Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer processes.However,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy costs.This paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research process.The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers.These sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for analysis.The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center environment.Through the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors.The model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power costs.Furthermore,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and AlexNet.The NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark algorithms.These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems.The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers.

基于NB-IoT的应用层数据安全传输机制

NB-IoT(Narrow Band Internet of Things)是基于蜂窝窄带物联网的一种新兴技术,是物联网的一个重要分支。随着NB-IoT终端设备的规模不断增大,物联网安全面临数据泄露、中间人攻击等安全威胁。本论文针对NB-IoT技术的数据安全传输研究,从物联网终端安全和应用服务安全两方面进行分析,结合密码技术给出了NB-IoT系统安全模型,提出了基于物联网应用层数据信源加密传输机制,给出了轻量级身份认证协议和数据加密传输协议,论证了该方案的安全性,通过实验验证了所提方案的可行性和适用性。

BIM-IoT技术在轨道交通工程智能化施工中的应用研究

建筑信息模型(BIM)和物联网(IoT)是建筑行业智能化转型升级的关键技术,通过在施工现场部署传感器和设备,BIM-IoT技术实现了对施工过程中包括实时监测材料运输、设备使用情况和施工进度等实时监测和数据采集。该技术还支持对施工人员的实时管理和安全监控,如人员考勤记录和视频监控系统的应用。基于IoT技术,通过数据分析和算法,实现资源的准确预测和合理分配,提高施工效率和质量。最后本文总结了BIM-IoT在实际应用过程中的重难点,为同类型工程提供参考。

IOT technology application model research of transportation industry in China

The paper studied the connection between internet of things (IOT) technology and transportation industry. Meanwhile,the definition of IOT in transportation was given. Concerning that many problems occurred during the process of traditional intelligent transportation system,the paper proposed a promising model of IOT in transportation. The advantage of the information utilization model from information to function was confirmed through comparative study. Finally,the model presented that a real interconnection of transportation would be achieved based on the unified information collection. It can greatly save cost on technology transfer,exploit potential value of information,and promote the emergence of a sustainable information service market and the industrial upgrade.

Development of Power Consumption Models for ESP8266-Enabled Low-Cost IoT Monitoring Nodes

The development of energy and cost efficient IoT nodes is very important for the successful deployment of IoT solutions across various application domains. This paper presents energy models, which will enable the estimation of battery life, for both time-based and event-based low-cost IoT monitoring nodes. These nodes are based on the low-cost ESP8266 (ESP) modules which integrate both transceiver and microcontroller on a single small-size chip and only cost about $2. The active/sleep energy saving approach was used in the design of the IoT monitoring nodes because the power consumption of ESP modules is relatively high and often impacts negatively on the cost of operating the nodes. A low energy application layer protocol, that is, Message Queue Telemetry Transport (MQTT) was also employed for energy efficient wireless data transport. The finite automata theory was used to model the various states and behavior of the ESP modules used in IoT monitoring applications. The applicability of the models presented was tested in real life application scenarios and results are presented. In a temperature and humidity monitoring node, for example, the model shows a significant reduction in average current consumption from 70.89 mA to 0.58 mA for sleep durations of 0 and 30 minutes, respectively. The battery life of batteries rated in mAh can therefore be easily calculated from the current consumption figures.

面向主动式BPM的IoT服务动态绑定方法

在现有的物联网(IoT)服务及业务流程管理(BPM)系统研究的基础上,提出一种面向主动式BPM的IoT服务动态绑定方法。首先,该方法构建了从时序序列中学习情境信息及IoT服务绑定规律的模型,并预测可绑定的IoT服务列表。该模型基于双向门控循环单元(Bi-GRU)搭建编码器解码器结构网络模型,引入注意力机制,通过优化神经网络结构提取时序数据依赖关系,从而通过该预测模型提升BPM系统的主动性。其次,将用户任务与边界非中断事件相结合实现IoT服务动态绑定活动,该活动通过集成上述预测模型和IoT服务动态切换及多IoT服务并行运行架构实现BPM系统在运行时动态绑定IoT服务。实现面向主动式BPM的基于情境感知的IoT服务动态绑定系统,基于液化天然气(LNG)危化品海上运输安全监管场景进行案例分析,对系统中预测模型的性能进行了定量的实验评价。经过对比,提出的预测模型在多项指标中均优于其他模型。案例分析和实验结果验证了方法的有效性。

