Radio Frequency Identification (RFID) based wireless manufacturing systems, a review

Radio frequency identification (RFID) is one of the most promising technological innovations in order to track and trace products as well as material flow in manufacturing systems. High Frequency (HF) and Ultra High Frequency (UHF) RFID systems can track a wide range of products in the part production process via radio waves with level of accuracy and reliability.   As a result, quality and transparency of data across the supply chain can be accurately obtained in order to decrease time and cost of part production. Also, process planning and part production scheduling can be modified using the advanced RFID systems in part manufacturing process. Moreover, to decrease the cost of produced parts, material handling systems in the advanced assembly lines can be analyzed and developed by using the RFID. Smart storage systems can increase efficiency in part production systems by providing accurate information from the stored raw materials and products for the production planning systems. To increase efficiency of energy consumption in production processes, energy management systems can be developed by using the RFID-sensor networks. Therefore, smart factories and intelligent manufacturing systems as industry 4.0 can be introduced by using the developed RFID systems in order to provide new generation of part production systems. In this paper, a review of RFID based wireless manufacturing systems is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers

Smart manufacturing for industry 4.0 using Radio Frequency Identification (RFID) technology

Industry 4.0 (I4.0) presents a unique challenge of efficiently transforming traditional manufacturing to smart and autonomous systems.Integrating manufacturing systems, materials, machinery, operators, products and consumers, improve interconnectivity and traceability across the entire product life cycle in order to ensure the horizontal and vertical integration of networked Smart Manufacturing (SM) systems. Manufacturing functions of Material Handling (MH)-control, storage, protection and transport of raw materials, work in process (WIP) and finished products- throughout a manufacturing and distribution process will need a revamp in ways they are currently being carried in order to transition them into the SM era. Radio Frequency Identification (RFID), an Automated Identification Data Capture (AIDC) technology increasingly being used to enhance MH functions in the (SM) industry, due to opportunities it presents for item tracking, out of sight data capturing, navigation and space mapping abilities. The technology readiness level of RFID has presented many implementation challenges as progress is being made to fully integrate the technology into the preexisting MH functions. Recently, many researchers in academia and industry have described various methods of using RFID for improving and efficiently carrying out MH functions as a gradual transition is being made into I4.0 era. This paper reviews and categorize research finding regarding RFID application developments according to various MH functions in SM, tabulates how various I4.0 enablers are needed to transform various traditional manufacturing functions into SM. It aims to let more experts know the current research status of RFID technology and provide some guidance for future research

Status and Future of Manufacturing Execution Systems

This paper proposes a taxonomy for characterizing manufacturing execution systems and discusses how they can benefit from the recent Developments of Industry 4.0. The study is based on a literature review. The taxonomy contributes to theory and practice by providing a framework for benchmarking of manufacturing execution systems. The taxonomy can be utilized in the selection or design process of the manufacturing execution systems. Outlining the further opportunities provided by Industry 4.0 technologies, the paper also provides directions for future improvements of manufacturing execution systems.acceptedVersio

Status and Future of Manufacturing Execution Systems

This paper proposes a taxonomy for characterizing manufacturing execution systems and discusses how they can benefit from the recent Developments of Industry 4.0. The study is based on a literature review. The taxonomy contributes to theory and practice by providing a framework for benchmarking of manufacturing execution systems. The taxonomy can be utilized in the selection or design process of the manufacturing execution systems. Outlining the further opportunities provided by Industry 4.0 technologies, the paper also provides directions for future improvements of manufacturing execution systems.acceptedVersio

An Online RFID Localization in the Manufacturing Shopfloor

{Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the non-stationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms

Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

The demand for positioning, localisation and navigation services is on the rise, largely owing to the fact that such services form an integral part of applications in areas such as human activity recognition, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, massive device connectivity, real-time response, flexibility, and integrability. Although many current solutions have succeeded in fulfilling these requirements, numerous challenges remain in terms of providing robust and reliable indoor positioning solutions. This dissertation has a core focus on improving computing efficiency, data pre-processing, and software architecture for Indoor Positioning Systems (IPSs), without throwing out position and location accuracy. Fingerprinting is the main positioning technique used in this dissertation, as it is one of the approaches used most frequently in indoor positioning solutions. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions for Global Navigation Satellite System (GNSS) denied scenarios. This first contribution identifies the current challenges and trends in indoor positioning applications over the last seven years (from January 2015 to May 2022). Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. This second contribution is devoted to reducing the number of outliers fingerprints in radio maps and, therefore, reducing the error in position estimation. The data cleansing algorithm relies on the correlation between fingerprints, taking into account the maximum Received Signal Strength (RSS) values, whereas the Generative Adversarial Network (GAN) network is used for data augmentation in order to generate synthetic fingerprints that are barely distinguishable from real ones. Consequently, the positioning error is reduced by more than 3.5% after applying the data cleansing. Similarly, the positioning error is reduced in 8 from 11 datasets after generating new synthetic fingerprints. The third contribution suggests two algorithms which group similar fingerprints into clusters. To that end, a new post-processing algorithm for Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering is developed to redistribute noisy fingerprints to the formed clusters, enhancing the mean positioning accuracy by more than 20% in comparison with the plain DBSCAN. A new lightweight clustering algorithm is also introduced, which joins similar fingerprints based on the maximum RSS values and Access Point (AP) identifiers. This new clustering algorithm reduces the time required to form the clusters by more than 60% compared with two traditional clustering algorithms. The fourth contribution explores the use of Machine Learning (ML) models to enhance the accuracy of position estimation. These models are based on Deep Neural Network (DNN) and Extreme Learning Machine (ELM). The first combines Convolutional Neural Network (CNN) and Long short-term memory (LSTM) to learn the complex patterns in fingerprinting radio maps and improve position accuracy. The second model uses CNN and ELM to provide a fast and accurate solution for the classification of fingerprints into buildings and floors. Both models offer better performance in terms of floor hit rate than the baseline (more than 8% on average), and also outperform some machine learning models from the literature. Finally, this dissertation summarises the key findings of the previous chapters in an open-source cloud platform for indoor positioning. This software developed in this dissertation follows the guidelines provided by current standards in positioning, mapping, and software architecture to provide a reliable and scalable system

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Indoor location identification technologies for real-time IoT-based applications: an inclusive survey

YesThe advent of the Internet of Things has witnessed tremendous success in the application of wireless sensor networks and ubiquitous computing for diverse smart-based applications. The developed systems operate under different technologies using different methods to achieve their targeted goals. In this treatise, we carried out an inclusive survey on key indoor technologies and techniques, with to view to explore their various benefits, limitations, and areas for improvement. The mathematical formulation for simple localization problems is also presented. In addition, an empirical evaluation of the performance of these indoor technologies is carried out using a common generic metric of scalability, accuracy, complexity, robustness, energy-efficiency, cost and reliability. An empirical evaluation of performance of different RF-based technologies establishes the viability of Wi-Fi, RFID, UWB, Wi-Fi, Bluetooth, ZigBee, and Light over other indoor technologies for reliable IoT-based applications. Furthermore, the survey advocates hybridization of technologies as an effective approach to achieve reliable IoT-based indoor systems. The findings of the survey could be useful in the selection of appropriate indoor technologies for the development of reliable real-time indoor applications. The study could also be used as a reliable source for literature referencing on the subject of indoor location identification.Supported in part by the Tertiary Education Trust Fund of the Federal Government of Nigeria, and in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement H2020-MSCA-ITN-2016 SECRET-72242

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society