Incorporating Artificial Intelligence Features To The Internet Of Things (IOT)
Tags: Artificial Intelligence, IoT, Ai And Iot, Internet of Things

Incorporating Artificial Intelligence Features To The Internet Of Things (IOT) Amal_cp Follow Dec 28 · 4 min read

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The Internet of Things is getting smarter. Companies are incorporating artificial intelligence in particular, machine learning into their IoT applications. The key idea is finding insights in data.

With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI.

How AI Helps IOT.

Artificial intelligence plays a growing role in IoT applications and deployments. Both investments and acquisitions in startups that merge AI and IoT have climbed over the past two years. Major vendors of IoT platform software now offer integrated AI capabilities such as machine learning-based analytics.

The value of AI in this context is its ability to quickly wring insights from data. Machine learning, an AI technology, brings the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate information such as temperature, pressure, humidity, air quality, vibration, and sound. Compared to traditional business intelligence tools which usually monitor for numeric thresholds to be crossed machine learning approaches can make operational predictions up to 20 times earlier and with greater accuracy.

Other AI technologies such as speech recognition and computer vision can help extract insight from data that used to require human review.

AI applications for IoT enable companies to avoid unplanned downtime, increase operating efficiency, spawn new products and services, and enhance risk management.

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Avoiding Unplanned Downtime.

In a number of sectors industrial manufacturing or offshore oil and gas, to name two unplanned downtime resulting from equipment breakdown can cost big money.

Predictive maintenance using analytics to predict equipment failure ahead of time in order to schedule orderly maintenance procedures can mitigate the damaging economics of unplanned downtime. Machine learning makes it possible to identify patterns in the constant streams of data from today’s machinery to predict equipment failure. In manufacturing, Deloitte finds predictive maintenance can reduce the time required to plan maintenance by 20–50 percent, increase equipment uptime and availability by 10–20 percent, and reduce overall maintenance costs by 5–10 percent.

Increases Operational Efficiency.

AI-powered IoT can also help improve operational efficiency. Just as machine learning can predict equipment failure, it can predict operating conditions and identify parameters to be adjusted on the fly to maintain ideal outcomes, by crunching constant streams of data to detect patterns invisible to the human eye and not apparent on simple gauges.

Machine learning often finds counterintuitive insights: A shipping fleet operator’s machine learning tools determined that cleaning their ships’ hulls more often an expensive, downtime-causing process actually increased the fleet’s overall profitability. The math went against shipping industry instincts: Hulls kept smooth through frequent cleaning improve fuel efficiency enough to vastly outweigh the increased cleaning costs.

Improved Products And Services.

Enhancing IoT with AI can also directly create new products and services. Natural language processing (NLP) is getting better and better at letting people speak with machines, rather than requiring a human operator. AI-controlled drones and robots which can go where humans can’t bring all-new opportunities for monitoring and inspection that simply didn’t exist before.

Fleet management for commercial vehicles is being reinvented through AI, which can monitor every measurable data point in a fleet of planes, trains, trucks or automobiles to find more efficient routing and scheduling, and reduce unplanned downtime. Cloudera claims its fleet management AI has cut downtime for fleet vehicles monitored by Navistar devices up to 40 percent.

Enhancing Risk Management.

A number of applications pairing IoT with AI are helping organizations better understand and predict a variety of risks as well as automate for rapid response, enabling them to better manage worker safety, financial loss, and cyber threats.

Applications already in use include detecting fraudulent behavior at bank ATMs, predicting auto driver insurance premiums based on their driving patterns, identifying potentially hazardous stress conditions for factory workers, and monitoring law enforcement surveillance data to identify likely crime scenes ahead of time.

Implications For Enterprises.

For enterprises across industries, AI is a natural complement to IoT deployments, enabling better offerings and operations to give a competitive edge in business performance.

Machine learning for predictive capabilities is now integrated with most major general-purpose and industrial IoT platforms, such as Microsoft Azure IoT, IBM Watson IoT, Amazon AWS IoT, GE Predix, and PTC Thing Worx.

A growing number of turnkey, bundled, or vertical IoT solutions take advantage of AI technologies, especially machine learning. It is often possible to use AI technology to wring more value from IoT deployments that were not designed with the use of AI in mind. IoT deployments generate huge, constant streams of data, which machine learning excels at examining to identify patterns that lead to greater value.

The Future Of IoT Is AI.

It may soon become rare to find an IoT implementation that does not make some use of AI. The International Data Corp. predicts that by 2022, AI will support “all effective” IoT efforts and without AI, data from the deployments will have “limited value.” If your company has plans for implementing IoT-based solutions, those plans should probably include AI as well.