Certrust:An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate flows.Traditional methods to identify the malicious flows in Crossfire attacks are rerouting,which is based on statistics.In these existing mechanisms,the identification of malicious flows depends on the IP address.However,the IP address is easy to be changed by attacks.Comparedwith the IP address,the certificate ismore challenging to be tampered with or forged.Moreover,the traffic trend in the network is towards encryption.The certificates are popularly utilized by IoT devices for authentication in encryption protocols.DTLShps proposed a new way to verify certificates for resource-constrained IoT devices by using the SDN controller.Based on DTLShps,the SDN controller can collect statistics on certificates.In this paper,we proposeCertrust,a framework based on the trust of certificates,tomitigate the Crossfire attack by using SDN for IoT.Our goal is threefold.First,the trust model is built based on the Bayesian trust system with the statistics on the participation of certificates in each Crossfire attack.Moreover,the forgetting curve is utilized instead of the traditional decay method in the Bayesian trust system for achieving a moderate decay rate.Second,for detecting the Crossfire attack accurately,a method based on graph connectivity is proposed.Third,several trust-based routing principles are proposed tomitigate the Crossfire attack.These principles can also encourage users to use certificates in communication.The performance evaluation shows that Certrust is more effective in mitigating the Crossfire attack than the traditional rerouting schemes.Moreover,our trust model has a more appropriate decay rate than the traditional methods.

A 3D Geometry Model of Vocal Tract Based on Smart Internet of Things

The Internet of Things(IoT)plays an essential role in the current and future generations of information,network,and communication development and applications.This research focuses on vocal tract visualization and modeling,which are critical issues in realizing inner vocal tract animation.That is applied in many fields,such as speech training,speech therapy,speech analysis and other speech production-related applications.This work constructed a geometric model by observation of Magnetic Resonance Imaging data,providing a new method to annotate and construct 3D vocal tract organs.The proposed method has two advantages compared with previous methods.Firstly it has a uniform construction protocol for all speech organs.Secondly,this method can build correspondent feature points between different speech organs.There are less than three control parameters can be used to describe every speech organ accurately,for which the accumulated contribution rate is more than 88%.By means of the reconfiguration,the model error is less than 1.0 mm.Regarding to the data from Chinese Magnetic resonance imaging(MRI),this is the first work of 3D vocal tract model.It will promote the theoretical research and development of the intelligent Internet of Things facing speech generation-related issues.

Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models

Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist.Manual observation and iden-tification take time and are always contingent on the involvement of experts.A system is proposed to alleviate this challenge that uses transfer learning techni-ques to classify the cephalopods automatically.In the proposed method,only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition.First,the efficiency of the chosen models is determined by evaluating their performance and comparing thefindings.Second,the models arefine-tuned by adding dense layers and tweaking hyperparameters to improve the classification of accuracy.The models also employ a well-tuned Rectified Adam optimizer to increase the accuracy rates.Third,Adam with Gradient Cen-tralisation(RAdamGC)is proposed and used infine-tuned models to reduce the training time.The framework enables an Internet of Things(IoT)or embedded device to perform the classification tasks by embedding a suitable lightweight pre-trained network.Thefine-tuned models,MobileNetV2,InceptionV3,and NASNet Mobile have achieved a classification accuracy of 89.74%,87.12%,and 89.74%,respectively.Thefindings have indicated that thefine-tuned models can classify different kinds of cephalopods.The results have also demonstrated that there is a significant reduction in the training time with RAdamGC.

物联网双层耦合动力学信息传播模型研究

信息传播模型的研究是物联网领域的重要组成部分,它有助于提高物联网系统的性能和效率,促进物联网技术的进一步发展,针对物联网通信中影响信息传播的因素复杂且不稳定的问题,该文提出一种双层耦合信息传播模型SIVR-UAD,通过分析物联网中不同状态的设备和用户对信息传播的影响,建立了6种耦合状态,并利用马尔科夫方法分析耦合节点的状态变化过程,找到信息传播平衡点,最后通过理论分析证明了模型的平衡点的唯一性以及稳定性。仿真结果表明,在3组不同的初始耦合节点数下,SIVR-UAD模型中的6种耦合节点数量变化始终趋向同一稳定水平,证明了该模型的平衡点和稳定性。

基于LSTM模型的无线物联网设备识别方法

随着无线物联网(Internet of Things,IoT)业务的兴起,海量设备的接入,无线网络受攻击的可能性大大增加,无线IoT设备的安全问题越来越重要。提出了一个基于深度机器学习长短期记忆(Long Short-Term Memory,LSTM)模型的无线IoT设备识别方法,用于甄别非法入侵的设备或者发现已经被入侵后通信异常的设备。所提方法的创新点在于通过深度机器学习对IoT设备公开传输的帧头信息进行分析识别,而不必深入分析承载信息,不依赖于易被修改和伪装的IP地址等身份信息,因此不受通信信息加密的影响,也不受各类伪装地址及其他入侵方法的影响。所提方法的应用可以自动快速地识别出非授权设备或者被入侵的授权设备,更好地保障网络安全。

