Founder and CEO of Mative. A mechanical designer in Bologna, then a software developer and cloud architect in healthcare, after twelve years of work with new technologies he designed a cloud-connected OBD reader based on ESP32, the start of the Synapsis project, and founded Mative.
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In Istanbul, ENLIL's vertical wind turbines placed next to the roads turn the breeze produced by passing traffic into clean energy: a single turbine topped with a solar panel can produce enough electricity to power two Turkish households for a day. The turbines also carry IoT sensors that track temperature, humidity, carbon footprint and earthquake activity, and pass the data on to the city's authorities and environmental scientists.
IoT and machine learning help an agricultural company save water, energy and money: soil moisture sensors allow irrigation only when needed, temperature and humidity sensors cut unnecessary heating and ventilation, and ML models support weather forecasting, crop management and production planning. For a medium-sized agricultural company, Mative's forecast analysis after one year points to 30% less water consumption, 25% lower energy costs, 20% more production and 15% savings in operating costs.
IoT and machine learning bring energy and economic savings to a construction company by monitoring machinery consumption, site climate and construction processes in real time, predicting equipment failures and improving the planning of resources, projects and supply chain. For a construction company, Mative's forecast analysis one year after adopting its IoT and ML solutions estimates 20% lower energy consumption, 15% lower operating costs, a 10% improvement in project quality and 10% savings on maintenance costs.
IoT and machine learning bring energy and economic savings to a manufacturing company by monitoring machinery and facilities in real time, enabling predictive maintenance and optimizing production schedules and processes. For a window, door and frame manufacturer, Mative's forecast analysis one year after adopting its IoT and ML solutions estimates 20% lower energy consumption, 15% lower operating costs, 25% higher production efficiency and 10% savings on maintenance costs.
In a public transport company, IoT sensors monitor fuel consumption, vehicle conditions, traffic and routes, and Machine Learning analyzes these data to forecast consumption, optimize routes and schedule maintenance. Mative's forecast for a company adopting its IoT and ML solutions estimates, after one year, 15% less fuel consumption, 20% lower operating costs, 25% better service punctuality and 10% savings on maintenance costs.
The CAN bus is a two-wire network (CAN low and CAN high) that lets every electronic control unit (ECU) of a vehicle or machine communicate with all the others, without complex dedicated wiring. Data travels in CAN frames, each with a CAN ID and a data payload: to use it you connect a CAN logger (in most cars through the OBD2 port) and decode the raw frames into physical values such as km/h or °C.
Predictive algorithms are machine learning methods that estimate values from existing data, and Mative applies four of them to its time series. Linear regression and its OLS form model the relationship between a target variable and its predictors, ARIMA predicts future values from past values and past errors, and the Fourier transform reveals cycles and periodic patterns in a signal.
Warning systems in agriculture monitor environmental conditions, plant diseases, pests and adverse weather, and alert farmers in time to make informed decisions, improve productivity and reduce losses. They are typically built on weather data APIs such as OpenWeatherMap, machine learning models that predict the spread of plant diseases, and Agricultural Decision Support Systems (ADSS) that turn field data into recommendations.
Smart farming uses software, IoT sensors and data analytics to optimize farming practices, improve productivity, reduce costs and mitigate environmental impacts. Software collects and analyzes real-time data from sensors and machinery to manage farm data, monitor crop conditions, optimize the use of water, fertilizers and pesticides, and forecast yields and plant diseases.
Car rental companies use software to handle online booking, fleet management, customer relationships and payments, and to let customers skip the counter. This article shows how Enterprise Rent-A-Car, Hertz and Avis Budget Group apply it, from mobile check-in and predictive fleet optimization to dynamic pricing, self-service kiosks, digital keys and telematics-based predictive maintenance.