Author | Big Data on the Web
Source | raincent_com
As the Internet of Things (IoT) evolves and grows, every conceivable thing (or things) and industry will become smarter: smart homes and smart cities, smart manufacturing machinery, smart cars, smart health, and more. The myriad of things authorized to collect and exchange data are forming a whole new network - the Internet of Things - a network of physical objects that can collect data, transmit it, and perform user tasks in the cloud.
The IoT and big data are on their way to victory. However, there are challenges and issues that need to be addressed in order to benefit from this innovation. In this article, we are pleased to share the knowledge we have gained over the years in the field of IoT consulting.
How IoT Big Data can be applied
First of all, there are multiple ways to benefit from IoT Big Data: in some cases, a quick analysis will suffice, while some valuable insights can only be gained after deep data processing.
Real-time monitoring. Data collected through connected devices can be used for real-time operations: measuring the temperature in your home or office, tracking physical activity (counting steps, monitoring exercise), and so on; real-time monitoring is widely used in healthcare (e.g., to obtain heart rate, measure blood pressure, sugar levels, and so on); it's also successfully used in manufacturing (for controlling production equipment), agriculture (for monitoring cattle and crops), and other industries.
Data analytics. When dealing with the big data generated by the IoT, we have the opportunity to go beyond monitoring and gain valuable insights from this data: recognizing trends, revealing unseen patterns and finding hidden information and correlations.
Process control and optimization. Data from sensors provides additional contextual information to reveal important issues that impact performance and optimize processes.
▲Traffic management: Tracking traffic loads on different days and times of day to develop recommendations for traffic optimization, such as increasing the number of public ****cars at specific times of day to see if there is an improvement, as well as recommending the introduction of new traffic signaling schemes and the construction of new roads to reduce traffic congestion on streets.
▲Retail: Tracking the sales of goods in supermarket shelves and informing staff to replenish goods in time before they are almost sold out.
▲Agriculture: Water crops when necessary based on data from sensors.
Predictive maintenance. Data collected through connected devices can be a reliable source for predicting risks and proactively identifying potentially dangerous conditions, such as:
▲Healthcare: monitoring patient health status and identifying risks (e.g., which patients are at risk for diabetes, heart attacks) so that timely action can be taken.
▲Manufacturing: Predict equipment failures so that they can be addressed in a timely manner before they occur.
It should also be noted that not all IoT solutions require big data (e.g., if a smart home owner wants to turn off the lights with the help of a smartphone, this can be done without big data). It is important to consider reducing the amount of work involved in processing dynamic data and avoid storing large amounts of data that will not be useful in the future.
Big Data Challenges in the IoT
Large amounts of data are completely useless unless they are processed to gain valuable insights. In addition, there are challenges in data collection, processing, and storage.
▲Data reliability. While big data will never be 100% accurate, before analyzing the data, make sure that the sensors are working properly and that the data used for analysis is of reliable quality and not corrupted by factors (e.g., unfavorable environments in which the machine operates, sensor failures).
▲What data to store. Connected devices generate trillions of bytes of data, and choosing what to store and what to delete can be a daunting task. What's more, some data is far from valuable, but you may need it in the future. If you decide to store data for the future, the challenge is to do so at minimal cost.
▲Depth of analysis. Once not all big data is important, another challenge arises: when is a quick analysis enough, and when do you need to go deeper to bring more value.
▲Security. There's no doubt that connected things in all areas can make our lives better, but at the same time, data security has become a very important issue. Cybercriminals can hack into data centers and devices, connect to transportation systems, power plants, factories, and steal personal data from telecom operators. IoT big data is still a relatively new phenomenon for security experts, and the lack of relevant experience increases security risks.
Big Data Processing in IoT Solutions
In IoT systems, the data processing component of the IoT architecture varies depending on the characteristics of the input data, the expected results, and so on. We have developed a number of approaches to handling big data in IoT solutions.
Data comes from sensors connected to things. A "thing" can be any object: an oven, a car, an airplane, a building, an industrial machine, a rehabilitation device, and so on. The data can be periodic or streaming. The latter is critical for real-time data processing and managing things quickly.
Things send data to the gateway for initial data filtering and preprocessing, which reduces the amount of data transmitted to the next IoT system.
Edge analytics. Prior to in-depth data analysis, it is necessary to perform data filtering and preprocessing to select the most relevant data needed for certain tasks. In addition, this phase ensures real-time analysis to quickly identify useful patterns previously discovered through deep analysis in the cloud.
For basic protocol conversion and communication between different data protocols, the cloud gateway is required. It also supports data compression and secure data transfer between the field gateway and the central IoT server.
Data generated by connected devices is stored in its natural format in a data lake. Raw data is "streamed" into the data lake. Data is kept in the data lake until it can be used for business purposes. Cleaned structured data is stored in a data warehouse.
The machine learning module generates models based on previously accumulated historical data. These models are periodically (e.g., once a month) updated with new data streams. Input data is accumulated and applied to train and create new models. Once these models have been tested and approved by experts, the control application can use them to send commands or alerts in response to new sensor data.
Summary
The IoT generates large amounts of data that can be used for real-time monitoring, analytics, process optimization, predictive maintenance, and more. However, it should be remembered that gaining valuable insights from massive amounts of data in various formats is no easy task: you need to make sure that sensors are working properly and that data is securely transmitted and processed efficiently. In addition, there is always the question of what data is worth storing and processing.
Despite some of the challenges and issues, it should be remembered that the IoT is going strong and can help open up new digital opportunities for businesses across multiple industries.