Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

DDoS Detection for 6G Internet of Things: Spatial-Temporal Trust Model and New Architecture

With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originating from IoT devices.In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness,which can be deployed in the access network of 6G IoT.Based on historical communication behaviors,this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices,thereby effectively distinguishing attack traffic.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method has advantages in terms of both accuracy and efficiency in identifying attack flows.

A Trusted NUMFabric Algorithm for Congestion Price Calculation at the Internet-of-Things Datacenter

The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information.However,it is difficult to predict the congestion state of the link-end accurately at the source.In this paper,we presented an improved NUMFabric algorithm for calculating the overall congestion price.In the proposed scheme,the whole network structure had been obtained by the central control server in the Software Defined Network,and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated.In this scheme,the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information.Based on the historical data,the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm.The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm.We evaluate this evolved NUMFabric algorithm in NS3,which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.

An Efficient SDFRM Security System for Blockchain Based Internet of Things

Blockchain has recently sparked interest in both the technological and businessfirms.The Internet of Things's(IoT)core principle emerged due to the connectivity of several new technologies,including wireless technology,the Inter-net,embedded automation systems,and micro-electromechanical devices.Manu-facturing environments and operations have been successfully converted by implementing recent advanced technology like Cloud Computing(CC),Cyber-Physical System(CSP),Information and Communication Technologies(ICT)and Enterprise Model,and other technological innovations into the fourth indus-trial revolution referred to as Industry 4.0.Data management is defined as the pro-cess of accumulation in order to make better business decisions,and process,secure and store information about a company.In the incipient model,there are interconnected contrivances and Machine-to-Machine(M2M)interactions,and transaction data are stored on the Blockchain.Security is a challenging aspect that must be punctiliously considered during the design and development phases of a CSP.In this research article,we proposed a Secure and Distributed Framework for Resource Management(SDFRM)in Industry 4.0 environments within a distribu-ted and collaborative Industry 4.0 system,the dynamic and trust-based Distributed Management Framework(DMF)of shared resource access.Such issues are focused by taking into account of the traditional characteristics of IoT/Industrial Internet of Things’(IIoT)-predicated environments,an SDFRM in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system.Also,to ensure strong privacy over the procedures associated with Access Control(AC),a privacy-preserving method is proposed and integrated into the DMF.The proposed DMF,based on blockchain technology and peer-to-peer networks,allows dynamic access management and system governance without using third parties who could be attacked.We worked hard to design and implement the pro-posal to demonstrate its viability and evaluate its performance.Our proposal out-performs the Multichain Blockchain in terms of successful storage transactions with an achieved average throughput of 98.15%.

Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.

Secure smart contract-based digital twins for the Internet of Things

The proliferation of Internet of Things(IoT)devices that operate unattended providing a multitude of important and often sensitive services highlights the need for seamless interoperability and increased security.We argue that digital twins of IoT devices,with the right design,can enhance the security,reliability,auditability,and interoperability of IoT systems.The salient features of digital twins have made them key elements for the IoT and Industry 4.0.In this paper,we leverage advances in W3C’s Web of Things(WoT)standards and distributed ledger technologies(DLTs)to present a novel design of the smart contract-based digital twins with enhanced security,transparency,interoperability,and reliability.We provide two different variations of that general design using two different blockchains(one public and one private,permissioned blockchain),and we present design trade-offs.Furthermore,we introduce an architecture for accessing and controlling IoT devices securely and reliably,providing full auditability,while at the same time using the proposed digital twins as an indirection mechanism(proxy).The proposed architecture leverages the blockchain to offer notable properties,namely,decentralization,immutability,auditability,non-repudiation,availability,and reliability.Moreover,it introduces mass actuation,easier management of IoT devices,and enhanced security to the IoT gateways,enables new business models,and makes consumer devices(vendor-)agnostic.

Recursive Approximation of Complex Behaviours With IoT-Data Imperfections

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect(noisy and incomplete) measurements in the internet of things(IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality(l0) optimization problem, known to be NP-hard.To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe(mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning(HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.

