Why IoT Needs AI?

Ahmed Banafa
May 14, 2019

 

Businesses across the world are rapidly leveraging the Internet-of-Things (IoT) to create new products and services that are opening up new business opportunities and creating new business models. The resulting transformation is ushering in a new era of how companies run their operations and engage with customers [6]. By 2020, the Internet of Things (IoT) is predicted to generate an additional $344B in revenues, as well as to drive $177B in cost reductions. IoT and smart devices are already increasing the performance metrics of major US-based factories. They are in the hands of employees, covering routine management issues and boosting their productivity by 40-60% [7][8].

Gartner forecasted there would be 20.8 billion connected things in use worldwide by 2020, but more recent predictions put the 2020 figure at over 50 billion devices. Various other reports have predicted huge growth in a variety of industries, such as estimating healthcare IoT to be worth $117 billion by 2020 and forecasting 250 million connected vehicles on the road by the same year. IoT developments bring exciting opportunities to make our personal lives easier as well as improving efficiency, productivity, and safety for many businesses [2][4].

However, tapping into the IoT is only part of the story. In order for companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies [6].

AI is the engine or the “brain” that will enable analytics and decision making from the data collected by IoT. In other words, IoT collects the data and AI processes this data in order to make sense of it. You can see these systems working together at a personal level in devices like fitness trackers and Google Home, Amazon’s Alexa, and Apple’s Siri [1].

With more connected devices comes more data that has the potential to provide amazing insights for businesses but presents a new challenge for how to analyze it all. Collecting this data benefits no one unless there is a way to understand it all. This is where AI comes in. Making sense of huge amounts of data is a perfect application for AI [3].

By applying the analytic capabilities of AI to data collected by IoT, companies can identify and understand patterns and make more informed decisions. This leads to a variety of benefits for both consumers and companies such as proactive intervention, intelligent automation, and highly personalized experiences. It also enables us to find ways for connected devices to work better together and make these systems easier to use.

It’s simply impossible for humans to review and understand all of this data with traditional methods, even if they cut down the sample size, simply takes too much time. The big problem will be finding ways to analyze the deluge of performance data and information that all these devices create. Finding insights in terabytes of machine data is a real challenge.

AI and IoT Data Analytics

There are six types of IoT Data Analysis where AI can help [4][5]:

  1. Data Preparation: defining pools of data and clean them which will take us to concepts like Dark Data, Data Lakes.
  2. Data Discovery: finding useful data in the defined pools of data
  3. Visualization of Streaming Data: on the fly dealing with streaming data by defining, discovering data, and visualizing it in smart ways to make it easy for the decision-making process to take place without delay.
  4. Time Series Accuracy of Data: keeping the level of confidence in data collected high with high accuracy and integrity of data
  5. Predictive and Advanced Analytics: a very important step where decisions can be made based on data collected, discovered and analyzed.
  6. Real-Time Geospatial and Location (logistical Data): maintaining the flow of data smooth and under control.

AI in IoT Applications

The following are only a few examples of Artificial Intelligence applied in the Internet of Things applications:

  • Visual big data will allow computers to gain a deeper understanding of images on the screen, with new AI applications that understand the context of images.
  • Cognitive systems will create new recipes that appeal to the user’s sense of taste, creating optimized menus for each individual, and automatically adapting to local ingredients.
  • Newer sensors will allow computers to “hear” gathering sonic information about the user’s environment.
  • Connected and Remote Operations- With smart and connected warehouse operations, workers no longer have to roam the warehouse picking goods off the shelves to fulfill an order. Instead, shelves whisk down the aisles, guided by small robotic platforms that deliver the right inventory to the right place, avoiding collisions along the way. Order fulfillment is faster, safer, and more efficient.
  • Preventive/Predictive Maintenance: Saving companies millions before any breakdown or leaks by predicting and preventing locations and time of such events.

These are just a few promising applications of Artificial Intelligence in IoT. The potential for highly individualized services are endless and will dramatically change the way people lives.

Figure 1: Challenges Facing AI in IoT [4] [5] (Image credit: Ahmed Banafa).

Figure 1: Challenges Facing AI in IoT [4] [5] (Image credit: Ahmed Banafa).

Challenges Facing AI in IoT

AI and IoT is a perfect mix if used with all the capabilities of both technologies , but challenges are real and can slowdown this magical integration , below is a list of some of the challenges:

  1. Compatibility: IoT is a collection of many parts and systems they are fundamentally different in time and space.
  2. Complexity: IoT is a complicated system with many moving parts and non –stop the stream of data making it a very complicated ecosystem
  3. Privacy/Security/Safety (PSS): PSS is always an issue with every new technology or concept, how far IA can help without compromising PSS? One of the new solutions for such a problem is using Blockchain technology.
  4. Ethical and legal issues: it is a new world for many companies with no precedents, untested territory with new laws and cases emerging rapidly. 
  5. Artificial Stupidity: back to the very simple concept of GIGO (Garbage In Garbage Out), AI still needs “training” to understand human reactions/emotions so the decisions will make sense.

Conclusion

While IoT is quite impressive, it really doesn’t amount to much without a good AI system. Both technologies need to reach the same level of development in order to function as perfectly as we believe they should and would.

Integrating AI into IoT networks is becoming a prerequisite for success in today’s IoT-based digital ecosystems. So, businesses must move rapidly to identify how they’ll drive value from combining AI and IoT—or face playing catch-up in years to come.

The only way to keep up with this IoT-generated data and gain the hidden insights it holds is by using AI in IoT.

References

  1. “ AI is the Brain IoT is the Body” https://aibusiness.com/ai-brain-iot-body/
  2. “AI, IoT, and Business Disruption” http://www.creativevirtual.com/artificial-intelligence-the-internet-of-things-and-business-disruption/
  3. “What are the major components of IoT” https://www.rfpage.com/what-are-the-major-components-of-internet-of-things/
  4. “The last mile of IoT is AI” https://www.bbvaopenmind.com/en/the-last-mile-of-iot-artificial-intelligence-ai/
  5. “Data Intelligence” http://www.datawatch.com/
  6. “AI and IoT” https://www.pwc.es/es/publicaciones/digital/pwc-ai-and-iot.pdf
  7. “IoT and AI” http://www.iamwire.com/2017/01/iot-ai/148265
  8. “IoT trends for business in 2018 and beyond” https://mobidev.biz/blog/iot-trends-for-business-2018-and-beyond

 


 

Ahmed BanafaAhmed Banafa has extensive research work with focus on IoT, Blockchain, cybersecurity and AI. He served as an instructor at well-known universities and colleges. He is the recipient of several awards, including Distinguished Tenured Staff Award, Instructor of the year and Certificate of Honor from the City and County of San Francisco. He was named as No.1 tech voice to follow, technology fortune teller and influencer by LinkedIn in 2018, featured in Forbes, IEEE-IoT and MIT Technology Review, with frequent appearances on ABC, CBS, NBC, BBC, and Fox TV and Radio stations. He is a member of MIT Technology Review Global Panel. He studied Electrical Engineering at Lehigh University, Cybersecurity at Harvard University and Digital Transformation at Massachusetts Institute of Technology (MIT). He is the author of the book “Secure and Smart Internet of Things (IoT) using Blockchain and Artificial Intelligence (AI)”.

 

 


 

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