Developing performance measurement system for Internet of Things and smart factory environment

To cope with large fluctuations in the demand of a commodity, it is necessary for the manufacturing system to have rapid reactive ability. This requirement may be secured by performance measurement. Although manufacturing companies have used information systems to manage performance, there has been the difficulty of capturing real-time data to depict real situations. The recent development and application of the Internet of Things (IoT) has enabled the resolution of this problem. In demonstration of the functionality of IoT, we developed an IoT-based performance model consistent with the ISA-95 and ISO-22400 standards, which define manufacturing processes and performance indicator formulas. The development comprised three steps: (1) Selection of the Key Performance Indicators of the Overall Equipment Effectiveness (OEE), and the development of an IoT-based production performance model, (2) Implementation of the IoT-based architecture and performance measurement process using Business Process Modelling and (3) Validation of the proposed model through virtual factory simulation. We investigated the effect of the IoT-workability on the OEE, based on the final results of the simulation, both for the planned and actual productions. The simulation results showed that the proposed model represented the timestamp data acquired by IoT and captured the entire production process, thus enabling the determination of real-time performance indicators.     

IoT patent roadmap for smart logistic service provision in the context of Industry 4.0

Abstract

Industry 4.0 enables the management of factories manufacturing products with complexity and flexibility. The corresponding logistic services must provide greater accuracy and efficiency in logistic operations. The Internet of Things (IoT) is an important aspect for smart logistics in the context of Industry 4.0. For instance, intelligent logistics models use IoT integrated technologies, e.g. radio frequency identification (RFID), wireless sensor network (WSN) and cloud computing, to enhance the traceability and decision supports of logistic processes in real-time speed, high accuracy, and flexibility. This research focuses on analyzing the related technology roadmaps for the adoption of IoT technologies in smart logistic services. A case research is conducted specifically to identify the relationship between IoT-oriented technologies and deployed advanced logistic services. The logistic operations are organized into an ontology schema based on a four level service framework. The research proposes a roadmap approach to visualize the patent allocations and evolutions corresponding to logistic services at each level. Although the patent roadmap methodology is generic, this research focuses on the two industry leaders, which are UPS and IBM. Using the roadmap methodology, the IoT enabled smart logistic patents are analyzed to identify technology-related business strengths and strategies.     

Internet of Things (IoT)–blockchain-enabled pharmaceutical supply chain resilience in the post-pandemic era

During the COVID-19 pandemic, the current operating environment of pharmaceutical supply chain (PSC) has rapidly changed and faced increasing risks of disruption. The Internet of Things (IoT) and blockchain not only help enhance the efficiency of PSC operations in the information technology domain but also address complex related issues and improve the visibility, flexibility, and transparency of these operations. Although IoT and blockchain have been widely examined in the areas of supply chain and logistics management, further work on PSC is expected by the public to enhance its resilience. To respond to this call, this paper combines a literature review with semi-structured interviews to investigate the characteristics of PSC, the key aspects affecting PSC, and the challenges faced by PSC in the post-pandemic era. An IoT–blockchain-integrated hospital-side oriented PSC management model is also developed. This paper highlights how IoT and blockchain technology can enhance supply chain resilience and provides a reference on how PSC members can cope with the associated risks.     

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.     

A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model (UTAUT)

Smart Farming is the application of modern technologies, tools and gadgets for increasing the agricultural crops quality and quantity. The Internet of Things (IoT) technology has had a prominent role in the establishment of smart farming. However, the application of this technology could be hard and, in some cases, challenging for the Middle Eastern users. Therefore, the research purpose is to identify the influential factors in the adoption and then application of IoT in smart farming by farmers with a contextualized approach in Iran, a typical Middle Eastern country. Thus, the Unified Theory of Acceptance and Use of Technology (UTAUT) has contextually been used as the theoretical model of the research. The results accentuated and proved the positive impacts of performance expectancy (H1), effort expectancy (H2), social influence (H3), individual factors (H4), and facilitating conditions (H5), on the intention to use IoT technology. Ultimately, the results were indicating the significant impact of behavioral intention on the actual usage of IoT technology (H6). One of the implications of the research is for the IT policymakers in the agricultural sector in the Middle East, where water and cultivable land are two valuable but scarce economic resources. Hence, smart farming could not be promoted unless the farmers had fulfilled its prerequisite factors proposed by the research results for using the IoT technology.     

