Author: antonellasant

The adoption of connected sensors and devices has grown rapidly across every industrial sector. Energy monitoring systems, environmental sensors, PLCs, gateways, and telemetry devices are often introduced at different times and by different vendors.  While this evolution has significantly increased the availability of data, it has also created a new challenge: the emergence of new data silos. Information is now spread across multiple platforms, separate dashboards, and applications that cannot communicate with one another. The result is increasingly complex plant management and a fragmented view of operational data.

The Limitations of Traditional SCADA Systems

SCADA systems remain essential tools for industrial control, but they were designed for a very different technological landscape. Today, companies must monitor distributed facilities, integrate heterogeneous devices, and manage data generated by a wide variety of technologies. In these scenarios, system integrations can become complex and expensive, while adding new devices or developing innovative services often requires significant customization. For system integrators, this means longer development times, higher implementation costs, and a strong dependence on proprietary technologies.

Why Interoperability Has Become Essential

The real challenge of Industrial IoT is no longer collecting data—it is making that data available in an integrated and meaningful way. A unified view of industrial assets enables organizations to correlate information from different sensors, build cross-functional KPIs, improve maintenance activities, and make faster, better-informed decisions. Interoperability has therefore become a fundamental requirement for companies seeking to unlock the full value of the data generated by their assets.

ADA: An Open and Vendor-Independent IIoT Platform

ADA was designed precisely to address this challenge. The platform enables the integration of sensors and devices from different vendors into a single management environment, collecting data from heterogeneous protocols and making it available through unified, fully customizable dashboards. Its multi-tenant architecture also allows organizations to manage multiple plants and multiple customers simultaneously while maintaining full control over both data and infrastructure. Rather than replacing existing technologies, ADA provides an integration layer that enhances their value and extends their capabilities.

A New Approach for System Integrators

For system integrators, an open platform such as ADA represents a genuine shift in perspective. It enables the development of vendor-independent solutions, reduces the time required to integrate new devices, and supports the creation of applications that can be replicated across multiple customers and industrial environments. In a market where interoperability, scalability, and data ownership are becoming strategic priorities, the ability to unify different technologies within a single platform provides an increasingly important competitive advantage.

From Monitoring to Data Value

The future of Industrial IoT does not lie in multiplying platforms, but in connecting different technologies and transforming isolated data into actionable insights. This is exactly where ADA creates value: by providing system integrators and industrial companies with an open, scalable, and vendor-independent ecosystem capable of overcoming the limitations of traditional architectures and turning data into a strategic asset.

The introduction of the EU ETS2 system is changing the way many companies manage energy consumption, emissions, and regulatory obligations related to fuels placed on the market. For many organizations, the challenge is not only about formal compliance with the regulation, but also about understanding procedures, organizing data correctly, and preparing for upcoming deadlines. In this article, we have collected key aspects not to underestimate, along with practical suggestions for approaching EU ETS2 with greater clarity and organization.

EU ETS2: Way it is important to prepare early

EU ETS2 introduces new obligations affecting several sectors, from road transport and residential or commercial buildings to energy operators and production companies not covered by ETS1. One of the most delicate aspects is the operational management of obligations, including:

• data collection and organization;
• information traceability;
• definition of documented procedures;
• monitoring of deadlines;
• preparation for verification activities carried out by accredited bodies.

Addressing these aspects only close to the deadlines can increase the risk of errors, delays, or difficulties in managing the required communications.

Key aspects not to underestimate

Among the most important elements to consider are:

Management of Documented Procedures
One of the central aspects of EU ETS2 is the ability to clearly and verifiably demonstrate how emissions data are collected, processed, and controlled. Defining structured internal procedures helps improve transparency and reduce critical issues during verification activities.

Monitoring Regulatory Deadlines
EU ETS2 introduces a progressive roadmap with specific dates and obligations that require advance planning. Main milestones include:

• RAE authorization;
• emissions reporting;
• verification by third-party accredited bodies;
• registration on the platforms dedicated to the future market phase.

