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A sea-faring vessel using a variety of sensors to manage its regular processes and navigation is a smart ship. Uses of this IoT technology range from single vessel to fleet management. However, even management of a single ship involves using massive amounts of data.

A distance learning course refers to online education method where students learn at their own pace but also have the benefit of teacher instruction.

Interest in distance learning has spiked massively over the past year, due to worldwide lockdowns forcing in-person educators to change tactics, so there is more data than ever on this subject. Often, the term is used interchangeably with “e-learning” and “virtual classroom” although there are important differences. E-learning, for example, refers to self-paced study without the instruction of a teacher or set study times. A virtual classroom, on the other hand, has a teacher overseeing students and a set class time.

Crop Disease and Pest Identification helps large-scale and subsistence farmers protect their crops and increase yields and health

Sensor-enabled vehicles use an autonomous navigation system to move through the atmosphere or down roads and terrain without direct, continuous human direction. Vehicles that use autonomous navigation systems include robots, spacecraft, ships, airplanes, and cars.

Naturally, there is a lot of overlap between this use case and remote navigation systems.

Remote employee management refers to the methods of managing remote workers. Whether they are seasoned in-field professionals or office workers newly adjusting to working from home, the approaches may be different but the data collection and analysis will be similar.

In these fraught times, especially, managers must take special care to account for their employees’ mental states.

A predictive maintenance model uses sensors and IoT technology to monitor the condition of equipment and predict when it will need repairs or replacements. This equipment may be anything from personal cars to large factory machines.

The machine learning programs used to run these models tend to take either the regression approach or the classification approach. Regression produces more accurate results but relies on more data; classification best predicts equipment failure within a set time period.

Smart building technology arms a building’s component systems with the ability to communicate with each other and with management. Security systems, plumbing, electricity, waste management, temperature control, and more work exchange data constantly in order to improve the comfort and safety of people living or working inside the buildings.

Smart building technology works for both residential and commercial buildings.

The process of economic forecasting is at heart the process of attempting to predict the future of an economy. Generally this refers to the economy of a country, but it may also refer to municipalities within a state.

A power grid resilience system monitors the status of a power grid and the threats to its function.

Traffic management, as part of a smart city model, enables private individuals and municipal officials to monitor and manage the flow of traffic.

A smart city is a city whose infrastructure and services have the ability to collect and share data. Smart cities data, therefore, refers to the data that make up or are generated by smart cities.

Fleet management uses IoT and other technology to monitor the movement and function of a fleet of vehicles. These vehicles may be trucks, cars, or ships.

V2X Technology uses wireless devices in vehicles and other objects to integrate internal and external data in real time via machine learning programs to improve road safety

A store’s location directly impacts supply, profits, marketing, and almost every other aspect of store performance. Much can be learned from the performance of different types of retail stores in different neighborhoods, countries, or even climate areas. Therefore, retailers use geospatial data machine learning models to plan store locations and predict profits.

Nowadays, the abundance of data and the advances in Machine Learning and big data applications reduce the need for top-down segmentation of customers. Smart customer clustering based on many commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.