基于Voronoi的无线传感器网络覆盖控制优化策略

针对无线传感器网络运行状态中存在覆盖空洞的问题,提出了一种基于Voronoi有效覆盖区域的空洞侦测修复策略。该策略以满足一定网络区域覆盖质量为前提,在空洞区域内合理增加工作节点以提高网络覆盖率为优化目标,采用几何图形向量方法对节点感知范围和Voronoi多边形的位置特性进行理论分析,力求较准确地计算出空洞面积,找寻最佳空洞修复位置,部署较少的工作节点保证整个网络的连通性。仿真结果表明,该策略能有效地减少网络总节点个数和感知重叠区域,控制网络中冗余节点的存在,同时其收敛速度较快,能够获得比现有算法更高的目标区域空洞修复率,实现网络覆盖控制优化.     

基于MOEA/P的无线传感器网络节点部署研究

在无线传感器网络节点部署中,提高网络覆盖性、连通性,降低网络成本,满足过度覆盖约束等是优化无线传感器网络的多个目标,提高无线传感器网络的服务质量的问题成为一个多目标优化问题。研究围绕在二维的平面区域进行传感器节点的部署。在保证连通性的基础上,采用基于投影面的MOEA/P算法实现最大化覆盖区域,最小化节点部署成本。实验结果表明,采用MOEA/P算法进行节点部署优化比采用MOEA/D算法在IGD指标上降低了18.5%。     

节点位置无关的无线传感器网络连通性部分覆盖协议

提出了一种节点位置无关的连通性覆盖协议。协议首先利用了随机部署网络在已知监测区域大小和节点感知范围情况下,应用期望的覆盖质量与所需的工作节点数量之间的数学关系,随机选取工作节点满足应用需求;然后根据每个节点距基站最小跳数,执行Add-On规则,增加额外节点保证网络连通。实验结果表明,EECPC协议能够在较长时间内能量有效地提供满足应用要求的覆盖率,而且保证网络连通。     

移动传感器网络非均匀事件区域节点部署优化

针对移动传感器网络中热点事件监测场景,研究传感器节点的快速优化部署策略.首先假定事件随机产生,针对事件优先模型及节点感知误差函数推导基于Voronoi剖分时感知误差最小,然后定义节点有效覆盖权值,证明了当所有节点有效覆盖权值一致时,整个网络覆盖效能将达到最大.结合虚拟力及节点有效覆盖权提出一种分布式优化部署算法SDOA(Sparse Deployment Optimization Algorithm),其在保证覆盖能效最大化时保证网络连通性.最后仿真比较了本文提出部署策略能够快速有效实现对热点区域部署,并保证较高的覆盖效能.     

无线传感器网络应用若干关键问题研究

本文主要从网络节点定位问题、网络覆盖连通性问题和网络路由问题三个方面对无线传感器网络的稳定应用进行研究,以此来为今后无线传感器网络在社会各领域的安全、稳定的应用提供一定的参考依据。     

网络分层的水下传感器网络覆盖保持路由算法

在以监测为目的的水下传感器网络中,较好的网络覆盖率和连通率是完成监测任务的重要保证.以改善覆盖效果为目标的水下覆盖保持路由算法NCPR算法相对比LEACH-Coverage-U算法有效的延长了网络覆盖时间,但是该算法连通性表现较差,同时存在靠近SINK节点的簇首由于需要转发大量数据而过早死亡的问题.本文提出一种分布式的网络不均匀分层的覆盖保持路由(Network Unevenly Layered Coverage Preserving Routing,NULCPR)算法,由SINK节点开始逐层向下建立网络,同时每层网络节点通信半径也随层号增加而逐渐增大.每层网络独立运行NCPR算法以使该层节点成簇,并通过簇首向上建立连通链路以保证网络连通.仿真结果表明,与NCPR算法相比,NULCPR算法提高了网络连通率以及覆盖率,并且降低了网络能耗,证明了该算法的有效性.     

容错能量均衡WSN拓扑控制方案研究

针对WSN（Wireless Sensor Net）结构中存在大量的网络覆盖冗余与节点唤醒时间的延长,耗费了资源同样也增加了节点的工作能耗的特点。设计了一个拓扑控制方案,减少网络冗余的同时,兼顾了网络的鲁棒性。并且保证生成拓扑具有最小的能量消耗。该方案根据节点的位置信息。建立各监测节点到簇节点的能量消耗最小并且可以保证...     

