Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios.

Small-scale farming can benefit from the usage of information and communication technology (ICT) to improve crop and soil management and increase yield. However, in order to introduce digital farming in rural areas, related ICT solutions must be viable, seamless and easy to use, since most farmers are not acquainted with technology. With that in mind, this paper proposes an Internet of Things (IoT) sensing platform that provides information on the state of the soil and surrounding environment in terms of pH, moisture, texture, colour, air temperature, and light. This platform is coupled with computer vision to further analyze and understand soil characteristics. Moreover, the platform hardware is housed in a specifically designed robust casing to allow easy assembly, transport, and protection from the deployment environment. To achieve requirements of usability and reproducibility, the architecture of the IoT sensing platform is based on low-cost, off-the-shelf hardware and software modularity, following a do-it-yourself approach and supporting further extension. In-lab validations of the platform were carried out to finetune its components, showing the platform’s potential for application in rural areas by introducing digital farming to small-scale farmers, and help them delivering better produce and increasing income.

The next generation of mobile broadband communication, 5G, is seen as a driver for the industrial Internet of things (IIoT). The expected 5G-increased performance spanning across different indicators, flexibility to tailor the network to the needs of specific use cases, and the inherent security that offers guarantees both in terms of performance and data isolation have triggered the emergence of the concept of public network integrated non-public network (PNI-NPN) 5G networks. These networks might be a flexible alternative for the well-known (albeit mostly proprietary) Ethernet wired connections and protocols commonly used in the industry setting. With that in mind, this paper presents a practical implementation of IIoT over 5G composed of different infrastructure and application components. From the infrastructure perspective, the implementation includes a 5G Internet of things (IoT) end device that collects sensing data from shop floor assets and the surrounding environment and makes these data available over an industrial 5G Network. Application-wise, the implementation includes an intelligent assistant that consumes such data to generate valuable insights that allow for the sustainable operation of assets. These components have been tested and validated in a real shop floor environment at Bosch Termotecnologia (Bosch TT). Results show the potential of 5G as an enhancer of IIoT towards smarter, more sustainable, green, and environmentally friendly factories.

Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and pocket, without a calibration step being required. This algorithm is able to be fully executed on a wearable device and no external devices are necessary for data processing. Additionally, a study of the accelerometer sampling rate, that allows the algorithm to achieve a better performance, was performed. The algorithm was validated with a continuous dataset with daily living activities and 272 simulated falls. Considering the trade-off between sensitivity and the number of false alarms the most suitable sampling rate found was 50 Hz. The proposed algorithm was able to achieve a trade-off of no false alarms and 89.5% of fall detection rate when wearing the sensor on the user’s waist with a medium sensitivity level of the algorithm. In conclusion, this paper presents a reliable solution for automatic fall detection that can be adapted to different usages and conditions, since it can be used in different body locations and its sensitivity can be adapted to different subjects according to their physical activity level.

The ongoing demographic changes in the European population are consistently stressing healthcare systems with an increasing number of elderly and chronically ill patients. In order to reach an economically and socially viable solution, home healthcare monitoring systems have been exploited. In this paper, we present the eCAALYX project, a manageable, expandable, inter-operable and low-cost Ambient Assisted Living (AAL) solution. Specifically we focus on the Home Gateway component. The eCAALYX Home Gateway relies in the use of open standardized protocols, as well as a modular architecture in order to create an unifying layer between sensors from several manufacturers and different caretaker entities. The performance analysis of our Home Gateway implementation indicates that such a system can run reliably on currently available off-the-shelf equipment, suggesting the practicability of a real-world deployment.

Internet of Things (IoT) driven digitalization is shifting data processing to the Edge, reducing the burden of constant Cloud communication. Advances in resource-constrained microcontroller-based IoT devices that interact with the environment, such as in Cyber-Physical Production Systems, enable them to assist in computation offloading, extending Edge Computing into the so-called Far-Edge that includes such devices. However, updating these devices often requires manual interventions, full firmware updates, or proprietary tools, leading to potential application downtime. To fully leverage Far-Edge enhanced computing capabilities, it is crucial to integrate Far-Edge devices with Cloud orchestration tools, streamlining service management and deployment along the Cloud to the Far-Edge continuum. Current approaches overlook these devices' computing power and their potential to host services, supporting IoT continuum orchestration only from the Cloud to the Edge. This paper introduces Far-edge IoT device mAnagement (FITA), the first platform that integrates Far-Edge devices into Kubernetes-based infrastructures. FITA provides a Far-Edge container- like solution compliant with the Open Container Initiative. It extends Kubernetes to support service deployment on heterogeneous Far-Edge devices seamlessly and provides a method for creating virtual representations of Far-Edge devices to expose their unique capabilities to the Kubernetes scheduler. Our evaluation shows a mean deployment time on clusters with 500 services and 100 devices of around 600 ms, and a device registration time of around 1080 ms, with CPU and memory consumption of around 23 milicores and 1500 MB, respectively. Overall, FITA improves service continuity, deployment speed, and application resilience, supporting the future of the IoT, particularly in industry.