基于结构相似性的RDF数据聚类分割

随着语义网的快速发展,RDF数据呈现出海量的增长特征,单机的RDF数据管理系统的可扩展性成为RDF数据发展的瓶颈,分布式的存储是解决这一难题的有效方法。而在数据的分布式存储中,数据分割是其中一个关键问题。文中根据RDF数据可以用有向图来描述特性,利用P-Rank基于结构的节点相似性度量方式计算图结点间的相似度,使用AP聚类算法对度量结果进行聚类,实现RDF数据的有效分割。实验结果表明,该方法能够有效地完成RDF数据的分割,使得类间相似度较小,而类内相似度较大。     

浅谈物联网应用中间件在公共领域中的应用

物联网应用中间件是物联网应用和感知设备之间的桥梁和纽带,其涉及物联网感知层海量感知设备的监控管理问题,是物联网数据的核心通道和国家信息安全的咽喉.物联网应用中间件能有力支撑大规模物联网应用,大大提高物联网应用的效益.本文主要介绍物联网应用中间件在智能交通、智能电网等公共领域的几种示范应用.     

基于改进PSO粒子群及K-Means聚类算法的物联网数据挖掘查全优化研究

大数据云计算环境下的物联网数据挖掘与分析,是企业大数据项集采集、数据样本挖掘与统计分析关注的重要方向之一。通过基于云计算Hadoop分布式软件服务架构,建构起改进粒子群(Particle Swarm Optimization, PSO)优化算法、K-Means聚类挖掘算法,设置自调节惯性权重、云变异算子进行不同类别物联网数据提取、全局搜索、极值追踪与更新,构造聚类函数完成不同数据项集的聚类分析,进而解决海量物联网数据的挖掘与计算问题、保证自适应聚类数组的采样查全准确率。     

边缘计算下物联网终端的可信评估安全技术

针对传统安全防护机制无法确保边缘计算下海量物联网终端接入安全,特别是数据泄漏等问题,提出一种边缘计算下物联网终端的可信接入安全技术,该技术设计一种基于分布式群智感知网络体系架构,通过分发的智能模型对行为特征进行检测分类,提供恶意行为识别等边缘服务,保证边缘环境下物联网终端的接入安全。     

密度敏感的谱聚类

谱聚类是近来出现的一种性能极具竞争力的聚类方法,它的成功很大程度依赖于相似性度量的选择.本文通过分析这一性质并结合数据聚类特性,提出一种数据依赖的相似性度量--密度敏感的相似性度量.该相似性度量可以有效描述数据的实际聚类分布.将其引入谱聚类得到密度敏感的谱聚类算法.与原有的谱聚类算法相比,新算法不仅能够处理多尺度聚类问题,而且对参数选择相对不敏感.算法有效性分析以及实验验证了所提算法的有效性和可行性.     

为嵌入式系统开发平台增加USB下载接口

本文介绍了如何利用USB接口为嵌入式系统开发板提供文件下载功能。扼要介绍了USB海量存储类设备的工作原理以及下载程序的流程,为广大嵌入式系统系统开发人员提供了一种快速的下载手段。     

基于本体的业务流程适应性配置方法研究

本文提出了一种基于描述逻辑的本体和规则的半自动业务流程配置框架。设计并实现了可配置节点本体及业务规则本体,提出七类元规则指导业务规则的撰写。最后提出基于C-iEPC的流程配置算法,对可配置流程进行配置。本文使用案例进行验证,结果证明本文方法能够在降低人工成本的基础上,取得与国际主流的问卷式流程配置方法相近的效果,并使用经验验证的方法证明了本文方法的实用性和有效性。     

STC算法的网络服务分类技术研究

针对现有的大部分网络服务分类机制基本上靠人工分类的缺陷,以及半自动分类技术准确率和查全率的效率较低等问题,进行了基于后缀树聚类算法的网络服务自动分类技术研究,同时提出概念与例子层次树结构来表示部分存在上下位关系或者同义关系的聚类标签,在后缀树聚类基础上对这些标签进行二次聚类。通过引入文本预处理和WordNet语义相似度计算的基础上来实现服务自动分类。实验结果表明,该服务自动分类算法具有较好的准备率和查全率,另外根据WordNet提取出抽象的聚类标签,有利于对日益剧增的网络服务进行抽象层次的分类,提高了海量网络服务分类的效率。     

