How to make your IOT system more cognitive?

Whether you are an a IOT system design architect building end to end IOT solutions ,an enterprise owner looking forward to harness the potential of IOT for business improvisation or an end user reaping the benefits of IOT in your day-to-day life, this article will give you an insight on the potential of machine learning in an IOT solution.

Before we embark on making our IOT system smarter and intelligent, let us look into what machine learning has to offer us. It is bound to revolutionize the way people think about and expect out of an IOT system.

 

Machine learning is often described as a new capability for computers wherein specialized algorithms help in better decision making without the need for explicit programming.Based on the nature of data set provided, machine learning can be applied to solve wide range of problems falling broadly into 3 categories.
First category is where the desired outcome in already known(known as supervised learning). Second type involves finding structure/pattern in data without any right answers provided (known as unsupervised learning) and learning is a continuous interaction between the model and the environment(reinforcement learning).
Supervised /Guided learning can be further designated as a regression or classification problem depending on whether we are trying to predict a continuous valued or discrete valued output.
For instance, supervised learning goes handy when one needs to predict the average temperature based on a historical pattern of weather data for the city (Regression problem). Given a set of images, using the appropriate computer vision algorithm to classify whether or not an image contains an object of interest can be the best example of classification problem.
Unsupervised learning is applied to find patterns in data set where right answers are not explicitly provided. For instance, social network analysis to find group of people with similar interests or human genome analysis to categorize individuals based on extent of specific gene presence.

With humongous data expected to be generated in couple of decades from billions of sensors embedded in IOT devices and connected to the cloud,decision making will be the need of the hour.Learning algorithms will then play a crucial role in providing insights on deciding which data to keep, ignore and forward to the cloud.
Since IOT endpoints have innate limitations in terms of cost,size and power, there comes the requirement for a separate companion device like a gateway to implement these decision making algorithms prior to data migration to cloud.This has become fairly easier since we can now afford more computational power at faster rates with large data sources than never before and this trend promises to improve in near future. Machine learning is in a way helping IOT to overcome its limitations of handling the perpetual growth in data and facilitates better data comprehension.

Continuously evolving models produce increasingly positive results thereby reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions.
Imagine a smart cognitive lifestyle where a wearable on your body automatically tracks the burnt calories and other health metrics and records to cloud, utilizing this data decision is made in your smart kitchen to prepare the appropriate drink necessary to supplement the essentials for the day.
Machine Learning algorithms help convert the raw IOT data to knowledge required for a cognitive system.
The future realization of IoT ’s promise is dependent on machine learning to find the patterns, correlations and anomalies that have the potential of enabling improvements in process performance.

Having understood the gravity of impact that machine learning can have on an IOT system, the next natural question that arises is –When should one start thinking of adopting the machine learning mindset for our IOT system? At what stage should one start looking for implementing Machine learning algorithms ?
The most common practice is to adopt a machine learning mindset once a full fledged IOT system is in place for quite some time and has generated data large enough to start analyzing and derive insights.
It is rather necessary that we cultivate and imbibe the machine learning mindset consciously right from the design step to ensure that the IOT system is equipped with ways(with embedded sensors) to measure features required for calculating the desired outcome for an anticipated machine learning problem in future.

System actuation with the help of Kinetic devices/actuators can also be realized based on the decision taken from the generated data using reliable algorithms in place.In such scenarios,one should ensure that the most realistic algorithm is used for making decisions.Most of these devices operate such that their action can neither tolerate long latency nor risk the possibility that the disconnection from cloud. In such cases,the key is to bring the decisions as fast and close as possible to the endpoints/devices.

Investing in ML along with IOT helps companies shift from a product/solution provider to proactive service providers.
Having realized the combined potential of IOT and machine learning, certain companies are now leveraging this opportunity to specifically offer machine-learning-as-a-service (MLaaS).

IOT and Machine Learning complement each other in the sense that IOT generates the big data required for implementing Machine Learning algorithms to gain meaningful insights which can be fed back to the IOT system making it more robust, proactive, intelligent and independent solution as a whole.