物联网技术在物流包装应用中的问题及对策

目的研究物联网技术在物流包装中的应用进展,提出待解决问题的对策。方法运用物联网和包装管理的基本原理,采用探究性、描述性和因果性研究方法系统阐释其工作机理。结果应提高芯片技术研发和生产能力,创建可视化智能管理系统,加强物流包装EDI技术的开发和应用,提高包装信息响应,加强电子产品代码(EPC)网络管理,提高网络安全技术水平,从而加强物联网技术在物流包装领域中的推广和应用。结论物流包装领域可通过实施物联网技术实现持续改进的目标。     

An Access Control Scheme Using Heterogeneous Signcryption for IoT Environments

When the Wireless Sensor Network (WSN) is combined with the Internet of Things (IoT), it can be employed in a wide range of applications, such as agriculture, industry 4.0, health care, smart homes, among others. Accessing the big data generated by these applications in Cloud Servers (CSs), requires higher levels of authenticity and confidentiality during communication conducted through the Internet. Signcryption is one of the most promising approaches nowadays for overcoming such obstacles, due to its combined nature, i.e., signature and encryption. A number of researchers have developed schemes to address issues related to access control in the IoT literature, however, the majority of these schemes are based on homogeneous nature. This will be neither adequate nor practical for heterogeneous IoT environments. In addition, these schemes are based on bilinear pairing and elliptic curve cryptography, which further requires additional processing time and more communication overheads that is inappropriate for real-time communication. Consequently, this paper aims to solve the above-discussed issues, we proposed an access control scheme for IoT environments using heterogeneous signcryption scheme with the efficiency and security hardiness of hyperelliptic curve. Besides the security services such as replay attack prevention, confidentiality, integrity, unforgeability, non-repudiations, and forward secrecy, the proposed scheme has very low computational and communication costs, when it is compared to existing schemes. This is primarily because of hyperelliptic curve lighter nature of key and other parameters. The AVISPA tool is used to simulate the security requirements of our proposed scheme and the results were under two backbends (Constraint Logic-based Attack Searcher (CL-b-AtSER) and On-the-Fly Model Checker (ON-t-FL-MCR)) proved to be SAFE when the presented scheme is coded in HLPSL language. This scheme was proven to be capable of preventing a variety of attacks, including confidentiality, integrity, unforgeability, non-repudiation, forward secrecy, and replay attacks.

     

Anomaly Detection for Internet of Things Cyberattacks

The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives. The IoT revolution has redefined digital services in different domains by improving efficiency, productivity, and cost-effectiveness. Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’ operation; however, IoT security issues remain a significant challenge. To minimize the risk of cyberattacks on IoT networks, anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT cyberattacks. Although various detection techniques have been proposed in the literature, existing detection methods address limited cyberattacks and utilize outdated datasets for evaluations. In this paper, we propose an intelligent, effective, and lightweight detection approach to detect several IoT attacks. Our proposed model includes a collaborative feature selection method that selects the best distinctive features and eliminates unnecessary features to build an effective and efficient detection model. In the detection phase, we also proposed an ensemble of learning techniques to improve classification for predicting several different types of IoT attacks. The experimental results show that our proposed method can effectively and efficiently predict several IoT attacks with a higher accuracy rate of 99.984%, a precision rate of 99.982%, a recall rate of 99.984%, and an F1-score of 99.983%.     

An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing Networks

Internet of Things (IoT) network used for industrial management is vulnerable to different security threats due to its unstructured deployment, and dynamic communication behavior. In literature various mechanisms addressed the security issue of Industrial IoT networks, but proper maintenance of the performance reliability is among the common challenges. In this paper, we proposed an intelligent mutual authentication scheme leveraging authentication aware node (AAN) and base station (BS) to identify routing attacks in Industrial IoT networks. The AAN and BS uses the communication parameter such as a route request (RREQ), node-ID, received signal strength (RSS), and round-trip time (RTT) information to identify malicious devices and routes in the deployed network. The feasibility of the proposed model is validated in the simulation environment, where OMNeT++ was used as a simulation tool. We compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection, communication cost, latency, computational cost, and throughput. The results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7 %.     

Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems

Intelligent Transportation System (ITS) is essential for effective identification of vulnerable units in the transport network and its stable operation. Also, it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things (IoT). Previous research on vulnerability has no congestion effect on the peak time of urban road network. The cascading failure of links or nodes is presented by IoT monitoring system, which can collect data from a wireless sensor network in the transport environment. The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure (V2I) channels to simulate key segments and their failure probability. Finally, the topological structure vulnerability index and the traffic function vulnerability index of road network are extracted from the vulnerability factors. The two indices are standardized by calculating the relative change rate, and the comprehensive index of the consequence after road network unit is in a failure state. Therefore, by calculating the failure probability of road network unit and comprehensive index of road network unit in failure state, the comprehensive vulnerability of road network can be evaluated by a risk calculation formula. In short, the IoT-based solutions to the new vulnerability assessment can help road network planning and traffic management departments to achieve the ITS goals.     

System dynamics analysis for an Internet-of-Things-enabled production logistics system

A production logistics system is often subject to high operational dynamics due to large working areas, frequent resource interactions, long operation periods and intensive human involvement. Researchers have applied system dynamics to design the structure of statistically robust systems which accommodate common dynamics. Yet this approach begins to lose its feasibility because dynamics anticipation and statistics are becoming more difficult in ever more competitive markets and adjustments to system structure typically incur high costs. In response, this study explores how a robust information structure can be designed and real-time control schemes for controlling the dynamics inherent to real-life systems applied. Motivated by the wide application of industrial Internet-of-Things (IoT) systems, this paper investigates the typical production logistic execution processes and adopts system dynamics to design cost-effective IoT solutions. The internal and external production logistic processes are first investigated separately. Using sensitivity analysis, the optimal IoT solutions are evaluated and analysed to provide guidance on IoT implementation. Internal and external production logistic processes are then combined into an integrated structure to offer a generic system dynamics approach. This research does not only enhance the use of system dynamics, but also presents a quantitative IoT system analysis approach.     

When Flexible Organic Field-Effect Transistors Meet Biomimetics: A Prospective View of the Internet of Things

The emergence of flexible organic electronics that span the fields of physics and biomimetics creates the possibility for increasingly simple and intelligent products for use in everyday life. Organic field-effect transistors (OFETs), with their inherent flexibility, light weight, and biocompatibility, have shown great promise in the field of biomimicry. By applying such biomimetic OFETs for the internet of things (IoT) makes it possible to imagine novel products and use cases for the future. Recent advances in flexible OFETs and their applications in biomimetic systems are reviewed. Strategies to achieve flexible OFETs are individually discussed and recent progress in biomimetic sensory systems and nervous systems is reviewed in detail. OFETs are revealed to be one of the best systems for mimicking sensory and nervous systems. Additionally, a brief discussion of information storage based on OFETs is presented. Finally, a personal view of the utilization of biomimetic OFETs in the IoT and future challenges in this research area are provided.     

An Internet-of-Things Initiative for One Belt One Road (OBOR)

A wide range of industrial Internet of Things (IoT) applications have been developed and deployed in recent years. IoT has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of RFID, wireless, mobile and sensor devices. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. As IoT has received support from governments and businesses across the globe, IoT will also greatly impact One Belt One Road (OBOR) in foreseeable future.     

Operations management of smart logistics: A literature review and future research

The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.     