基于Spark框架的CFSFDP改进算法

CFSFDP算法是一种基于密度的新型聚类算法。文中针对算法需使用决策图人工选取聚类中心点的问题,利用斜率思想找出聚类中心点与非聚类中心点间的分界点,在消除主观误差的同时实现了中心点的自动求取,并最终将算法使用Spark框架进行了并行化实现。实验结果表明,文中算法在消除人为误差的同时提升了算法效率,且并行后的算法具有良好的加速比与扩展性,适用于海量数据的聚类分析。     

一种层次初始的聚类个数自适应的聚类方法研究

K均值聚类算法是一种常见且有效的基于划分的聚类算法。为解决该聚类算法对初始中心敏感的问题,常用的方法是层次化初始聚类中心。然而,层次初始的聚类算法仍然需要将聚类个数作为输入参数,在高维数据和海量数据中不易应用。基于能够自动确定聚类数目的目的,采用DBI度量,提出一种层次初始的聚类个数自适应的聚类方法（简称DHIKM）。通过UCI数据集和仿真数据上的实验,证明DHIKM可以在采样数据中快速找到合适的聚类个数,实验结果表明该算法在聚类质量与收敛速度上的有效性。     

SALA: Smartphone-Assisted Localization Algorithm for Positioning Indoor IoT Devices

This paper proposes a Smartphone-Assisted Localization Algorithm (SALA) for the localization of Internet of Things (IoT) devices that are placed in indoor environments (e.g., smart home, smart office, smart mall, and smart factory). This SALA allows a smartphone to visually display the positions of IoT devices in indoor environments for the easy management of IoT devices, such as remote-control and monitoring. A smartphone plays a role of a mobile beacon that tracks its own position indoors by a sensor-fusion method with its motion sensors, such as accelerometer, gyroscope, and magnetometer. While moving around indoor, the smartphone periodically broadcasts short-distance beacon messages and collects the response messages from neighboring IoT devices. The response messages contains IoT device information. The smartphone stores the IoT device information in the response messages along with the message’s signal strength and its position into a dedicated server (e.g., home gateway) for the localization. These stored trace data are processed offline through our localization algorithm along with a given indoor layout, such as apartment layout. Through simulations, it is shown that our SALA can effectively localize IoT devices in an apartment with position errors less than 20 cm in a realistic apartment setting.     

Intelligent Contextual Information Collection in Internet of Things

As we are moving towards the internet of things (IoT), a significant growth of stationary and mobile sensing and computing IoT devices continuously generate enormous amounts of contextual information, e.g., environmental data. Contextual information collection, reasoning, and inference plays critical role in IoT. In this paper, we consider the contextual information collection and harvesting problem in which stationary sensing and computing devices (sources), which are incapable to communicate with each other either due to their long distance, or for energy efficiency, or spatially dispersed network, rely on mobile IoT devices (collectors) to ‘drain’ their acquired contextual information. (e.g., generating from IoT applications: smart cities, smart metering, and smart agriculture). At the contact instances with the collectors, sources have to decide whether to deliver the contextual information obtained so far or postpone their delivery for later hitting epochs in an effort to sense fresher (or more critical) contextual information. We rest on the principles of Optimal Stopping Theory and propose an intelligent context collection scheme in IoT environments. We show through simulations with synthetic and real mobility data the effectiveness of our scheme compared to other approaches.     

物联网平台安全接入方案研究

物联网发展过程中,面临的一个重要问题是感知数据如何从感知设备安全地传输到物联网平台的问题。文章针对三种物联网平台部署模式,提出三种物联网平台的安全接入方案,并分别对三种方案的安全性进行讨论。     

An energy-efficient power allocation algorithm under reliability constraint for industrial Internet of Thing systems

With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat-reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms.     

Imp Raft: a consensus algorithm based on Raft and storage compression consensus for IoT scenario

In order to meet various challenges in the Internet of things (IoT), such as identity authentication, privacy preservation of distributed data and network security, the integration of blockchain and IoT became a new trend in recent years. As the key supporting technology of blockchain, the consensus algorithm is a hotspot of distributed system research. At present, the research direction of the consensus algorithm is mainly focused on improving throughput and reducing delay. However, when blockchain is applied to IoT scenario, the storage capacity of lightweight IoT devices is limited, and the normal operations of blockchain system cannot be guaranteed. To solve this problem, an improved version of Raft (Imp Raft) based on Raft and the storage compression consensus (SCC) algorithm is proposed, where initialization process and compression process are added into the flow of Raft. Moreover, the data validation process aims to ensure that blockchain data cannot be tampered with. It is obtained from experiments and analysis that the new proposed algorithm can effectively reduce the size of the blockchain and the storage burden of lightweight IoT devices.     