On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing

This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

针对MR-OFDM波形的降峰均比机制研究

针对IEEE 802.15.4g标准的多速率多区域正交频分复用(MR-OFDM)波形,提出了一种通过对加扰种子优化选择来降低发送信号峰均比(PAPR)的机制。其充分利用了发送信号的帧格式,在业务负荷信号的产生过程中引入加扰种子的优化选择机制,从而改善了发射信号的PAPR性能。和传统降PAPR算法相比,新算法无需对标准内容进行任何变动,在发送端仅需少量修改现有信号处理流程,而在接收端则无需任何修改,从而实现对大量已部署产品的通信兼容。理论分析和仿真结果表明,优化算法不影响传输速率,且计算复杂度低,不同参数OFDM信号的互补累积分布函数在10^(-4)数值处减少了约2~2.6 dB,取得了较好的降峰均比值性能。另外,相关机制经过适当修改后可以应用于其他同类型的物联网OFDM通信标准中。

改进PBFT算法的配电物联网接入认证方法

随着物联网与配电网深度融合,海量终端设备接入系统给配电物联网安全稳定运行带来巨大挑战。针对传统身份认证方式过于中心化且无法承载大规模终端等现状,设计一种基于区块链共识机制的配电物联网终端接入认证方法。由配电物联网网关负责对待接入终端节点登记注册,采用共识算法进行分布式认证,将合法终端上链存储。在传统PBFT算法的基础上,设计了配电物联网终端共识算法。该算法引入权重机制,根据终端节点权重选取认证节点,缩小共识规模;引入可验证随机函数增强主节点安全,避免启动视图切换协议,提高共识效率;结合实际应用场景优化一致性协议,降低通信开销。实验分析表明该方法可有效规避多种网络攻击,通信开销和吞吐量优于其他方法,系统抗攻击性较强,满足配电物联网对认证效率和系统可靠性等要求。

物联网平台下的分布式数据融合管理系统设计与实现

随着物联网技术的快速发展,感知终端之间的数据融合、交互与共享成为智能物联网高层应用的基础。目前,我国各行业的管理系统还存在较大的“信息孤岛”现象,本文为了解决上述问题而提出的总体方案和总体方案。在此基础上,设计了数据融合平台、分布式实时库管理等各子系统的功能结构,针对个问题提出来系统的总模块设计与总架构设计。并对系统五大子系统:数据融合平台、分布式实时库管理、客户端代理、集群、元数据服务器的功能架构进行了设计。为高效处理、分布式存储等提供借鉴。

配电物联网云边协同系统计算资源双目标优化配置方法

云边协同系统能够支持计算资源弹性扩展,适应配电物联网技术的需求。针对云边协同系统的计算资源配置问题,刻画了云边协同系统的计算资源,建立了计算业务模型,提出了批量计算业务的业务超时系数以对系统效果进行定量评价。基于上述模型,以最小化云边协同系统开销和最小化业务超时系数为目标,建立了考虑云边互动的计算资源优化配置双目标规划模型,并采用改进差分进化算法得到帕累托前沿。基于仿真算例讨论了不同双目标处理方法、通信质量、并发业务规模等对所提计算资源配置方法的影响。

面向配电物联网的配电终端一体化建模

针对当前基于IEC61850的配电终端信息模型对配电物联网的描述和覆盖能力的欠缺,以及未考虑面向终端微应用的开发,存在功能灵活扩展不足等问题,提出一种面向配电物联网的配电终端一体化信息模型。该模型融合扩展了IEC61850、CIM和物模型,采用扁平化模型层次结构和基于本体的模型抽象方法,建立物、数据、设备和服务4个顶层本体。所建模型适用于云主站与终端之间设备的信息模型交互,以及大规模接入场景下的配电终端即插即用,有助于降低终端微应用间信息交互管理的复杂度,以及配电终端功能一体化的实现,能够全力支撑当前配电终端的数字化转型。

基于多优先级排队论的配电物联网带宽分配方法

针对数据量剧增的配电物联网中存在的带宽利用率低和业务数据服务质量(quality of service,QoS)难以满足通信需求等问题,提出一种多优先级排队论的带宽分配方法。首先,对感知终端到边缘物联网关的业务数据传输过程进行改进,改进后的传输过程可根据不同业务数据对QoS的不同要求进行数据优先级的划分,对不同优先级数据设置不同的服务机制;然后,对业务数据传输中的马尔科夫过程进行分析,基于改进后的数据传输过程建立以带宽利用率为目标,丢包率和延时时间为约束的多优先级排队论带宽分配模型;并将所提出的带宽分配方法与传统方法进行对比。结果表明:QoS指标有所改善,而且带宽利用率比传统不分优先级带宽分配方法高9.73%,比弹性系数法高31.17%。最后,探究多优先级排队论带宽分配方法的动态性能,结果表明适当地提高带宽可以改善QoS指标,但要注意带宽增大时所带来的带宽利用率减小问题。合理的带宽分配可以避免资源的浪费。