一种无线传感器网络全连通群的休眠调度算法

该文针对无线传感器网络的覆盖性和连通性问题,在假设传感器节点地理位置信息已知的条件下,设计了一种包含全连通群的建立和维护以及群内节点休眠调度的全新算法。该算法采用保证群内节点彼此一跳可达的全连通群分群方法,以及分布式节能的休眠调度策略,最大程度上减少传感器网络的能量消耗,延长了网络寿命。仿真结果表明:该算法能较好地保证无线传感器网络的覆盖性和连通性,且能耗较低。     

无线传感器网络覆盖优化控制技术分析

覆盖控制问题是无线传感器组网的一个基本问题，本文对传感节点的组成、定位方法，网络的拓扑结构等进行了简要的介绍。分析了无线传感器网络覆盖问题的背景，并针对覆盖冗余检测的主要方法和数学模型，讨论了无线传感器网络能量高效覆盖优化与网络连通性的关系。重点阐述了覆盖控制的技术手段，最后对无线传感器网络的覆盖控制关键技术进行了展望。     

基于多目标生物习性激励的WSN传感器节点部署

为确保无线传感器网络(WSN)覆盖和连通性最大化以及能量消耗最小化的有效监测,提出一种基于多目标生物习性激励(MOBHI)的传感器节点部署算法。首先,将传感器节点的区域(领地)根据诸如最大覆盖、最大连通性和最小能耗等多个目标,基于领地捕食者气味标记行为进行标记,并模仿气味匹配识别其监测的位置;其次,对多个目标的优化问题应用非受控Pareto最优,将其分解为多个单目标优化子问题并同时对它们进行优化,得到所需目标的解。仿真实验结果表明,本文提出算法在网络覆盖、连通性和能耗等性能指标方面都优于其他传感器节点部署的多目标和单目标优化算法。     

认知无线传感器网络中能耗有效的协作频谱感测算法

提出了一种新的认知无线传感器网络中能耗有效的协作频谱感测算法。首先,为了降低分布式传感节点的能耗,假定传感节点的瞬时信噪比和平均信噪比已知,分析频谱感测节点的能耗与最优检测门限值之间的数学模型。然后,结合感测节点选择和判决门限设定理论,研究基于判决节点选择的有效协作频谱感测方案。理论分析和仿真结果表明,算法有效地降低了认知传感器网络的节点总能耗,提高了能耗效率。     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

基于能量有效性的协作节点配置问题研究

无线通信的能耗主要由功放能耗和电路能耗两部分组成。在大距离传输中,通信能耗由功放能耗主导,电路能耗往往被忽略不计。而在以短距离传输为主的传感器网络中,电路能耗成为不可忽略的一部分,甚至有可能超过功放能耗成为通信能耗的主导。本文就如何通过节点间协作降低网络的通信能耗展开研究,拟解决协作通信中的协作节点配置问题。本文将信源-协作节点间距离纳入协作通信系统模型,提出一种针对调制参数、协作中继数、信源-协作簇距离对协作通信能耗进行联合优化的策略,并在无线传感器网络环境下对该模型的能耗进行了仿真。仿真结果表明,在传输距离一定的情况下,通过协作节点的数量、调制参数和信源-协作簇距离的联合优化配置,可以更有效地提高协作传输的节能效果。      

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

Sleep scheduling with expected common coverage in wireless sensor networks

Sleep scheduling, which is putting some sensor nodes into sleep mode without harming network functionality, is a common method to reduce energy consumption in dense wireless sensor networks. This paper proposes a distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. The scheme can activate and deactivate the three basic units of a sensor node (sensing, processing, and communication units) independently. The paper also provides a probabilistic method to estimate how much the sensing area of a node is covered by other active nodes in its neighborhood. The method is utilized by the proposed scheduling and routing scheme to reduce the control message overhead while deciding the next modes (full-active, semi-active, inactive/sleeping) of sensor nodes. We evaluated our estimation method and scheduling scheme via simulation experiments and compared our scheme also with another scheme. The results validate our probabilistic method for coverage estimation and show that our sleep scheduling and routing scheme can significantly increase the network lifetime while keeping the message complexity low and preserving both connectivity and coverage.     