Having a clear overview of timelines helps companies better organize internal activities and responsibilities.

Data Organization
Managing emissions-related information requires continuity, consistency, and the ability to quickly retrieve the necessary data in the event of inspections or verifications. For this reason, establishing an organized approach to data collection and storage from the very beginning can be highly beneficial.

A Clearer and More Sustainable Approach to Compliance

For many companies, the main challenge is not only understanding the regulation, but also transforming obligations into an operational process that can be managed over time. Organization, activity monitoring, and the definition of shared procedures can help make EU ETS2 management more predictable and less fragmented.

Learning More About EU ETS2

Artificial intelligence for personalized learning represents today a strategic tool for improving the effectiveness of digital learning pathways. Through the automatic analysis of results, the generation of targeted feedback, and personalized recommendations, users can be supported in a more informed, data-driven growth journey.

A smarter, results-oriented learning experience

Over the past months, our team has designed and integrated intelligent components within an advanced learning platform, with the goal of delivering a more personalized, interactive, and results-oriented experience.

Thanks to solutions based on machine learning principles and data analysis, the system is able to:

• automatically analyze the results of users’ learning activities;

• generate clear and personalized feedback;

• suggest targeted content to effectively support skills development.

Turning data into learning value

Artificial intelligence–based solutions for personalized learning make it possible to transform data generated by educational activities into useful and immediately actionable insights. The system interprets user performance, identifies areas for improvement, and recommends tailored content, fostering progressive learning paths that are truly centered on individual needs.

The adopted approach transforms raw data into valuable information, improving the effectiveness of learning pathways and enabling users to access intelligent recommendations built around real individual needs. This data-driven approach enhances the quality of the learning experience, reduces the gap between assessment and corrective action, and supports continuous skills development.

The technology ecosystem behind intelligent features

At the core of these capabilities lies an ecosystem of advanced artificial intelligence techniques and tools, including large language models (LLMs), semantic search and embedding systems, data augmentation strategies, and retrieval mechanisms based on RAG and agentic RAG.

These components make it possible to enrich responses with reliable contextual information (web grounding), improve the quality of recommendations, and effectively support skill growth and upskilling processes, also through the integration of cloud-based AI models and dedicated AI tooling.

This innovation represents a further step toward a data-driven learning experience, capable of dynamically accompanying users throughout their learning journey.

Learn more about artificial intelligence and digital transformation in education on the European Commission’s Digital Education Action Plan.

Stay tuned!

The SafeForPorts project, to which InformAmuse contributed actively together with Tenova e Prosoft Intesys S.r.l., was successfully completed in August 2025. Considering the value of the results achieved, an extension was granted until December 2025 with the launch of SafeForPorts+, which consolidates and enhances what has been developed so far.

Among the main achievements of InformAmuse:

• development and consolidation of the on-site software architecture;

• implementation of the AI Module for the automatic recognition of anomalies and wear, deployable on edge to enable real-time predictive maintenance.

MV Maintenance also contributed actively to the project: thanks to its expertise and support during on-site trials, it made it possible to test the system under real operating conditions, thus facilitating the validation of the developed solutions.

The project, carried out within the RAISE (Robotics and AI for Socio-economic Empowerment) initiative supported by Italy’s PNRR and the European Union – NextGenerationEU, integrated VR simulation for the training of ship unloader operators and on-field systems based on wearable devices and computer vision, supporting predictive maintenance and hands-free operational workflows.

With SafeForPorts+, the work carried out is further validated and extended. The main innovations include:

• an enhanced VR training environment, which introduces kinematic variables such as hold depth and trolley travel, while also simulating new types of ship unloaders;

• an advanced on-field support system, integrating thermographic data into the wearable device, expanding diagnostic capabilities and paving the way for multimodal models based on both visible and infrared imaging.

Stay tuned!

In recent years, artificial intelligence (AI) has transformed many sectors, and environmental monitoring is no exception. AI technologies, along with advanced solutions like OpenAI, are revolutionizing data management and document verification, increasing the efficiency and accuracy of environmental control processes.

