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.

Internet of robotic things for mobile robots:Concepts,technologies,challenges,applications,and future directions

Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sensors and actuators improved by communication technologies to powerful computing systems utilizing advanced Artificial Intelligence(AI)algorithms have rapidly driven the development of MRS,so the Internet of Things(IoT)in MRS has become a new topic,namely the Internet of Robotic Things(IoRT).This paper summarizes a comprehensive survey of state-of-the-art technologies for mobile robots,including general architecture,benefits,challenges,practical applications,and future research directions.In addition,remarkable research of i)multirobot navigation,ii)network architecture,routing protocols and communications,and iii)coordination among robots as well as data analysis via external computing(cloud,fog,edge,edge-cloud)are merged with the IoRT architecture according to their applicability.Moreover,security is a long-term challenge for IoRT because of various attack vectors,security flaws,and vulnerabilities.Security threats,attacks,and existing solutions based on IoRT architectures are also under scrutiny.Moreover,the identification of environmental situations that are crucial for all types of IoRT applications,such as the detection of objects,human,and obstacles,is also critically reviewed.Finally,future research directions are given by analyzing the challenges of IoRT in mobile robots.

Design of Intelligent Pension Online Monitoring System Under the Environment of Internet of Things

With the improvement of the degree of aging,the traditional pension model can no longer meet the growing needs of the elderly.Therefore,it is necessary to use the intelligent means of information technology to improve the level of pension services.This paper will integrate multi-sensor fusion technology,NB-IoT communication technology and cloud platform technology to develop and design a smart pension online monitoring system to realize real-time collection of human health and motion status information and realize monitoring platform management.In this system,STM32 microcontroller will be used as the main control module,and MAX30102,ADXL345 and DS18B20 sensors will be used to collect the heart rate,blood oxygen,displacement and body temperature of the human body in real time.On the one hand,the communication part is completed by the BC20 Internet of Things module.The data transmission between the terminal detection device and the cloud platform,on the other hand,the HC-42 Bluetooth module is used to complete the data communication with the mobile phone.The test results show that the system can collect and process data accurately in real time and maintain good communication with the cloud platform and mobile phone.The designed system has strong pertinence,easy operation,high reliability and broad development prospects.

Internet of Things Cloud Framework for Smart Homes

Smart appliances and renewable energy resources are becoming an integral part of smart homes. Nowadays, home appliances are communicating with each other with home short-range home area gateways, using existing network communication protocols such as ZigBee, Bluetooth, RFID, and WiFi. A Gateway allows homeowners and utilities to communicate remotely with the appliances via long-range communication networks such as GPRS, WiMax, LTE, and power liner carrier. This paper utilizes the Internet of Things （IoT） concepts to monitor and control home appliances. Moreover, this paper proposes a framework that enables the integration and the coordination of Human-to-Appliance, Utility-to- Appliance, and Appliance-to-Appliance. Utilizing the concepts of Internet of Things leads to one standard communication protocols, TCP/IPV6, which overcomes the many diverse home area networks and neighborhood area networks protocols. This work proposes a cloud based framework that enables the IoTs integration and supports the coordination between devices, as well as with device-human interaction. A prototype is designed, implemented, and tested to validate the proposed solution.

IoT data analytic algorithms on edge-cloud infrastructure:A review

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massive data stream to edge devices and the cloud for adequate storage and processing.This further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data streams.This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load balancing.The review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load balancing.In addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been reviewed.The classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.

Analysis of identifiers in IoT platforms

The Internet of Things(IoT)provides new opportunities for different IoT platforms connecting various devices together.The need to identify those devices is the foremost important to perform any kind of operation.Many organizations and standard bodies that provide specifications and frameworks for the IoT currently have their own identification mechanisms.Some existing industrial identification mechanisms can also be used in the IoT.There is no common Identification Scheme(IS)for the IoT as yet,because of the political and commercial differences amongst the standard bodies.The unavailability of a unified IS method makes the inter-working among IoT platforms challenging.This paper analyses and compares ISs used by several selected IoT platforms.This work will help in understanding the need for a common identification mechanism to provide inter-working among different IoT platforms.

