# A Comprehensive Survey on the Societal Aspects of Smart Cities
## 1 Introduction
Second, due to limitations, such as bandwidth and signal interference, the communication networks had to be wired and confined to a singular location. However, with the advent of wireless technologies, they became more affordable and widespread, enabling the use of sensors and computing devices in almost any location, which allowed for the implementation of functions that previously would be impossible or prohibitively expensive. Additionally, as wireless networks became able to provide bandwidth and throughput comparable to wired networks, it became possible to transmit significantly larger amounts of data, allowing for more complex sensing packages, which in turn opened new possibilities. For instance, the real-time monitoring of multiple health data streams (e.g., pulse, oxygen level, or glucose level) from a patient. These advancements enable the creation of complex systems, which include multiple sensors for data collection, wireless capabilities for communication with other devices, and computing resources for storing and analyzing collected data. This is most well exemplified today by the modern smartphone, which allows for any citizen in a smart city with the appropriate infrastructure to become a mobile sensor for that city. This is also at the root of the Internet of Things (IoT) concept, which involves integrating sensors into everyday objects and enabling them to connect to the internet to share the data collected, which facilitates the acquisition of detailed data, namely from previously unavailable sources, and thus enables more accurate problem prevention and resolution [23; 24]. Advances in the gathering of data from multiple sources are being accompanied by advances in data storage and analysis. In terms of data storage, relational databases are commonly used to store large volumes of data on digital systems, but the massive amounts of data generated by smart cities make them unsuited for the task of retrieving that data even if they can store it without issues. This is because they are designed to perform create, read, update, and delete (CRUD) operations on small amounts of data [25], and due to having to import data into their native format, they have problems with real-time data [26]. This becomes the domain of big data technologies, such as NoSQL databases, which are non-relational and thus allow for vastly increased speeds. Additionally, data warehouses of various types, such as the massively parallel processing (MPP) databases, which coordinate multiple operations in multiple hardware devices in parallel, also allow for much improved efficiency [27; 28]. Moreover, concerning data analysis, machine learning (ML) and artificial intelligence (AI) can efficiently search through vast amounts of data to quickly identify previously unseen patterns and automate tasks. For instance, it can be used to monitor traffic conditions in a city and automatically implement changes to improve traffic flow [29]. As technology has progressed, the focus has shifted from creating better hardware and providing the necessary infrastructures to improve the required software to handle the massive amounts of data generated, to provide services with impact in people's lives, and to integrate them in the smart cities' ecosystems. This last aspect represents a huge challenge that assumes a great importance to avoid dehumanization or giving more importance to the technological goals of the smart cities than the effective needs of their inhabitants. As a result, there has been a refinement of the purposes of smart cities by emphasizing the perspectives of their inhabitants, which is a crucial strategy to translate the predicted sustainable economic growth of the smart cities into improvements of their inhabitants' quality of life [30; 31].