一种基于势博弈的无线传感器网络拓扑控制算法

在实际的应用中,无线传感器网络常常由大量电池资源有限的传感器节点组成.如何降低网络功耗,最大化网络生存时间,是传感器网络拓扑控制技术的重要研究目标.随着传感节点的运行,节点的能量分布可能越来越不均衡,需要在考虑该因素的情况下,动态地调整节点的网络负载以均衡节点的能耗,达到延长网络生存时间的目的.该文引入博弈理论和势博弈的概念,综合考虑节点的剩余能量和节点发射功率等因素,设计了一种基于势博弈的拓扑控制模型,并证明了该模型纳什均衡的存在性.通过构造兼顾节点连通性和能耗均衡性的收益函数,以确保降低节点功耗的同时维持网络的连通性.通过提高邻居节点的平均剩余能量值以实现将剩余能量多的节点选择作为自身的邻居节点,提高节点能耗的均衡性.在此基础上,提出了一种分布式的能耗均衡拓扑控制算法.理论分析证明了该算法能保持网络的连通性.与现有基于博弈理论的DIA算法和MLPT算法相比,本算法形成的拓扑负载较重、剩余能量较小的瓶颈节点数量较少,节点剩余能量的方差较小,网络生存时间更长.     

基于改进免疫遗传算法的K覆盖异构传感器节点调度

为了实现无线传感器网络监测区域目标点的多重覆盖,设计了一种基于改进免疫遗传算法的异构传感器节点调度算法实现目标点的K重覆盖;首先,在传统的概率感知模型中加入剩余能量和感知能力因素,得到改进的概率感知模型,并设计了以最小化节点数并满足覆盖度约束的目标函数;然后,采用改进的免疫遗传算法对节点进行调度,最后,给出了具体的采用改进免疫遗传算法实现WSN异构节点调度的具体算法;仿真实验表明:文中方法能在满足K覆盖约束前提下实现监测区域的节点调度,与其他方法相比,活动节点数平均多7%,具有较长的网络生命周期和较少的网络能耗。     

基于QoS和分簇机制的WMSNs路由算法研究

针对无线多媒体传感器网络中如何设计具有服务质量保证的路由算法问题,综合考虑的了节点间标准化后的丢包率、延迟、剩余能量、可用存储四个参数,提出算法ED-ACO(Energy and best distribution of Cluster Head Distance-Ant colony optimization )。ED-ACO算法采用基于剩余能量和簇首最佳距离分布的分簇结构,均匀划分网络,将节点的丢包率,延迟,剩余能量,可用存储标准化为具体参数,考虑到蚁群算法的状态转移概率公式中,利用该公式去选择下一跳路径传送感知数据,同时满足了服务质量要求。NS2仿真结果表明,与经典的AODV算法相比,在丢包率,延迟上保证了服务质量要求。     

无线传感器网络中一种能量高效的skyline查询算法

针对分布式无线传感器网络环境下的skyline查询问题,提出了KSkySenor算法,有效地从传感器网络中获取更有意义的skyline结果;KSkySenor算法对感知数据进行预处理计算每个元组的支配能力,按照支配能力与各维度值之和对其进行排序,设计了一个基于聚簇的结构用于收集所有传感器读数,提出了一个剪枝方法用于渐进的从传感器网中获取skyline结果集;实验中分别改变传感器网络规模大小、数据维度、支配属性个数k,对KSkySensor算法进行测试,实验结果表明KSkySenor算法与先前的skyline查询处理算法相比具有很高效率,减少了无线传感器网络中的数据传输量,延长了网络生命周期。     

WSN中基于多分辨率和压缩感知的数据融合方案

提出一种基于多分辨率和压缩感知的传感器网络数据融合方案;首先,对传感器网络进行配置,以生成多个层次不同类型的簇结构用于过渡式数据收集,在该结构上,最低层的叶结点只传输原始数据,其他层上的数据收集簇进行压缩采样,然后将其测量值向上发送,当母数据收集簇收到测量值时,利用基于反向DCT变换和DCT模型的CoSaMP算法来恢复原始数据;最后,我们在SIDnet-SWANS平台上部署了本文方案,并在不同的二维随机部署传感器网络规模下进行了测试;实验结果表明,随着分层位置不同,大部分结点的能耗均显著降低,与NCS方案相比,能耗下降50%~77%,与HCS方案相比,能耗下降37%~70%。     

