In recent years, the concept of the smart city has gained widespread popularity, referring to a model of urban innovation capable of exploiting the functions of the digital world and data obtained through IoT technologies to improve public services such as healthcare, mobility, water supply quality, etc., while raising the quality of life for citizens and city users. In general, the smart city model is linked to characteristics inherent in resilience, sustainability, governance, service and resource management, as well as improving the quality of lifestyles and life itself (Al Nuaimi et al., 2015).

In this perspective – and in order to achieve these goals – the importance of Big Data, IoT technologies and the integration between the two is clear: in fact, the key themes of smart cities can be implemented through the acquisition of data, its storage and, finally, its processing for decision-making, i.e. the process of making decisions (e.g. improving urban mobility infrastructure, etc.). One of the clearest definitions of what is meant by smart cities is: 

 

The connection between physical, IT, social and economic infrastructure to harness the collective intelligence of the city” (Chourabi et al., 2012).

From this, it is therefore possible to arrive at the logical framework underlying the concept itself and define its operating criteria:

  • As already discussed, data flows can be produced from a huge number of different sources, such as social networks, purchase transaction data, etc. This Big Data can also be obtained through IoT technologies;
  • Sensors embedded in IoT devices generate additional data flows which, once processed, can provide information about the environment and people: think, for example, of cameras active in urban areas, traffic detection sensors, air quality sensors, etc.;
  • In order for all these flows to be stored and made available for processing – through which the decision-making phase can then be reached – the Cloud is the tool of choice because, as already mentioned, it is an economically sustainable and reliable system;
  • Through data processing – or data analysis – it is possible to understand what decisions to take in urban areas to streamline government and land management actions and, at the same time, improve the quality of life for everyone.

A practical example of how these technologies can make a city smarter and more functional is traffic management: given the high number of individuals gravitating towards urban areas, the integration of Big Data, IoT and the Cloud can support the definition of strategies that can be adapted in real time to mitigate the negative effects of congestion and the resulting pollution. In this case, sensors can collect traffic data which, once processed, can give instructions to actuators in traffic lights and other objects so that they can streamline activities and intervene to prevent congestion and other negative externalities.