How Artificial Intelligence Will Solve IoT's Big Data Challenges

If IoT is going to deliver on its transformational promise, it will have to provide greater value and importance than a single internet enabled sensor such as a wearable device. The technology to create a central hub around a small collection of sensors, for example in home automation, has been around for decades. What is revolutionary today is that home automation is cheaper to implement and gives home owners better software to monitor and control their homes remotely.

As I've said in a previous post, the real magic happens when a hub of managed sensors can easily communicate with other neighboring systems. Each hub has to be programmed to intelligently broadcast signals to its neighbors and also make intelligent decisions on how to process signals from its neighbors. 

Examples of IoT Magic


Here are some examples. Maybe there are heavy rains and my basement is beginning to take on water. Can my home automation system alert neighboring systems that flooding is eminent and that the home owner should consider precautionary measures? What if my fire alarms also made similar communications if there is an emergency? These are all examples of neighboring systems sharing information.

Information sharing scales regionally as well. What if the town's facility's team was alerted when multiple homes on a block were flooding. They could then come on sight, review the environment for issues, and maybe fix sewer drains that were flooding. What if fire departments received information on the severity of a fire and its location before a 911 call?

The Challenges of Information Sharing


Information sharing has two fundamental starting problems. First, the security and privacy of what information is shared needs definition. What information and under what circumstances am I willing to share with neighbors and the extended region? It's hard enough for most users to set their Facebook privacy settings, so home automation and wearable device manufacturers will need to consider how to simplify selecting these preferences.

Then there is the question of how various systems will process a larger scope of data coming from neighboring and regional systems. Now for small scale systems with well understood patterns, a rule based approach implementing "if this then that" may be sufficient. But for ecosystems with millions of sensors, thousands of neighboring systems and hundreds of regional systems, rule based systems are likely to be too complex to define and program - assuming patterns are well understood.

Where AI Meets IoT


AI bases algorithms have a better chance to succeed in places where rule based systems are too complex to program or need to process too much data. Neural networks identifying patterns, fuzzy logic based controllers that can respond to local, neighboring and regional inputs, reinforcing learning algorithms instituted at regional systems to identify macro conditions are all AI possibilities to help transform dumb internet connected sensors to intelligent ecosystems.

AI could be used to determine "dangerous" conditions when local systems may be permissioned to "share" additional information with neighbors and regions. For example, upon sensing a flooding danger, a neighbor's home automation may proactively turn on the basement sump pump in response. A health monitoring wearable device could be programmed to seek out a nearby and volunteering doctor, medic or nurse on an emergency condition.

These are all interesting and promising AI applications in IoT. The trick will be in getting enough participants and early adopters to establish data sets, test user interfaces, and validate AI's logic and response.


 

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