AI & Data Strategy
We offer companies new market opportunities through the use of cutting-edge technologies and AI-based data analysis processes.
The United Nations predicts that by 2050, two-thirds of the world's population will reside in urban areas. In Europe, this trend is even more pronounced, with 75 percent of citizens already living in cities, according to Eurostat. This rapid urbanization puts a strain on existing infrastructure and management systems, highlighting the need for innovative solutions. The Smart Cities emerge as a response to these challenges, aiming to transform urban areas into centers of sustainability and efficiency through advanced technologies, particularly the Internet of Things (IoT).
Cities, although they occupy only 2-3% of the earth's surface, are responsible for 70% of carbon dioxide emissions and significant energy consumption. This high concentration of people and activities makes them a major contributor to climate change. Therefore, the challenge is to effectively integrate people, infrastructure and technologies to minimize environmental impact and improve the quality of urban life.
The Internet of Things (IoT) represents a strategic and promising solution to address complex urban issues. With its ability to automate data collection and analysis, IoT facilitates timely and informed decisions, substantially improving various aspects of urban life.
IoT sensors play a crucial role in continuous environmental monitoring. They collect real-time data on air quality, tracking and measuring all substances that may be harmful to humans. Similarly, sensors installed in water networks monitor water quality, detecting contamination and pH changes in real time, thus ensuring access to safe water resources. In urban settings, noise pollution sensors help identify areas where noise exceeds recommended limits, enabling administrators to take action, such as creating traffic-restricted zones. Another significant example is the use of sensors in garbage collectors to monitor their filling, thus optimizing collection routes and reducing emissions from service vehicles.
Urban traffic management benefits greatly from IoT technologies. Sensors and cameras installed along roads collect data on traffic flow, congestion and accidents, facilitating dynamic traffic light management to optimize travel times. IoT applications allow drivers to view real-time maps of available parking spaces, helping reduce the time spent looking for a spot. In addition, the integration of IoT sensors in public transportation gives operators the ability to monitor the status of vehicles and intervene promptly in case of anomalies or delays, thus ensuring a more reliable and efficient service.
Urban lighting is another area that benefits from IoT adoption. Light and motion sensors can adjust the intensity of light in streets based on the actual presence of people or vehicles, maximizing energy efficiency and reducing light pollution. This not only saves energy, but also improves visual comfort and safety at night.
IoT contributes significantly to improving urban safety through integrated video surveillance and advanced sensor systems. These systems enable faster and more effective detection and response to emergency situations. For example, smart cameras can analyze real-time video streams to recognize suspicious behavior or dangerous situations, such as sudden gatherings or traffic accidents, automatically triggering appropriate emergency responses.
Theadvanced monitoring of water infrastructure through IoT is a crucial breakthrough for urban sustainability. Specific sensors installed in water networks not only identify contamination and ensure safe drinking water, but are also critical in detecting leaks along pipelines. This is vital in a global context where water resources are becoming increasingly scarce and precious. By quickly identifying leaks, prompt action can be taken to repair them, reducing significant water wastage and minimizing environmental impact. Such efficient management not only conserves a critical resource but also reduces the cities' operational costs and carbon footprint.
The use of IoT sensors to monitor urban infrastructure provides an additional layer of safety that is essential for disaster prevention. Sensors installed on bridges, viaducts, tunnels, buildings, and aqueducts can detect changes and anomalies that could forewarn of structural failure. This real-time monitoring enables the application of preventive and predictive maintenance strategies, which are significantly cheaper than post-damage interventions. Predictive maintenance not only lowers operating costs but also drastically reduces public safety risks, ensuring the longevity and reliability of vital infrastructure.
The adoption of IoT in Smart Cities represents more than a technological advance: it is a strategic imperative for urban sustainability and raising the quality of life. Successful implementation of these technologies requires a holistic vision that transcends sectoral boundaries and is based on synergistic collaboration between public agencies, private businesses and local communities. Such collaboration is essential to building resilient infrastructure, promoting sustainable resource management, and ensuring a better quality of life for all citizens.
