There is a clear intersection between the Internet of Things (IoT) and Artificial Intelligence (AI). IoT is about connecting machines and making use of the data generated from those machines. AI is about simulating intelligent behaviour in machines of all kinds. Clearly an overlap.
As IoT devices will generate vast amounts of data, then AI will be functionally necessary to deal with these huge volumes if we’re to have any chance of making sense of the data.
Data is only useful if it creates an action. To make data actionable, it needs to be supplemented with context and creativity. IoT and AI together is this context, i.e. ‘connected intelligence’ and not just connected devices.
Traditional methods of analysing structured data and creating action are not designed to efficiently process the vast amounts of real-time data that stream from IoT devices. This is where AI-based analysis and response becomes critical for extracting optimal value from that data.
AI is beneficial for both real-time and post event processing:
- Post event processing – identifying patterns in data sets and running predictive analytics, e.g. the correlation between traffic congestion, air pollution and chronic respiratory illnesses within a city centre
- Real-time processing – responding quickly to conditions and building up knowledge of decisions about those events, e.g. remote video camera reading license plates for parking payments
Actually to be more accurate when I say AI, I really mean machine learning. It is machine learning that provides the ability to detect patterns in data presented. It learns from these patterns in order to adjust the ways in which it then analyses that data or triggers actions.
With machine learning embedded into an IoT environment you get more ‘connected intelligence’:
- Predictive analytics – ‘What will happen?’
- Prescriptive analytics – ‘What should we do?’
- Adaptive/continuous analytics – ‘What are the appropriate actions or decisions? How should the system adapt to the latest changes?’
We are now also seeing AI being implemented in the edge. With greater processing power and longer battery life manufacturers are implementing AI processes in ‘edge’ devices. Referring to the remote video camera example – you don’t need to transmit the whole video, only data based on certain triggers, e.g. number and location of parking spaces or ANPR. This can be determined on the edge device.
We’re now seeing significant investment in the convergence of IoT and AI and even more sure with this ‘intelligent edge’. Microsoft announced in May its vision for intelligent cloud / Intelligent Edge. Azure IoT Edge will enable low-power devices to run containers and perform artificial intelligence locally but retain a connection to the cloud for management and modelling. Similarly in April, Amazon Web Services (AWS) updated its edge computing platform, Greengrass, to incorporate machine learning.
So what does this all mean for the public sector? As the technology matures we will start to see the scenarios for IoT develop significantly beyond the traditional use cases we see today.
A few examples:
- Real-time public safety – thinking back to the video camera analysis example above – vehicle, facial and other visual patterns can be actioned sooner for quicker decision and response by the emergency services
- The ability of machine learning algorithms to foresee possibilities of a device failing will enable remote predictive maintenance to be a reality within a smart city context from street furniture to intelligent building management.
- The technology will be critical for autonomous vehicles to ingest millions of events from vehicles to ensure safety, reliability, and efficiency for driver less transportation.
IoT and AI combined could be the trigger to really drive smart city business cases – creating not just the connected city but the connected intelligent city.
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