Reduce your operating costs through real time automation

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With the rapid growth of artificial intelligence, the environmental impact of AI is a hot topic. Green AI aims to create sustainable, energy-efficient, and environmentally-friendly AI systems. However, achieving this goal requires a combination of different technologies and one of the most critical ones is Edge Computing. In this article, we'll explore Green AI, its importance, and the critical role of Edge Computing in its success.

The main goal of virtualization is to provide a new operational environment, which is not bound to any computer hardware or operating system. Hardware components are typically designed to be robust and reliable, but they can also be expensive and difficult to modify or upgrade. Separating hardware from software allows for the software to be updated or modified without affecting the hardware.

In this Masterclass given by some of the top leaders in the industry, we will review the most important Machine Learning applications and how their deployment is getting closer and closer to the source of the data to ensure its privacy and enable real-time decision making. Don't miss this webinar on May 3rd at 17:30.

Under the name SEC2GRID, Barbara together with Ingeteam, Iberdrola, Ormazabal, Arteche, PwC, Zigor ZIV, Ikerlan and the GAIA Cluster will provide cybersecurity to the electricity grid. A collaborative framework composed of competing companies that will extend to 2024 with a total investment of €6.4 million.

In general, it is important to clearly understand your business requirements and the problem you are trying to solve when determining the best approach to automate the retraining of an active machine learning model. It is also important to continuously monitor the performance of the model and make adjustments to the retraining cadence and metrics as needed.

To operate more efficiently, water management operators face the challenge to monitor in a unified and comprehensive way all components that make up the OT and industrial automation architectures. The use of Edge Nodes allows connectivity with different equipment in each installation regardless of the various data reporting equipment. Discover how utility companies can access sophisticated intelligence to support water operators using Edge Computing.

Although much of the water infrastructures in place are dozens of years old, in recent times their processes have changed radically thanks to a large extent, to digital technology. Edge computing is one of those technologies that will change the shape of the water industry due to the speed and reliability it provides when modernizing this type of infrastructure.

The rise of Industry 4.0, has brought a sheer amount of data. With the advent of IoT, automation, and advanced analytics, organizations are collecting and generating more data than ever. This data has the potential to revolutionize the way industries operate and improve decision-making. However, with the vast amount of data being generated, its crucial that organizations have the ability to effectively control and manage it.

One of the main applications of artificial intelligence in industrial environments is predictive maintenance. If before there was a human maintenance team, with protocols and routines for 'review and monitoring' (and repair if necessary) of each segment or area of the production chain, AI can do it much more accurately and with fewer resources.

Artificial Intelligence (AI) is the greatest catalyst for technological innovation in history, with countless applications in the energy sector. Key examples are the optimisation of production and storage, smart market trading, consumer savings and efficient consumption models for reducing carbon footprints, among others.

A new concept resonates in recent times among analysts and professionals in the energy sector: the Internet of Energy. It is a trend that has arisen from the urgency caused by the accumulation of events affecting a sector that is more in crisis than ever. Factors such as climate change, international conflicts, the supply crisis, escalating prices and new regulatory requirements have put the sector in the spotlight.

Thanks to IoT nodes, power grids acquire the flexibility to face a more electrified future. Smart grids provide insight into consumption and demand and enable the adoption of predictive maintenance strategies.

How can the water industry benefit from IoT Edge and AI? IoT is one of the technologies that has made a strong entry into the water industry, developing solutions rich in monitoring, analytical, and automation features. Meanwhile, Artificial intelligence (AI) is making its mark on the water industry, powering intelligent operations using machine learning to optimize resource use and operational budgets for organizations.

The true potential of Industrial IoT can only be achieved through the introduction of Artificial Intelligence. In this article we will go beyond IoT and will focus on Data Analytics and Data exploitation because for us IoT without Big Data is nothing.

The use of Docker containers in the Internet of Things is becoming increasingly widespread, but being able to use this technology in IoT requires tools that allow simple, centralised, and secure management of the entire process.

Barbara’s Edge Platform allows the deployment of algorithms from different authors in the same Edge Node and facilitates its remote management in a cybersecure fashion.

IoT Edge Computing has the potential to transform the energy industry through its ability to process large amounts of data in real time ultimately improving the operational safety and efficiency.

The electricity sector is undergoing a revolution and Edge Computing is positioning itself as a great ally. But in which use cases is this technology being applied?

With the Fourth Industrial Revolution, many small companies and start ups appear with much more affordable, much cheaper technology and start deploying data collection architecture in all types of large, medium and small companies and start providing services to companies that previously could not afford it.

The industry is moving towards a computing paradigm capable of distributing and subordinating real-time decision making to whatever their nodes «think». Edge Orchestrators enable this decision-making process, facilitating the execution of increasingly complex Machine Learning models in a parallel and distributed manner.

We talked about distributed computing with Alex Cantos, VP of Product at Barbara IoT about the great challenges of the Connected Industry and Distributed Intelligence. According to him, decision making in Industry is centralized due to the technological challenge of distributing it, but this is going to change; we have the technology that allows us to bring artificial intelligence to plants and create cognitive machines.

