一种优化算法物联网技术分布式协作路由研究

研究无线传感器网络分布式协作优化问题。针对无线传感器网络资源利用率和传输效率低下等问题,建立了一种基于遗传优化算法的无线信道质量预测的分布式优化协作路由技术。该技术充分利用遗传算法,采用启发式方法建立无线链路信道信噪比预测模型,然后根据信道质量选择最优者作为协作节点,以较小代价在动态无线网络拓扑中搜寻到最优路由。数学分析表明,遗传算法收敛速度快、可靠性高,可以准确地预测无线链路质量;同时该协作路由技术对无线传感器网络具有更好的适应性,并有效延长了网络生命周期。     

WSN in cyber physical systems: Enhanced energy management routing approach using software agents

Recently, the cyber physical system has emerged as a promising direction to enrich the interactions between physical and virtual worlds. Meanwhile, a lot of research is dedicated to wireless sensor networks as an integral part of cyber physical systems. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensors to monitor physical or environmental conditions. These autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. Shrinking size and increasing deployment density of wireless sensor nodes implies the smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization to increase the WSN lifetime. The network lifetime is defined as the time duration until the first sensor node in a network fails due to battery depletion. One solution for enhancing the lifetime of WSN is to utilize mobile agents. In this paper, we propose an agent-based approach that performs data processing and data aggregation decisions locally i.e., at nodes rather than bringing data back to a central processor (sink). Our proposed approach increases the network lifetime by generating an optimal routing path for mobile agents to transverse the network. The proposed approach consists of two phases. In the first phase, Dijkstra’s algorithm is used to generate a complete graph to connect all source nodes in a WSN. In the second phase, a genetic algorithm is used to generate the best-approximated route for mobile agents in a radio harsh environment to route the sensory data to the base-station. To demonstrate the feasibility of our approach, a formal analysis and experimental results are presented.     

Algorithms for Fault-Tolerant Topology in Heterogeneous Wireless Sensor Networks

This paper addresses fault-tolerant topology control in a heterogeneous wireless sensor network consisting of several resource-rich supernodes, used for data relaying, and a large number of energy-constrained wireless sensor nodes. We introduce the k-degree anycast topology control (fc-ATC) problem, with the objective of selecting each sensor's transmission range such that each sensor is k-vertex supernode connected and the total power consumed by sensors is minimized. Such topologies are needed for applications that support sensor data reporting, even in the event of failures of up to k - 1 sensor nodes. We propose three solutions for the k-ATC problem: a k-approximation algorithm, a greedy centralized algorithm that minimizes the maximum transmission range between all sensors, and a distributed and localized algorithm that incrementally adjusts sensors' transmission range such that the k-vertex supernode connectivity requirement is met. Extended simulation results are presented to verify our approaches.     

无线传感器网络中基于数据融合的移动代理曲线动态路由算法研究

和传统的C/S模型相比,移动代理模型在数据融合方面更适合无线传感器网络.在基于移动代理的数据融合算法中,移动代理访问传感节点的顺序以及总数对算法的效率、网络寿命等有着重大影响.为此提出了一种基于数据融合的移动代理曲线动态路由算法设计方案.通过构造特定数据结构的数据报文和数据表,给出了目标节点基本信息收集算法获取目标节点到处理节点的最优路径;将移动代理路由归结为一个优化问题,由静态路由算法求出移动代理迁移的静态最优路由节点序列,进而获得了移动代理基于曲线的动态路由算法.理论分析和模拟实验表明,随着传感器网络规模的增大和传感数据量的增加,和其它算法相比,该算法有更小的网络耗能和延时.     

一种适合Underlay认知无线传感网络的跨层路由算法

在Underlay认知无线传感网络（Underlay Cognitive Radio Sensor Network,Underlay CRSN）中,由于每个信道被主用户所使用的状态随机变化,设计路由算法不仅需要考虑网络中节点的能量消耗以及延长网络寿命的需求,还要考虑路由路径上各链路的信道选择策略对路径数据传输可靠性的影响。该文研究了Underlay认知无线电模型下的CRSN路由问题,提出了一种适合该路由问题的链路Utility函数,并将这类问题建模为以最小化Utility为目标的最优化问题。在讨论了几种传统的无线传感器网络（Wireless Sensor Network,WSN）路由算法之后,提出了一种适合Underlay CRSN的跨层路由算法,并给出了该算法的分布式实现方案。计算机仿真表明所提出的路由算法能很好地适应UnderlayCRSN中信道可用状态的随机变化。     