In this context, the integration of Artificial Intelligence (AI) with the IoT is proving crucial. AI amplifies the potential of the IoT through advanced algorithms that can analyze large volumes of data collected from sensors in real time. This continuous learning and improvement capability allows not only proactive optimization of urban operations, but also predicts trends and behaviors, significantly improving urban planning and response to unexpected events. For example, AI can predict energy or water demand spikes and automatically adjust resources to maximize efficiency and reduce waste. Similarly, embedded AI systems can improve public safety by analyzing video streams to recognize suspicious behavior or emergencies in real time, directing resources where they are most needed.
Adoption of this advanced technology also presents significant challenges, including the need to ensure citizen privacy and data security in an increasingly digital age. Addressing these issues with appropriate policies and regulations will be critical to maintaining public trust and promoting widespread adoption of IoT and AI technologies.
If you would like more information on the integration of Artificial Intelligence and the Internet of Things within city management, please contact us using the form at the bottom of this page.
By 2050, about 75% of the world's population will residein cities, leading to a significant increase in urban density: this transition brings with it crucial challenges in terms of urban management and urban decay. Artificial Intelligence (AI) could play a crucial role in monitoring and improving the quality of urban life.
Often perceived as an abstraction relegated to research labs or sensationalist headlines, artificial intelligence (AI) is actually an increasingly tangible and influential presence in the daily fabric of our lives. Let's talk not only about technology, but how it shapes our cities and neighborhoods, concretely improving the quality of urban life day after day. Take, for example, the problem of urban decay-a complex challenge that requires accurate data collection and analysis to be effectively managed. AI gives us advanced tools to measure, monitor and respond to the needs of the urban environment, demonstrating that its application can be as practical as it is revolutionary.
With the expected increase in urban population, problems related to urban decay will be inevitable: cities will become more densely populated and the complexity of management and social problems will inevitably grow. Density can exacerbate problems such as congestion, infrastructure maintenance, and access to essential services, making effective management essential.
A recent study conducted by the University of Notre Dame and Stanford University illustrated how AI can be used to address these challenges. The researchers developed a machine learning-based method to map urban decay in three model cities-San Francisco, Mexico City and South Bend, Indiana.
Using the YOLOv5 AI model, the researchers analyzed thousands of images from Google Street View to identify visible signs of urban decay: potholes, graffiti, trash, curtains, broken bars or windows, discolored or dilapidated facades, and weeds. This approach allows detailed mapping and monitoring of changes over time, providing a valuable database for urban planning.
In analyzing images collected from the same urban contexts, AI technology demonstrates its ability to identify the incidence of urban decay with spatial and temporal accuracy. This detailed analysis makes it possible to recognize specific factors of degradation in different neighborhoods, thus providing urban planners and policy makers with the tools they need to intervene effectively. The elements identified by AI can then be prioritized in maintenance and improvement activities, with the goal of elevating the quality of urban life. Through this information, targeted strategies can be developed to transform degraded urban spaces into livable and welcoming environments, contributing significantly to the well-being of citizens.
Accuracy
Artificial Intelligence technology enables the identification and tracking of urban decay with high accuracy. Using advanced analytics on continuous images over time, AI is able to detect both small variations and long-term degradation trends, ensuring detailed and reliable monitoring of urban conditions.
Proactivity
The ability to predict and identify signs of degradation before they become major problems is one of the main advantages of AI. This proactivity allows administrations to act early, preventing the escalation of degradation and keeping the quality of the urban environment high.
Data-driven planning
The use of real, up-to-date data greatly improves the effectiveness of urban planning. Decisions based on sound data analysis enable optimization of resource allocation and implementation of public policies aimed at solving the most pressing problems.
Scalability
AI methods can be applied on a large scale, covering entire cities or even regions, without requiring a commensurate increase in human and financial resources. This makes AI an extremely efficient tool for urban land monitoring and management, adaptable to different contexts and city sizes.
Despite the obvious benefits, the use of Artificial Intelligence in urban management also raises some significant concerns. Privacy is a major ethical issue: the continuous collection of large volumes of data through cameras and sensors raises questions about how this data is used and who can access it. In addition, the reliability of collected data is crucial; errors or bias in the data can lead to incorrect decisions that could negatively affect entire communities.
