智慧能源体系信息通信技术构架及实施方案

基于社会经济发展、环境保护需求、产业政策引导等多方因素,智慧能源将为传统能源结构带来新一轮的巨变。现代信息通信技术(information communications technology,ICT)的飞速发展,为传统能源向智慧能源转型升级提供了强有力的技术支撑。整个智慧能源体系将以创新型的场景需求为导向,以跨界科技力量的深度加持为支撑,驱动整个能源及相关产业链的结构性升级。结合云计算、大数据、物联网(internet of things,IoT)、移动通信、人工智能、区块链等新兴技术,赋能传统能源体系,提出一种适用于智慧能源应用场景下的能源体系ICT构架,贯通能源体系的底层至顶层,实现智慧能源体系能量流、信息流和价值流的全面融合。     

Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.     

Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

Fault detection of the photovoltaic (PV) grid is necessary to detect serious output power reduction to avoid PV modules’ damage. To identify the fault of the PV arrays, there is a necessity to implement an automatic system. In this IoT and LabVIEW-based automatic fault detection of 3 × 3 solar array, a PV system is proposed to control and monitor Internet connectivity remotely. Hardware component to automatically reconfigure the solar PV array from the series-parallel (SP) to the complete cross-linked array underneath partial shading conditions (PSC) is centered on the Atmega328 system to achieve maximum power. In the LabVIEW environment, an automated monitoring system is developed. The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem, and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW. The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system. Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity. The IoT-based Blynk app is employed for visualizing the fault status of the 3 × 3 PV array. The dashboard of Blynk visualizes every array with the status.     

M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture known as a Multi-class Classification based Intrusion Detection Model (M-IDM), which typically relies on data collected by real devices and the use of convolutional neural networks (i.e., it exhibits better performance compared with conventional machine learning algorithms, such as naïve Bayes, support vector machine (SVM)). Unlike existing studies, the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices, such as a patient’s monitors (i.e., electrocardiogram and thermometers). The proposed architecture classifies the data into multiple classes: Critical, informal, major, and minor, for intrusion detection. Further, we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms, including naïve Bayes, SVM, and logistic regression, using neural networks.     

Super Compact UWB Monopole Antenna for Small IoT Devices

This article introduces a novel, ultrawideband (UWB) planar monopole antenna printed on Roger RT/5880 substrate in a compact size for small Internet of Things (IoT) applications. The total electrical dimensions of the proposed compact UWB antenna are 0.19 λ_o × 0.215 λ_o × 0.0196 λ_o with the overall physical sizes of 15 mm × 17 mm × 1.548 mm at the lower resonance frequency of 3.8 GHz. The planar monopole antenna is fed through the linearly tapered microstrip line on a partially structured ground plane to achieve optimum impedance matching for UWB operation. The proposed compact UWB antenna has an operation bandwidth of 9.53 GHz from 3.026 GHz up to 12.556 GHz at −10 dB return loss with a fractional bandwidth (FBW) of about 122%. The numerically computed and experimentally measured results agree well in between. A detailed time-domain analysis is additionally accomplished to verify the radiation efficiency of the proposed antenna design for the ultra-wideband signal propagation. The fabricated prototype of a compact UWB antenna exhibits an omnidirectional radiation pattern with the low peak measured gain required of 2.55 dBi at 10 GHz and promising radiation efficiency of 90%. The proposed compact planar antenna has technical potential to be utilized in UWB and IoT applications.     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

State-of-the-art survey on digital twin implementations

Digital twin (DT) has garnered attention in both industry and academia. With advances in big data and internet of things (IoTs) technologies, the infrastructure for DT implementation is becoming more readily available. As an emerging technology, there are both potential and challenges. DT is a promising methodology to leverage the modern data explosion to aid engineers, managers, healthcare experts and politicians in managing production lines, patient health and smart cities by providing a comprehensive and high fidelity monitoring, prognostics and diagnostics tools. New research and surveys into the topic are published regularly, as interest in this technology is high although there is a lack of standardization to the definition of a DT. Due to the large amount of information present in a DT system and the dual cyber and physical nature of a DT, augmented reality (AR) is a suitable technology for data visualization and interaction with DTs. This paper seeks to classify different types of DT implementations that have been reported, highlights some researches that have used AR as data visualization tool in DT, and examines the more recent approaches to solve outstanding challenges in DT and the integration of DT and AR.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00375-w     

Governing the progress of internet-of-things: Ambivalence in the quest of technology exploitation and user rights protection

This paper discusses the dynamics between the pursuit of competitiveness and governance of data security in strengthening the Internet of Things (IoT) readiness in developing economies using Malaysia as a case study. It explores the potential of the IoT regulatory framework in guarding the privacy and interests of IoT users. This paper also reveals the collaborative model of technology push-market pull for technological capabilities development as well as the measures that uphold the principles of good privacy practice. The model incorporates privacy-by-design measures that would result in higher user confidence in this emerging technology, which is vital to a healthy IoT ecosystem. Through the collaborative model of Penang as evidence, our findings indicate that Malaysia seeks to create a structure that fosters technology push-market pull forces for IoT technological capabilities development. While the model paves a co-evolutionary path for diffusion and upgrading of IoT, several issues related to the volatility of online data and databases were identified as well as the lack of responsibility and accountability of corporations in handling the sensitive personal data of customers. We see that it is essential for the regulators to play a (more) significant role in safeguarding the interests of IoT users. In this regard, the privacy-by-design, a citizen-centric regulatory framework should be considered in policy reviews in deploying IoT-based competitive promotion initiatives. This paper breaks new ground by elaborating on the common route of IoT technology capabilities development, which is typical in the developing context. While it highlights the common issues that emerge as technology advances, we propose a regulatory framework that features embedded privacy-by-design to protect the interests of the IoT users.     

面向异构IoT业务的LoRa网络自适应参数配置策略

前针对LoRa组网技术的研究主要受单一应用需求驱动, 可配置参数利用率低, 网络性能存在进一步优化的空间. 随着异构多类型IoT业务传输需求的日益增长, 优化网络的性能使之能够适应多类型业务显得尤为重要. 针对上述问题, 本文提出了一种基于模拟退火遗传算法的动态LoRa传输参数自适应配置策略, 在能耗约束的条件下可实现对多种异构业务的数据传输需求, 并可提高单网关网络可支持的终端设备数量和数据吞吐量. 基于LoRaSim的仿真结果表明: 与传统ADR (Adaptive Data Rate)相比, 本文所提方法的平均吞吐量提高了25.6%; 对于超过1000台终端设备的单网关LoRa网络, 当每个设备分组生成率小于1/100 s时, 网络的实际分组交付率(Packet Delivery Rate, PDR)超过90%. 该方法可适应多种异构业务的数据传输需求并在有效提高数据吞吐量的同时保证各业务的PDR.     

泛在电力物联网可信安全接入方案

为全面提高全业务泛在电力物联网安全综合防御能力,解决目前全业务泛在电力物联网安全防护指导和终端认证机制的缺失和不足,本文提出一种泛在电力物联网可信安全接入方案。首先给电力物联网终端层设备确定一个唯一标识的指纹信息;然后结合该指纹信息,采用身份标识密码技术实现终端层设备的接入认证,阻断非法终端的接入;最后设计合法终端的身份信息安全传递机制,根据身份信息对合法终端的异常行为进行溯源。