无线传感器网络中基于最小延时的数据汇集树构建与传输调度算法

无线传感器网络的数据通信模式问题是目前的研究热点,针对现有的无线传感器网络数据汇集算法延时较大这一不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照 BFS 方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效地数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法：WIRES-G 和 DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对文中方法的性能进行了全面评估。结果表明,与当前最优算法相比,文中调度算法与文中汇集树构建算法结合起来,可显著降低数据汇集的延时。     

基于人工蜂群的无线传感器网络能耗均衡算法

由于无线传感器网络中的节点链路状况、数据传输能耗及节点剩余能量的限制,造成网络中部分感知节点寿命缩短,影响网络生存周期,提出了一种基于人工蜂群算法的WSNs能耗均衡算法,优化网络能耗均衡,从而提高网络寿命;文章给出了网络能耗相应的数学模型及优化求解算法,介绍人工蜂群算法的寻找食物过程,阐述了人工蜂群算法在网络能耗均衡方面的实现步骤;通过实验仿真证明,文章提到的算法与LEACH分簇算法、蚁群优化算法相比,具有更好的能耗和负载均衡能量、丢包率和时延性,有效地提高了网络生存周期。     

邻域搜索蚁群算法在无线传感器网络数据融合中的应用

为了减少网络中的数据传输量,提高数据融合率,降低网络延时,针对无线传感器网络数据融合问题的研究,提出了一种邻域搜索蚁群算法。首先利用蚁群算法寻找最短路径的优势,构造最短路径。为了避免蚁群算法的早熟收敛和收敛速度慢的问题,当达到一定的迭代次数后,运用具有可变邻域搜索的变异算子对搜索结果进行优化。算法不但考虑了无线传感器网络节点能量消耗也考虑了数据传输的网络延时问题。实验结果表明,该算法减少了网络能耗,降低了网络延时,稳定性更好,性能更优。     

一种无线视频传感器节点协作跟踪方法

由于网络通信带宽以及节点能量等因素限制,信息的有效获取与能耗的平衡优化是无线视频传感器网络近期研究的热点,面向目标跟踪的无线视频传感器网络实现节能的关键在于节点的高效协作;文章目的在于研究一种无线视频传感器节点协作跟踪方法,通过综合考虑目标跟踪效果和节点能耗等因素,采用自适应混合高斯算法进行背景建模,分布式均值漂移算法进行目标跟踪,并构建一种基于效能函数的最优节点选择方法;实验结果显示该方法能在真实场景下高效地进行目标跟踪。     

基于改进蚁群算法的无线传感器网络路由的优化

为了延长无线传感器网络(Wireless Sensor Network ,WSN)的生命周期,均衡各个节点间能量消耗,针对现有的WSN路由优化算法存在的问题,提出了一种基于改进蚁群算法的路由优化算法。首先通过对蚁群算法和遗传算法的优劣性比较,在蚁群算法的基础上,结合遗传算法的选择、交叉和变异的操作,从而提高蚁群算法的搜索速度和寻优能力。最优路径评价函数综合考虑节点能耗及节点的剩余能量,使剩余能量多的节点优先参与数据转发,均衡节点间的能量消耗。通过与经典蚁群算法及遗传算法的对比实验表明,随着数据转发轮数增加,改进的蚁群算法能耗小,剩余能量多,网络生命周期明显延长;随着整个网络运行时间的增长,改进的蚁群算法,节点均衡能耗性好,最优路径搜索的成功率也明显优于其他两种算法。     

Local adaptive sampling for wireless sensor network powered by energy harvesting

For a sensor network, energy limitation is always a key factor to affect the continuous work of a sensor node. A good idea is harvesting energy from the environment to support the node to work continuously. However, energy from environment is varied with time, weather and season. So in order to use the varied environment energy, it is necessary to find a way to achieve real-time monitoring and adaptive working. In this paper, an algorithm called “Adaptive Sampling” was proposed to adapt the sample mode to the present energy condition. Simulation proves that the algorithm can make the CO₂ sensor flexibly achieve adaptive sampling under different energy condition with the least MSE 2.7767. This algorithm can be widely used in wireless sensor network power by energy harvesting for local adaptive sampling.     

