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.     

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.     

IoT-Enabled Autonomous System Collaboration for Disaster-Area Management

Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical, but also very challenging and risky. Despite first responders putting their lives at risk in saving others, human-physical limits cause delays in response time, resulting in fatality and property damage. In this paper, we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster. The framework consists of unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), a cloud-based remote control station (RCS). A light-weight message queuing telemetry transport (MQTT) based communication is adopted for facilitating collaboration between autonomous systems. To effectively work under unfavorable disaster conditions, antenna tracker is developed as a tool to extend network coverage to distant areas, and mobile charging points for the UAVs are also implemented. The proposed framework’s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language (AADL). Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols, and the implemented UAV control mechanisms are functioning properly. Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.      

基于人工智能的堤防工程大数据安全管理平台及其实现

针对堤防工程综合安全监测体系,基于物联网技术,建立堤防工程海量数据资源的采集、汇集、交换与共享云平台;同时针对堤防工程质量、险情演化、致溃机理,基于大数据和人工智能技术,构建堤防工程大数据开放平台和人工智能计算平台,提供海量、多源、异构数据的融合、共享,实现风险识别、评估、预警模型的构建、运算。针对堤防工程安全防护、加固中填筑石料粒径级配要求,利用堤防工程物联网监测平台进行数据采集汇集,在堤防工程人工智能计算平台构建砂石爆破开采级配预测模型,解决多层非线性问题,进行爆破开采优化设计和石料粒径级配控制。实例证明,基于人工智能的堤防工程大数据安全管理平台可将堤防填筑石料开采级配平均相对误差率控制在21%以内,满足开采级配设计要求,控制石料块度,从而保障了堤防工程质量,实现了堤防工程的安全管理。研究成果可为堤防工程填筑石料开采设计提供技术参考,保障工程质量。     

Dynamic framework to mining Internet of Things for multimedia services

The rapid and unprecedented technological advancements are currently dominated by two technologies. At one hand, we witness the rise of the Internet of Things (IoT) as the next evolution of the Internet. At the other hand, we witness a vast spread of social networks that connects people together socially and opens the door for people to share and express ideas, thoughts, and information. IoT is overpopulated by a vast number of objects, millions of multimedia services, and interactions. Therefore, the search of the right object that can provide the specific multimedia service is considered as an important issue. The merge of these two technologies resulted in new paradigm called Social IoT (SIoT). The main idea in SIoT is that every object can mine IoT in search for certain multimedia service. We investigate the issue of friends' management in SIoT and propose a framework to manage friends' requests. The proposed framework employs several mechanisms to better manage friends' relationships. The proposed framework consists of friend selection, friendship removal, and an update module. It proposes a weight-based algorithm and Naïve Bayes Classifier-based algorithm for the selection component. Moreover, a random service allocation model is proposed to construct service-specific network model. This model is then used in the simulation setup to examine the performance of different friends' management algorithms. The performance of the proposed framework is evaluated using simulation under different scenarios. The obtained simulation results show improvement over other strategies in terms of average degree of connections, average path length, local cluster coefficients, and throughput.