# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 2 Background  
### Dimensions and Projections of Smart Cities  
Urbanization brings several advantages to the society but also implies big challenges for city managers, since it increases the demand for energy, water, sanitation, education, health services, and other public services. Statistical data indicate that 4.50 billion of the world population, which is close to 68% of the global population, will live in cities by 2050 [5], and 426 million people will be over 65 years of age by the same year [6]. Additionally, projections indicate that 80% of the global energy will be consumed in cities in 2030 [7], and about $7 trillion will be invested by 2050 in the automobile sector, especially in self-driving cars [8]. In this situation, the adoption of smart cities models to make cities more sustainable, competitive, socially cohesive, and safe has become an indispensable task [9].  
Smart city models are based on the idea of sustainable development considering people as the central axis. They has six pillars: governance, economy, living, environment, people, and mobility [10] (see Figure 1). From this perspective, city managers' decision-making processes can be improved through the sensorization of the different aspects of the city such as environment, safety, traffic, pollution, and so on, for mitigating and preventing urban challenges. For example, Figure 2 shows a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Criteria of Circularity** & **Indicator of Circularity** \\ \hline Reuse of old buildings & Number of existing reused buildings \\ Energy efficiency & \% of energy reduction in buildings \\ Greenhouse gas emissions avoided & \% CO\({}_{2}\) emissions avoided (tons/year) \\ Water consumption avoided & \% of water use reduction in buildings \\ Waste avoided in the construction & \% of waste reused \\ \hline \hline \end{tabular}
\end{table}
Table 1: Criteria and indicators of circularity in built environments [2].  
smart city sensor model, which considers the sensorization of different aspects related to industrial automation, pollution, energy, and traffic; the model also includes social interactions generated by people through different social network channels and components of data analytics using technologies such as cloud computing, big data, and high-performance computing (HCP).  
In order to establish the sensorization of a city, smart cities require information and communication technologies (ICTs) [11], specifically, an emergent technology called Internet of Things (IoT) that allows a deeper level of sensorization. According to a study published by Gartner in 2015, the number of devices connected was close to 1.1 billion in 2015 and predicted to be approximately 2.6 billion in 2017 [12]. The same study indicated the biggest subcategories as the smart home, with approximately 1.067 million connected devices, and the smart building, with 648.1 million devices (see also Table 2).  
Figure 1: Dimensions in the development of an effective smart city model [10].  
Figure 2: Sensorization and communication technologies (ICT) integration for the development of smart cities.  
On the other hand, a report published by STATISTA in 2019 indicated that the number of IoT devices in the smart city sub-category was 260.6 million in 2015, 314 million in 2016, 380.6 million in 2017, and 463.5 million in 2018 [13]. These numbers re lower than those estimated by Gartner in 2015. However, they still reflect the significant growth of IoT implementations for smart cities. Gartner, in another study published in 2018, estimated that IoT would grow to reach 1.1 billion devices in 2019 and close to 9.7 billion by 2020. This study also indicated that IoT will expand the usage of new technologies such as smart machines and robotics [14]. According to a McKinsey's study, IoT technologies will have a potential economic impact of $2.7 to $6.2 trillion by 2025 [15].