Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

Semantics Interaction Control for Constructing Intelligent Ecology of Internet of Things and Critical Component Research

Intelligent equipment is a kind of device that is characterized by intelligent sensor interconnections, big data processing, new types of displays, human-machine interaction and so on for the new generation of information technology. For this purpose, in this paper, first, we present a type of novel intelligent deep hybrid neural network algorithm based on a deep bidirectional recurrent neural network integrated with a deep backward propagation neural network. It has realized acoustic analysis, speech recognition and natural language understanding for jointly constituting human-machine voice interactions. Second, we design a voice control motherboard using an embedded chip from the ARM series as the core, and the onboard components include ZigBee, RFID, WIFI, GPRS, a RS232 serial port, USB interfaces and so on. Third, we take advantage of algorithms, software and hardware to make machines “understand” human speech and “think” and “comprehend” human intentions to structure critical components for intelligent vehicles, intelligent offices, intelligent service robots, intelligent industries and so on, which furthers the structure of the intelligent ecology of the Internet of Things. At last, the experimental results denote that the study of the semantics interaction controls based on an embedding has a very good effect, fast speed and high accuracy, consequently realizing the intelligent ecology construction of the Internet of Things.

Design and Implementation of Environment Monitoring and Alarm System for Broiler Breeding Based on Internet of Things

Environment plays a vital role in the breeding process of broilers. In order to effectively monitor and control the breeding environment of broilers, a broiler breeding environment monitoring and alarm system based on internet of things is studied and established. The system adopts the narrow band internet of things communication technology, and can transmit the temperature, humidity, illumination and ammonia and other environ-mental data in the chicken house remotely through terminal collection unified interface device. Meantime, it realizes the control of fans, wet cur-tains, small windows and illumination in the chicken house by threshold method. The system software is designed and implemented by C#, SQL Server, WeX5 and other development tools, including platform terminal and enterprise terminal. Since its operation, the system is featured by stable state, reliable data and timely alarm, which solves the problem of unified control of different sensors and realizes the effective control of house envi-ronment.

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.

Intrusion detection and prevention system for an IoT environment

Internet of Things(IoT)security is the act of securing IoT devices and networks.IoT devices,including industrial machines,smart energy grids,and building automation,are extremely vulnerable.With the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection Systems(IDSs)and Network-based Intrusion Prevention Systems(NBIPSs)are proposed in this study.An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering.The proposed NBIPS inspects network activity streams to identify and counteract misuse instances.The NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe substances.Networkbased IPS sensors can be installed either in an inline or a passive model.An inline sensor is installed to monitor the traffic passing through it.The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large amounts of data from various sources.In IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization.The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics.In this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud environment.The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished.Besides,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the work.The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan.The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

Implications COVID-19 on Performance and Energy Management in the Production Electricity

The COVID-19 pandemic has directly impacted the electric power industry;the energy sector has experienced huge losses in electricity production.These losses have also affected the reliability of communication and employees’performance,hence destabilizing the electric power system.This article aims at achieving two objectives.First,analyzing the impact of the COVID-19 pandemic on the communication of performance(human error and human factors)and energy management in electricity production.Second,to develop a conceptual framework model to alleviate effects of the pandemic on the power sector and then improve energy management and human performance.This paper involves investigating the influence of the COVID19 pandemic on the global production of electricity in the first quarters of 2019 and 2020.A conceptual model was developed based on a case study.Additionally,to ensure reliability,a variable,namely COVID-19,was used as a moderator to examine the effects of the independent and dependent variables.The results show that scores for the internet of things(IoT)with awareness and communication(A&C)and workplace environment management were high with Cronbach’s alpha value of 0.87 for the IoT and 0.89 for A&C.These numbers are important indicators of factors that could affect performance and energy management and should not be overlooked by the top management.The results also indicate that the pandemic has had a direct effect on the electricity production sector,and the conceptual framework model revealed that COVID-19,as a moderator,has a direct effect on the variables that significantly affect the improvement of both energy management and employee performance.The case study’s results confirm the poor performance in power plant maintenance and operation,in which human error would increase especially in Iraqi power plants that have not yet adopted any internationally recognized standards for energy management.This paper contributes to the literature studying COVID-19’s impact on the electricity sector in two ways:first,by developing a model to assist the electricity production sector mitigating effects of the COVID-19 pandemic,and second,by providing a detailed investigation into the pandemic’s impact on the electricity sector’s global production.The findings are hoped to assist researchers and research centers in understanding the general and specific framework to manage the pandemic’s effects on electricity production.

