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Visiting a new city as a tourist is exciting, visiting it as an engineer might be even more exciting, particularly in these times when technology dominates, although it is mostly invisible to citizens and tourists alike. It takes an engineer’s eye, and some help from cities’ managers and operators to look through the “application layer” and see the machinery at work.
This idea of an application layer, hiding whatever is making the city tick, can only come to an … engineer, used to separate the layer seen by users to the many layers required to make it all work seamlessly.
To her trained eye the sensors detecting the flow of cars, trucks, people and goods are clearly visible, even though a casual passer-by will miss the ones embedded in the tarmac, those security cameras that can double up as visual detection points and clearly he will also fail to realise that the antennas providing radio connectivity to smartphones, vehicles, and more generally “things” (IoT) are actually very sophisticated sensors detecting many parameters and letting applications extract further meaning, from traffic flows to the attraction of a particular shop window, from the ever changing patterns of people aggregation to their use of resources.
Each city pulses in its own specific way, so specific –actually- that one can create a digital signature of the whole city and through this signature separate it from any other cities. At the same time this signature consists of ever changing details and by looking at these details one (an engineer…) can tell if something is wrong.
Within this signature, like a fractal construction, the engineer can detect other signatures, each representing a system like the public transportation system, the waste management system, the goods distribution system, the power and water distribution grids. All these “systems” would probably be placed at layer 2, 3 and 4. Layer 1 would consist of wires, pipes and tarmac, whilst layer 5 and 6 are for the coordination of various systems and delivery of hooks, gates, handles used to create and deliver services.
However, looking closer, as an engineer is bound to do, she will realise that something is different from this nicely laid out model that fit so well the cities of the past.
Self-driving vehicles are changing the way city roads are used. No longer you see signs indicating one way streets, rather depending on the traffic flow vehicles behave like they were on a one way street at a certain moment or on a two way street at a different moment. At some times they occupy all the lanes in one direction and a moment later they squeeze into a few lanes in a direction to free the others for traffic coming in the opposite direction. There is no control in a classical sense. It is much more likely a flock of birds generating amazing and ever changing patterns with no one in charge. There is no defined protocol to agree on a sharing of the road, rather each vehicle makes autonomous choices “sensing” the presence (and behaviour) of other vehicles in the vicinity and applies local rules (basically: “let’s not crash!”).
More difficult to perceive are the mutual relationships among different autonomous systems. The digital signature of the overall vehicles flow has an impact on the public transportation flow and on the goods delivery flow. Each of these two systems are influenced by the overall traffic flow and self-adapt to achieve their goals, like maximising transport capacity to meet demand, decreasing fuel consumption, speeding up delivery and so on. Again, there need not be an orchestrator and explicit negotiation among the various systems and their component parts. Rather, each one is autonomous and takes local decisions being influenced by the perceived context. Clearly, a smart city “brain”, a control centre with a global view of resources available and pending requests on access to these resources may provide directives to the various systems under its control and sometimes override local autonomous decisions but in general the city relies on autonomous decisions that –normally- ends up in a desired emerging behaviour.
Large panels are drawing the engineer attention. They are showing snapshots of the city state, both the present one and the expected evolution. They look a little bit like the weather radar map, showing the traffic loads in the different city areas and the expected evolution. In this “expected” evolution there is a lot of technology (sensors and data processing based on past experience –AI/Deep Learning) as well as a lot of sociology and psychology. Their aim is to generate awareness among the citizenship and through awareness influence their behaviour. Citizens are “autonomous systems” and collectively they form an “autonomous systems” that smart cities are learning to manage (influence) in very effective ways. More than that. Smart cities can measure the degree of influence and work on improving it, at the same time making do with what is achieved.
Citizens live in a symbiotic relation with the various autonomous systems characterising a smart city. Notice that symbiotic is a bidirectional relation and this symbiosis is a recent phenomena among smart cities and its citizens. In the past the city systems influenced the behaviour of its citizen but the reverse was not true, at least in terms of having short term effects. Citizens behaviour used to influence the city behaviour through a planning process that city’s administrators run to adapt city resources to changing demands. In present smart cities planning still exist but the mutual impact is felt immediately as systems respond to citizens behaviour and viceversa.
Panels are useful to provide a shared awareness among citizens. Personalised awareness is also useful and made possible by customised information made visible to single individual. Often this leverages on augmented reality technologies, showing “meaning on the person ambient”, and it may involve a digital self that lives in the cyberspace along with the other digital copies of resources, systems and processes making up a smart city.
The existence of a digital twin of a city (digital representation of the city in terms of resources, their behaviour and the rules steering it) is a crucial enabler in the overall coordination of the autonomous systems. Whilst each of these autonomous systems operates independently of the other (it is autonomous, isn’t it?!) and it is influenced by the others in terms of the local context it perceives, the digital twin exists in a space where locality is no longer an issue (bits are fundamentally de-localised) and simulation can study the global emergent behaviour and stimulate contextual changes (and even goal changes that in turns result in a different reaction to a given context by an autonomous system). Notice the different approach and the departure from the ISO 7 layer model in the case of a smart city based on autonomous systems. The coordination relies on changing goals, not on prescribing specific actions. Autonomous behaviour is at the core of the behaviour and this requires strategy for influencing the reactions rather than prescriptive approaches that are very difficult to apply in a context of autonomous entities.
