Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix

A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.

Energy-balanced clustering protocol for data gathering in wireless sensor networks with unbalanced traffic load

Energy-efficient data gathering in multi-hop wireless sensor networks was studied,considering that different node produces different amounts of data in realistic environments.A novel dominating set based clustering protocol (DSCP) was proposed to solve the data gathering problem in this scenario.In DSCP,a node evaluates the potential lifetime of the network (from its local point of view) assuming that it acts as the cluster head,and claims to be a tentative cluster head if it maximizes the potential lifetime.When evaluating the potential lifetime of the network,a node considers not only its remaining energy,but also other factors including its traffic load,the number of its neighbors,and the traffic loads of its neighbors.A tentative cluster head becomes a final cluster head with a probability inversely proportional to the number of tentative cluster heads that cover its neighbors.The protocol can terminate in O(n/lg n) steps,and its total message complexity is O(n2/lg n).Simulation results show that DSCP can effectively prolong the lifetime of the network in multi-hop networks with unbalanced traffic load.Compared with EECT,the network lifetime is prolonged by 56.6% in average.

A Power Graded Data Gathering Mechanism for Wireless Sensor Networks

The data gathering manner of wireless sensor networks, in which data is forwarded towards the sink node, would cause the nodes near the sink node to transmit more data than those far from it. Most data gathering mechanisms nowdo not do well in balancing the energy consumption among nodes with different distances to the sink, thus they can hardly avoid the problem that nodes near the sink consume energy more quickly, which may cause the network rupture from the sink node. This paper presents a data gathering mechanism called PODA, which grades the output power of nodes according to their distances from the sink node. PODA balances energy consumption by setting the nodes near the sink with lower output power and the nodes far from the sink with higher output power. Simulation results show that the PODA mechanism can achieve even energy consumption in the entire network, improve energy efficiency and prolong the network lifetime.

Single Mobile Sink Based Energy Efficiency and Fast Data Gathering Protocol for Wireless Sensor Networks

Recently, the exponential rise in communication system demands has motivated global academia-industry to develop efficient communication technologies to fulfill energy efficiency and Quality of Service (QoS) demands. Wireless Sensor Network (WSN) being one of the most efficient technologies possesses immense potential to serve major communication purposes including civil, defense and industrial purposes etc. The inclusion of sensor-mobility with WSN has broadened application horizon. The effectiveness of WSNs can be characterized by its ability to perform efficient data gathering and transmission to the base station for decision process. Clustering based routing scheme has been one of the dominating techniques for WSN systems;however key issues like, cluster formation, selection of the number of clusters and cluster heads, and data transmission decision from sensors to the mobile sink have always been an open research area. In this paper, a robust and energy efficient single mobile sink based WSN data gathering protocol is proposed. Unlike existing approaches, an enhanced centralized clustering model is developed on the basis of expectation-maximization (EEM) concept. Further, it is strengthened by using an optimal cluster count estimation technique that ensures that the number of clusters in the network region doesn’t introduce unwanted energy exhaustion. Meanwhile, the relative distance between sensor node and cluster head as well as mobile sink is used to make transmission (path) decision. Results exhibit that the proposed EEM based clustering with optimal cluster selection and optimal dynamic transmission decision enables higher throughput, fast data gathering, minima delay and energy consumption, and higher

Energy equalizing routing for fast data gathering in wireless sensor networks

Energy saving and fast responding of data gathering are two crucial factors for the performance of wireless sensor networks. A dynamic tree based energy equalizing routing scheme （DTEER） was proposed to make an effort to gather data along with low energy consumption and low time delay. DTEER introduces a dynamic multi-hop route selecting scheme based on weight-value and height-value to form a dynamic tree and a mechanism similar to token passing to elect the root of the tree. DTEER can simply and rapidly organize all the nodes with low overhead and is robust enough to the topology changes. When compared with power-efficient gathering in sensor information systems （PEGASIS） and the hybrid, energy- efficient, distributed clustering approach （HEED）, the simulation results show that DTEER achieves its intention of consuming less energy, equalizing the energy consumption of all the nodes, alleviating the data gathering delay, as well as extending the network lifetime perfectly.

