IoT (the Internet of Things) refers to the automated intelligent control and command of connected devices over vast regions via sensors and other computing capabilities. At its core, IoT is a fairly simple concept to grasp. It’s all about making out products smarter. IoT is on its path to becoming one the biggest technological revolutions the world has ever seen. By 2020, the amount of revenue generated by IoT technology is expected to be in the figures north of $300 billion, and this is just a tip of the iceberg. One of the most critical components of the IoT process is data. For connected devices to perform commands, data has to be sent to a centralized location ” say gateway or the cloud ” where it’s processed and sent back to the sensors of these devices.
It is, therefore, imperative to have an efficient way of collecting small amounts of data and transmitting this data to the centralized location for processing, and sending it back to the sensors ” all in real-time. Taking into account the type, the enormous explosion in numbers and capabilities of these devices and sensors, the size of the data that needs processing can be extremely large (volume), fast (velocity), and diverse (variety) ” all characteristics associated with big data. The role of big data on the Internet of Things is tremendous, but the most visible applications will be in analytics, data security, and data storage fronts.
Big Data Analytics
IoT devices generate continuous streams of data in a scalable manner. Users must be able to handle this data and make it actionable. The actions performed on these high volumes of stream data may include analytics, statistics operation, metric calculation, or event correlation. The actions may vary depending on the big data scenario, and the data may not always be stream data. Put these factors into consideration before you build an analytic solution for your IoT data.
Proper IoT data analytics should be growth, right-size infrastructure, and performance focussed. A hybrid approach is best for performance and future growth. A hybrid deployment consists of dedicated hosting, colocation, managed hosting, and the cloud. It provides a single optimal environment by combining the best features from multiple platforms. For performance, the best option would be a single customer dedicated server. When setting up proper analytics infrastructure for IoT data, companies should remember that not all IoT data is important.
Data Security
IoT data generation carries inherent data security risks. Data generated and the type of IoT devices used to generate are naturally different. The diversity of the IoT world presents new challenges to security professionals. Companies should be extra careful during website data extraction or web scraping ” the process of copying specific data from a website using dedicated software for later use ” as an attack threatens more than just the data, the connected devices will also be at risk.
Data Storage
For IoT to work well, companies must equip themselves with functional data centres to handle the big loads of heterogeneous data streaming in. Picture massive, continuous loads of data coming in every second and you won’t be able to perform any actions on it if you can’t store it. Instead of keeping their own storage infrastructures, organizations are moving towards cloud-based Platform as a Service (PaaS) models as they offer the necessary architecture, compliance, flexibility, and scalability. IoT data can be stored in public, private, or hybrid clouds.
The Internet of Things heralds a technological revolution in the making. Organizations wishing to take advantage of this technology must make the necessary adjustments to accommodate these changes ” data sources and various types of data ” which is just a tip of the iceberg. There will be more problems to solve as businesses grow with IoT.