Rate-lifetime tradeoff for reliable communication in wireless sensor networks

The network lifetime and application performance are two fundamental, yet conflicting, design objectives in wireless sensor networks. There is an intrinsic tradeoff between network lifetime maximization and application performance maximization, the latter being often correlated to the rate at which the application can send its data reliably in sensor networks. In this paper we study this tradeoff by investigating the interactions between the network lifetime maximization problem and the rate allocation problem with a reliable data delivery requirement. Severe bias on the allocated rates of some sensor nodes may exist if only the total throughput of the sensor network is maximized, hence we enforce fairness on source rates of sensor nodes by invoking the network utility maximization (NUM) framework. To guarantee reliable communication, we adopt the hop-by-hop retransmission scheme. We formulate the network lifetime maximization and fair rate allocation both as constrained maximization problems. We characterize the tradeoff between them, give the optimality condition, and derive a partially distributed algorithm to solve the problem. Furthermore, we propose an approximation of the tradeoff problem using NUM framework, and derive a fully distributed algorithm to solve the problem.     

一种基于语义的无线传感器网络路由算法

在无线传感器网络中,为了监测多个事件,监测区域可能需要布置多种不同类别的传感器节点以监测不同的事件属性,通过对每个传感器节点感知属性信息的融合处理,来判别事件是否发生。如何快速地将生成的事件路由到sink节点是个重要的研究课题。文中将传统的路由算法与分布式事件存储和语义思想相结合,提出了一种基于语义的无线传感器网络路由算法。在该算法中,一个传感器节点只需要关心自己通信范围内的邻居节点,不需要知道整个网络的状况,算法具有冗余数据少,节能性好,网络生存周期比较理想,实现简单等特点。该算法也可以为无线传感器网络的时空查询算法的研究提供一定基础。     

一种基于语义的无线传感器网络路由算法

在无线传感器网络中，为了监测多个事件，监测区域可能需要布置多种不同类别的传感器节点以监测不同的事件属性，通过对每个传感器节点感知属性信息的融合处理，来判别事件是否发生。如何快速地将生成的事件路由到sink节点是个重要的研究课题。文中将传统的路由算法与分布式事件存储和语义思想相结合，提出了一种基于语义的无线传感器网络路由算法。在该算法中，一个传感器节点只需要关心自己通信范围内的邻居节点，不需要知道整个网络的状况，算法具有冗余数据少，节能性好，网络生存周期比较理想，实现简单等特点。该算法也可以为无线传感器网络的时空查询算法的研究提供一定基础。     

无线传感器网络满足QoS带宽需求 的能量最优路由方案

有效使用片上受限资源以保持最长的生命周期是无线传感器网络的核心问题.QoS路由技术解决了无线传感器网络要求支持多种业务的差别服务和全网范围的资源有效利用的问题.本文提出了一种可以满足QoS带宽需求的能量最优路由发现方法.该方法构建了节点选择模型、能量评价模型和节点能耗权重模型,使用遗传算法利用可用节点集合建立满足QoS带宽需求的能量最优路由.利用Markov链证明该算法是可收敛并且是以指数速度概率强收敛的.另外,通过实验仿真与分析,给出了本文遗传算法的控制参数的选择区间,实验显示本文提出的满足QoS带宽需求的路由发现方法对无线传感器网络具有更好的适应性和更长的网络寿命.     

无线传感器网络中非均匀的节点布置

在无线传感器网络中,传感器节点将收集到的数据传输到簇头,经簇头聚合后数据包以多跳方式发送到基站。靠近基站的节点,因转发的数据较多而提早死亡,出现所谓的能量空洞问题。为此,对无线传感器网络中节点的能耗情况进行了研究,提出了一种非均匀的节点布置算法,得出了一个布置传感器节点的密度函数,在靠近基站的区域内布置较多的节点。仿真实验表明,非均匀的节点布置算法能有效延长网络的生命周期。     

势博弈与时变Log-linear分布式拓扑控制算法

针对UWSNs由网络拓扑控制不稳定、频繁变化引起网络的能耗不均衡、网络生存周期短等问题,从分析传感器节点受水下不确定性因素对UWSNs的拓扑结构演化入手,构建分布式水下传感器节点状态变量描述模型,归纳出节点之间和节点与环境之间多目标交互、协同、决策的UWSNs拓扑控制优化问题,将多目标优化问题映射成博弈论优化问题,再利用势博弈与Log-linear分布式学习规则实现博弈中节点策略行为的更新过程,采用非齐次马尔可夫链理论证明网络拓扑控制目标函数的优化问题收敛到最大化势博弈函数的解,从而达到保持网络均衡、延长网络生存周期的目的.     