The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN

The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.

Design and Implementation of the Sense Egypt Platform for Real-Time Analysis of IoT Data Streams

Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.

Novel Architecture of OneM2M-Based Convergence Platform for Mixed Reality and IoT

There have been numerous works proposed to merge augmented reality/mixed reality(AR/MR)and Internet of Things(IoT)in various ways.However,they have focused on their specific target applications and have limitations on interoperability or reusability when utilizing them to different domains or adding other devices to the system.This paper proposes a novel architecture of a convergence platform for AR/MR and IoT systems and services.The proposed architecture adopts the oneM2M IoT standard as the basic framework that converges AR/MR and IoT systems and enables the development of application services used in general-purpose environments without being subordinate to specific systems,domains,and device manufacturers.We implement the proposed architecture utilizing the open-source oneM2M-based IoT server and device platforms released by the open alliance for IoT standards(OCEAN)and Microsoft HoloLens as an MR device platform.We also suggest and demonstrate the practical use cases and discuss the advantages of the proposed architecture.

Design and Simulation of IoT Systems Using the Cisco Packet Tracer

Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SOx emissions, converter head temperature, NOx emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.

A Quick Survey on Cloud Computing and Associated Security, Mobility and IoT Issues

This survey paper provides a general overview on Cloud Computing. The topics that are discussed include characteristics, deployment and service models as well drawbacks. Major aspects of Cloud Computing are explained to give the reader a clearer understanding on the complexity of the platform. Following this, several security issues and countermeasures are also discussed to show the major issues and obstacles that Cloud Computing faces as it is being implemented further. The major part of countermeasures focuses on Intrusion Detection Systems. Moving towards Mobile Cloud Computing and Internet of Things, this survey paper gives a general explanation on the applications and potential that comes with the integration of Cloud Computing with any device that has Internet connectivity as well as the challenges that are before it.

基于NB-IoT的停车场管理系统设计

阐述一个基于NB-IoT技术的智慧停车场系统的设计,该系统以STM32F103ZE作为系统核心控制器,融合窄带物联网技术、红外传感技术以及机智云平台,实现数据实时监测及传输、用户端远程控制,车位管理更便捷高效,车位分配更快速和合理。

基于NB-IOT的农作物大棚监测系统研究

为了解决农作物大棚种植环境复杂、通信差、监管不严等问题,借助NB-IOT无线通信技术,设计了一种农作物大棚监测系统。该系统分为环境数据采集、物联网云平台、监测中心3大部分,主要工作原理:通过分布在大棚内的终端采集设备,采集对应的空气温湿度、光照强度、二氧化碳浓度及土壤湿度等环境数据,发送给主处理器STM32F103C8T6;对数据进行初步处理后,借助串口传送给BC95型NB-IOT通信模块,由通信模块上传至Ocean Connect DEV物联网云平台,用户可通过上位机监测中心实时监测大棚内的环境数据及设备状态。为此,提出该系统的总体设计方案,对其硬件和软件进行了详细的设计,并通过上位机进行测试,证明了该方案的可行性。

基于NB-IoT技术的变压器故障实时监测系统的设计与应用

随着物联网(Internet of Things,IoT)技术的不断发展,基于窄带物联网(Narrow Band Internet of Things,NB-IoT)技术的变压器故障实时监测系统已经成为一种重要的监测手段。基于NB-IoT技术,设计了一个变压器故障实时监测系统,并在实际应用中进行了验证。系统采用了基于NB-IoT的无线通信技术,实现变压器温度、湿度、电流等参数的实时监测和传输。系统采用了传感器和物联网技术进行数据采集和传输,通过云平台处理和分析数据,实现变压器故障的实时监测和预警。实际结果表明,该系统能够实时监测变压器的状态,提前发现变压器的故障,有效降低了电网事故的发生率。

