Published on July 18th, 2019 | by Emergent Enterprise0
Why IoT Needs Machine Learning to Thrive
When IoT devices and sensors deliver all that data, how can it be practically managed to make good business decisions? Enter machine learning says Marta Robertson at iotforall.com with an overview on how the two technologies can work together to transform businesses. The velocity of business requires 24/7 monitoring and split second decisions when data reveals action items. ML can work alongside humans to make it happen.
Photo Source: Illustration: © IoT For All
Machine Learning can eliminate human errors and enable big data to generate real-time insights and allow Internet of Things devices to reach their full potential.
There’s an unceasing buzz around big data and AI, the opportunities and threats of these technologies and concerns about their future. Meanwhile, companies are installing more and more sensors hoping to improve efficiency and cut costs. However, machine learning consultants from InData Labs say that without proper data management and analysis strategy, these technologies are just creating more noise and filling up more servers without actually being used to their potential. Is there a way to convert simple sensor recordings into actionable industrial insights?
The simple answer is yes, and it lies in machine learning (ML).
Machine Learning Capabilities
The scope of ML is to mimic the way the human brain processes inputs to generate logical responses. If people rely on learning, training or experience, machines need an algorithm. Also, as each of us learns more, we adapt our reactions, become more skilled and start to apply our efforts selectively. Replicating this self-regulatory behavior in machines is the finish line of ML development.
To learn, a computer is presented with raw data which it tries to make sense of. As it progresses, it gets more and more experienced, producing ever more sophisticated feedback.
Under the broad umbrella of the Internet of Things (IoT), we can find anything ranging from your smartphone to a smart fridge to sensors monitoring industrial processes.
Yet, there are at least four essential concerns related to IoT implementation, which need to be addressed:
- Security and Privacy: Any algorithm that processes this kind of data needs to embed ways to keep all communication safe, especially if we’re talking about personal data such as that collected by medical sensors.
- Accuracy of Operation: Sensors implemented in harsh conditions can send faulty data, or no data, disrupting the algorithm.
- The 3 Vs of Big Data: Most IoT devices generate what can be classified as big data because it checks the 3Vs: volume, velocity, and variety. Tackling the 3Vs means finding the best algorithms for the type of data you’re using and the problem you’re trying to solve.
- Interconnectivity: The value of IoT is in making disconnected items and tools “talk” to each other. However, since these are all created differently, they need to have a common language, which is usually the smallest common denominator. If computers already have protocols like TCP/IP, how would your fridge talk to your coffee machine?