ONNECTED project seeks to digitise, connect and enable the communication of all equipment in the HORECA and Industrial Laundry sector in a cybersecure manner. Supported by AI and Big Data, manufacturers can now incorporate sensors, software and wireless connectivity to add new features to their products and differentiate them in the market.

Barbara IoT publishes the first Industrial Edge Computing Barometer, a report to help understand the degree of development of this technology in Spain.

Edge AI or AI on the Edge has emerged as a game-changer technology for the Industrial World. Industries with highly distributed critical assets will be the great beneficiaries of taking advanced computing to the Edge.

The Consortium created by Barbara IoT, RKL and Keynetic, will develop the pilot project - Global Safey and Security Center - for Puertos del Estado whose objective is to manage security, physical and logical, through Machine Learning and Artificial Intelligence techniques.

As the market moves from proof of concepts to large multi-application deployments that require scalability, different technological alternatives emerge at the Edge. In this article, we explore the foundation for a successful Edge Computing project.

Docker brings enormous flexibility to application development teams: it gives them the freedom to develop in the programming language of their choice and deploy them easily without worrying about the final hardware on which they will run. It also simplifies the maintenance and version control of the applications themselves, which is very useful for homogeneous deployments in distributed environments.

Barbara has been selected amongst 12 startups to take part in this cohort of the accelerator out of over 424 applicants from 58 countries. The programme will connect startups with leading energy organizations including ENEL, ENI, Iberdrola, GALP, Siemens Gamesa and Total Energies, to work together in solving complex clean energy and decarbonization challenges.

The energy sector is undergoing a profound transformation and Edge Computing is positioned as one of the enabling technologies to manage an increasingly distributed energy.

When we talk about "water sustainability", experts use some fundamental indicators. For us, the most important is the resilience of their infrastructures, i.e. how prepared they are for adverse situations, and the efficiency of their processes, measured as the number of resources to be invested in meeting a specific demand. AI plays a fundamental role in these processes.

An increasingly complex Smart Grid requires two main aspects: visibility and, above all, the ability to automate decisions. This ability to make decisions autonomously is made possible through virtual applications on the edge nodes.

The growth of Edge technology is causing a revolution on the path to Industry 4.0, and the need has arisen to define different parts of the Edge and their functionalities. Concepts such as thin edge and thick edge have emerged from the exponential growth of Edge Computing technology, which industry experts claim is the key to the digital transformation of critical industrial infrastructures. As new use cases emerge, edge diversification is continuing to expand alongside technology and business requirements. ‍Here is an explanation of these concepts, before comparing the two extremes and then looking at some of their technical requirements for industrial implementations.

Advances in Artificial Intelligence continue to act as a driver of the industry's digital transition. With the power of AI and analytics pushing into IT operations, it is only a matter of time before artificial intelligence is integrated throughout all industrial processes.

As part of the rise of the Industrial Internet, all machinery, transportation and facilities are being equipped with a new generation of instruments and sensors that generate petabytes of data and that, thanks to the advance of artificial intelligence and Edge Computing, make it possible to implement predictive maintenance applications with real-time responsiveness.

We are still at the dawn of Machine Learning and Artificial Intelligence in the Industry. Still, as we envision new use cases and develop them in our environment, we realise success in the future depends on the proper implementation today.

As the microservice trend grows, the use of containers is spreading. To scale its deployment, it is necessary to orchestrate the entire process and this is where Kubernetes, a trendy tool among DevOps and a reference in the cloud world in recent years, emerges.

In this future context, where neither production nor distribution are optimally guaranteed, all eyes are on the so-called Active Demand Management or "Demand Response" mechanisms, which could be translated into English as "demand response".

Artificial Intelligence and Machine Learning are the most popular technologies used to create intelligent systems and although they are related, they are not the same. Because of this relationship, when you look at Artificial Intelligence versus Machine Learning, you are actually analyzing their interconnectedness.

Four out of ten Spanish companies will double their investments in Edge Computing by 2022. Next we will analyze: Why is this technology making such a strong impact, what advantages does it have for organizations, what is its degree of maturity in Spain and how to scale its deployment in an efficient and simple way?

The digital twin is one of the main topics of discussion about connected industry or industry 4.0. There are sectors where they are now not only a reality, but a key part of operations. Manufacturing uses digital twins in their day-to-day operations, helping to operate machinery, monitor material, predict behaviour, or plan tasks, using a virtual copy of the systems involved, and thus saving hundreds of field visits.

Edge Mesh proposes that all computing tasks and data be shared using a meshed network of edge devices and routers, offering many advantages, such as distributed processing, low latency, fault tolerance, greater scalability and enhanced security and privacy. These benefits are paramount for critical applications that require real-time processing, increased reliability or mobility support.

As the number of connected devices increases, so does the amount of data generated. This ability to analyze data, extract insights from it and make autonomous decisions based on the analysis is the essence of Artificial Intelligence (AI) of things, also known as AIoT.

The purpose of this article is to help the industrial ecosystem (service providers, integrators,, companies...) to better understand the industrial landscape of Edge technology. It is known that by 2026, 30% of packaged enterprise capabilities will be deployed on the Edge with integrated resources (gateways with local compute) to drive inference and business outcomes on nearby data. Find out more on the survey we conducted over 200 industrial companies.