The Future Formula IoT + AI = $

We Have Been Here Before

Monitoring a process and tuning it based on feedback can be traced back to 3^rd century B.C. when Kteisbios of Alexandria created valves to control water clock levels. Heron, also from Alexandria, in the 1^st century BC created a fill valve like today’s toilet fill valves. The 1660’s and early 1700’s implemented controls by sensing pressure and temperature. The Industrial Revolution created controls focusing on making processes more efficient and replacing human labor. Continuous variable control with electronic sensors, proprietary network and PID controllers was introduced in the 1990’s. Today’s solutions are more sophisticated with the improvement in sensing operational variability, communicating changes to a central processing unit and sending adjustments to control units.

IoT Emergence

IoT or internet of things is part of everyday life. But it is just a variation on a theme. The ubiquity of the internet has boosted its acceptance. It is just “things,” with internet compatibility (open standard) sharing data with other “things,” on the internet. This affords the opportunity to collect, analyze and make decisions. Device sensitivity in identifying process variability, network communication speed and algorithms for managing outcomes determine the sophistication level of an IoT implementation.

IoT + AI

The algorithms are created by applying artificial intelligence to IoT. This marriage delivers system optimization, better decision-making insights, and enhanced data creation enabling machine learning. The sophistication level determines the solution implementation. At a basic level, artificial intelligence predicts in a forecasting mode or can improve quality and manage process risk. Intermediate artificial intelligence using logic-empowered sensors can act limiting outages and reducing safety risks. At the advanced level, artificial intelligence incorporates continuous data inputs allowing the system to learn and make optimal operational decisions without human intervention.  

Artificial Intelligence identifies data anomalies and patterns through data supplied from IoT intelligent sensors and devices. With IoT, it is only possible to notify when setpoints are exceeded, with the addition of artificial intelligence, machine learning predictions can be made 20 times faster with improved accuracy. How the IoT + artificial intelligence, or AIoT, achieves this is by building  intelligent machines which make optimal decisions with limited or no intervention.

Major Segments

IoT and AI markets are growing across four segments

• Wearables – wearable devices monitoring preferences and habits for applications in sports, fitness, and healthcare
• Home – leveraging appliances, lighting and electronic devices for automated support and energy efficiency
• Municipal – applications for urban life making improvements in traffic control, public safety, and energy management
• Industry – digitization of manufacturing improving efficiency, safety, and quality.

Applications that are generating value across these segments include:

• Predictive Maintenance
• Natural Language Processing (NLP)
• Fleet Management
• Banking Fraud Protection
• Insurance Premium Prediction
• Crime Scene Identification

Leveraging the addition of artificial intelligence is adding value to existing IoT installations and optimizing the value creation of new installations.

Use Cases

With facial recognition, Retail businesses are identifying customer’s gender, flow, and product preferences predicting behavior for store operations and locating products. Drone collected traffic data is input into algorithms which are deciding on how to improve flows through speed limit adjustments and light timing. Truck fleet management of routing and scheduling for energy saving, and predictive maintenance yielding reduced unplanned downtime. Risk prediction and automated decisions for managing process safety, monetary gain and addressing cyber threats. These are notable solutions where AIoT is generating significant value propositions.

IoT + AI + 5G + Big Data = Infinity

How do we soar higher with automated intelligence? With emerging technologies to turbocharge AIoT, 5G networks with next to zero latency will support real time data processing. Unlimited data feeding a variety of sources fueling machine learning yielding new knowledge sources for augmented intelligence. The digitizing of data will be more impactful with 5G speed delivery to algorithms and back to the process control point. Computing power complemented by IoT and 5G speed make the artificial intelligence and analytical toolset even more important than when they were originally conceived. 

Challenges

All this potential has its share of challenges. Artificial intelligence will get the most out of the combination of IoT +AI+5G+Big Data. Challenging aspects of AIoT include:

• Analyzing and creating value propositions with IoT data
• Predictable latency and accurate data analysis
• Balancing the need for speed and local smart devices vs. centralized control
• Providing personalization while protecting data privacy and confidentiality
• Protecting against increasing cyber-attack threats.

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