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无线传感器网络(WSN)中传输的数据具有相关性和冗余性。如何有效降低网络中的数据量,延长网络生命周期,始终是WSN的研究热点之一。该文基于WSN中数据序列的相关性,提出一种两步数据压缩算法(TSC-SC)。网络中的簇首和簇内节点执行各自的压缩算法:簇首首先执行相关性分组算法,将数据分组,减少簇内节点的计算量以及消除簇内数据的空间相关性;簇内节点对多属性数据分类压缩,并将压缩参数传至簇首,簇首解压后再次进行分类压缩,进一步消除数据相关性,减少节点数据冗余度,降低通信能耗。为实现对压缩算法的综合性能评价,考虑基本的压缩要求和算法的计算能耗,提出了基于能量判别的算法评估模型(NCER)。仿真结果表明TSC-SC算法可以有效降低压缩比和压缩误差,充分减少数据传输量和网络的通信能耗,利用NCER指标能够直观地评价算法的性能。 相似文献
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为了降低无线传感器网络(WSN)能量消耗,延长网络生存周期,提出了一种基于混沌粒子群(CPSO)和蚁群算法相结合的路由协议。该协议针对典型的分簇协议LEACH(Low-Energy Adaptive Clustering Hierarchy)协议的簇头选择进行了优化,考虑了节点剩余能量和簇内密集性等因素,采用新的混沌粒子群算法对簇头选择进行优化。然后,针对LEACH协议簇头到基站采用单跳通信,容易使簇头早亡的问题,采用蚁群算法优化簇头到基站的路由路径,减少通信消耗的能量。仿真结果表明,与传统的LEACH协议相比,新的协议能有效减少能量消耗,延长网络生命周期。 相似文献
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基于UAV动态特性限制的WSN分簇路由方法研究 总被引:2,自引:0,他引:2
本文针对目前的WSN分簇算法研究中没有考虑到UAV动态特性,导致UAV采集信息过程中飞行距离过长、采集难度大的问题,提出了基于UAV动态特性限制的WSN分簇路由方法(CR).CR算法首先考虑到UAV飞行中与簇头通信时间较短的情况,控制了成簇的大小,能够保证UAV访问过簇头节点后可以完全采集该簇信息;第二,簇头选择阶段在兼顾簇内节点能量消耗一致的同时,对簇头进行调整,使得簇头选择方案更利于UAV采集,减少UAV绕行距离;第三,考虑到了UAV可供飞行能量的局限性,在分簇的同时加入总飞行能量的限制,使得规划方案在可行的前提下执行.实验表明,CR算法能够有效地减少节点能量消耗差异,使得网络节点剩余能量趋于一致,延长了网络生存时间. 相似文献
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如何有效降低WSN(Wiretess Sensor Net work)网内数据传输量,延长WSN的寿命,是WSN领域的研究热点.在分簇WSN基础上,实现了一种误差实时可控的数据融合算法.通过该算法,节点可自行根据近期采集的历史数据实时调整传输阈值,不同节点可保持接近的数据传输率,实现均匀耗电;自适应的阈值可以有效控制数据融合的误差.理论分析与仿真实验表明,该算法能够保证不同节点数据传输的公平性;在数据传输率相同的情况下,其求和查询及均值查询的平均绝对误差均远低于当前优秀的基于伯努利采样的数据融合方法.此算法无需先验知识,在多种WSN应用场景中具有较强的可用性与适应性. 相似文献
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针对传统时变图信号重构算法重构精度较低的问题,提出了一种基于全局采样集优化的联合多维度平滑性时变图信号重构算法(Joint Multidimensional Smoothness Time-Varying Graph Signal Reconstruction Based on Global Sampling Set Optimization,JMSR-GSSO-TVGS)。研究对象为加权无向图上的时变图信号。首先提出一种采样集优化方法,采样时根据图结构中节点的区域关联性对图结构分簇,并在每个簇中按一定采样比例对节点进行优选,获得优化采样集;其次进一步挖掘时变图信号在空时域的平滑性;最后在优选采样集的基础上利用时变图信号多维度的平滑性对信号进行全局重构。仿真实验验证了JMSR-GSSO-TVGS的重构可靠性。 相似文献
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该文采用随机矩阵理论(RMT)直接对压缩采样得到的观测数据进行分析,设计出了一种基于广义似然比检验(GLRT)的非重构宽带压缩频谱感知新算法。该算法无需任何先验知识就能对宽带频谱中的每个子带进行盲检测。此外,为了减轻次用户(SU)在数据获取和频谱感知过程中的通信开销,该文提出一种基于传感器节点(SN)辅助感知的合作频谱感知架构。理论分析和仿真结果均表明,与传统基于信号重构的GLRT感知算法以及Roy最大根检测(RLRT)算法相比,该算法不仅具有计算复杂度低、开销小、感知性能稳定等诸多优点;而且只需较少的SN就能获得较好的检测性能。 相似文献
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为了降低水下无线传感网(UWSN)中数据收集的能耗和保证实时性,提出一种基于压缩感知的移动数据收集方案。以分布式能量均衡非均匀分簇(DEBUC)协议和压缩感知理论为基础,簇内节点依据设计的稀疏测量矩阵决定是否参与压缩采样,并将获得的测量值传输至簇头。然后,通过自主式水下潜器(AUV)的移动来收集各个簇头上的数据到数据中心,该问题被建模为基于信息质量最大化的旅行商问题(TSP),并提出近似算法进行求解。仿真实验结果表明,相比于已有的水下移动数据收集算法,本文方案在保证数据收集可靠性的同时,缩短了数据收集延时,延长了网络寿命。 相似文献
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To solve the problem that the ubiquitous unreliable links in the WSN influence the performance of the compressive sensing (CS) based data gathering,first the relationship between the reconstruction SNR of CS-based data gathering algorithm and the bit-error-ratio (BER) were simulated quantitatively.Then classify two cases were classified,namely light-payload and heavy-payload,relying on the analysis of wireless link packet loss characteristics.The random packet loss model was conceived to describe the packet loss under light-payload scenario.Further the neighbor topology spatial correlation prediction-based CS data gathering (CS-NTSC) algorithm was proposed,which utilized the nodes spatial correlation to reduce the impact of error.Additionally,the node pseudo-failure model was conceived to describe the packet loss occurred in network congestion,and then the sparse schedule-aided CS data gathering (CS-SSDG) algorithm were conceived,for the purpose of changing the sparsity of measurement matrix and avoiding measurements amongst the nodes affected by unreliable links,thus weakening the impact of error/loss on data reconstruction.Simulation analysis indicates that the proposed algorithms are not only capable of improving the accuracy of the data reconstruction without extra energy,but also effectively reducing the impact affected by the unreliable links imposed on CS-based data gathering. 相似文献
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无线传感器网络在探测目标源时会碰到处理能力不足和能量缺乏的问题。为了克服这些问题,该文提出了基于能量均衡的自适应压缩感知算法。与传统自适应压缩感知算法不同,所提出的算法在选择观测向量时不仅考虑了重构性能,还考虑了节点的能量均衡,防止某些节点过快消耗能量而导致整体网络结构的破坏。同时为了适应不同应用场景的需求,将自适应压缩感知算法和能量均衡压缩感知算法相结合,通过门限值的选择达到灵活配置的目的。仿真实验的结果表明,该文所提出的算法能够有效延长网络生存时间,同时能够实现能耗和收敛性的兼顾。 相似文献
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压缩感知理论指出,稀疏信号可以通过以低于奈奎斯特采样的测量数据重建出原始信号。针对高分辨率SAR成像在奈奎斯特理论下所面临的高速A/D采样、大数据量存储、传输等问题挑战。本文提出了一种基于压缩感知理论的多发多收高分辨率SAR二维成像算法。该算法减轻了高分辨率SAR成像的压力,采用压缩感知处理降低了A/D采样速率、数据量... 相似文献
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Rani T P Vemireddi Srinadh Mano Paul P Ananth J.P 《International Journal of Communication Systems》2023,36(15):e5574
The arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS-based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network-long short term memory (CNN-LSTM) is employed for the data aggregation process. The proposed HGCIO-based CNN-LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%. 相似文献
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In view of high efficiency and security requirements in WSN encryption algorithm,a lightweight chaotic block encryption algorithm was designed and a novel scheme of dynamic sub keys extension was proposed.To greatly reduce the computing burden of WSN nodes,this scheme made full use of WSN cloud servers monitoring platform,which was powerful in data computing and processing,and transfered the sub keys synchronization task from nodes to cloud servers.Experimental results and performance analysis show that the scheme has good characteristics of diffusion,confusion and statistical balance,strong key security and high algorithm efficiency.It has a good application prospect in the field of WSN communication encryption. 相似文献
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A routing algorithm, based on a dual cluster head redundant mechanism combined with compressive sensing data fusion algorithm, is proposed to improve reliability and reduce data redundancy of the industrial wireless sensor networks. The Dual cluster head alternation mechanism is adopted to balance the energy consumption of cluster head nodes. Through the compressive sensing data fusion technology to eliminate redundancy, effectively improve the network throughput of the sensor network. The simulation results show that the proposed algorithm is able to enhance the networks performance, significantly reduces the number of lost packets and extend the network’s lifetime. 相似文献