选择性选择一个SC活动,而其他SC休眠,进而提高能量利用率,延长了网络寿命.EMNL算法构建了不同不相邻SC,进而最大化网络寿命.最后,建立仿真环境,并进行性能仿真.此环境下的数据表明,在EMNL算法有效地扩延生存时间,也提升了覆盖率.     

面向异构网络的基于 k-覆盖的休眠调度算法

异构无线传感网络WSNs(Wireless Sensor Networks)的多数监测应用要求兴趣区域FoI(Field of Interest)是k覆盖(k-cover),且k≥1.而冗余节点被安排为休眠,进而最小化能量消耗.为此,提出面向异构网络的基于k-覆盖的冗余节点休眠算法k-CRSS(k-cover based sleep Scheduling algorithm for redundant node).k-CRSS算法引用概率方法判断节点是否为冗余节点,并推导判断一个节点是否为冗余节点的概率表述式.然后,引用调度算法识别所有冗余节点,并让它们进行休眠,且在FoI内不出现覆盖空洞.k-CRSS算法属分布式算法,并无需任何地理信息,仅通过少量控制消息收集邻居节点信息.实验数据表明,k-CRSS算法通过调度算法减少了活动节点数,进而延长了网络寿命.     

一种超低功耗无线传感器网络MAC协议

在无线传感器网络中,能量是一个关键资源。传感器网络节点通常在大部分时间里处于休眠状态以节约能量。其中,节点间精确地同步和超低的休眠功耗能够本质上延长无线传感器网络节点的寿命。然而现实中节点在唤醒周期设置、时钟源选择和网络节点同步时很难满足理论研究时提出的要求。因此,提出了一种低功耗无线传感器网络MAC协议:允许节点使用多种时钟源实现功耗最优配置,在休眠时采用内部时钟以达到最低功耗,在工作时采用外部晶振以保证射频性能,同时为了解决多时钟源误差增大且休眠周期变化带来的问题,提出了多时钟源休眠唤醒机制和节点同步策略。最后文章在IEEE802.15.4硬件测试平台上完成了多时钟源MAC协议与SMAC协议的实证测试,结果表明对比SMAC协议的唤醒和同步机制,低功耗无线传感器网络MAC协议在传感器网络节点上能够极大地减少休眠功耗并显著地节约同步的时间,从而大大延长节点寿命。     

基于阿基米德螺旋的WSNs节点部署的能耗均衡算法

无线传感网络WSNs(Wireless Sensor Networks)是由资源受限的节点构成.而能量是节点最稀缺资源,据此需要保存能量,才能优化网络寿命.为此,先分析了通过平衡能耗优化网络寿命问题,然后提出基于阿基米德螺旋的WSNs节点部署的能耗均衡算法AS-DBEC(Archimedes' Spiral-Based Deployment balancing energy consumption algorithm).AS-DBEC算法提出基于阿基米德螺旋的部署函数,将阿基米德螺旋转换成离散形式,再将节点部署于离散位置.最后,评估了AS-DBEC算法在能耗均衡和网络寿命方面的性能.     

无线传感网中基于优先级的节点休眠调度机制

当前,无线传感网的广泛应用给人们带来便捷的同时,与日俱增的能耗也对全球环境造成了重大影响。因此,无线传感网的节能问题始终是业界关注的焦点。节点休眠调度策略有助于合理组织网络中众多传感器节点的工作状态,从而有效均衡节点负载和降低网络整体能耗。首先,对现存的无线传感网休眠调度策略进行了调研分析,并指出了存在的边界效应问题。然后,针对现有节点休眠调度策略存在的不足,面向无线传感网目标监测应用场景的需求,综合考量网络覆盖度和节点剩余能量,构造了节点进行休眠决策的优先级,并据此设计了一种基于优先级的分布式节点休眠调度机制(Priority Based Distributed Sleep Scheduling, PBDSS)。仿真实验结果表明,与典型的贪心休眠调度策略相比,PBDSS能够降低信息交互带来的能量开销,有效解决了边界效应问题,均衡了节点能量消耗,提高了节点能量效率,延长了网络寿命。     

一种新型唤醒机制的无线传感器网络节点设计

寿命过短一直困扰无线传感器网络(WSNs)与实际应用结合的难题。通过对WSNs工作效率低问题的研究,提出了一种新型射频唤醒机制的WSNs节点的设计方法,对降低节点的功耗和延长WSNs的寿命都有帮助。详细说明了节点设计的硬件结构和软件中的程序流程。可行性分析论证了节点的实用性和低功耗特性。     

基于电量检测的WSNs智能休眠方法

为使无线传感器网络（WSNs）中传感器节点的电池电量得到充分利用并降低传感节点能量消耗,提出了一种基于传感节点电量检测的WSNs智能休眠方法,以达到智能化管理WSNs传感节点电池电量的目的。详细地论述了WSNs智能休眠方法的设计思路和实现方法。测试结果表明：此休眠方法可＂智能＂地降低WSNs节点的功耗,为WSNs的进一步发展与应用奠定了一定的基础。     