Low-latency Data Gathering with Reliability Guaranteeing in Heterogeneous Wireless Sensor Networks

In order to achieve low-latency and high-reliability data gathering in heterogeneous wireless sensor networks(HWSNs),the problem of multi-channel-based data gathering with minimum latency(MCDGML),which associates with construction of data gathering trees,channel allocation,power assignment of nodes and link scheduling,is formulated as an optimization problem in this paper.Then,the optimization problem is proved to be NP-hard.To make the problem tractable,firstly,a multi-channel-based low-latency(MCLL)algorithm that constructs data gathering trees is proposed by optimizing the topology of nodes.Secondly,a maximum links scheduling(MLS)algorithm is proposed to further reduce the latency of data gathering,which ensures that the signal to interference plus noise ratio(SINR)of all scheduled links is not less than a certain threshold to guarantee the reliability of links.In addition,considering the interruption problem of data gathering caused by dead nodes or failed links,a robust mechanism is proposed by selecting certain assistant nodes based on the defined one-hop weight.A number of simulation results show that our algorithms can achieve a lower data gathering latency than some comparable data gathering algorithms while guaranteeing the reliability of links,and a higher packet arrival rate at the sink node can be achieved when the proposed algorithms are performed with the robust mechanism.

基于多模态神经网络的微地震事件检测

针对微地震有效信号时序特征存在的局限导致微地震事件识别准确率不高的问题,提出了一种基于多模态学习的神经网络微地震事件检测方法。首先,利用道集数据的相关性以目标道为轴对称制作多道时域模态,对目标道进行时频分析得到S域模态特征;然后,联合时域模态和S域模态设计微地震事件检测神经网络,综合多模态的特征进行训练学习,提高微地震事件识别的精度;最后,为验证方法的有效性,对合成微地震信号进行低信噪比数据分析、小幅值数据分析以及实际油井微地震监测信号事件分析。结果表明,该方法可以有效检测低信噪比及微弱的微地震事件;与支持向量机、卷积神经网络、基于监督机器学习方法的对比实验结果表明该方法具有更高的抗噪性与准确率。

基于压缩感知的波形指示反演在薄储层预测中的应用

鄂尔多斯盆地东缘大牛地气田二叠系下石盒子组有效砂体致密、规模小、厚度薄,储层非均质性强且地震波阻抗叠置严重。为实现下石盒子组5~6 m致密薄砂岩储层的有效刻画,文中选取了盒1段气藏主力单砂体作为研究对象。在地震正演分析基础上,形成了叠前道集优化提质+压缩感知拓频+纵波标定+波形指示GR反演的技术组合,该技术组合将压缩感知技术与波形指示反演技术的优势有机结合起来,并在盒1段气藏主力单砂体进行了应用。预测结果表明:地震剖面横、纵向分辨率显著提高,其预测成果更加符合地质沉积规律,与实钻井吻合良好,实现了5~6 m致密薄砂岩的有效刻画。该研究为致密砂岩气藏薄储层预测提供了一种新方法。

Enabling Energy Efficient Sensory Data Collection Using Multiple Mobile Sink

Mobile sink is the challenging task for wireless sensor networks(WSNs).In this paper we propose to design an efficient routing protocol for single mobile sink and multiple mobile sink for data gathering in WSN.In this process,a biased random walk method is used to determine the next position of the sink.Then,a rendezvous point selection with splitting tree technique is used to find the optimal data transmission path.If the sink moves within the range of the rendezvous point,it receives the gathered data and if moved out,it selects a relay node from its neighbours to relay packets from rendezvous point to the sink.Proposed algorithm reduces the signal overhead and improves the triangular routing problem.Here the sink acts as a vehicle and collect the data from the sensor.The results show that the proposed model effectively supports sink mobility with low overhead and delay when compared with Intelligent Agent-based Routing protocol(IAR) and also increases the reliability and delivery ratio when the number of sources increases.