Adaptive design optimization of wireless sensor networks using genetic algorithms

We present a multi-objective optimization methodology for self-organizing, adaptive wireless sensor network design and energy management, taking into consideration application-specific requirements, communication constraints and energy-conservation characteristics. A precision agriculture application of sensor networks is used as an example. We use genetic algorithms as the optimization tool of the developed system and an appropriate fitness function is developed to incorporate many aspects of network performance. The design characteristics optimized by the genetic algorithm system include the status of sensor nodes (whether they are active or inactive), network clustering with the choice of appropriate clusterheads and finally the choice between two signal ranges for the simple sensor nodes. We show that optimal sensor network designs constructed by the genetic algorithm system satisfy all application-specific requirements, fulfill the existent connectivity constraints and incorporate energy-conservation characteristics. Energy management is optimized to guarantee maximum life span of the network without lack of the network characteristics that are required by the specific application.     

Primal and dual-based algorithms for sensing range adjustment in WSNs

Coverage and tracking of multiple targets, are viewed as important challenges in WSNs, mainly aimed for future ubiquitous and pervasive applications. Target coverage in WSNs with large numbers of sensor nodes and targets, and with a predefined placement of sensors, may be conducted through adjusting the sensing range and considering the energy consumption related to this operation. In this paper, we encounter the problem of multiple target coverage in WSNs by determining the sensing range of each sensor node to maximize the total utility of the network. We solve this Network Utility Maximization (NUM) problem via two approaches, primal and dual decompositions, which result in iterative distributed price-based algorithms. Convergence of sensing ranges to optimal values is proved by means of stability analysis and simulation experiments. Simulation results show convergence to optimal values in few iterations, with near optimal values for the total objective function and energy consumption of nodes. These results show scalability of our algorithm, in terms of the number of iterations needed for convergence when compared with the other two methods. Furthermore, the distributed algorithm based on dual decomposition is used to cover efficiently moving targets in consecutive time intervals.     

一种多属性目标监测的无线感知网络覆盖算法

本文研究了一种无线感知网络应用中多属性目标的覆盖问题。这种覆盖问题与单一类型数据的目标不同,其待测区域中的每个目标同时包含多种类型的现场数据。如果布置一个无线感知网络去担任监测任务,其节点需要配置多种不同类型的传感器单元。针对这种需要采集多种类型的数据才能对目标进行监测的无线感知网络的应用,节能而有效的的覆盖目标更是一个突出的问题。本文首先用ILP模型将问题进行了形式化,然后通过设计一种分布式算法求得问题了模拟仿真。仿真结果表明,这种分布式算法比直接求解ILP求出的网络寿命很接近。由于直接求解ILP问题必须依靠中心节点完成,对于节点较多并且电量受限的无线感知网络,这种分布式算法更适合。     

A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP)

Wireless sensor networks have become increasingly popular because of their ability to cater to multifaceted applications without much human intervention. However, because of their distributed deployment, these networks face certain challenges, namely, network coverage, continuous connectivity and bandwidth utilization. All of these correlated issues impact the network performance because they define the energy consumption model of the network and have therefore become a crucial subject of study. Well-managed energy usage of nodes can lead to an extended network lifetime. One way to achieve this is through clustering. Clustering of nodes minimizes the amount of data transmission, routing delay and redundant data in the network, thereby conserving network energy. In addition to these advantages, clustering also makes the network scalable for real world applications. However, clustering algorithms require careful planning and design so that balanced and uniformly distributed clusters are created in a way that the network lifetime is enhanced. In this work, we extend our previous algorithm, titled the zone-based energy efficient routing protocol for mobile sensor networks (ZEEP). The algorithm we propose optimizes the clustering and cluster head selection of ZEEP by using a genetic fuzzy system. The two-step clustering process of our algorithm uses a fuzzy inference system in the first step to select optimal nodes that can be a cluster head based on parameters such as energy, distance, density and mobility. In the second step, we use a genetic algorithm to make a final choice of cluster heads from the nominated candidates proposed by the fuzzy system so that the optimal solution generated is a uniformly distributed balanced set of clusters that aim at an enhanced network lifetime. We also study the impact and dominance of mobility with regard to the variables. However, before we arrived at a GFS-based solution, we also studied fuzzy-based clustering using different membership functions, and we present our understanding on the same. Simulations were carried out in MATLAB and ns2. The results obtained are compared with ZEEP.     