The Importance of Document Verification in Environmental Monitoring

In environmental monitoring, data collection and analysis are crucial for assessing the impact of human activities on the environment. Governments, companies, and organizations gather vast amounts of data from sensors, satellites, and field studies, often accompanied by technical reports and regulatory documents. Document verification ensures that this information is accurate and complies with current regulations—a complex task that artificial intelligence can greatly simplify.

How Artificial Intelligence is Transforming the Process

With OpenAI and other AI solutions, a significant part of the document verification process can be automated. AI quickly analyzes large volumes of documents and data, identifying potential errors or anomalies that might go unnoticed by the human eye.

1. Automated Data Extraction

A key aspect of document verification is the extraction of relevant data. Through machine learning, AI can be trained to recognize and collect key information from various types of documents, such as technical reports or environmental regulations. For example, in water quality monitoring, AI can automatically extract pollution levels and compare them with regulatory limits.

2. Compliance Verification

With AI, it’s possible to automatically compare collected data against national and international regulations. An AI-based system can analyze thousands of pages of regulations, flagging potential non-compliance issues in a timely and efficient manner.

Benefits of AI in Document Verification

Artificial intelligence reduces human error, speeds up manual processes, and optimizes business resources. AI solutions ensure data always comply with regulations, providing greater operational efficiency.

Integrating AI into environmental monitoring processes represents a step toward sustainability, helping companies make more informed decisions and reduce their environmental impact.

Revolutionizing Water Resource Management with IoT Technologies

In the quest for sustainable resource management, the integration of Internet of Things (IoT) technologies has emerged as a game-changer, particularly in the realm of water resource management. With water scarcity becoming an increasingly pressing global issue, the ability to monitor, analyze, and optimize water usage has never been more critical. IoT solutions offer precisely the tools needed to address these challenges effectively.

1 Real-time Monitoring IoT in Water Resource Management

IoT sensors can be deployed across various water infrastructure components, including pipelines, reservoirs, and treatment plants, enabling real-time monitoring of water quality, flow rates, pressure levels, and leak detection. This continuous data collection empowers water managers to promptly identify anomalies, potential leaks, or contamination events, thus facilitating swift response measures.

2 Data Analytics

The vast amounts of data generated by IoT sensors can be analyzed using advanced analytics techniques such as machine learning and predictive modeling. By harnessing this data, water resource managers can gain valuable insights into consumption patterns, demand fluctuations, and infrastructure performance. These insights enable informed decision-making, allowing for proactive maintenance, resource allocation, and demand forecasting.

3 Optimization and Efficiency

IoT-enabled smart water systems facilitate optimization and efficiency improvements across the entire water supply chain. By dynamically adjusting water distribution based on real-time demand and supply conditions, these systems can minimize losses, reduce energy consumption, and optimize resource utilization. Additionally, IoT-driven automation can streamline operational processes, leading to cost savings and enhanced reliability.

4 Remote Management and Control

IoT platforms enable remote monitoring and control of water infrastructure, providing flexibility and scalability for managing distributed systems. Whether it's adjusting valve settings, regulating pump operations, or implementing demand-based scheduling, remote management capabilities empower water utilities to efficiently manage assets and respond promptly to changing conditions, regardless of geographic location.

5 Environmental Sustainability

By promoting efficient water usage and reducing wastage, IoT technologies play a crucial role in promoting environmental sustainability. By conserving precious water resources and minimizing energy consumption in water treatment and distribution processes, IoT-driven solutions contribute to mitigating the environmental impact of water management practices.

6 Stakeholder Engagement

IoT platforms can also enhance stakeholder engagement by providing transparency and real-time access to water usage data. By empowering consumers with information about their water consumption patterns and associated costs, IoT technologies promote awareness and encourage responsible water usage behaviors among individuals and businesses alike.

In conclusion, the integration of IoT technologies holds immense potential to revolutionize water resource management practices. By providing real-time insights, optimizing operations, and promoting sustainability, IoT-driven solutions offer a holistic approach to addressing the challenges of water scarcity and ensuring the efficient utilization of this vital resource. As we navigate the complexities of water management in an increasingly water-stressed world, embracing IoT innovations will be key to building resilient and sustainable water systems for future generations.