物联网技术支持下的城市热环境时空变化分析

热环境是城市生态系统最为关键的大气环境要素之一,开展城市热环境研究的前提是在时空维度上获取足量的热环境参数。利用先进的物联网技术构建了在线式热环境监测设备,收集2022年10月16日至24日广州大学校园气温、风速、太阳辐射和地面温度四种热环境参数,分析小尺度下城市热环境的时空变化特征。研究结果表明:1)不同测点之间的风速特征具有一定的相关性,极个别测点的风速特征与其它测点相关性仅在0.5左右,显示其小气候的独特性;2)不同测点风速和热岛强度变化时空差异明显,即使距离靠近的测点,受邻近建筑和植被特征的影响热环境特征会有所差异,各测点的日间风速大,热岛强度较为明显,夜间风速较小,热岛强度较弱;3)地面温度与气温的相关性达0.8左右,这种相关性在夜间表现更为密切,并且这种关系受风速的影响不大。研究结果反映了城市热环境参数在小尺度上的高度异质性,并揭示了物联网技术在城市热环境监测领域的可行性、便捷性和高效性。

粤港澳大湾区城市群生态系统智能化管理

开展生态系统数字化、信息化、智能化管理,全面提升粤港澳大湾区生态环境质量,是建设国际一流湾区的必然趋势。以城市群生态系统智能化管理为目标,系统整合各类生态环境相关数据资源,形成生态系统管理数据和决策支撑体系,并以此为基础构建生态智能管理平台。研究以生态系统要素和功能管理逻辑为核心,构建了生态系统管理业务流程:(1)精准剖析生态环境问题,确定问题发生的尺度、范围并对其进行分类和定性;(2)确立生态管理目标,制定适宜的管理策略;(3)根据现状与基线进行生态系统服务权衡,通过生态管理恢复工程提升生态系统质量;(4)通过环境物联网监测生态系统变化,及时调整和改进生态系统管理计划。针对城市群生态系统多尺度、多层次、复杂化等特点,在制定管理决策时应充分权衡管理目标和生态服务,兼顾各类生态系统服务效益;需通过示范性生态工程印证管理方案的可行性、适用性和协同性;以趋善化理念为指导思想,不断优化调整生态管理目标;同时在管理活动实施的过程中不断积累、凝练、总结所获得的反馈信息和经验。面向生态管理体制和管理能力的现代化提升需求,融合大数据、地理信息系统(GIS)、全球广域网络(Web)等信息化技术,构建粤港澳大湾区生态管理智能平台,实现多主体信息共享,打破管理决策的“黑箱”,为推进生态环境管理现代化提供可靠可行的方案。构建的生态系统管理业务流程和管理策略,将知识充分融入管理决策的制定流程,能服务于粤港澳大湾区的生态文明建设,推动可持续和高质量发展。

物联网环境下复杂光纤通信传输网络节点调度

为了解决物联网环境下,光纤通信传输网络节点能耗高、调度效率低的问题,提出一种物联网环境下复杂光纤通信传输网络节点调度方法。在物联网环境下,分析光纤通信传输网络节点的分布情况,根据动态联盟理论,以最快调度速度和最小投资为目标,构建复杂光纤通信传输网络节点调度模型。通过蚁群和粒子群算法相结合的融合算法展开全部搜索,获取最佳调度方案,实现光纤通信传输网络节点调度。实验结果表明,所提方法的节点平均能耗在6.0 mJ以下,LBF值始终高于0.20,且节点调度耗时始终低于200 ms,具有良好的调度效果。

基于物联网技术的育雏鸡舍环境监控系统设计与应用

本文设计基于物联网技术的育雏鸡舍环境监控系统,在监控系统设计中从软件与硬件两个部分对整体结构进行构建。其主要功能包括育雏鸡舍环境温湿度监测,有害气体浓度监测以及自动加温与报警。主要功能设计完成后,分别从软件与硬件两个部分进行系统调试,设计系统整体功能实现符合育雏鸡舍环境内部监控系统使用要求,能够帮助提升育雏鸡舍管理能力,降低管理中投入的人力成本。

基于物联网的城市网格化大气环境监测系统设计

针对城市的大气环境的综合治理问题,提出网格化环境监测部署方案。综合运用物联网、嵌入式系统、Web等相关开发技术,设计并搭建大气环境质量要素的数据远程监测与管理系统。通过在小区域进行部署实验发现,系统能够长时间稳定运行,且监测数据结果稳定可靠,可为城市大气环境相关研究提供充足的数据来源,为城市环境治理提供一定参考。

基于激光测距仪的智能物联网体感交互感知研究

当前的智能物联网设备在识别用户指令方面存在不足,时常需要用户多次尝试,降低了用户的使用体验和对设备的信任度。为了解决问题,提出了一种基于激光测距仪的智能物联网体感交互感知方法。首先,拟合激光波绘制结果,并利用距离变换算法设定智能物联网的体感交互方式。然后,通过误差传递公式,计算激光测距仪的协方差矩阵,依据此调整参数和测量点。最后,融合激光图像特征,识别操作者的动态指令。实验结果表明,所提方法能够显著提升智能物联网设备对体感指令的采集完整度,正确完成所有体感指令,且平均体感动作类别划分精准度高达97.02%,说明所提方法具有较高的体感交互感知精度。

