One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future

Biological age estimation using an eHealth system based on wearable sensors

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A Versatile Machine Vision Algorithm for Real-Time Counting Manually Assembled Pieces

The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term “complex environment”, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures

A Computer Vision System for Staff Gauge in River Flood Monitoring

Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames

Machine Learning Approach for Care Improvement of Children and Youth with Type 1 Diabetes Treated with Hybrid Closed-Loop System

Type 1 diabetes is a disease affecting beta cells of the pancreas and it’s responsible for a decreased insulin secretion, leading to an increased blood glucose level. The traditional method for glucose treatment is based on finger-stick measurement of the blood glucose concentration and consequent manual insulin injection. Nowadays insulin pumps and continuous glucose monitoring systems are replacing them, being simpler and automatized. This paper focuses on analyzing and improving the knowledge about which Machine Learning algorithms can work best with glycaemic data and tries to find out the relation between insulin pump settings and glycaemic control. The dataset is composed of 90 days of recordings taken from 16 children and adolescents. Three Machine Learning approaches, two for classification, Logistic Regression (LR) and Random Forest (RL), and one for regression, Multivariate Linear Regression (MLR), have been used for the purpose. Specifically, the pump settings analysis was performed based on the Time In Range (TIR) computation and comparison consequent to pump setting changes. RF and MLR have shown the best results, while, for the settings’ analysis, the data show a discrete correlation between changes and TIRs. This study provides an interesting closer look at the data recorded by the insulin pump and a suitable starting point for a thorough and complete analysis of them

A Cross-Protocol Proxy for Sensor Networks Based on CoAP

Smart technologies able to support people during their daily life, such as those linked to the Ambient Assisted Living (AAL) world, are gaining importance in today's context. In this regard, solutions to problems arising from the need to connect constrained networks with the internet are essential. In fact, constrained network, being a particular wireless network made of devices that have limited computational power and limited storage capacity, strongly differ from the internet network. Communication protocols try to overcome the issues related to the interconnection of smart devices in our smart world with the internet. A lot of efforts have been made in this direction, ending up with the creation of several different protocols and among them, in the applications context, the CoAP (Constrained Application Protocol) one is becoming increasingly relevant. The emergence of new protocols forces the need for developing proxying systems able to intermediate between the two kind of networks and to translate between the relative protocols. In this paper we are going to present a cross-protocol proxy able to broker among the HTTP, MQTT and CoAP protocols and also able to implement the caching function, that as we will deepen, is an essential contributor for the timeliness of communications. The proposed cross-protocol proxy has been tested under four operating conditions, in terms of Throughput and Round Trip Time. The results show excellent performances for both metrics taken into account, especially when the caching feature is enabled