20 posts tagged with "IoT"

Industrial IoT (IIoT) connects machines, sensors and management systems in a continuous, real-time data flow that companies use to monitor performance, detect imminent failures and reduce downtime. Mative Cloud is Mative's IIoT platform: independent of hardware, it collects and stores device data in the cloud, runs remote commands and over-the-air firmware updates, creates intelligent alerts and integrates PLC plants through protocols such as MQTT, Modbus, OPC UA and CANopen.

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.

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.

Keyless systems let users enter buildings, vehicles and other assets without physical keys, authenticating them with technologies such as biometrics, RFID and mobile apps. This article looks at three examples: Kisi, cloud-based access control for offices; Openpath, hands-free access with mobile credentials and Bluetooth; and August Smart Lock, keyless entry for homes.

MQTT is a lightweight, binary, data-centric publish/subscribe protocol: unlike HTTP, AMQP or REST, it pushes messages over persistent TCP connections with minimal packet overhead. This article compares MQTT with those protocols and explains its advanced features, from Clean Start, Keep Alive and Last Will to QoS levels, retained messages, shared subscriptions and topic aliases.

MQTT is a lightweight publish/subscribe messaging protocol for the Internet of Things: clients publish messages on topics, and a central broker filters them and pushes them to the clients subscribed to those topics. It needs minimal bandwidth, copes with unreliable networks and is easy to implement, which makes it a leading choice for machine-to-machine (M2M) communication between constrained devices and server applications.