However, the implementation of AI in city management promises to revolutionize the way we deal with urban problems. As technology evolves, our ability to manage urban challenges will also grow, allowing us to create more livable and sustainable urban environments. Research from Notre Dame and Stanford is a promising example of how AI can be used to significantly improve the quality of the urban environment and, consequently, the well-being of its inhabitants.
With studies like this, published in journals such as Scientific Reports, we are shaping a future in which technology and innovation drive the continuous improvement of our cities.
If you would like more information about the potential and applications of Artificial Intelligence in the context of city management and the urban challenges of the future, please contact us using the form at the bottom of this page.
Ongoing socio-economic changes impose innovative and bold mobility choices in daily routines from which significant improvements can result from an environmental perspective as well as in terms of safety and traffic reduction.
The transport sector is responsible for 25 percent of greenhouse gas emissions in Europe, the European Environment Agency's "Transport and Environment Report" found. In first place, as can be easily guessed, is road traffic, followed by maritime traffic and then air traffic. In contrast, rail transport is the most sustainable by far, producing only 0.4 percent of emissions.
The FS Group's Sustainability Report states that "overall, the benefit of sustainable mobility through the use of the FS Group's collective means of transport has been estimated for both rail and road passenger transport and rail freight transport at about 4.8 million tons of CO2e saved." Suffice it to say that a traveler to go from Rome and Milan produces 25Kg of CO2 by traveling by train, 67.5Kg of CO2 by car, and 117.3Kg of CO2 by taking the airplane."All in all, the benefit of sustainable mobility through the use of the FS Group's collective means of transportation has been estimated for both rail and road passenger transport and rail freight transport at about 4.8 million tons of CO2e saved."
This is why the issue of sustainability is at the heart of the FS Group's latest 2022-31 Business Plan, which on the one hand encourages the so-called modal shift, i.e., a change in the travel habits of people and goods, and on the other aims to achieve carbon neutrality by 2040, 10 years ahead of the target set by the EU.
Rail transportation also has to deal with the open issue of emissions from generating the electricity needed to move trains and operate stations and the rail network, which is why the State Railways has launched a more than 1.6 billion euro self-generation plan.
The goal is to produce about 2.6 TWh of energy, which would guarantee to reduce CO2 emissions by about 800,000 tons. A decisive move for the decarbonization of transport: renewable and clean energy will come from real photovoltaic fields placed on the roofs of stations and other buildings of the FS Group, and for this, a widespread monitoring of the entire real estate will be initiated to identify areas and buildings to be used for this purpose.
An ambitious project with enormous potential, but it is not the only one.
We also recall Sustainable Construction Sites, which involves the high-speed line to be built between Naples and Bari. This Infrastructure is the first work certified with the Envision Protocol in Europe and has achieved the Platinum level, the highest level achievable. Special attention has been paid to the management of excavated materials, which provides for a reuse of more than 96 percent of the excavated land, in full circular economy view. At the Florence Passante construction sites, these excavated materials are transported precisely by train, with significant savings in terms of pollution and road traffic congestion.
Modern mobility is not just mobility. It also requires very advanced integration with the issues of connectivity and digitization, issues on which the FS Group is far ahead.
It is precisely digitization that plays a crucial role in making effective predictive maintenance, which in the transportation sector is undermining "reactive" maintenance that creates huge costs in terms of time and inconvenience. Following reactive maintenance, a certain component is changed when it breaks down, resulting in failure, delay and inconvenience to passengers, expenses that cannot be budgeted for and are often incurred by having to take emergency action. Not to mention the danger and possible consequences to people.
What is predictive maintenance based on instead? On the collection, analysis, and processing of data-everything is monitored, from the network to the support facilities to the rolling stock, which thus becomes much safer. The FS group has deployed its two main entities: Trenitalia, which is responsible for fleet management and efficiency, and RFI which is responsible for managing the rail network. With this paradigm shift comes a new methodology that aims to prevent breakdowns and improve the efficiency of maintenance activities, precisely through data collection and analysis.
