IOT

Internal sensors collect data from IoT consumer devices, such as security systems, smart appliances, smart TVs, and wearable health meters. Data are collected from commercial devices, as well, including commercial security systems, traffic monitoring devices, and weather tracking systems. The data are transmitted, saved, and can be retrieved at any time.

IoT analytics is performed by applying data analysis tools or procedures to the various types of data IoT devices generate. Using IoT analytics, valuable information can be extracted from massive data collections that can then be used to improve on procedures, applications, business processes, and production. Several types of data analytics can be used on IoT data:

Prescriptive analytics. Prescriptive analytics is used to analyze which steps to take for a specific situation. It’s often described as being a combination of descriptive and predictive analysis. When used in commercial applications, prescriptive analytics helps decipher large amounts of information to obtain more precise conclusions.

Spatial analytics. This method is used to analyze location-based IoT data and applications. Spatial analytics deciphers various geographic patterns, determining any type of spatial relationship between various physical objects. Parking applications, smart cars, and crop planning are all examples of applications that benefit from spatial analytics.

Streaming analytics. Streaming analytics, sometimes referred to as event stream processing, facilitates the analysis of massive “in-motion” data sets. These real-time data streams can be analyzed to detect emergency or urgent situations, facilitating an immediate response. The types of IoT data that benefit from streaming analytics include those used in traffic analysis, air trafficking, and the tracking of financial transactions.

Environmental monitoring is a broad application
for the Internet of Things. It involves everything from monitoring levels of ozone in a meat packing facility to monitoring national forests for smoke. Using IoT environment sensors for these various applications can take an otherwise highly labor-intensive process and make it simple and efficient.

8 IoT Environment Monitoring Use Cases

1. Monitoring air for quality, carbon dioxide and smog-like gasses, carbon monoxide in confined areas, and indoor ozone levels.
2. Monitoring water for quality, pollutants, thermal contaminants, chemical leakages, the presence of lead, and flood water levels.
3. Monitoring soil for moisture and vibration levels in order to detect and prevent landslides.
4. Monitoring forests and protected land for forest fires.
5. Monitoring for natural disasters like earthquake and tsunami warnings.
6. Monitoring fisheries for both animal health and poaching.
7. Monitoring snowfall levels at ski resorts and in national forests for weather tracking and avalanche prevention.
8. Monitoring data centers
   for air temperature and humidity.

An IoT system consists of sensors/devices which “talk” to the cloud through some kind of connectivity. Once the data gets to the cloud, software processes it and then might decide to perform an action, such as sending an alert or automatically adjusting the sensors/devices without the need for the user.

The IoT interface is a middleware which enables devices/systems to communicate with one another. The data can then be transferred on to higher-level systems. The IoT interface is only supported by the Blue e+ cooling units from Firmware 1.11. 0.