基于STM32的铁路运输站内NB-IoT可视化云BAS系统设计

BAS系统(机电设备监控系统)主要完成车站内的机电设备如空调机组、新风换气机、送/排风机、风幕机、潜污泵、电/扶梯、风冷热泵等设备的自动监视、控制和管理;为了更好地优化BAS系统的可视化界面设计、提高监控质量,以达到节能、安全、提高管理水平的目的,该设计将NB-IoT技术、Oceanconnect云平台设计及嵌入式技术综合融入到BAS系统中,实现远程监管各个站内数据并实时控制各站内设备以应对突发情况;该系统采用STM32作为微控制器,设计温湿度及光照总控中心和调控节点电路,总控中心通过NB-IoT无线通信模块组网与各安装在站内的多个调控节点进行指令下发和数据上报的双工通信,将通过各节点的温湿度及光照传感器采集到的数据经STM32单片机处理后,通过NB-IoT无线通信模块上传至根据实际要求设计的Oceanconnect云模型中,实现总控制中心及节点电路的云端数据可视化及数据分析,同时还可以由上位机云端模块直接控制各站内下位机的空调机组,换气机污水泵等设备,以实现实时的监管与控制各站内现场环境;经过实验测试结果表明,系统可视化界面清晰简洁易操作且性能稳定可靠,能够实现实时数据的通信分析与控制,提高车站内现场控制的有效性,能够对突发情况进行快速自动处理。

NB-IoT网络部署方案探讨

首先介绍了NB-IoT技术特征及适应的几种典型业务特征模型,基于传统无线网络覆盖规划经验,结合不同业务特征对网络的性能需求,提出了NB-IoT网络建设规划覆盖目标。最后对现网站点覆盖区仿真,给出了NB-IoT在不同站点比例情况下的覆盖情况,为网络规划部署提供参考。

基于SD-IoT的DDoS攻击防御方法

为解决软件定义物联网中防御DDoS攻击的问题,提出一种攻击溯源与防御方法。该方法部署在软件定义物联网控制器中,当检测到网络中有DDoS攻击发生时,进行节点流量特征提取,利用提出的基于信任模型-自组织映射(trust model-self organizing map,T-SOM)算法的DDoS攻击溯源方法,根据提取到的节点流量特征对网络内节点进行聚类,通过控制器中的网络拓扑找出攻击节点的Mac地址。由防御模块进行端口封禁和packet_in报文过滤实现DDoS攻击防御。实验结果表明,该方法能够对DDoS攻击节点进行有效溯源,快速下发流表隔离攻击节点并过滤packet_in报文。

基于人工智能的即插即用型配电站房物联边缘终端研制

配电网核心节点的配电站房位于供用电用户边界,近年来,随着光伏、储能、电动车等分布式资源的快速发展,配电站房数量增速提升,新的安全风险点不断出现,导致配电站房运维人员、设备、环境等安全问题多发,安全生产形势严峻,运维管理面临着极大的压力。针对上述问题,设计了基于人工智能的即插即用型软硬件平台,研制了配电站房物联边缘终端,实现配电站房状态全面感知,模型统一管理,数据高效共享,应用灵活部署。通过设备故障预警、视频图像分析和规则引擎等边缘计算功能,实现配电站房全景监控,提升精益化运维和智能化管控水平。

智能配电系统的通信网络建模与性能分析

由于我国的综合能源系统(IES)迅速发展,智能配电系统(SPDS)中集成了越来越多的先进设备和组件,伴随而来的是多样化的信息交互、海量的复杂数据、先进的智能设备使用所带来的技术挑战,因而如何可以在有限的通信网络资源内满足各种应用需求成为一个关键的问题。首先分析了在智能配电系统中的各类应用对于系统通信网络的性能要求,然后介绍了一种基于物联网技术的通信网络架构,并描述了其在智能配电系统中的应用和服务质量QoS要求。进一步地,基于排队论的带宽模型提出了一种考虑QoS的数据采集应用的预测带宽方法。最后,考虑三种典型智能配电站应用场景对网络性能进行分析,通过OPNET仿真,证明所提方法具有合理性与有效性。

数字化技术在水务工程中的应用

阐述水务行业数字化转型的目标和模式,分析水务企业数字化现状,探讨解决方案,包括规范化、标准化、智慧化、平台化的运营阶段、物联网平台的实现。

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.

Data Security Storage Model of the Internet of Things Based on Blockchain

With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the IoT has brought great convenience to people’s production and life.However,the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them.The traditional centralized data storage and management model of the IoT is easy to cause transmission delay,single point of failure,privacy disclosure and other problems,and eventually leads to unpredictable behavior of the system.Blockchain technology can effectively improve the operation and data security status of the IoT.Referring to the storage model of the Fabric blockchain project,this paper designs a data security storage model suitable for the IoT system.The simulation results show that the model is not only effective and extensible,but also can better protect the data security of the Internet of Things.

面向NB-IoT的核心网业务模型和组网方案

窄带物联网(NB-IoT)的市场空间巨大,是未来重点的发展方向,全球主流电信运营商均开始积极布局。为更好地确定网络部署的建设方案和资源配置,从NB-IoT的业务场景出发,对NB-IoT业务特性进行分析,给出面向NB-IoT的核心网业务模型参数和组网方案,对后续NB-IoT核心网资源配置和规划方案有重要指导意义。