LEO星座边缘计算网络中的动态计算卸载策略

在卫星边缘计算网络中,当过多用户通过同一信道接入卫星时,产生的同信道干扰会导致边缘计算性能下降。为了解决该难题,在动态环境低地球轨道(LEO)星座边缘计算网络的系统模型下,提出了一种基于随机博弈的多用户计算卸载策略。在考虑用户的自私性、星地信道的随机特性和地面用户接入的动态特性的前提下,从博弈论的角度,将动态环境下地面用户的卸载决策过程表述为随机博弈,证明了所制定的随机博弈等价于具有至少一个纳什均衡(NE)的加权势博弈,并且NE最小化系统开销。为了在动态环境下以分布式方式达到NE,基于随机学习设计了一种智能随机学习算法,以高效达到所提随机博弈的NE。仿真结果表明,与基准算法相比,所提算法能够显著降低同信道干扰和系统开销,并达到接近最优的性能。

基于“云计算+物联网”的高校智慧实验室探索与实践

针对当前高校实践教学中设备管理、安全管理、环境管理、资源管理等方面的问题,充分发挥人工智能时代新兴技术的优势,基于“云计算和物联网”技术整合实验室软硬件资源,解决高校实验教学的痛点。首先分析了传统实验室存在的问题和不足,阐述了人工智能时代高校智慧实验室的功能定位,并对高校智慧实验室架构进行了剖析,将EHS理念融入高校智慧实验室的构建与管理,降低实验室后期的维护成本,提升了实验室管理效率和安全性。最后对高校智慧实验室的未来发展进行了展望。

基于物联网的羽毛球馆内环境监控系统设计

目的:实现羽毛球馆内环境的长期系统监测,提升智慧化全民健身公共服务能力。方法:设计一套基于物联网的羽毛球馆内环境监控系统。结果:系统主要包括硬件端、服务器及数据库、软件端等。系统各控制参数可以在客户端实时查看,且能够在客户端自动控制场馆内风扇、可调灯、空调等设备,烟雾报警器还可以按照设置阈值触发并在客户端实时显示,能够实现对羽毛球馆内环境数据的实时采集、存储及分析功能。结论:系统有软件和硬件两大部分,同时基于服务器将各传感器所检测到的数据存储于数据库中,有助于后续对这些数据进行分析。

移动物联网环境下无线传感网络异常节点快速定位算法

为了提升无线传感网络异常节点定位精度,提出一种移动物联网环境下无线传感网络异常节点快速定位算法。通过高通图滤波器对网络信号展开处理后获取高频分量,利用高频分量划分异构哈希网络,确定子图对应输出信号的特征频率分量。通过对比疑似异常节点集合和子图节点集合进行异常节点检测。利用锚节点和未知节点之间的跳数展开区域划分,并选择对应跳距的锚节点估算展开距离。通过筛选方式排除异常因子并优化全局最优解,完成异常节点定位。实验结果表明,所提方法能够实现异常节点精准检测,检测概率高达99%以上,虚警概率最高仅为0.02%,针对不同数量的锚节点数、通信半径与未知节点数,其平均定位误差均处于5%以下,提升了系统的实用性。

基于开源物联网技术的温室环境监测系统设计

物联网是对传统互联网的拓展与延伸,可将不同信息传感设备与互联网结合,实现“人”、“机”和“环境”之间的信息交互。近年来,物联网在农业生产领域得到了广泛的应用与发展。在农业生产中使用传感器、嵌入式系统和执行器实现对农业环境信息的采集与通信。数据传输与集合是农业物联网技术的核心,如何实现数据高性能传输与存储是当前农业物联网系统亟待解决的关键问题。为此,提出了一种基于无线传感器网络和物联网开源网关的环境监测系统,允许在AWS上实现可扩展的物联网解决方案,且使用低成本的硬件和基于Linux系统的开源网关平台形成完整的新型物联网系统。通过搭建测试环境对系统运行功能和性能进行测试与分析,结果表明:提出的方案具有良好的功能体验与性能表现。研究结果对于解决农业物联网的发展与数据通信具有一定的推动作用。

设施温室物联网智能测控系统研究

针对温室环境调控中手动控制和阈值条件调控存在调控精度低、参数超调严重等问题,以设施番茄温室为例,研究提出一种基于环境预测的精准调控策略。首先,构建专用物联网“六域”架构,采用MSP430F5438A作为主控模块,设计感知、通信等功能模块。其次,构建SSA-LSTM预测模型实现对温室环境的精准预测,并根据模型预测结果确定环境调控策略,通过PSO-PID控制模型实现对温室风口电机的精准控制。实验结果表明,相较于传统LSTM模型,SSA-LSTM预测模型的MAE降低58.52%,MAPE降低61.68%,RMSE降低63.84%。同时,相较于传统PID模型,PSO-PID控制模型的超调量降低89.99%,调节时间降低59.85%。系统经过实地部署验证,在保持种植品种和农事管理操作一致的情形下,智能调控的温室产量相较于传统温室提升约8.5%,证明了系统的有效实用性。

6G关键技术及其面临的挑战

6G及未来的通信系统网络将满足一个世界性完全连接的要求,革命性的技术方案预计将推动快速增长的智能设备和服务应用。针对实现6G互联互通目标的重大技术应用进行深入的相关主题研究,包括在太赫兹波段与更广泛的网络操作实现通信、智能通信环境、人工智能、网络自动化、环境反向散射通信、用立方体卫星和无人机实现的卫星物联网、无蜂窝大规模MIMO通信等,并讨论技术应用可能面临的问题挑战。