Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

REMUDA: A Practical Topology Control and Data Forwarding Mechanism for Wireless Sensor Networks

In wireless sensor networks, topology control plays an important role for data forwarding efficiency in the data gathering applications. In this paper, we present a novel topology control and data forwarding mechanism called REMUDA, which is designed for a practical indoor parking lot management system. REMUDA forms a tree-based hierarchical network topology which brings as many nodes as possible to be leaf nodes and constructs a virtual cluster structure. Meanwhile, it takes the reliability, stability and path length into account in the tree construction process. Through an experiment in a network of 30 real sensor nodes, we evaluate the performance of REMUDA and compare it with LEPS which is also a practical routing protocol in TinyOS. Experiment results show that REMUDA can achieve better performance than LEPS.

Trajectory-Based Data Forwarding Schemes for Vehicular Networks

This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv- ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe how to take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks. The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net- works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectory in the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, we explain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net- work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to- end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu- lar traffic statistics in a privacy-preserving manner.

Enhancing Interest Forwarding for Fast Recovery from Unanticipated Data Access Failure in NDN

We show that an aggregated Interest in Named Data Networking (NDN) may fail to retrieve desired data since the Interest previously sent upstream for the same content is judged as a duplicate one and then dropped by an upstream node due to its multipath forwarding. Furthermore, we propose NDRUDAF, a NACK based mechanism that enhances the Interest forwarding and enables Detection and fast Recovery from such Unanticipated Data Access Failure. In the NDN enhanced with NDRUDAF, the router that aggregates the Interest detects such unanticipated data access failure based on a negative acknowledgement from the upstream node that judges the Interest as a duplicate one. Then the router retransmits the Interest as soon as possible on behalf of the requester whose Interest is aggregated to fast recover from the data access failure. We qualitatively and quantitatively analyze the performance of the NDN enhanced with our proposed NDRUDAF and compare it with that of the present NDN. Our experimental results validate that NDRUDAF improves the system performance in case of such unanticipated data access failure in terms of data access delay and network resource utilization efficiency at routers.

Covariance Estimation Using High-Frequency Data： An Analysis of Nord Pool Electricity Forward Data

The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM （Capital Asset Pricing Model betas）, derivate pricing and risk management in general. Recent access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange has made it possible to apply new and promising methods for analyzing volatility and correlation. The concepts of realized volatility and realized correlation are applied, and this study statistically describes the distribution （both distributional properties and temporal dependencies） of electricity forward data from 2005 to 2009. The main findings show that the logarithmic realized volatility is approximately normally distributed, while realized correlation seems not to be. Further, realized volatility and realized correlation have a long-memory feature. There also seems to be a high correlation between realized correlation and volatilities and positive relations between trading volume and realized volatility and between trading volume and realized correlation. These results are to a large extent consistent with earlier studies of stylized facts of other financial and commodity markets.

基于模糊评判的无线传感器网络簇间数据转发算法

由于无线传感器网络在簇间数据转发过程中消耗能量较大、转发效率低,导致数据在转发过程中容易失效。为此,提出基于模糊评判的无线传感器网络簇间数据转发算法。建立无线传感器网络模型和通信模型,明确节点的运行方式和转发数据所消耗的能量,构建模糊综合评判模型,选取梯形隶属度函数计算传感器节点的相似度和活跃度,找出最佳转发节点,完成数据转发任务。仿真结果表明,所提算法的第一个节点死亡轮数和网络失效轮数最高为167和178,在仿真轮数达到1000轮时,该算法剩余的网络能量和节点平均能量分别为4 J和0.01 J,证明所提算法在数据转发过程中可消耗最少的能量,高效率地完成数据转发任务。

A Modified Back/Forward Sweep Method Based on the Electricity Consumption Data

With the development of distribution automation system, the centralized meter reading system has been adopted more and more extensively, which provides real-time electricity consumption data of end-users, and consequently lays foundation for operating condition on-line analysis of distribution network. In this paper, a modified back/forward sweep method, which directly uses real-time electricity consumption data acquired from the centralized meter reading system, is proposedto realize voltage analysis based on 24-hour electricity consumption data of a typical transformer district. Furthermore, the calculated line losses are verified through data collected from the energy metering of the distribution transformer, illustrating that the proposed method can be applied in analyzing voltage level and discovering unknown energy losses, which will lay foundation for on-line analysis, calculation and monitoring of power distribution network.