无线传感器网络覆盖控制算法研究

无线传感器网络(Wireless sensor network, WSN)覆盖控制通常采用基于二元感知模型的几何计算方法休眠冗余节点, 其算法在实际应用中受到局限, 不够精确. 针对此问题, 本文采用概率感知模型, 提出新的覆盖控制算法, 将提高能量利用效率作为重要指标, 采用节点轮换周期工作机制, 每个周期逐个唤醒部分节点, 组成满足网络覆盖要求的覆盖集, 实现降低能耗、均衡节点能量的目的. 概率感知模型描述网络的覆盖能力更精确, 算法不受感知模型的限制, 原理简单, 易实现, 仿真结果验证了本算法的有效性.     

面向异构WSNs的基于能量感知的簇路由算法

有效地使用传感节点的能量,进而延长网络寿命成为设计无线传感网路由协议的一项挑战性的工作.为了延长网络,现存的多数簇路由是面向同构网络.为此,提出分布式能量感知的异构WSNs非均匀分簇路由DEAC(Distributed Energy Aware unequal Clustering)算法.DEAC算法是以EADUC(Energy Aware Distributed Unequal Clustering)为基础,并进行优化.与EADUC不同,DEAC算法从簇头竞选机制、簇间多跳通信中的下一跳转发节点的选择策略以及自适应的节点通信半径的设置三方面进行优化.在簇头竞选机制中,采用退避算法,利用节点的剩余能量以及邻居节点的平均能量设置延时时间;在选择下一跳转发节点时,建立节点的关于能量的度量函数,选择具有最大剩余能量的节点作为下一跳;而在设置节点通信半径时,考虑了距离、剩余能量以及邻居节点数信息.仿真结果表明,与EADUC协议相比,提出的DEAC算法能够有效地延缓第1个节点失效的时间,减少了能耗,扩延网络寿命.     

WSNs能量异构节点部署与区域覆盖优化

针对无线传感器网络（WSNs）的热点区域问题所导致的节点能量异构、能量空洞等问题,对网络进行重新部署以满足区域覆盖的要求。建立WSNs能量异构节点区域覆盖优化模型,以网络覆盖率为目标函数,节点位置作为决策变量,采用差分进化算法优化该目标,同时获得各节点的最佳位置。仿真实验表明：该模型能充分调度各节点的剩余能量,对热区问题导致的能量空洞进行重新部署,该策略能够延长网络的生命周期,提高网络的可靠性。     

HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks

Wireless sensor networks (WSNs) require energy management protocols to efficiently use the energy supply constraints of battery-powered sensors to prolong its network lifetime. This paper proposes a novel Heuristic Algorithm for Clustering Hierarchy (HACH), which sequentially performs selection of inactive nodes and cluster head nodes at every round. Inactive node selection employs a stochastic sleep scheduling mechanism to determine the selection of nodes that can be put into sleep mode without adversely affecting network coverage. Also, the clustering algorithm uses a novel heuristic crossover operator to combine two different solutions to achieve an improved solution that enhances the distribution of cluster head nodes and coordinates energy consumption in WSNs. The proposed algorithm is evaluated via simulation experiments and compared with some existing algorithms. Our protocol shows improved performance in terms of extended lifetime and maintains favourable performances even under different energy heterogeneity settings.     

C3: an energy-efficient protocol for coverage,connectivity and communication in WSNs

The lower layer of ubiquitous and pervasive systems consists of wireless ad hoc and sensor networks. In wireless sensor networks (WSNs), sensors consume most of their energy in data transmission and idle listening. Hence, efficient usage of energy can be ensured by improved protocols for topology control (i.e., coverage and connectivity), sleep scheduling, communication, and aggregation and compression of data. Though several protocols have been proposed for this purpose, they are not energy-efficient. We propose an integrated and energy-efficient protocol for Coverage, Connectivity, and Communication (C3) in WSNs. The C3 protocol uses received signal strength indicator to divide the network into virtual rings, defines clusters with clusterheads more probably at alternating rings, defines dings that are rings inside a cluster and uses triangular tessellation to identify redundant nodes, and communicates data to sink through clusterheads and gateways. The proposed protocol strives for near-optimal deployment, load balancing, and energy-efficient communication. Simulation results show that the C3 protocol ensures partial coverage of more than 90 % of the total deployment area, ensures one connected network, and facilitates energy-efficient communication while expending only one-fourth of the energy compared to other related protocols such as the coverage and connectivity protocol, and the layered diffusion-based coverage control.     