乡村振兴视域下农产品交互式智能包装设计研究

目的 围绕乡村振兴开展农产品包装设计,研究交互式智能包装在农产品包装上的创新应用。方法 针对农产品,以交互式智能包装设计为研究重点。首先,通过国内外相关文献、资料研究,对农产品包装设计的现状进行梳理,综述交互式智能包装的概念、特征、设计方法与原则;其次,利用案例法重点分析交互式智能包装在食品包装上的运用;最后,结合农产品包装设计的现状问题,为今后农产品应用交互式智能包装设计提出对策性的方法与设计方案。结果 交互式智能包装设计中的趣味性和互动性,满足了消费者对农产品在包装上的形式和功能以外的新诉求。借由交互式智能包装设计的科技性、互动性、传播性和创新性,让消费者和产品之间建立起了紧密联系,从而赋能农产品的价值提升。结论 交互式智能包装设计可以为乡村振兴提供一条好的路径。将交互式智能包装应用到农产品包装设计中,旨在更好地满足用户交互体验需求,从而更好地传播乡村品牌文化,提升特色农产品市场经济效益,助力乡村振兴。     

An Internet of Energy Things Based on Wireless LPWAN

Under intense environmental pressure, the global energy sector is promoting the integration of renewable energy into interconnected energy systems. The demand-side management (DSM) of energy systems has drawn considerable industrial and academic attention in attempts to form new flexibilities to respond to variations in renewable energy inputs to the system. However, many DSM concepts are still in the experimental demonstration phase. One of the obstacles to DSM usage is that the current information infrastructure was mainly designed for centralized systems, and does not meet DSM requirements. To overcome this barrier, this paper proposes a novel information infrastructure named the Internet of Energy Things (IoET) in order to make DSM practicable by basing it on the latest wireless communication technology: the low-power wide-area network (LPWAN). The primary advantage of LPWAN over general packet radio service (GPRS) and area Internet of Things (IoT) is its wide-area coverage, which comes with minimum power consumption and maintenance costs. Against this background, this paper briefly reviews the representative LPWAN technologies of narrow-band Internet of Things (NB-IoT) and Long Range (LoRa) technology, and compares them with GPRS and area IoT technology. Next, a wireless-to-cloud architecture is proposed for the IoET, based on the main technical features of LPWAN. Finally, this paper looks forward to the potential of IoET in various DSM application scenarios.     

Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning

The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic and lossy environment and impact performance. The ranking mechanism also requires large control overheads to update rank information. The resource-constrained IoT devices operating in a low-power and lossy network (LLN) environment need an efficient solution to handle these problems. Reinforcement learning (RL) algorithms like Q-learning are recently utilized to solve learning problems in LLNs devices like sensors. Thus, in this paper, an RL-based optimization of dense LLN IoT devices with heavy heterogeneous traffic is devised. The proposed protocol learns the collision information from the MAC layer and makes an intelligent decision at the network layer. The proposed protocol also enhances the operation of the trickle timer algorithm. A Q-learning model is employed to adaptively learn the channel collision probability and network layer ranking states with accumulated reward function. Based on a simulation using Contiki 3.0 Cooja, the proposed intelligent scheme achieves a lower packet loss ratio, improves throughput, produces lower control overheads, and consumes less energy than other state-of-the-art mechanisms.     

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.     

时间–温度指示器在冷链运输中的应用进展

目的 研究时间–温度指示器(Time-Temperature Indicator,TTI)在产品包装和冷链物流中的价值和意义,并对冷链物流过程实施严格的监控义,以保障公众医疗卫生安全和食品卫生安全。方法 简述几种常见TTI的类型及原理,并概述TTI的研究现状,同时总结TTI技术的发展趋势并对其在我国的商用前景进行展望。结果 TTI历经几十年的发展技术已较为成熟,已在冷链运输、物流管理等领域发挥很大作用。结论 TTI技术的应用和发展前景广阔,与智能包装、物流管理等领域的技术结合发展会加速其在我国的商业化进程。     

Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

An IoT-based wireless sensor network (WSN) comprises many small sensors to collect the data and share it with the central repositories. These sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting it. During data sharing, security is an important concern in such networks as they are prone to many threats, of which the deadliest is the wormhole attack. These attacks are launched without acquiring the vital information of the network and they highly compromise the communication, security, and performance of the network. In the IoT-based network environment, its mitigation becomes more challenging because of the low resource availability in the sensing devices. We have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their methodology. The analysis of literature has motivated our research. In this paper, we developed the ESWI technique for detecting the wormhole attack while improving the performance and security. This algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its operation. The simulation results of our technique show competitive results for the detection rate and packet delivery ratio. It also gives an increased throughput, a decreased end-to-end delay, and a much-reduced consumption of energy.