协作频谱感知中基于可靠度原则的节点选择方法研究

多节点协作频谱感知通过融合不同地理位置节点的检测信息,提高了对主用户使用 状态的感知性能。但感知性能与感知节点数目之间是非线性关系,感知节点的增加导致能量 消耗的增加,而信息量少的节点参与协作感知不利于提高感知性能,反而增加了额外的能量 消耗。为提高感知效率,降低能量消耗,提出一种节点选择算法,该算法只调度可信度高的 节点参与协作感知,排除性能差的节点参与协作感知,融合中心通过机器学习机制与外部环 境不断交互信息,对节点性能进行实时评,及时剔除可靠性下降的节点,动态选择高可靠性 的节点参与协作感知,维持高可靠性感知的动态平衡,提高认知网络的鲁棒性。实验结果表 明,本文中的算法在6个能耗单位下检测概率可达到99%,在有效降低 能量消耗的同时显著提高了感知性能,远优于传统感知方法。     

面向认知无线电网络能量高效协作感知的在线节点选择算法

面向认知无线电网络中能量高效协作感知任务需求,提出了面向单次协作感知过程的能量最小化节点选择问题EMNS和面向在线协作感知的能量高效节点选择问题OENS。证明了两问题均为NP-hard难题。针对EMNS问题,提出采用分枝定界算法BAB求最优解和贪婪节点选择算法GS求近似解。针对OENS问题,提出为每个节点引入考虑能量消耗负载均衡的动态权重系数,基于BAB和GS算法设计了启发式的在线节点选择算法OBAB、OGS1。仿真实验结果表明,提出的算法可显著增加网络完成的协作感知过程次数,可有效延长网络"生存期"。     

Square partition‐based node scheduling algorithm for wireless passive sensor networks

Wireless passive sensor networks play an important role in solving the energy limitation of nodes in the Internet of Things, and node scheduling is a significant method used to improve the energy utilization of nodes. In this work, an unused energy model based on analyzing the energy consumption characteristics of passive nodes is proposed because no unified model of passive sensor nodes is reported in previous studies. A rapid square partition clustering method is proposed according to the analysis of the relation between the sensing and communication radii of nodes, and the secondary grouping and node scheduling in each cluster are implemented to ensure the coverage rate of networks. Experimental results show that the state distribution of nodes in the proposed algorithm is favorable. The performance of the proposed algorithm is significantly affected by the P ratio between the working and charging powers of nodes. When the value of P is less than 100, the network coverage and connectivity rate are maintained at more than 95% and 90%, respectively, and are both higher than the existing algorithm.     

Minimum energy mobile wireless networks

We describe a distributed position-based network protocol optimized for minimum energy consumption in mobile wireless networks that support peer-to-peer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each node guarantees strong connectivity of the entire network and attains the global minimum energy solution for stationary networks. Due to its localized nature, this protocol proves to be self-reconfiguring and stays close to the minimum energy solution when applied to mobile networks. Simulation results are used to verify the performance of the protocol     

Some Fundamental Results on Complex Network Problem for Large-Scale Wireless Sensor Networks

Wireless sensor networks (WSNs) are typically constituted by a large number of connected wireless sensors (nodes), generally distributed at random on a given surface area. In such large-scale networks, the desired global system performance is achieved by gathering local information and decisions collected from each individual node. There exist two fundamental global issues on WSNs that we consider here, i.e. full network connectivity and network lifetime. Full connectivity can be obtained either by increasing transmission range, at the expense of consuming higher transmission power, or by increasing the number of sensors, i.e. by increasing network costs. Both of them are closely related to global network lifetime, in the sense that the higher the power consumption or the more sensors deployed the shorter the network lifetime [31]. So the main question is, how can one design large-scale random networks in order to have both global connectivity and maximum network lifetime? Although these questions have been addressed often in the past, a definite, simple predicting algorithm for achieving these goals does not exist so far. In this paper, we aim to discuss such a scheme and confront it with extensive simulations of random networks generated numerically. Specifically, we study the minimum number of nodes required to achieve full network connectivity, and present an analytical formula for estimating it. The results are in very good agreement with the numerical simulations as a function of transmission range. In addition, we study in detail several other statistical properties of large-scale WSNs, such as average path distance, clustering coefficient, degree distribution, etc., also as a function of the transmission range, both qualitatively and quantitatively. We discuss results on how to further improve network energy consumption from the original networks considered by switching off (deleting) some nodes at random but keeping whole network connectivity. The present results are expected to be useful for the design of more efficient large-scale WSNs.