Load Shedding Strategy Based on Combined Feed-Forward Plus Feedback Control over Data Streams

In data stream management systems (DSMSs), how to maintain the quality of queries is a difficult problem because both the processing cost and data arrival rates are highly unpredictable. When the system is overloaded, quality degrades significantly and thus load shedding becomes necessary. Unlike processing overloading in the general way which is only by a feedback control (FB) loop to obtain a good and stable performance over data streams, a feedback plus feed-forward control (FFC) strategy is introduced in DSMSs, which have a good quality of service (QoS) in the aspects of miss ratio and processing delay. In this paper, a quality adaptation framework is proposed, in which the control-theory-based techniques are leveraged to adjust the application behavior with the considerations of the current system status. Compared to previous solutions, the FFC strategy achieves a good quality with a waste of fewer resources.

NDN-AdHoc网络中基于多属性决策的可靠转发策略

为了应对传统自组织网络高度动态拓扑、短寿命连接等特性导致的传输失效问题,基于命名数据网络(NDN)架构,设计了一种联合链路稳定性和内容可达性的可靠转发(RF-LSCA)策略。该策略基于多属性决策方法,综合考虑链路持续时间、节点间距离和内容可达性3个关键属性,从而更加精确地选择最优转发节点,提升了内容传输性能;此外,鉴于单一最优路径可能存在的不稳定性,该策略进一步将决策模型与概率转发思想相结合,设计了兼具低开销与高可靠性的冗余转发机制。这一设计充分利用了NDN的多路径特性,以保障移动环境下的内容传输性能。通过仿真实验验证,所提RF-LSCA在内容获取平均时延、平均跳数、内容满足率等多个关键性能指标上优于现有策略,充分证明了其在提高NDN-AdHoc网络传输效率方面的优越性。

一种基于重力正演理论的海底地形反演迭代算法

重力数据反演海底地形的方法是获取全球海底地形数据的重要途径。针对传统地形反演算法需要间接引入经验参数的问题,本研究以不采用经验公式为原则,从空间域方法入手,结合重力正演公式,建立重力异常与地形之间的解析观测方程,采用最小二乘方法进行求解。为了得到观测方程最优解,建立牛顿迭代关系,引入正则化参数,增强方程组的收敛性。顾及边缘效应对正演的影响,采用双线性插值算法对原始重力异常数据进行加密处理和无约束网格扩充以削弱误差。在太平洋海域(155°E~156°E,16°N~17°N)构建了1′×1′分辨率的海底地形模型,并通过船测水深数据验证。相比于传统空间域算法——重力地质法,本研究方法的均方根误差降低了12.7%,验证了其可行性与准确性。

基于UVM和AXI VIP的SoC外设组件仿真模型设计

随着片上系统(System on Chip,SoC)复杂度和集成度的提高,其对验证效率的要求也不断提高。传统的瀑布形式的系统设计流程中,各个开发子过程顺序执行。为了将功能模块开发与系统架构验证并行开展以缩短项目的交付时间,提出了一种利用通用验证方法学(Universal Verification Methodology,UVM)验证IP(Verification IP,VIP)进行SoC外设组件快速建模的方法。使用事务级建模(Transaction-Level Modeling,TLM)模型模拟硬件行为代替未开发完成的功能模块产生数据流,使得在设计早期系统架构搭建完成后能更早地开展系统级测试,帮助评估总线架构性能和功能,提高验证效率。以网络处理器芯片中的数据转发模块为例,对SoC外设组件的建模方法进行了介绍,在系统级测试中进行了仿真验证。

针对回波数据异常时的雷达前视超分辨快速成像方法

机载扫描雷达前视成像可广泛应用于态势感知、自主导航和地形跟随。在雷达扫描过程中受到不经意的电磁脉冲干扰或设备性能异常等影响时,雷达回波数据出现异常值。已有的超分辨方法可以抑制回波中的异常值、提高角度分辨率,但没有考虑计算实时性问题。针对上述问题,该文提出了一种机载雷达超分辨方法实现回波数据异常时的快速前视成像。为了更好地拟合回波噪声,引入对异常值更加鲁棒的学生t分布,并采用期望最大化方法对成像参数进行估计。受截断奇异值分解方法的启发,将截断的酉矩阵引入目标散射系数的估计公式中。通过矩阵变换降低了求逆矩阵的尺寸,从而降低了参数估计的计算复杂度。仿真结果表明该文提出加速方法可以用更短的时间提高前视成像的角度分辨率,抑制回波数据中的异常值。

增强TLS 1.3中Early data安全性的协议

将新型0-RTT密钥交换协议思想借鉴到TLS 1.3会话重用阶段,构建r FSOPKE协议,改进了Early data的加密和传输过程。r FSOPKE协议可以在Ticket有效期内保护Early data的前向安全性并使其抵抗重放攻击。与改进前Early data的发送过程相比,本协议大幅增强了Early data的安全性。在实现效率方面,由于在发送Early data时增加了本协议的计算和传输开销,所以实现效率有所降低。但是本协议可以根据应用场景的不同嵌入适合的算法,所以可以选择更加高效的算法提高协议实现速度。