视觉及多传感器数据融合的关键技术研究

多传感器融合是现代信息技术领域一个前景很广阔的研究方向,由于单一传感设备所采集的数据在融合算法计算时会具有一定的局限性,因此将2种或2种以上的传感设备所采集来的数据通过融合算法进行计算与优势集成的方案,已经逐渐成为数据融合领域的研究热点。主要介绍传感器数据融合的创新及关键技术,多传感器底层数据收集的复杂性,往往有大量的数据需要处理,将不同类型的传感器数据进行实时采集、融合、存储、传输并显示。

基于大数据赋能的PCDN业务识别技术研发

随着宽带中国、三千兆改造、提速降费等战略部署,河南联通家宽用户上行带宽不断提高,部分投机商户利用业务漏洞采用汇聚捆绑等技术手段,通过路由器、智能网络盒子等带有PCDN功能的设备,为抖音、哔哩哔哩、京东等厂家提供边缘计算加速服务,赚取带宽租赁的收益。文章基于大数据赋能实现PCDN业务识别技术研发,实现对大规模汇聚捆绑带宽干扰网络良性发展行为的管控。

GSVM:一种支持Gather/Scatter的向量存储器

宽单指令多数据流(Single Instruction Multiple Data,SIMD)架构数字信号处理器一般都能高效支持地址连续或等距跨步等规则应用的向量访存,但对于科学与工程计算中广泛存在的不规则应用的数据访存则带宽利用率往往较低,从而大幅降低了其整体运算能效。为了提高不规则应用的向量访存性能,基于某SIMD数字信号处理器的体系结构,设计了一种支持Gather/Scatter访存的向量存储器GSVM。通过设计与SIMD宽度相匹配的向量地址计算单元和合适深度的冲突缓冲器阵列,实现了Gather/Scatter指令向量地址计算、仲裁与缓存的全流水访存操作。实验结果表明,相比以前不支持Gather/Scatter访存的存储器,GSVM在增加22%的硬件代价基础上,基于稀疏矩阵向量乘的测试程序集获得了2~8的性能加速比。

大数据时代高校网络舆情管理机制的优化策略

大数据时代高校网络舆情具有师生主动发表意见、网络舆情载体多元化、聚焦性强等特征,但也存在网络舆情管理机制不健全、缺乏统一的大数据管理平台、网络舆情管理专业技术人员技能弱等问题。因此,高校要建立网络舆情立体化预警处理机制、应急处理机制、引导机制和多元互动机制,构建以大数据为核心资源的舆情管理模式,并依托大数据管理平台优化网络舆情管理队伍,从而提升大数据时代高校网络舆情管理水平。

新媒体技术在新闻采编中的应用

在当今数字化时代,新媒体技术以其迅猛的发展势头深刻改变着传媒格局,尤其在新闻采编领域发挥着日益重要的作用。分析新媒体技术的内涵、常见类型,通过具体案例,深入探讨虚拟现实、网络通信、大数据和数字技术等新媒体技术在新闻采编中的实际应用,为新闻从业者提供更具前瞻性的参考,从而推动传媒行业的发展与进步。

大数据技术在新闻采编播环节的应用

介绍大数据的定义及特点、产生和发展、处理和分析技术以及产业五大核心领域,重点分析大数据技术在新闻采编播环节的具体应用并举例进行说明,阐述大数据在新闻采编播环节应用面临的挑战以及未来发展趋势,以推动大数据技术与新闻行业的深度融合应用。

人工智能技术在新闻采编中的应用研究

介绍人工智能(Artificial Intelligence,AI)的定义、主要特点、发展历程、最新的技术动态以及AI赋能新闻采编的具体表现,重点分析AI技术在新闻采编中的应用,包括AI+大数据、AI+VR/AR、AI+5G等在新闻采编环节的具体应用,为AI与新闻行业的深度融合应用提供参考,推动新闻采编向自动化、智能化发展。