试析配电自动化技术在智能电网中的应用

配电自动化技术是指利用先进的信息技术和控制手段对电力系统中的配电设备和线路进行实时监测和管控的先进技术。配电自动化技术具有先进性、安全性、兼容性等诸多优势,能够显著提高电网供电的稳定性、对电网形成有效监管、提高用户用电质量,是智能电网建设中不可或缺的关键技术。文章首先分析配电自动化系统的特点,接着分析在智能电网中应用配电自动化技术的意义,并提出配电自动化技术在智能电网中的应用措施,旨在为新时期智能电网发展相关研究提供参考和建议。

基于异步分层联邦学习的数据异质性处理方法研究

在物联网设备遍布的时代,时刻都在产生大量数据,数据分布和数据量各不相同,因此数据异质性普遍存在。针对物联网环境中智能设备的联邦学习挑战,传统联邦学习的同步机制解决数据异质性(NON-IID)问题并不理想,且面临着单点故障和维护全局时钟的复杂性问题,而异步机制则可能带来额外的通信开销和NON-IID数据分布导致的过时性问题。分层联邦学习结合异步机制在应对数据异质性的问题时更加灵活,为此,提出了一种基于分层联邦学习的异步分层联邦学习方法。首先,使用BIRCH算法分析物联网中各节点的数据分布并进行簇的划分;然后,对簇中的数据进行拆分与验证,目的是找到数据质量高的节点,然后将数据质量高的簇中的节点打散,重组到其他数据质量低的簇中,形成新的簇;最后,进行簇内聚合和全局聚合的两阶段模型训练。此外,基于MNIST数据集,对提出的方法进行了评估。结果表明,与经典方法相比,所提方法在NON-IID数据集上收敛速度提高,而且在模型精度上提高了15%以上。

基于Niagara的分布式智慧光伏充电站

在“双碳”目标及电动车迅猛发展的背景下,为解决电动车充电问题,同时进行能源优化,以太阳能作为基础能源,结合物联网和人工智能等新兴技术,构建分布式智慧光伏充电站。基于Tridium公司的Niagara框架搭建分布式智慧光伏充电站物联网平台,结合云服务和人工智能技术实现分布式集成管理,平台可实时采集各种数据,并根据采集的数据、光伏发电预测数据及用户用电预测数据进行系统层面的优化调度,实现蓄电池的充放电智能动态管理。分布式智慧光伏充电站可显著提高系统的管理水平,充分利用光伏发电,降低运行费用,实现系统整体节能降耗。

Distributed sensing and cooperative estimation/detection of ubiquitous power internet of things

The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information interconnection, and commercial interconnection. Due to the integration of renewable energy, the reform of the electricity market, and the deployment of the Smart Grid, a large amount of data will be generated. Thus, it is necessary to establish a Ubiquitous Power Internet of Things (UPIoT) to realize connections among people and things, things and things, and people and people in power systems. This paper studies the concept and architecture of the UPIoT and indicates the deployment of the perception layer and network layer as the key to building UPIoT in the initial stage. As UPIoT tends to cover a wide area and produce massive and distributed data, signal processing and data analytics theories and techniques are needed to handle the data and observe the state of the large-scale system. Further studies on distributed sensing and cooperative estimation theories and techniques of UPIoT are also required. Finally, the application prospects of UPIoT and the directions for future research are discussed.

基于物联网的配网一二次融合跨区协同全景监测技术

针对配网一二次融合跨区协同全景监测技术过度依赖网络环境,当网络发生堵塞时会造成监测存在死角的问题,提出基于物联网的配网一二次融合跨区协同全景监测技术。设计监测智能物理架构,支持多网多工下监测节点之间的横向交互和纵向交互,在此基础上,计算监控摄像头的旋转角度,采集监测环境及监测目标图像,通过全景图像拼接配准,实现对目标的全景监测。实验结果表明:设计的基于物联网的全景监测技术在通信组网内,全景监测角度精准,没有出现监测死角,实用性较好。

两芯环形通信物联网中压配网设备管理系统研究

针对中压配网设备管理松散、管理效率低的问题,通过融合物联网技术对管理模式进行优化改进,设计了两芯环形通信物联网中压配网设备管理系统。采用网格化管理模式组成严谨的设备管理方式,利用多种终端提高指令的强控性。设计两芯环形通信线路,避免了传输信号的中断,从而保证管理指令的顺利传达。在技术上,为了提高数据信息处理能力,构建了改进信赖域算法。该算法通过运行状态中的有功和无功功率,有效保证了系统节点传输过程的安全性。通过Proteus软件,仿真配网设备运行过程。试验结果表明,网格化中压配网设备管理系统负荷最大为473.58 kVA,管理指令传输速度为8.4 MB/s,设备管理效率为96.5%。通过仿真对比三种不同管理模式指令传输速度和设备管理效率曲线,验证了所提系统的优越性。