面向无人机无人船自组网的NDN智能数据转发策略

无人机和无人船组成的移动自组织网络存在通信环境恶劣和网络拓扑结构变化频繁等挑战,导致网络性能变差。针对这一问题,建立以数据为中心的命名数据网络(Named Data Networking, NDN)网络架构,在此基础上提出基于深度强化学习的智能数据转发策略。利用深度强化学习实时感知网络动态变化,优化数据转发策略,设计优先采样和双重Q网络算法,加快深度强化学习收敛速度。实验结果表明,该策略可以有效降低时延并提高兴趣包满足率。

基于Forward平台的固井资料预处理研究

临盘地区后期开采大多以电缆射孔为主,对射孔深度精度要求较高,其深度依据主要来自于测井所提供的磁定位曲线。磁定位曲线受干扰因素较多,且测井过程中会存在一定的拉伸误差。Forward平台的预处理模块并未将固井资料进行单独研究,为满足该地区的生产需要,专门开展针对固井资料的预处理技术研究。该技术包括自动磁定位噪声消除、自动深度校正;校正时以每根套管间拉伸/压缩量小于0.2m为约束条件,达到减小局部深度误差的目的。计算机预处理方法的成功实现,代替了传统低效、繁杂的手工处理方式,提高工作效率的同时降低了资料处理中的人为误差。在临盘地区的实践表明,该预处理方法高效、可靠,值得在以电缆射孔为主的油田及区块推广应用。

智能矿山IoT边缘数据网关技术研究

随着工业4.0和工业互联网的发展,矿山行业也开始走向智能化,面对煤矿工业互联网接入终端复杂、通信协议多样、接入网络以及多源数据异构等现状,提出一种智能矿山IoT边缘数据网关技术。通过构建边缘网关管理架构,北向上行采用TCP、HTTP、WebSocket、MQTT等传输协议连接云平台,南向下行支持多种通信接口连接感知层设备,实现不同类型网络的协议转换以及各个系统多源异构数据的统一接入。该研究成果已应用于多个煤矿自动化相关设备数据采集与转发。

电力信息物理系统建模与故障分析

针对电力信息物理系统分层建模中信息层数据转发关系表达不清晰的问题,建立了考虑前置机数据转发功能的电力信息物理系统模型。基于信息物理交互视角,通过对信息流流动过程进行分析并结合电力信息物理系统结构与功能特性,将电力信息物理系统分为数据上传与数据下发两个部分进行建模。数据上传与下发过程中分别建立决策接收矩阵与前置机数据下发矩阵以表达信息流横向流动过程。在数据上传部分建立数据预处理矩阵以表征电力信息系统自动调控能力。最后以IEEE30节点系统为例进行模型推演与故障分析以验证模型的有效性,仿真结果表明,所提模型对多种故障情形下的数据流动分析具有普适性。

多普勒雷达DATA文件安全模型的设计

从计算机DATA文件安全的内涵出发,针对目前DATA文件面临的安全威胁,分析了影响DATA文件的安全因素,提出了DATA文件安全体系层次结构。重点论述DATA文件层次的安全控制所采取的技术手段和应急措施,在此基础上设计了一种新型DATA文件安全防护模型,使之能够控制DATA文件多层安全,保证DATA文件的安全运行。

Forward、Watch平台软件中的测井深度制式转换技术

Forward、Watch软件平台采用的公制深度系统,不适应英制深度单位测井资料处理和绘图需要。针对测井深度制式转换问题,通过数据动态解编技术、深度制式自动识别技术、人工干预转换方法在Forward、Watch软件平台上实现了测井资料数据格式与深度制式的统一。应用表明,该技术提高了Forward、Watch平台解释处理测井资料的适应能力,保证了国际测井服务对不同深度制式的特殊资料处理需要。

基于前视声呐图像的AUV目标识别与跟踪

声呐图像由于水体不均匀、边界不规则以及声呐设备本身性能的限制,导致图像噪声明显、亮度不均、分辨率低,使得水下AUV装备在使用前视声呐进行水下目标检测时难度较大。针对该问题,基于m750d声呐探测获得的AUV声呐数据,进行了数据提取、高斯滤波处理、扇形映射处理,并采用Jet映射对声呐灰度图像进行了伪彩色映射提高数据标注速度和精度,制作获得了4组2 500张声呐图像的AUV目标检测数据集;采用YOLOv4-tiny目标检测算法开展AUV目标检测研究,研究结果表明该方法在该数据集上表现优秀,mAP@0.50达到94.17%,FPS在22帧左右,说明该轻量级网络在水下AUV目标识别与跟踪应用上具有较好的应用价值。