Product failure detection in smart manufacturing is important because it allows manufacturers to quickly identify and isolate faulty products before they reach the end of the production line. In today’s fast-paced and highly competitive business environment, manufacturers need to quickly and accurately identify product failures to stay competitive and meet customer expectations. Previously the product quality was inspected manually and now it is examined using a machine learning algorithm to overcome the limitations of manual inspection. However, in the latter case AI and ML experts are required to do the task. Therefore, to overcome such dependency, this work proposes a microservice to allow the end user (an industry person, as well as an automated hardware/software agent) to test different combinations of data science and AI tools and technologies without any AI expertise. The proposed microservice exposes APIs that make it possible to select different combinations of feature selection, sampling, and classification algorithm. A demo environment with a Postman collection that includes few API calls to demonstrate how the proposed module enables customized selection to detect faulty products, is also discussed.

Reducing product failure rates is crucial to ensure a healthy production line. However, the current approach for inspecting product quality is inefficient, costly, and time-consuming, relying on manual inspection at the end of the production process. This research paper focuses on the utilization of transfer learning, an intelligent machine-learning technique, to improve the accuracy and efficiency of product quality inspection in production lines. The proposed approach utilizes transfer learning to adapt a pre-trained model from a related domain to the target domain, enabling accurate product quality prediction with limited data. The reference architecture provides a framework for implementing the proposed approach in a manufacturing environment, enabling real-time monitoring and decision-making based on product quality predictions. The proposed approach can improve the accuracy of faulty product detection by up to 11% compared to traditional techniques, as demonstrated by evaluations on a real-world production dataset.

In modern industry, adaptation to market changes, as well as prompt reaction to a variety of predictable and unpredictable events, is a key requirement. Ubiquitous computing, real-time analytics, reconfigurable hardware/software components, often coexist in the complex, internally variegated, and often proprietary systems that are traditionally deployed to meet such requirement. However, such tailor-made systems meet only in part the requirements of openness, security, monitorability, geographical distribution, and most of all, remote extendability and changeability, which are crucial for prompt reaction to unforeseen circumstances. In this work, a containerized service application named Network Factory is presented. It enables the remote construction, configuration and operation of resilient computation systems that meet the above-mentioned requirements, and distinguish for their logical simplicity and for the uniform addressing of elaborations and human-computer interfaces, which are achieved through few reconfigurable components and communication mechanisms that are used from the production line up to the Cloud. Source code, documentation, and step-by-step introductory guides are publicly available in a dedicated GitHub repository, and distributed under the CC-BY-4.0 license.

Modern manufacturing systems characterize for the multiple dimensions of their complexity. They are numerically complex, as they consist of several components. They are logically complex, as multiple and variegated links exist among the different components. They are technologically complex, as a mix of different hardware and software technologies and architectures is typically found. They are geographically complex, as they often extend across multiple physical locations and sometimes involve multiple organizations. However, resilience to predictable and unpredictable events through timely, efficient, and effective reconfiguration of the whole manufacturing ecosystem remains a key objective, being it a key enabler of industry competitiveness. In this work, an innovative approach based on API request collections, containerization technologies, and past research about remotely reconfigurable distributed systems, is proposed for achieving ultimate resilience in modern industry.

Smart Factories characterize as context-rich, fast-changing environments where heterogeneous hardware appliances are found beside of also heterogeneous software components deployed in (or directly interfacing with) IoT devices, as well as in on-premise mainframes, and on the Cloud. This inherent heterogeneity poses major challenges particularly when a high degree of resiliency is needed, and the ubiquitously deployed software components must be replaced or reconfigured at real-time to respond to the most diverse events, ranging from an out-of-range sensor detection, to a new order issued by a customer. In this work, a software framework is presented, which allows to deploy, (re)configure, run, and monitor the most diverse software across all the three layers of the Smart Factory (edge, fog, Cloud), from remote, via API calls, in a standardised uniform manner, relying on containerization technologies, and on a variety of software technologies, frameworks, and programming languages, including Node-RED, MQTT, Scala, Apache Spark, and Kafka. The most recent advances in the framework design, implementation, and demonstration, which led to the introduction of the so-called Crazy Nodes, are presented and motivated. A comprehensive proof-of-concept is given, where user interfaces and distributed systems are created from scratch via API calls to implement AI-based alerting systems, Big Data stream filtering and transformation, AI model training, storage, and usage for one-shot as well as stream predictions, and real-time Big Data visualization through line plots, histograms, and pie charts.

