# IoT-Enabled Smart Cities: Evolution and Outlook
## 3 Key Technologies Developed
### Artificial Intelligence and Big Data  
The true value of the Internet of Things, especially applied to the smart city domain, lies in insights that can be gained from the sensor data and user-provided information collected on the IoT platform. The insights are the basis for enabling humans to take the right decisions or successfully automate important processes, all with the goals of increasing the operational efficiency of the city as well as improving the quality of life for the citizens. To get such insights, the raw data and low-level information constituting Big Data needs to be processed, using a variety of algorithms from the area of artificial intelligence, which are going to be discussed in the following. Big Data is often characterized by five Vs-Volume, Velocity, Variety, Veracity and Value [85]. _Volume_ relates to the amount of information. In a smart city a huge amount of information may be related to streams from video cameras or the creation of detailed 3D models, e.g., based on point clouds. _Velocity_ refers to the speed at which information flows and needs to be handled. Again, this could be video streams or traffic information that needs to be processed in real-time. _Variety_ relates to different types of information as well as different formats. For example, location can be provided in a variety of formats with different accuracies. _Veracity_ refers to the quality of the data-how complete and accurate it is and to what extent is it a realistic representation of the situation, e.g., a temperature sensor may have good accuracy, but may not provide a correct representation of the room temperature if positioned closed to a window through which the sun is shining. The final V stands for _Value_, which represents the utility of the data, i.e., the insight that can be gained and what can be done with it. As discussed in the context of IoT platforms in Section 3.3, it is important to agree on a common API for accessing and finding relevant city information as a minimum interoperability mechanism. However, with the variety and heterogeneity of information available in a smart city, not all information will be on the same abstraction level and on the same conceptual level. In particular, raw data may be required from which the high-level insights can be extracted. Depending on the Big Data Vs introduced above, different storage systems are required for handling the data and to enable the required processing. The storage of very heterogeneous data in different systems is often called a _data lake_. To be able to find the data in a data lake, key information can be extracted from the data lake and made available as homogenous information accessible through the IoT platform API, but linking to the complete original information, enabling to discover and further process the raw data. The artificial intelligence-based algorithms for processing information can be categorized into data analytics, machine learning and deep learning [86]. For data analytics, there are the following four different approaches:  
* **Descriptive Analytics** processes historic data and provides metrics and measures to summarize the information. It is often taken as an initial step followed by further analytics. For example, it can be used to find the peak of traffic congestion. * **Diagnostic Analytics** after an issue has been detected, e.g., as a result of descriptive analytics, diagnostic analytics can be applied to attempt to understand the cause, e.g., rush hour traffic in combination with construction sites. * **Predictive Analytics** can be used to forecast situations and events in the future. Historic information can be used to create a function that predicts a value, e.g., traffic congestion at a certain time on a weekday under certain weather conditions, which can be applied to predict the situation at a future point in time. * **Prescriptive Analytics** goes one step further than predictive analytics as it tries to understand how different factors influence a situation.