Analysis,design and simulation of Internet of Things routing algorithm based on ant colony optimization

In this paper, a routing algorithm to optimize the selection of the best path for the transmitted data within the Internet of Things (IoT) system is proposed. The algorithm controls the use of ant colony ideas in the IoT system to obtain the best routing benefit. It divides the IoT environment into categorized areas depending on network types. Then, it applies the most suitable ant colony algorithm to the concerned network within each area. Furthermore, the algorithm considers routing problem in intersected areas that may arise in case of IoT system. Finally, Network Simulator 2 is used to evaluate the proposed algorithm performance. Simulation results demonstrate the effectiveness of the proposed routing algorithm in terms of end‐to‐end delay, packet loss ratio, bandwidth consumption, throughput, overhead of control bits, and energy consumption ratio. Copyright © 2016 John Wiley & Sons, Ltd.     

What’s Critical about Critical Infrastructure?

Abstract

Critical infrastructure (CI) has received much attention in research, policy and political discussions in recent years, following concern over exposure of infrastructure to terrorist attack, disruption through disasters, rising awareness of the interdependent nature of infrastructure in modern urban systems, and changes in the ownership of and responsibility for infrastructure assets. In this paper, we explore the implications of different framings of both “critical” and “infrastructure”, through two questions: critical how and for whom; critical when and at what scale? Framings of CI, and their increasingly important manifestations in policy and law, have deep but too-often unexposed implications: the lines drawn between what is defined as critical and that which is not concerns not only the physical or informational assets, but the inclusion/exclusion of communities and their places and values as important aspects of modern urban governance. We argue that a better understanding of what is critical about urban infrastructure is not just recognition of their vulnerability and interconnectedness, but also of the key linkages between critical infrastructure and human and environmental system integrity and equity within the context of capitalist urbanisation.     

物联网开放服务平台

针对目前物联网以垂直服务系统为主、缺乏跨系统的资源共享等问题,提出了一个可扩展的物联网智慧服务体系架构.在此基础上构建了统一的服务数据模型,通过消息发布中间件,屏蔽底层设备的数据结构、数据平台、操作系统等多源异构问题,加入认证授权机制,从而形成一个可扩展、可以接入各种服务、安全可靠的物联网智慧开放服务平台.最后设计了物联网开放服务平台的系统原型,通过实现具体的应用系统,验证了所提架构的可行性与优越性.     

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.     

Review of operational management in intelligent agriculture based on the Internet of Things

This review aims to gain insight into the current research and application of operational management in the area of intelligent agriculture based on the Internet of Things (IoT), and consequently, identify existing shortcomings and potential issues. First, we use the Java application CiteSpace to analyze co-citation networks in the literature related to the operational management of IoT-based intelligent agriculture. From the literature analysis results, we identify three major fields: (1) the development of agricultural IoT (Agri-IoT) technology, (2) the precision management of agricultural production, and (3) the traceability management of agricultural products. Second, we review research in the three fields separately in detail. Third, on the basis of the research gaps identified in the review and from the perspective of integrating and upgrading the entire agricultural industry chain, additional research directions are recommended from the following aspects: The operational management of agricultural production, product processing, and product sale and after-sale service based on Agri-IoT. The theoretical research and practical application of combining operational management theories and IoT-based intelligent agriculture will provide informed decision support for stakeholders and drive the further development of the entire agriculture industry chain.     

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。     

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.     

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.     

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