Intelligent, smart, Cloud, reconfigurable manufac-turing, and remote monitoring, all intersect in modern industry and mark the path toward more efficient, effective, and sustain-able factories. Many obstacles are found along the path, including legacy machineries and technologies, security issues, and software that is often hard, slow, and expensive to adapt to face unforeseen challenges and needs in this fast-changing ecosystem. Light-weight, portable, loosely coupled, easily monitored, variegated software components, supporting Edge, Fog and Cloud computing, that can be (re)created, (re)configured and operated from remote through Web requests in a matter of milliseconds, and that rely on libraries of ready-to-use tasks also extendable from remote through sub-second Web requests, constitute a fertile technological ground on top of which fourth-generation industries can be built. In this demo it will be shown how starting from a completely virgin Docker Engine, it is possible to build, configure, destroy, rebuild, operate, exclusively from remote, exclusively via API calls, computation networks that are capable to (i) raise alerts based on configured thresholds or trained ML models, (ii) transform Big Data streams, (iii) produce and persist Big Datasets on the Cloud, (iv) train and persist ML models on the Cloud, (v) use trained models for one-shot or stream predictions, (vi) produce tabular visualizations, line plots, pie charts, histograms, at real-time, from Big Data streams. Also, it will be shown how easily such computation networks can be upgraded with new functionalities at real-time, from remote, via API calls.

Graphical User Interfaces (GUI) and real-time in-teractive Big Data charts play a key role in a wide variety of Big Data applications. The software libraries that are available at today are not suitable for displaying huge volumes of data in a single chart, because they require all the data to be collected at a single node. In this work, an innovative approach to the problem is presented, that consists in using a network of cyber-devices that is created and configured via API calls and that interfaces with a Scala Spark server application through a multiplicity of communication technologies, to produce and display a variety of time-space- infinite Big Data stream visualizations, including line plots, pie charts, histograms, that are updated at real-time as new data come, without ever collecting the data or the charts markup at a single node. The proposed approach characterizes for being (i) Web-based, (ii) API-based, (iii) Cloud-based, (iv) portable, (v) customizable/extendable, (vi) plug and play, and for relying on (i) Node-RED, (ii) MQTT, (iii) Scala, (iv) Akka HTTP, (v) Spark, (vi) Kafka, (vii) Docker. Remarkably, the same network used for Big Data visualization can be reconfigured in a matter of milliseconds and used for Big Data (streams) filtering, transformation, merge, analytics, and for training Machine Learning models, storing trained models on a Cloud storage, using stored models for one-shot or stream predictions, and much more. Although being at an advanced stage, we consider this research as a work in progress, since an extensive benchmarking and application to variegated real-world scenarios are still to be carried out.

Smart Cities are probably on the more complex environment for IOT data collection. IOT data could have different producers, sample rates, periodic and aperiodic, typical trends, structures and stacks, faults, etc. Thus, a strongly flexible and scalable solution is needed to avoid investing huge amount of resources in anomaly detection that has to be done in real time and has to be agnostic to the above-mentioned problems. This paper presents a solution for automatic detection of anomalies. The proposed approach scales seamlessly and integrates in different contexts, featuring different sensor types, protocols, and data formats, and computationally cheap. The research has been developed in the context of Snap4City PCP Select4Cities project and is presently implemented in the Https://www.snap4city.org solution adopted in several cities and regions.

In the context of Smart City, it is quite frequent the usage of Smart City API for providing services at web and mobile applications. Most of the solutions using Smart City APIs are focused on a single city. This means that passing from one city/area to another, the users must change application. This happens for the lack of interoperability among Smart City APIs and/or services. In this paper, the problem of federation of smart city services is addressed by proposing a solution for federating smart city APIs. To this end, a formal model has been proposed to federate API services, with efficiency, security, scalability, and capacity of managing overlapped areas of competence, distributed searches, etc. These features are typically not all satisfied by classic GIS solutions which federate the services at level of databases. The solution has been developed in the context of Snap4City European platform enhancing former Km4City API of Sii-Mobility national project with Snap4City (https://www.snap4city.org).

Real time traffic flow data in terms of road-segments traffic densities are relevant information at the basis of several smart services, such as smart routing, smart planning for evacuations, planning of civil works on the city, etc. The traditional methods for traffic flow measured via sensors using data from navigator Apps (e.g., TomTom, Google map, Waze) could be very expensive to be acquired. For this reason, there is the space for low cost and fast solutions for dense traffic flow reconstruction from scattered data coming from a limited number of street sensors spread on the city. The proposed method is based on differential equations and physical constraints applied to a detailed street graph which is enriched of several features. A stochastic learning approach has been adopted to estimate the weights representing in certain sense the road-segments capacity at each time slot of the day. The proposed solution allows computing in real-time the traffic density reconstruction in unmeasured road-segments. The solution has been validated estimating the error in the places where the sensors are positioned, excluding each of them iteratively and reconstructing the flow without it. Then, it has been possible to estimate the error between the reconstructed traffic density and the measured values. This approach allowed setting up a converging algorithm for estimating the traffic density in the whole city graph from detailed parameters. The proposed reconstruction model has been created by exploiting open and real-time data in the context of Sii-Mobility research project by using Km4City infrastructure in the area of Florence, Italy, for its corresponding Smart City solution.