改进的对向传播网络及其在多传感器目标识别中的应用

针对多传感器数据融合和目标识别的特点,提出了改进的对向传播网络(MCPN),并与Dempster-Shafer(D-S)证据推理相结合,实现了决策层数据融合目标识别.文中利用仿真数据对所提出的网络训练算法和融合结构进行了实验研究.结果表明:改进后的对向传播网络识别性能优于传统的对向传播网络(CPN),融合后的目标识别率较单传感器明显提高.最后,将该方法应用于前视红外(FLIR)和可见光摄像机目标跟踪系统对算法和融合结构进行验证,结果表明文中提出的方法是可行的.     

Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.     

Power management in heterogeneous networks with energy harvesting base stations

In this paper, heterogeneous cellular networks (HCNs) with base stations (BSs) powered from both renewable energy sources and the grid power are considered. Based on a techno-economic analysis, we demonstrate that by controlling both transmit power and stored energy usage of BSs, energy costs can be effectively reduced. Specifically, we propose a two-stage BS operation scheme where an optimization and control subproblem is solved at each stage, respectively. For the first subproblem, transmit power of BSs is adjusted while quality of service (QoS) experienced by users is preserved. In the second subproblem, we consider the strategic scheduling of renewable energy used to power the BSs. That is, harvested energy may be reserved in the battery for future use to minimize the cost of on-grid power that varies in real-time. We propose: (1) an optimization approach built on a lattice model with a method to process outage rate constraint, and (2) a control algorithm based on nonlinear model predictive control (NMPC) theory to solve the two subproblems, respectively. Simulation results include a collection of case studies that demonstrate as to how operators may manage energy harvesting BSs to reduce their electricity costs.     

D2D-assisted user-centric adaptive video transmission in next generation cellular networks

Next-generation cellular networks need to provide seamless connectivity with higher data rates, increased capacity, and enhanced network coverage. As multimedia service demands in various heterogeneous devices grow rapidly compared to the underlying network’s capacity and bandwidth, the adaptation in multimedia streaming services is essential for providing satisfactory Quality of Experience (QoE). This paper develops a Device-to-Device (D2D)-assisted Utility-based Adaptive Multimedia (video) Streaming scheme (UAMS) using D2D communication in a 5th Generation (5G) cellular network where low-battery users may extend their streaming duration by spending lower reception energy with the help of D2D-assisted communication. The adaptation algorithm considers four utility functions: quality, power consumption, packet error ratio, and remaining battery of the user devices to adapt the bitrate dynamically and augment viewers’ experience. We formulate an optimization problem to maximize the joint utility function to provide the best adaptive multimedia content selected for transmission to the end-users either directly or via D2D Relay Nodes (DRNs) in every scheduling interval. We use a graph theoretic approach for choosing the best DRNs. Extensive simulations show the efficacy of the proposed scheme in terms of saved battery energy, churn rate, and QoE metrics compared to a few well-known existing schemes in the literature that do not use D2D communication.     

A cluster-based barrier construction algorithm in mobile Wireless Sensor Networks