We are pleased to collaborate with Tenova  and Prosoft Intesys S.r.l.  to the #SafeforPorts project, recently awarded in response to a CFP from Istituto Italiano di Tecnologia and RAISE Liguria.

SafeForPorts aims to revolutionize the maritime industry with cutting-edge Industry 4.0 technologies to enhance safety and efficiency in port operations, prioritizing the safety of machine operators during training, operation, and maintenance phases.

Through #VR simulators and Computer Vision algorithms, we’re creating a comprehensive solution for remote training and on-field assistance for bucket ship unloaders – rail-mounted mobile units typically installed on port docks.

Informamuse will contribute in the creation of voice-controlled applications dedicated to mobile devices, in the implementation of personalized video communication systems based on WebRTC and in the management of solutions that exploit AI techniques.

Specifically in the field of port innovation we will deal with:

• customize a remote video assistance system that will allow the operator to contact, through a hands-free system, an expert operator to achieve remote support during the movement of the machine
• develop a software solution to allow execution of handling and maintenance workflows on hands-free devices
• define and implement algorithms based on Computer Vision and Artificial Intelligence techniques for automatic maintenance procedure

Special thanks will go to MV Maintenance for their valuable support and availability during the on-site testing activities, which will significantly contribute to the success of the project.

In the ever-evolving landscape of technology, businesses are constantly faced with the challenge of adapting legacy systems to meet the demands of the digital age. One such challenge is the migration of COBOL (Common Business-Oriented Language) applications, which have been the backbone of many critical business operations for decades. As newer technologies emerge and the need for agility and scalability increases, organizations are exploring strategies to modernize their COBOL-based systems.

COBOL, with its robustness and reliability, has been the cornerstone of numerous industries, including banking, finance, and government sectors. However, the language's age and inherent limitations in integration with modern technologies have made COBOL migration a pressing concern for many enterprises.

So, what does COBOL migration entail, and how can businesses navigate this complex process?

1. Assessment and Planning: The first step in any migration journey is a comprehensive assessment of the existing COBOL applications and their dependencies. This involves analyzing the codebase, identifying obsolete functionalities, and assessing the compatibility with modern platforms and architectures. A detailed migration plan should be developed, outlining the goals, timelines, and resources required for the transition.
2. Choosing the Right Approach: There are several approaches to COBOL migration, each with its pros and cons. Organizations can opt for a full rewrite of the application using modern programming languages, rehosting on a cloud platform, or encapsulating COBOL code within a wrapper to enable integration with newer systems. The choice of approach depends on factors such as budget constraints, time frame, and the desired level of disruption to business operations.
3. Ensuring Data Integrity: Data is at the heart of any business operation, and ensuring its integrity during the migration process is paramount. Organizations must implement robust data migration strategies, including data cleansing, transformation, and validation, to prevent loss or corruption of critical information.
4. Testing and Quality Assurance: Thorough testing is essential at every stage of the migration process to identify and rectify any issues or discrepancies. Automated testing tools can streamline the testing process, ensuring the reliability and functionality of the migrated application before deployment.
5. Training and Support: Transitioning to a new system requires adequate training and support for the end-users. Employees should be provided with training sessions to familiarize themselves with the new interface and functionalities. Additionally, ongoing technical support should be available to address any issues or concerns that may arise post-migration.
6. Continuous Improvement: COBOL migration is not a one-time endeavor but an ongoing process of adaptation and refinement. Organizations should continuously monitor the performance of the migrated application, gather feedback from users, and implement updates and enhancements to optimize efficiency and usability.

In conclusion, COBOL migration represents a significant undertaking for organizations reliant on legacy systems. By following a systematic approach, leveraging the right tools and expertise, and prioritizing data integrity and user satisfaction, businesses can successfully navigate the journey towards modernization and ensure the longevity and competitiveness of their IT infrastructure in the digital era.