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.

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.

Due to the increasingly alarming consequences of climate change, pests are becoming a growing threat to grape quality and viticulture yields. Estimating the quantity and type of treatments to control these diseases is particularly challenging due to the unpredictability of insects’ dynamics and intrinsic difficulties in performing pest monitoring. Conventional pest monitoring programs consist of deploying sticky traps on vineyards, which attract key insects and allow human operators to identify and count them manually. However, this is a time-consuming process that usually requires in-depth taxonomic knowledge. This scenario motivated the development of EyesOnTraps, a novel AI-powered mobile solution for pest monitoring in viticulture. The methodology behind the development of the proposed system merges multidisciplinary research efforts by specialists from different fields, including informatics, electronics, machine learning, computer vision, human-centered design, agronomy and viticulture. This research work resulted in a decision support tool that allows winegrowers and taxonomy specialists to: (i) ensure the adequacy and quality of mobile-acquired sticky trap images; (ii) provide automated detection and counting of key insects; (iii) register local temperature near traps; and (iv) improve and anticipate treatment recommendations for the detected pests. By merging mobile computing and AI, we believe that broader technology acceptance for pest management in viticulture can be achieved via solutions that work on regular sticky traps and avoid the need for proprietary instrumented traps.

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.

Digitalization of production processes is rather important to improve the industrial ecosystem. When implemented, such digitalization has positive impacts in the production value chain, from helping to choose the best raw materials to providing high quality products. However, adoption of technologies in industrial settings may not be straightforward given the associated challenges. To mitigate this inherent adoption reluctance and encourage the implementation of the Industry 4.0 vision, we developed the Wireless Gateway for Industrial IoT (WGW4IIoT) that allows easy adaptation to specific installation needs and further development upon identified future requirements in the industry context. To illustrate its potential, WGW4IIoT is validated in real industrial settings.