It is important to maximize network lifetime while meeting coverage requirements in Wireless Sensor Networks (WSN). This paper focuses on maximizing the network lifetime of barrier coverage in WSN with mobile sensors. For mobile sensors, movement energy consumption can be much higher than during sensing and communication. Since the battery capacity of the sensor is fixed, more energy can be used for sensing and communication if the movement distance required to build the barrier can be reduced. Therefore, we focus on reducing the movement distance required by the mobile sensors to build the barriers. Also, we can build as many barriers as possible if we use as few mobile sensors as possible to build a barrier. By rotating multiple barriers, we can further extend the lifetime of the network. Based on the above concept, an energy-efficient algorithm is proposed for building barriers in WSNs with mobile sensors. The contribution of this paper are: (1) We find the minimum number of sensors required to construct a barrier for a rectangular region. (2) We explain how to cluster the sensors in the region, and then calculate the linear equations of the barriers based on the results of the clustering. (3) We explain how to set the sojourn points for these linear equations of the barriers, and then assign the mobile sensors to every one of these sojourn points. In short, the proposed algorithm can construct multiple barriers depending on the location of the sensor, with a minimum number of sensors connecting the left and right boundaries of the target field. Finally, the simulation results show that the proposed algorithm has better performance in terms of network lifetime than the previous result.     

一种应用于桥载设备监控网络CSMA/CA机制算法

在机场桥载设备在线监测管理网络的现场应用测试中,针对网络突发事件中高优先级节点传输延时长,节点能耗利用率低的问题,采用基于优先级调度策略思想的网络节点能量优化机制,对网络MAC层协议中的CSMA/CA算法参数做出调整,达到数据快速传输的目的,利用NS2软件进行网络仿真,结果表明该方法提高了高优先级节点的信道访问率,均衡了网络能耗并延长了网络生命周期。     

基于生命期模型的无线传感器网络信道分配博弈算法

针对无线传感器网络中节点因干扰过大导致重传能耗增加, 进而节点过早失效、网络生命期缩短的问题, 根据网络拓扑信息和路由信息设计节点的负载模型, 从而构建了节点的生命期模型. 然后利用博弈论将路径增益、交叉干扰和节点生命期等性能参数融入到效益函数中, 构建信道分配博弈模型. 理论分析证明该博弈模型存在纳什均衡. 进而运用最佳回应策略, 在所构建的信道分配博弈模型的基础上, 设计了一种优化网络生命期的抗干扰信道分配算法. 该算法使节点在选择信道时避免与网络中交叉干扰较大的节点和生命期较小的节点使用相同信道, 实现干扰小、能耗低且均衡的信道选择. 理论分析与仿真结果证明该算法最终能够快速地收敛到纳什均衡, 且具有较小的信息复杂度, 从而减小算法本身的通信能耗. 同时, 该算法具有良好的抗干扰性和信道均衡性, 能够有效地延长网络生命期.     

基于神经免疫生长可免域网络的红外光谱图像分割算法

管道运输对远距离输送石油天然气有着较大优势,而与之伴随的管道安全问题使得管道安全检测至关重要。为确保任何时间下管道状况的有效检测,红外成像技术由于其根据对象的热辐射信息反映目标特征的特殊性,能够忽视可见光的影响检测管道状态,因而在管道检测领域有重要意义。但由于户外环境的多样性,交错的管道和复杂环境使得采集的红外管道图像具有目标特征分布不均匀,目标遮挡和背景类目标干扰等问题。这些问题增加了提取管道目标的难度,不利于管道的分割和检测。生物免疫系统在抗原检测、提取和消除上表现出识别、学习、记忆、耐受和协调配合等目前复杂系统优化策略所缺乏的优异特性,借鉴生物神经系统调控免疫系统的机理,设计一种基于神经免疫网络的复杂背景下红外管道目标的检测与提取算法。根据生物神经网络在免疫系统中的调控机制,利用基础管道形状特征模型构建用于红外管道目标定位的神经网络,并将最优神经免疫可免域和区域种子生长结合,解决管道遮挡影响提取目标完整性的问题。选择三种典型的红外管道图像,将传统目标检测算法与基于神经免疫网络的算法进行了效果对比分析。结果表明,传统算法的平均真阳性率为40.56%,Jaccard相似性指数为27.18%,绝对误差率为11.75%,而基于神经免疫网络算法的真阳性率为98.05%,Jaccard相似性指数为94.44%,绝对误差率为1.18%。对比可知,神经免疫网络算法的真阳性率比传统方法高57.49%,绝对误差率则低10.57%,验证了复杂背景下,本文算法相比传统方法能够更加准确地提取完整的红外管道目标,这对管道安全检测效率的提高有着重要意义。