Introducing AI to IoT Data Analytics
Introducing AI to IoT Data Analytics
The availability of high-speed internet connectivity has transformed the way we interact with and benefit from technology. The ability to send and receive vast amounts of data quickly and reliably supports the expanding Internet of Things (IoT).
All devices are gathering data — non-stop.
Google Home, Alexa, Furbo, and Ring are just a few of the players that make up the realm of Internet-enabled devices. These home-based brands, smartphones, sensors, and wearable devices — among many others — all gather valuable data analytics that acts as the foundation for future decision-making.
However, to extrapolate these insights, we must first analyze data.
While legacy systems can adequately handle this task, new solutions are necessary to support the IoT’s rapid expansion. Estimates suggest there will be 1.3 billion IoT device subscriptions by 2023 and 35 billion IoT devices installed worldwide by 2021.
The arrival of 5G cellular networks will further amplify the data generated by this growing technology pool. But what’s the most appropriate solution for handling all of this information?
Data Analytics and Artificial Intelligence
For many, the answer is artificial intelligence (AI) — a term now synonymous with the concept of machines carrying out tasks in a way humans deem intelligent. Machine Learning (ML), a subset of AI, can generate even more value as machines learn for themselves instead of relying on a preprogrammed algorithm.
Given the vast pools of IoT data, leveraging the power of ML is now a real possibility.
Forecasts suggest the world’s data will amount to around 44 zettabytes by 2020, with 10% coming from IoT. This database provides an ample supply of reference material.
Data analysis is sped up dramatically through ML or AI algorithms, which benefits all of those looking to extrapolate insights — consumers, businesses, and governments. The resulting Artificial Intelligence of Things (AIoT) accelerates decision-making and bolsters valuable information exchange.
However, there are specific ways in which the merger of these technologies delivers such results.
How AI Handles Data
Conventional data analysis facilitates IoT deployment, but AI can do it faster and with greater accuracy. More specifically, AI can structure a data set, improve IoT device interoperability, and draw conclusions in real-time.
Unstructured Data: The IoT ecosystem is diverse, which means the format of data is too. In contrast to many existing data analysis techniques, AI algorithms can save valuable time by aggregating unstructured data from multiple sources, processing it, and representing it in a cohesive format. Making this process less cumbersome offers an immediate benefit and allows stakeholders to take action faster.
Metadata: Metadata is data about data and enables IoT devices to communicate with one another. For instance, metadata might include the model number of one device, which tells another which communication protocol to use and organizes the resulting data. Here, AI might also contribute to the organization of data analytics while streamlining interoperability through its learnings.
Transformed Data: After AI processes unstructured data, systems can draw further insights. While traditional data analysis achieves the same outcome, AI or ML hold the potential to deliver this information dynamically and with greater context and even in real-time. This functionality expands the potential applications of IoT.
The Current AIoT Ecosystem
Today, several examples of companies are entering the AIoT space — an industry that’s estimated to reach a value of $ 5.7 billion globally by 2025. In a recent development, the Honeywell Connected Life Safety Services (CLSS) was launched as a commercial fire safety solution. The cloud platform transforms the way fire systems are designed, commissioned, monitored, and maintained.
The system’s IoT components generate constant feedback that AI processes to provide actionable insights and informed recommendations.
Honeywell defines this category as enterprise performance management (EPM) and has recently entered a partnership with Microsoft to bolster its efforts. Microsoft has also put together an independent team that explores the integration of IoT and AI to provide greater visibility and better control of internet-enabled devices and sensors.
Integrations of the Future
Although traditional IoT solutions continue to generate immense value, the next iteration of this technology expands on system monitoring and data collection.
Through the integration of AI and IoT, real-time data synthesizing is possible. AI and ML technologies hold the potential to process vast amounts of data quickly while structuring data and improving interoperability.
The merger of these technologies will facilitate the decision-making necessary to support smart cities of the future while accelerating digital transformation. The resulting benefits will dramatically impact the way consumers, businesses, and governments operate as real-time data is leveraged to add a new dimension of logic.
Image credit: RF._.studio; pexels
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