物联网与标准

物联网本质是 ICT 与国民生产中更多行业的联姻。标准是促进物联网应用形成规模经济效应和避免碎片化的重要手段,在涉及重大基础设施建设的项目中标准更是客户采购的前提条件。通信行业达成共识成立全球性的物联网标准组织迈出了重要的一步,以业务层和终端管理的标准化作为起点是合理的步骤,也符合整个 ICT 行业的利益。     

国产算法安全芯片与市场应月

随着信息技术的发展,物联网进程逐渐加快,信息安全工作愈加重要。安全芯片可以应用在信息系统安全的各个方面,安全芯片具有更高的安全性和其他方案不可比拟的优点;国家密码管理局颁布的国密算法具有安全性和制度性两方面优势。因此,将国产算法与安全芯片技术相结合,采用国产算法的安全芯片必将在信息社会各领域中得到广泛应用,并具有巨大的市场前景。     

LTE无线通信技术与物联网技术的结合与发展

在介绍物联网与云计算定义的基础上,对其特有的业务形态进行了分析与总结,对LTE宽带无线技术如何更好地服务于物联网与云计算领域进行了探讨,并对现在的标准现状进行了分析。通过对物联网、云计算业务数据形态的分析,对未来由物联网带来的无线通信技术发展进行了展望。     

物联网分布式路由中基于Beta函数的抗攻击信任管理模型

With the rapid development of Internet of Things (IoT), the issue of trust in distributed routing systems has attracted more research attention. The existing trust management frameworks, however, suffer from some possible attacks in hostile environments, such as false accusation, collusion, on-off, and conflicting behavior. Therefore, more comprehensive models should be proposed to predict the trust level of nodes on potential routes more precisely, and to defeat several kinds of possible attacks. This paper makes an attempt to design an attack-resistant trust management model based on beta function for distributed routing strategy in IoT. Our model can evaluate and propagate reputation in distributed routing systems. We first describe possible attacks on existing systems. Our model is then proposed to establish reliable trust relations between self-organized nodes and defeat possible attacks in distributed routing systems. We also propose a theoretical basis and skeleton of our model. Finally, some performance evaluations and security analyses are provided to show the effectiveness and robustness of our model compared with the existing systems.     

基于累计量和SPWVD谱对消的物联网OFDM和FH信号识别

Considering the Gaussian asymptotic features of OFDM signals, the identification method of it is proposed in this paper by using the cumulants of the wavelet transform coefficients in different layer in a low SNR circumstance. Furthermore, taking the coexistence of the OFDM and Frequency Hopping (FH) signals into account, a new way to separate FH and OFDM signals is proposed based on SPWVD spectrum cancellation, and it can be used to estimate the FH parameters. The simulation results show that the OFDM and single-carrier signals can be identified with a high correct rate of 95% even at -6 dB SNR; meanwhile, the separation of mixed OFDM and FH signals can be achieved with a low SNR of-6 dB, and FH parameters can be estimated accurately. It shows that the recognition performance is improved by about 5 dB compared with the traditional method.     

上行干扰抑制中的功率控制

在上行OFDM蜂窝系统中,小区间干扰成为系统内的主要干扰。文章重点介绍小区间干扰抑制中的功率控制方法,并引入一种基于小区间干扰信息的功率控制方法。文该方法能更有效地降低小区间干扰对系统的负面影响、使系统IoT（Interference over Thermal）更加稳定。     

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.     

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.     

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     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.