Sensor data management, IoT exploration and analysis
Definition of Internet of Things (IoT )
The Internet of Things stands for IoT. Things refer to the objects that we use in our daily life (for example, appliances and electronics). These elements, called the Internet of Things, are accessible or connected via the Internet. A network of physical elements embedded in software, electrical devices, and sensor systems that enable these elements to collect and share data can be referred to as the Internet of Things.
The goal of IoT is to increase internet connectivity from ordinary devices such as computers, cell phones, and electrical gadgets. Due to various technological convergences, real-time analytics, machine training, computing, commodity sensors and embedded systems, things have evolved. The Internet is complemented by the traditional areas of embedded systems, wireless sensor networks, control systems, automation (including home automation and home automation) and others. IoT technology is the consumer technology most synonymous with goods that support one or more common ecosystems under the idea of ’smart home’, incorporating gadgets and devices (e.g. lighting systems, thermostats, security systems, cameras, etc.). In medical systems, IoT can also be used.
The growing risk of IoT is a series of serious issues, especially in the area of privacy and security.
Fig. 1: Description image for the IoT (Source: https://www.tutorialandexample.com/iot-tutorial/)
2. Sensor data management
Data management is the process of collecting and improving all of the accessible data. Different gadgets send huge amounts and types of information from different apps. Managing all of this IoT data requires creating and implementing architectures, rules, practices, and methods that meet all of the data lifecycle requirements.
Things are controlled by smart gadgets for task automation so that our time is saved. Smart objects can gather, transmit and understand information, and a tool to aggregate information and draw conclusions, trends and patterns will be needed.
Fig. 2: Steps in IoT data management (Source: https://iot.electronicsforu.com/content/tech-trends/data-management-systems-iot-devices/)
Thousands of sensors interact with and beyond individuals, homes, cities, farms, factories, workplaces, automobiles. The Internet of Things (IoT) is transforming our lives, from appliances to cars. Devices can tell us what to do, when to do it, and where to go today. In controlling operations and forecasting problems and disasters, industrial IoT applications support us. IoT platforms make it possible to correctly specify and manage data parameters.
3. Exploration and analysis of the IoT
The Internet of Things (IoT) is the extension to physical devices and ordinary things of an Internet link. Integrated with electronics, Internet connectivity and other technologies; these devices communicate, interact with, and can be monitored and controlled remotely via the Internet. The IoT is used in the mining sector to optimize costs and efficiency, improve guarantees and improve their requirements for artificial intelligence.
Fig. 3.1: Below are some examples of IoT in the mining industry (Source: https://www.infosysbpm.com/blogs/sourcing-procurement/iot-in-mining.html)
IoT in the mining industry
Given the various incentives it offers, many large mining companies are planning and evaluating approaches to mining to begin their digital journey and digitize to manage day-to-day mining operations. For example:
- Optimization of costs and increase in productivity through the deployment of sensors and systems for the control and performance of mining equipment. These huge amounts of data – “big data -” are used by mining companies to find more economical methods of operation and also to minimize overall operational inactivity.
- Ensure the safety of people and equipment with real-time IoT monitoring of ventilation and toxicity levels in deep mines. It makes evacuations or safety drills faster and more efficient
- Move from preventive maintenance to predictive maintenance
- Improved and Fast Decision Making The mining industry has great unpredictability in emergency situations almost every hour. In cases where many factors act simultaneously to turn ordinary activities into algorithms, IoT helps compensate for conditions and make appropriate judgments.
4. What is IoT analysis
Internet of Things (IoT) analytics is a data analysis tool for evaluating a wide variety of data from IoT devices. IoT analytics assesses and generates valuable information on huge volumes of data.
At the same time, the analysis of industrial IoT is frequently discussed (IIoT). Data is collected from a wide variety of sensors on production facilities, weather stations, smart metering systems, delivery vehicles and other types of machinery. For the management of retail and healthcare data centers, IoT analytics can be used.
IoT data is similar to big data in many ways. The main difference is not only in the amount of data, but also in the variety of sources from which it is derived. All of this information needs to be turned into a single, understandable data stream. Considering various types of information sources, integrating data becomes very difficult and it can be difficult to design and apply IoT analytics.
Fig.3.2: Description image for IoT analysis (Source: https://www.robomq.io/blog/iot-analytics-what-is-that/)
5. Types of IoT data analysis
- Analysis of the description of IoT data
Focus on how the health of the device, machine, product, and IoT assets is monitored. Determine if everything is on schedule and alert if anything extraordinary happens. Typically, descriptive analytics are used to present current and historical sensor data, key performance indicators (KPIs) as well as statistics and warnings.
- Diagnostic analysis of IoT data
Analyze IoT data to discover and improve a service, product or process for fundamental issues.
Diagnostic capability is typically an addition to dashboards that allows users to uniquely perceive correlations and patterns in data, analyze the data, and compare it. Many organizations, rather than data scientists, diagnose data using domain experts with an understanding of a particular process, device, device, or product.
- Predictive analysis of IoT data
Evaluate the likelihood, based on past evidence, that something will happen within a certain period of time. It is designed to take proactive action, reduce risk or isolate opportunities before an unwanted consequence occurs.
Usually used by learning machines formed with historical data and stored in the cloud so that end user applications are available.
- Descriptive analysis of IoT data
Suggest actions based on prediction or diagnostic results or provide visibility of a diagnostic prediction or explanation. There are suggestions to optimize or fix something
6. IoT data analysis applications
To increase crop production, plan and maintain farm operations, farmers use data obtained through IoT devices. The Climate Corporation uses IoT devices that sense soil quality and moisture, to allow farmers to determine how crops are turned and when irrigated. Farms also use IoT equipment to obtain data from farm tractors and use IoT drones for analytical aerial imagery.
- IoT data analysis in food services
Restaurants and bars are using IoT to check their inventory and develop more efficient management methods. A company named I-TAPR2 Technologies uses a wireless smart faucet, which monitors the flow of beer and helps food service managers identify which products are selling the most, when fresh stocks are ordered, which drinks are marketable and which. are supposed to stop being transported.
- IoT data analysis in logistics management
We have already shown, through our example, how IoT data analysis can affect logistics management. UPS says on its website that it can save more than $ 400 million a year through data analysis. Most of it is on the transport side, but there are even more opportunities to use IoT on the warehouse side of logistics. In order to optimize layout, minimize labor costs, monitor inventory and orders throughout the supply chain, and automate inventory management to eliminate errors, l The warehouse can use sensors and robotics.
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*** This is a Syndicated Security Bloggers Network blog by Blog IoT – Speranza written by Allen. Read the original post on: https://www.speranzainc.com/sensors-data-management-iot-mining-and-analytics/