IoT and Machine Learning: Why Collaboration is Key
“Internet of Things” and “Machine Learning” can seem like buzzwords. They are both in the “Peak of Inflated Expectations” stage on Gartner’s emerging technologies hype cycle, but together they will change the world in various capacities in the next couple of years.
Internet of Things (IoT) refers to the network of devices and data shared from them. You know that Fitbit that you wear? That’s an IoT device. Machine Learning (ML) finds patterns in data and does something based on those patterns without being explicitly programmed. The data collected from IoT devices overtime is enormous and would be difficult for one person or even a team to uncover all insights. That’s where machine learning comes in – it can scale and simplify IoT data analysis. Internet of Things and Machine Learning complement each other. Here are some use cases to illustrate how IoT and ML can work together..
IoT and ML use cases
Analyze traffic patterns for city planning
Use machine learning to forecast traffic and peak demand within smart cities to make recommendations on alternative transportation or travel times. You would need to collect hourly or daily traffic data so city officials can make predictions to identify bottlenecks and make city planning recommendations.
Predictive maintenance for wind turbines
Predict cooling system usage on wind turbines and schedule for preventative maintenance optimization. You would need to collect hourly or daily usage data. A manager can then dispatch a maintenance crew when the predicted aggregate usage exceeds known maintenance thresholds.
Optimize device / systems efficiency
By gathering device and systems data you can optimize efficiency by linking usage forecast to supply chain and device operations. Developers or analysts can use machine learning to send alerts when predicted usage has exceeded a known threshold.
Collaboration is key
The list goes on and on of possible use cases for IoT and ML to achieve greater insights together, but collaboration will yield the best results. By collaboration we mean minimizing siloes that separate departments, companies, or industry verticals. A good example of this is a grocery store chain’s data on customer food purchases by customer ID collaborating with a healthcare company who has data on health history to uncover relationships between food and health overtime. This type of partnership enables the dynamic duo of machine learning and internet of things to work together effectively.
We need to start thinking of internet of things and machine learning as a dynamic duo that together will create positive social impact, especially with collaboration.
Originally published on Nexosis.
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