无线传感网络中基于无线能量补给的能量感知路由算法

针对无线传感网络中随机分布传感器节点能量消耗不均衡的问题,提出了一种基于无线能量补给的能量感知路由算法。休眠节点不仅可以在无线携能通信(Simultaneous Wireless Information and Power Transfer,SWIPT)传输方式下通过功率分割方法进行无线能量补给,还可以在信息传输方式下通过无线能量收集方法进行能量补给,重新进入活跃状态,为信息传播提供更好的路由,提高传感器节点的能量利用,延长传感网络的使用寿命。在该算法中,通过优化节点间的信息和能量分配,最小化传输功率,引入能量路由度量方法,选择能耗最小的路径作为传输路径。仿真结果表明,本文提出的算法可以有效地利用节点资源,均衡多跳能量受限无线传感器网络中的能量分布。     

Robotic data mules for collecting data over sparse sensor fields

We present a robotic system for collecting data from wireless devices dispersed across a large environment. In such applications, deploying a network of stationary wireless sensors may be infeasible because many relay nodes must be deployed to ensure connectivity. Instead, our system utilizes robots that act as data mules and gather the data from wireless sensor network nodes. We address the problem of planning paths of multiple robots so as to collect the data from all sensors in the shortest time. In this new routing problem, which we call the data gathering problem (DGP), the total download time depends on not only the robots' travel time but also the time to download data from a sensor and the number of sensors assigned to the robot. We start with a special case of DGP in which the robots' motion is restricted to a curve that contains the base station at one end. For this version, we present an optimal algorithm. Next, we study the two‐dimensional version and present a constant factor approximation algorithm for DGP on the plane. Finally, we present field experiments in which an autonomous robotic data mule collects data from the nodes of a wireless sensor network deployed over a large field. © 2011 Wiley Periodicals, Inc.     

WSN中考虑节点磨损的分布式自稳定网络寿命优化算法

针对无法预估的节点故障影响无线传感器网络寿命的问题,本文提出了一种考虑节点磨损的分布式自稳定优化算法。首先,利用韦伯函数拟合网络生命周期的分布,定期唤醒睡眠节点进行故障检测;然后,采用分布式调度,无需知道传感节点的位置信息,使用多个节点同时工作,从而提高效率;最后,通过较低的网络通信代价,及时使用空闲节点替代故障节点,保持网络的连通性。理论分析和仿真实验验证了本文算法的有效性及可靠性,仿真结果表明,当传感器节点的可靠性随着使用时间与磨损下降时,本文算法可以更好地延长无线传感器网络寿命,相比分布式多目标概率覆盖协议,本文算法在寿命延长、覆盖率、节点唤醒次数等方面均取得更好的性能。     

异构无线传感器网络中异构节点的部署与优化

在无线传感器网络中适当地部署少量的异构传感器节点能够有效地延长网络寿命,提高网络的可靠性.本文主要研究无线传感器网络中异构节点的部署问题,提出了基于选址问题的异构节点部署算法,以优化无线传感器网络中异构节点的数量和位置.与其它算法相比,该算法对无线传感器网络的拓扑结构没有特定的要求,可以支持随机部署或人工部署的各种传感器网络,最后还给出了该算法的仿真测试结果.     

基于蚁群优化解决传感器网络中的能量洞问题

基于多跳的无线传感器网络,越靠近sink的传感器节点因需要转发更多的数据,其能量消耗就越快,从而在sink周围形成了一种称为“能量洞”的现象.“能量洞”问题会导致整个网络由于内部节点能量过早耗尽而结束寿命,同时,网络中离sink较远的节点仍有大量能量剩余.研究“能量洞”现象,基于改进的分级环模型,总结出调节各环内节点的数据传输距离是实现网络节能的有效方法.证明搜索各区域最优的传输距离是一个多目标优化问题,即是NP难问题.从而提出一种基于蚁群优化的分布式算法,各区域根据其节点分布情况自适应地探索近似最优的传输距离,延长网络寿命.模拟实验结果表明,该算法在较短的时间内能够收敛到合理的解,并且得到的网络寿命接近于理想情况下的最优时间,与现有的类似算法相比,该算法提供了更长的网络寿命,并能适用于非均匀节点分布情况.