AI’s explosion in the public imagination through the likes of ChatGPT has industries scrambling about to see how AI can be applied into their respective sectors. AI in automation, AI in copywriting, AI in customer service, the possible applications this revolution is witnessing seem endless. IoT, is no different in that regard. AI can and will indeed effect its use and operation. But it will not be in a minimised measure, it could go on to revolutionise it and become an integral part of its operation.
Over the past few decades, businesses have steadily adopted IoT. Although the concept of IoT evolution has been anticipated for a while with AIoT and machine learning, the recent events highlighting how far AI outside of an IoT setting may have businesses scrambling to unleash the combination these two systems can deliver in their synergy.
What benefits AI brings to IoT
AIoT in a nutshell combines the power of AI algorithms and data analytics with the network of connected devices in the IoT ecosystem. It takes the data collected from the physical devices, sensors, and actuators that are connected to the Internet, and analyses it.
This analysis enables IoT devices several benefits. For instance, AI algorithms can enable IoT devices to learn from data patterns, recognise anomalies, and make autonomous decisions in real-time.
This real-time decision making is key for the future implementation and rollout of IoT in a number of industries. For instance, some of these will require decisions on the data to be made immediately in order to function effectively and adapt to changing situations.
Even more slower processing IoT systems can benefit from this AI analysis. Currently, many businesses are having trouble effectively processing the data & using it for practical decision-making and insights due to the sheer volume accumulated. AI analysis can detect the patterns and send a streamlined view of the data.
Equally, as the use rises of a particular IoT device, the amount of data it sends over may be so large that it becomes slow, especially true of IoT devices connected in remote areas, where the connectivity is already low. Even the Cloud can experience scaling issues with this, and so the processing power and space can be better utilised by data being interpreted and analysed at the source, or as near to as possible as with AI Edge computing, before being sent.
AI’s capabilities to process the massive amounts of data generated by IoT devices through machine learning, deep learning, and other AI techniques, allow IoT to operate at an optimal rate for the device and processor and will give those adopting this combination an edge over those who don’t.
The use of AI in IoT is increasingly being utilised, especially as IoT is proving pivotal in the use of new emerging industries – like autonomous driving.
But IoT has even went on to improve even already existing industries through their implementation, and the introduction of AI will only add to that.
For instance, in manufacturing. IoT’s use of sensors has allowed companies to harvest an enormous amount of data that can be analysed to make efficiencies on a production line. Yet, if AI is added to this mix, changes can be enacted in real-time by using the data to, for instance, change speed of the process.
The other industries AI’s symbiosis with IoT is primed for:
Autonomous vehicles: they are only one example of an IoT application that depends on quick, in-the-moment decisions. Autonomous vehicles need to analyse data and make quick judgments to be efficient and secure. Latency, inconsistent connectivity, and inadequate bandwidth cannot be a barrier for them.
Biometrics: they are frequently employed in security to limit or permit access to particular regions. Without quick data processing, there may be lags that affect performance and speed, not to mention the dangers in emergencies. High security and extremely low latency are required for these applications. As a result, processing must be carried out at the Edge. Data transfer from a local system to the Cloud and back is not practical.
Predictive maintenance: IoT using sensors that gather data from connected devices can then be processed by AI to spot when the state of something changes then alert the user. This working together of the two shifts maintenance approaches from reactive to proactive, meaning potential issues can be identified before they become bigger problems.
Energy management: smart thermostats, lighting systems, and appliances collect data on energy consumption, which is subsequently analysed by AI. This can not only applied to individual domiciles and commercial buildings, but even the energy grid as a whole. Through the aggregation of data from intelligent meters and meteorological stations, algorithms scrutinise patterns of energy consumption, pinpointing opportunities for conservation. As a result, utilities and energy providers can forecast demand with enhanced accuracy. Even renewable energy sources benefit from it, with smart algorithms able to optimise the performance of wind turbines, solar panels, and other renewable sources to achieve maximum power generation.
These are just a few examples of the benefits AIoT can bring to industries already implementing IoT solutions.
The future of AIoT
The fusion of AI within IoT has established the foundation for another revolution in an already game-changing technology. While IoT devices collect vast amounts of data, AI can analyse it with smart behaviour to support real-world and even real-time decision-making processes with minimal human intervention.
AIoT, like AI applied to many other fields, therefore has the ability to expand the use that IoT is currently able to provide. Market research company IoT Analytics predicted that there will be more IoT applications developed globally with the infusion of 47 per cent AI element in all developed IoT applications by 2027.
As AI & IoT continue to evolve to greater heights separately, the synergy of the two with AIoT will become more abundant and the benefits more apparent.