基于元胞蚁群优化算法的农业无线传感器网络路由研究

无线传感器网络(WSNs)进行农业信息采集时,针对传感器节点数量多,能量消耗不均衡的特点,提出基于元胞蚁群优化(CACO)的WSNs路由算法。算法将网络中的节点映射成CACO算法中的元胞,通过改进蚂蚁信息素更新模型,引入睡眠唤醒机制,有效地减少了节点间的通信,从而改善能量消耗不均衡。仿真结果表明:该算法有效地减少了网络耗能、节点消亡数量,延长了网络生命期。     

无线传感器网络密度控制研究

无线传感器网络节点能量受限，部署环境恶劣、复杂。在监测区域内部署大量节点，通过节点之间的状态轮换与合作策略延长网络寿命是非常重要的方法。本文论述了设计密度控制算法状态转移条件需要解决的两个基本问题：区域覆盖问题和节点连通性优化问题，并分别以ILP的形式给出了一个集中式算法解决方案。通过节点的覆盖效用范围与连通性效用范围的几何分析，提出了传感器网络密度控制算法中解决两个基本问题的一致性策略。     

Connectivity and coverage maintenance in wireless sensor networks

One of the main design challenges for wireless sensor networks (WSNs) is to obtain long system lifetime without sacrificing system original performance such as communication connectivity and sensing coverage. A large number of sensor nodes are deployed in redundant fashion in dense sensor networks, which lead to higher energy consumption. We propose a distributed framework for energy efficient connectivity and coverage maintenance in WSNs. In our framework, each sensor makes self-scheduling to separately control the states of RF and sensing unit based on dynamic coordinated reconstruction mechanism. A novel energy-balanced distributed connected dominating set algorithm is presented to make connectivity maintenance; and also a distributed node sensing scheduling is brought forward to maintain the network coverage according to the surveillance requirements. We implemented our framework by C++ programming, and the simulation results show that our framework outperforms several related work by considerably improving the energy performance of sensor networks to effectively extend network lifetime.     

基于差分算法的异构无线传感器网络多重覆盖节点调度方案

针对部署区域中存在多个不同覆盖质量需求的目标,本文提出一种基于多重覆盖算法的异构节点调度机制。该算法在满足区域覆盖要求和重点区域监测目标多重覆盖的要求,及节点能量的约束条件下,以网络的有效覆盖率最大和工作节点数目最少为目标,借助改进的差分算法来对节点状态进行优化达到提高网络覆盖性能和降低网络能耗的目的。仿真结果显示,本文的算法在满足热点目标监测要求的前提下,最大限度地兼顾网络的区域覆盖质量,减少了网络的能耗;较之随机调度算法,本文算法在覆盖率和网络能耗方面优于后者。     

A testbed for coverage control using mixed wireless sensor networks

Wireless sensor networks (WSNs) is a relatively new technology that has been proposed for several applications including wide area monitoring. Such applications may include stationary or mobile sensor platforms or they may include several stationary and some mobile-robotic sensor nodes that can move in the area in order to achieve certain objectives, e.g., monitor areas that are not adequately covered or assist in the transfer of data to prevent the energy depletion of certain critical nodes. Such networks that consist of both stationary and mobile nodes are referred to as mixed WSNs. This paper presents the development of an experimental testbed for mixed WSNs consisting of stationary and mobile sensor nodes that collaborate to improve the sensing coverage and event detection of the network in a given deployment area. The paper describes the hardware and infrastructure of the testbed as well as a case study for coverage control that was investigated using the testbed. We point out that the developed testbed can be used for the evaluation and validation of different algorithms for coverage control that involve collaboration between stationary and mobile sensors to improve the WSN's monitoring capabilities. In addition, it can also be used to investigate other objectives as well as other concepts (e.g., network control).     

无线传感器网络服务质量的随机控制策略

为使网络具有给定的感知范围或覆盖率,必须保证WSN传感器具有一定空间密度,因此网络中活动的传感器数量可作为QoS的一种度量[1]。在这种定义下,提出两种QoS控制方法,基于贪心算法的集中式控制方法和基于寿命的分布式随机控制方法。如果传感器能够存储运行状态信息或每隔一段时间能够交换节点寿命信息,前者可以获得最优的网络寿命;后者则通过交换初始传感器节点寿命,利用平均寿命信息随机调度活动的节点,从而延缓存活节点数的降低。仿真结果证实了这两种方法的有效性。     

基于Memetic算法的WSN分簇协议的研究 *

针对无线传感器网络的寿命和覆盖优化,提出了一种基于Memetic算法和节点休眠-唤醒调度策略的复合文化基因聚类协议(Composite Memetic Algorithm Clustering Protocol,CMACP)。算法首先运行文化基因算法初始化需要激活的节点并规划相邻冗余节点,其中遗传算法和局部搜索算法能保障得到最优的初始节点分布。随着网络的运行,当某个节点因能量耗尽而丢失覆盖目标时,休眠调度策略选择激活最优相邻节点弥补覆盖漏洞。仿真实验表明,与其他WSN分簇协议相比,CMACP能较好的延长WSN稳定周期生存时间,并且提高WSN对感知区域的覆盖能力。