How do the Internet of Things and AI work together?
IoT and AI are two of the hottest topics in technology, which is a good reason why enterprise technologists need to understand them. The two technologies are very symbiotic, so planning how they can support each other for the benefit of business users is essential.
What is IoT?
The IoT is a network of devices rather than people. IoT applications are normally built from devices that sense real-world conditions and then trigger actions to react in some way. Often the answer includes steps that influence the real world. A simple example is a sensor that, when activated, turns on certain lights, but many IoT applications require more complicated rules to link triggers and actions.
Messages that represent triggers and actions/commands in the IoT go through what is commonly called a control loop. The part of an IoT application that receives triggers and initiates actions is the center point of this loop and where the IoT rules reside.
The control loop is only part of the total information flow in an IoT application – the part that actually receives information about real-world process conditions and generates real-world responses. Most IoT applications also generate business transactions. For example, reading a shipping manifest at the entrance to a warehouse can open the door for the driver — a control loop decision — and also generate a transaction to receive the goods represented on the manifest. in inventory — a business transaction. Decisions made in the control loop must meet application latency requirements, often referred to as length of the control loop.
Often control loops only require simple processing to close the loop and create an actual response to an event. Entering a code to open a portal is an example. In other cases, the treatment needed to decide is more complicated. When processing needs to apply more decision factors, the time required to make those decisions can affect the length of the control loop and the ability of the IoT to deliver the expected functionality. A half-minute delay for a worker to scan a manifest before admitting a truck to a freight yard, for example, could reduce the station’s capacity. The IoT could read a QR code on the manifest and make the necessary decisions much faster, thus speeding up the movement of goods.
What is AI?
AI is a class of applications that interpret conditions and make decisions, much like people react to their senses, but without requiring direct human intervention.
There are three major forms of AI in use today, which are:
- Simple or rule-based AI is software that has rules or policies that link trigger events to actions. These rules are programmed, so some people might not recognize this as a form of AI. However, many AI platforms rely on this strategy.
- Machine Learning (ML) is a form of AI where the application learns the behavior rather than programming it. Learning can take the form of monitoring a live system and relating human responses to events, then repeating them when the same conditions occur, either by analyzing past behaviors or asking an expert to provide the data.
- Inference or neural networks use AI to build an “engine” designed to mimic a simple biological brain and make inferences that generate responses to triggers based on what the engine “infers” from conditions. Today, this technology is most often applied to image analysis and complex analyses.
These three forms of AI are designed to replace human intelligence, but their ability to represent something even approaching actual human intelligence is greater the further you progress through the three in the order above. .
How can IoT and AI support each other?
In the IoT, real world events are reported and processed to create an appropriate response. In a simple sense, any IoT application that uses software to generate a response to a triggering event is at least a basic form of AI, and then AI is essential to IoT. The question for IoT users and developers is not whether to use AI, but how far AI can be taken. It depends on the complexity and variability of the real systems supported by the IoT.
A simple rules-based AI would say “If the trigger switch is pressed, turn on light A”, and a more sophisticated evolution might say “If the trigger switch is pressed, and it’s dark, turn on light A.” This represents not only event recognition (trigger-switch), but also state recognition (it’s dark). Programmers use state/event tables to describe how a series of events is interpreted in multiple states, but this only works if there are a limited number of states that can be easily recognized.
Referring to the example of a truck arriving at a warehouse with goods to be stored, a simple AI could provide the driver with a way to enter a code to pass a security barrier. This would eliminate the cost of hiring a gate worker. It is also possible to read a barcode or RFID tag on the vehicle itself and allow entry without entering a code. This would allow the truck to continue moving while its right to enter was validated, further speeding up the process.
If more conditions need to be analyzed to determine a response to an IoT event, the process is beyond the capabilities of the simple AI application. If the It’s dark state has been replaced by a called, i need more lightand the IoT system had to respond not to a specific trigger switch but to the task a person was trying to perform, a simple AI would not suffice.
In this situation, IA’s ML form can monitor the arrival of a load of goods at the warehouse. Over time, it could learn when drivers and workers needed more light and activate the switch without the person needing to act. Alternatively, an expert can perform the expected tasks and “teach” the software when more light would be appropriate. AI/ML software would then eliminate the need for a programmer to create every IoT application.
In the form of AI inference, the IoT application tries to gather as much information as possible, mimicking what a person is feeling. It then applies inference rules, such as people cannot work where light levels are less than xand from the conditions felt and the application of these rules, decides to turn on a light.
Inference-based AI requires more complicated software to gather conditions and define inference rules, but it can respond to a wider range of conditions without being programmed. The same level of inference processing could determine whether additional workers should be assigned to unloading, because the goods are needed, work is running late, or simply because workers are available. All of this could improve the movement of goods and the overall efficiency of truck drivers and warehouse staff.
IoT is about using IT tools to automate real-world processes, and like all automation tasks, it should reduce the need for direct human involvement. Although the IoT aims to reduce human labor, it does not eliminate the need for human judgments and decisions. This is where AI can step in and dramatically improve the IoT system.