基于虚拟网关的交通基础设施监测点位数据接入方法

针对交通基础设施点位监测数据来源多样、结构各异、协议不一的特点,分析具体接入需求,提出一种基于Netty架构的交通基础设施监测点位数据接入虚拟网关。阐述网关接入监测点位的配置方法及集群分配策略,定义超文本传输协议(HTTP)、传输控制协议(TCP)、用户数据报协议(UDP)下的数据传输格式,设计一种数据传输消息的编码和校验机制。在真实监测数据样本增强的基础上,利用分布式消息模拟工具对虚拟网关性能进行测试,结果表明,该虚拟网关实现了多点位多协议的交通基础设施监测数据统一接入,每亿条数据接入时间、存储时间分别达到8.14 s和9.75 s,平均数据溯源时间为2.96 s,具有亿级规模点位监测数据的接入能力,可为交通基础设施数字化监测的研究和应用提供理论支撑。

基于OneNET云平台的智慧消防远程监控系统的设计

针对传统消防监控系统存在开发成本高、误警率高、实时监控不便的问题,提出一种基于物联网云平台的智慧消防远程监控系统。采用STM32单片机作为中枢控制芯片,经多传感器采集温度、湿度、烟雾、火焰等环境数据,通过窄带物联网(NB-IoT,Narrow Band Internet of Things)上传至OneNET云平台。经数据分析后以可视化方式呈现,对异常数据触发报警实时响应。通过手机APP实现数据实时监测及一键处置。经测试,监控系统报警准确率高于97.2%,数据延迟低于50 ms,表明该系统能够实现消防火警的无线远程监控,并做出快速反应,满足中小微企业和普通家庭用户的消防监控需要。

基于物联网的隧道通风控制系统的研究与设计

针对隧道通风采用的分档控制与模糊控制的各自缺点,提出一种基于LSTM交通流预测的模糊PID智能算法。利用LSTM算法预测未来的污染物趋势,按照计算出的需风量提前开启风机,改善系统的滞后性,并利用模糊PID算法对风机进行精确控制。引入物联网技术,构建“互联网+隧道通风”的控制系统,利用PLC网关将采集的数据上传至云平台,供运营商随时随地查看隧道各节点的数据并对设备进行远程调控。仿真实验数据表明,算法超调量为4%,调节时间较模糊控制缩短68.60%,并具有较强的鲁棒性。

基于可穿戴设备医用防护服微环境监测系统的研究

随着全球气温不断升高,生态环境不断变化,重大传染病疫情时有发生。个体防护装备是医护人员避免生物污染危害的直接防护屏障,但长期穿戴专业的医用防护服装备会导致体表温度升高、大量出汗、虚脱等状况。为此,基于一次性医用防护服设计一种监测系统,以实时监测一次性防护服内的温湿度、二氧化碳含量及穿着人员的心率和血氧饱和度等数据。针对穿戴者长时间在污染区域作业,与外界通信受限的问题,搭建物联网云平台并采用无线传输技术实现作业人员与后方控制人员的信息交互,提高了一次性防护服的安全性和控制能力。针对微环境的温湿度、二氧化碳浓度及人体的心率和血氧饱和度等设定不同的阈值,随着工作的进行,通过微环境参数的改变来触发报警系统。所设计的监测系统满足设备向小型化发展的要求,方便医护人员穿戴,且获取监测数据后能及时通过物联网上传。

基于物联网的病虫害绿色防控系统研发与应用

为减少农药施用量、发展病虫害绿色防控技术,保障农产品质量安全,基于物联网技术,结合臭氧灭菌防病、光源诱导除虫、作物健康识别和病虫害辨识预警等技术,研发了设施蔬菜病虫害绿色防控系统,主要由多功能植保机和信息管理系统组成。结果表明,绿色防控系统下草莓白粉病发病率较常规施药法降低27.7%,草莓产量提高19.8%,具有显著的草莓白粉病防控效果和增产潜力。同时,系统的预警服务、多方式消杀、远程控制和智能托管等功能有效缓解了我国植保技术装备落后、过度依赖农药使用、劳动力结构性短缺和维保运营困难等问题。系统的应用在一定程度上实现了设施蔬菜病虫害防治的绿色化、专业化和智能化。