Wear, temperatures and vibrations are among the most important aspects to monitor, since if these parameters are out of control the life cycle of the components involved is greatly shortened. Of course, the fundamental acquisition of data with thousands of sensors on the vehicles and structures is then accompanied by their analysis and processing, activities on which personnel must also be constantly updated.
An example of this approach is Trenitalia's DMMS (Dynamic Maintenance Management System) that has been in operation for a few years and allows real-time monitoring of the entire fleet of regional, Intercity and Frecce trains: each train sends 5,000 pieces of information per minute.
A very detailed level of continuous analysis that no human being could carry out as effectively.
The Industrial IoT (IIoT) represents the application of the Internet of Things (IoT) in the industrial domain. Researcher Kevin Ashton has defined IoT as the set of technologies that enable the control, monitoring, and transfer of information by connecting devices to the Internet.
The IIoT is a verticalization of the IoT, focused on the industrial ecosystem and enabled by technologies such as cybersecurity, cloud computing, edge computing, big data analytics, artificial intelligence and machine learning.
According to a report by Industry ARC, the Industrial IoT market will exceed $771 billion by 2026, with an estimated compound annual growth rate of 24.3 percent. Real-time data enables better management of the production process and a clear view of business performance.
An IIoT system consists of four levels:
Device layer: hardware, machines and physical sensors.
Network layer: communication protocols, cloud computing and WiFi networks for data transfer.
Service layer: applications and software for analyzing and transforming data into viewable information.
Content layer: user interface devices.
IIoT enables the monitoring and predictive maintenance of strategic infrastructure, using AI and machine learning to predict risks and suggest preventive measures. For example, in the infrastructure sector, IoT sensors and predictive algorithms enable continuous monitoring of tunnels, bridges, buildings and sewer systems, reducing energy and maintenance costs.
Cybersecurity is a challenge for IoT devices, as current measures are inferior to traditional systems. In addition, the lack of standardization in industrial communication protocols complicates the interconnection between machines with varied hardware.
Frontiere's Team specializing in IIOT can support you in the remote management and control of complex infrastructure systems. Contact us and discover the ideal solution for your company's needs.
The IoT has given rise to a concept known as 'Industry 4.0', in which industrial production is made more efficient and intelligent through the connection of devices and the collection of real-time data.
Some examples of the application of IoT in the manufacturing industry include:
Through the IoT, machines and production facilities can communicate with each other and with management systems, enabling the automation of production processes. Sensors collect data on production, quality and resource utilisation, providing information to optimise operations and reduce waste.
The IoT has a significant impact on industrial automation, enabling more efficient production, predictive maintenance and improved safety. The integration of connected devices and artificial intelligence systems makes it possible to optimise production processes, reduce downtime and improve product quality.
The integration of the IoT into supply chain management provides greater traceability and visibility into the movement of materials and products throughout the supply chain. IoT devices, such as RFID (Radio-Frequency Identification) tags, enable the automatic tracking and recording of the passage of products, providing real-time information on their location and status. This simplifies inventory management, reduces errors and optimises logistics processes.
In conclusion, the Internet of Things (IoT) has had a significant impact on the manufacturing industry, giving rise to the concept of 'Industry 4.0'. The adoption of IoT has made it possible to connect devices and collect data in real time, leading to more efficient and intelligent industrial production.
Automation and process monitoring have improved through communication between machines and management systems, enabling optimisation of operations and reduction of waste. In addition, IoT has enabled the implementation of predictivemaintenance, allowing companies to predict and prevent machine breakdowns and downtime.
Finally, the integration of IoT into supply chain management has improved the traceability and visibility of products throughout the supply chain, optimising logistics processes and reducing errors.
The IoT has opened up new opportunities for the manufacturing industry, enabling greater operational efficiency and better resource management. It is clear that the IoT will continue to play a key role in the evolution of Industry 4.0 and the innovation of the manufacturing sector.
The adoption of the IoT represents both a challenge and an opportunity for companies, which will have to be prepared to exploit its full potential to remain competitive in the global marketplace.