Design of Two-Step Random Access Procedure for URLLC Applications

The idea of Smart City incorporates a few ideas being technology, economy, governance, people, management, and infrastructure. This implies a Smart City can have distinctive communication needs. Wireless technologies, for example, WiFi, Zig Bee, Bluetooth, WiMax, 4G or LTE have introduced themselves as a solution for the communication in Smart City activities. Nonetheless, as the majority of them utilize unlicensed interference, coexistence and bands issues are increasing. So to solve the problem IoT is used in smart cities. This paper addresses the issues of both resource allocation and routing to propose an energy efficient, congestion aware resource allocation and routing protocol (ECRR) for IoT network based on hybrid optimization techniques. The first contribution of proposed ECRR technique is to employ the data clustering and metaheuristic algorithm for allocate the large-scale devices and gateways of IoT to reduce the total congestion between them. The second contribution is to propose a queue based swarm optimization algorithm for select a better route for future route based on multiple constraints, which improves the route discovering mechanism. The proposed ECRR technique is implemented in Network Simulator (NS-2) tool and the simulation results are compared with the existing state-of-art techniques in terms of energy consumption, node lifetime, throughput, end-to-end delay, packet delivery ratio and packet overheads.

     

A self‐adaptive load‐dispatching control framework for device data accessing in IoT‐based systems

The Internet of things (IoT) information system plays important roles in disposing of huge volumes of real‐time service requests from heterogeneous devices, targeting for different complex application requirements. Load‐dispatching control (LDC) is a key problem to be solved for devices accessing concurrently in cluster systems. Self‐adaptive LDC optimizes the resource allocation to ensure no overloading node, thus, improving the performance of IoT systems. This paper focuses on adaptive dispatching control problem in IoT information system. First, a device data access platform is proposed for reducing the load imbalance and improving the efficiency of data processing. Then, we propose a processing capability prediction model to evaluate the system performance. On the basis of the model, we present a practical self‐adaptive LDC framework with a self‐adaptive control strategy and a load dispatching method. Finally, a case study is given to verify the framework and the control strategy. Experimental results show that the proposed strategy can meet the requirements of dynamic load balancing with the ability to avoid the load imbalance problem, and the LDC‐based device access platform can process data accessing effectively and ubiquitously.     

Enhancing IoT anomaly detection performance for federated learning

Federated Learning (FL) with mobile computing and the Internet of Things (IoT) is an effective cooperative learning approach. However, several technical challenges still need to be addressed. For instance, dividing the training process among several devices may impact the performance of Machine Learning (ML) algorithms, often significantly degrading prediction accuracy compared to centralized learning. One of the primary reasons for such performance degradation is that each device can access only a small fraction of data (that it generates), which limits the efficacy of the local ML model constructed on that device. The performance degradation could be exacerbated when the participating devices produce different classes of events, which is known as the class balance problem. Moreover, if the participating devices are of different types, each device may never observe the same types of events, which leads to the device heterogeneity problem. In this study, we investigate how data augmentation can be applied to address these challenges and improving detection performance in an anomaly detection task using IoT datasets. Our extensive experimental results with three publicly accessible IoT datasets show the performance improvement of up to 22.9% with the approach of data augmentation, compared to the baseline (without relying on data augmentation). In particular, stratified random sampling and uniform random sampling show the best improvement in detection performance with only a modest increase in computation time, whereas the data augmentation scheme using Generative Adversarial Networks is the most time-consuming with limited performance benefits.     

一种基于深度学习的异常数据清洗算法

在物联网(IoT)中采用合适的异常数据清洗算法能极大地提升数据质量.许多研究人员采用统计学方法或分类聚类等方法对时-空相关数据进行清洗.但这些方法需要额外的先验知识,会给汇聚节点带来额外的计算开销.该文根据低秩-稀疏矩阵分解模型,提出一种基于深度神经网络的快速异常数据清洗算法,来解决物联网中时-空相关数据的清洗问题.结...     

Machine learning-based clustering protocols for Internet of Things networks: An overview

The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML-based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology-centered ML-based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML-based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.