基于变循环预估寿命均衡的无线传感网数据采集算法

智能电网等大型工业系统数字化转型需求逐年提升,相应的无线传感器近年来被广泛应用。然而,无线传感器的自主续航十分有限,面向对应自组网的能效优化因可显著提升其生命周期而意义重大。为此提出一种基于变循环预估寿命均衡的无线传感网(WirelessSensorNetworks,WSN)数据采集算法,首先构建面向指定区域远程感知需求的WSN系统模型,然后设计基于变循环预估寿命均衡的高能效数据采集算法,最后通过仿真分析证明所提出的算法可通过拓扑重构有效均衡节点剩余能量,进而优化网络能效,显著提升无线传感网生命周期。

气田无人机巡检系统应用探讨

某气田集输管网分布广、地理环境恶劣,巡检工作量大,给人工巡检带来了较大难度。为进一步提高应急处置能力,提出了采用无人机代替部分人工进行智能化巡检的方案;介绍了无人机巡检系统的组成,给出了飞行平台、传输链路、手持遥控系统、移动无人机机场、无人机自助机场和地面站操作系统等无人机巡检主要设备的技术参数和功能。应用无人机巡检系统可以有效提升气田管理的数字化水平和应急处置能力。

基于角道集数据地层Q值提取方法研究

地震波衰减是实际介质的一种重要属性。地震波衰减的提取方法对地震资料的品质要求比较高,角道集资料比叠后地震资料含有更加丰富的地层信息,比叠前地震资料具有更高的信噪比;同时S域谱比法不仅充分考虑了因地层吸收而引起的振幅、频率等信息的变化,而且避免了通常品质因子计算中的平均效应,使估算的品质因子更加准确。基于以上的考虑,本文基于角道集资料利用S域谱比法提取地层频谱比斜率值,再通过三角度归零处理得到零炮检距处的地层Q值,消除了入射角对地层Q值的影响,使得到的结果更加精确。模型数据和实际资料的应用表明,该方法的准确性更高、适用性更强。

灾后煤矿物联网无人机辅助节能数据采集方法

煤矿物联网在煤矿生产监测控制和灾害预测预警中具有重要意义。然而,煤矿物联网在数据传输过程中很容易受到煤矿事故的影响,事故往往会导致部分物联网节点损毁,残存的物联网节点受限于较低的数量和能量约束,难以完成对事故巷道中大量监测数据的采集和传输任务。为了保证灾后煤矿物联网可靠、节能的数据通信,构建了一种无人机(UAV,Unmanned Aerial Vehicle)辅助的分簇式煤矿物联网通信系统架构。在此架构基础上,提出了一种基于分簇和A*搜索的UAV辅助数据采集方法。首先,利用物联网节点的能耗和UAV的路径长度构造目标函数,通过分别绘制节点到簇中心的距离方差、UAV数据采集的路径长度和不同K值之间的关系图来确定最优分簇数K。然后,采用K均值算法将所有物联网节点划分为K个簇。接着,通过综合考虑物联网节点的数据采集能耗和UAV的数据采集能耗,将UAV的路径规划问题建立为一个最小化煤矿物联网数据采集系统整体能耗的优化问题,并提出了一种改进的A*搜索UAV数据采集路径规划算法。在该算法中,利用指针网络将UAV的起始点和所有分簇的信息输入到A*网络中,A*网络输出的一组簇头和簇头访问顺序即为UAV的飞行路径。仿真结果表明,与平面式UAV数据采集方法相比,所提方法显著降低了UAV的能耗;与两种分簇式UAV数据采集方法相比,所提方法有效降低了物联网节点的平均能耗和总能耗。因此,所提UAV辅助的数据采集方法改善了灾后煤矿物联网系统的能耗问题,延长了网络生存期,对于提高灾后煤矿物联网数据采集系统的可靠性起到了重要作用。