By Aveekshith Bushan

The future of the Internet of Things (IoT) has the potential to be limitless. The total installed base of IoT-related devices in the world is estimated to grow to nearly 31 billion by 2025. From connected cars, smart cities, smart home devices to connected industrial devices, a exciting wave of IoT applications, brought to life through intuitive human-machine interaction.

These advances in IoT will be accelerated by the increase in network versatility and the ability to automate various usage cases. The potential of the IoT is not just the deployment of billions of devices, but the use of data from those devices, for actionable knowledge. It is predicted that in the next four years, the world’s IoT devices will generate 90 zettabytes of data.

Some technologies are inevitably linked together. Artificial Intelligence (AI) and IoT are a perfect example of two technologies complementing each other while being closely linked. In the fast-growing world of IoT applications, which connect and share data across a wide array of devices, organizations need analytics.

This is the ability to make quick decisions and discover in-depth knowledge while constantly learning from massive volumes of IoT data. AI is an essential part of analytics that helps expand the overall value of the IoT. Utilizing Deep / Mechanical Learning (ML) and AI, businesses can anticipate the needs of their clients and networks, automate preventive actions, and tailor their products and services based on learned behavioral knowledge.

The key aspect of autonomous systems is better decision making and providing smart automatic behavior for industrial machines, intelligent cities and equipment working in any environment. IoT sensors digitize the physical world, data is generated at different speeds – this data is sometimes raw as a video format or structured as RFID data. To process this data, raw or structured, at the edge, in-depth machine learning models are required.

For example, a smart city project should install high-definition traffic cameras for better law enforcement. These cameras must capture speed, registration numbers and record illegal driving activities. Sensors should statistically compress data at source, extract information from noise to send relevant information centrally, and help provide local knowledge of equipment in the environment.

As data is swallowed at different speeds, they must provide an accurate context. Also, the speed with which these events must be processed and how many of them must be stored to extract actionable knowledge is critical. Such systems need historical data to improve knowledge and ensure better decision making. As data is captured in different layers and shapes, they need to be merged together in real time, for the best knowledge.

This is where ML and in-depth learning tools help generate useful knowledge. These tools not only direct the sensors to what they capture, but bring the layers together to share reports with the authorities in real time. AI, ML and analytics can help optimize the client’s life cycles (in this case law enforcement authorities) and make them use all resources efficiently to strengthen their activities. Data views boost the customer life cycle, create a plan to use accurate resources, and protect against risk.

AI and ML analyzes in IoT realize the benefits of productivity, efficiency and effectiveness using semantics to transform raw data into applicable knowledge. It provides value from exploiting the challenges posed by the volume and variety of big data, and in turn provides workable information and improved decision making. The convergence of AI and ML paves the way for progress in efficiency, accuracy, productivity and overall cost savings for resource-limited IoT devices. When AI and ML and IoT analytical algorithms work together, organizations can use this for better overall communication, real-time needs calculation, and better data controllability.

Common challenges facing organizations today are the application, accessibility and analysis of IoT data. While most are using AI and ML to perform some form of statistical analysis, key players are using it to be proactive and predict events for future knowledge. Such AI-enabled IoT systems can automatically and consistently provide relevant knowledge to these organizations, taking advantage of the large amount of data that is constantly being streamed into their internal systems.

These technologies are enabling the next level of automation and productivity while doing so at ever-decreasing costs. As consumers, businesses, and governments begin to control IoT in a variety of ways, optimizing data through analytics will change the way we walk in our lives and allow us to make better choices.

The author is Regional Director and General Director – APAC, Aerospike.

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