You can find many, many smart cities that share a common architecture. They have one or many sensors producing data. And this data needs to be processed in real time to provide insights, either to take action or to predict trends.
Let me introduce you to the fascinating work we’re doing in the Swiss city of Geneva, partnering with Securaxis, who provide sound analytics for smart cities, HEPIA, Geneva’s university of landscape, engineering and architecture, and HEG, the city’s school of business administration. In the context of the MEDInA project, we’re using the Securaxis SONAL machine learning app to measure sound so we can monitor and categorise traffic. We’re also using cameras and more machine learning from HEG to understand traffic lanes. Combined, this provides valuable real-time insights into traffic flow and helps to control street lighting to provide safer streets while saving energy.
Another example comes from the gorgeous Italian City of Florence where, in the context of the ELASTIC project, with partners such as Thales providing smart sensors for transportation, Barcelona Supercomputing Center, ICE and Ikerlan, we are providing a range of new services on the city’s tram network. One of these services is predictive maintenance – we use laser scanners on the rails to monitor rail profile degradation to predict the need for maintenance operations. Another is synchronisation of traffic intersections to favour public transport traffic while minimising impact on all other traffic. And a third is providing proactive driver assistance with real-time emergency alerts in case of intrusion onto the tram path.
The really smart cities do more than share a common architecture. The also share edge infrastructure.
Their IT architectural anatomy includes:
It is extremely wasteful to replicate this architecture for each and every use case – all the applications mentioned above plus many more, such as measuring air quality and monitoring temperature and sound pollution.
Think of the energy consumed in manufacturing all this redundant hardware… and the energy needed to power it. And what if new use cases come along that require mixing or fusing different data (say, audio and video to identify the source of traffic accidents or noisy or polluting vehicles)? If everything is built in silos, interoperability is much harder, and with independent systems there can be no cost sharing and only limited economies of scale to improve the ROI.
Instead, what makes much more sense is to use a shared edge infrastructure. This means that sensors are connected to a multi-tenant edge computing unit, able to host several apps or algorithms, connected to a range of sensors. It also means the unit can create many insights which enable the city to take actions in real time.
Further, once smart city operators are able to remotely control this architecture it becomes much more future proof. Updates, upgrades and even new use cases can be deployed with minimal, if any, intrusion into the smart city physical infrastructure.
In North America, people are starting to describe this type of shared edge infrastructure for smart cities as ‘Intelligent Infrastructure’. I call it common sense, since cities must evolve quickly to provide citizens with the level of services they require.
The climate emergency is also accelerating the need for such architecture, since public transport systems must become more inclusive, delivering a greater sense of security along with higher performance. Understanding the impact of inescapable rising temperatures is also essential and, for this, situational awareness and behaviour trend analysis are key. As discussed in previous blogs, only edge computing is capable of doing all this while also protecting citizen privacy.
We at SixSq have the technology and a growing ecosystem to address these challenges. Get in touch if you want to better understand what benefits a shared edge infrastructure can bring to your community. .
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