# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 5 Discussion
### Impact of LoRa Research Proposals in the Smart City Initiative  
In this subsection, we analyze the contribution of research proposals related to IoT and LoRa to build a smart city in the context of a circular economy according to the New Urban Agenda approach. To carry out this analysis, three hypotheses were taken from reference [95]. These hypotheses were adapted for this study and are described below. The first hypothesis is loose integration. in other words, the proposals of IoT-LoRa are focused on solving a specific need for a subcategory of the smart city, such as energy, water, health, but they have not considered a wider scope from the perspective of smart city. LoRa's proposals have been developed independently from the criteria or indicators of the smart city. The second hypothesis is tight integration, in other words, IoT-LoRa proposals focus on covering a criterion or specific indicators of the circularity of an intelligent city. The proposals were developed in the context and following the guidelines of the smart city. The third hypothesis is about data. The IoT-Lora proposals focus on the generation of data that can provide insights on certain criteria or indicators, based on which a decision can be made. To perform the analysis, we classified 96 studies identified in the phase of inclusion of the PRISMA methodology (see the Table 16. This methodology was used in this study to produce a qualitative analysis based on the relationship of each study with one of the three above-mentioned hypotheses. We subsequently validated the dependence of each of the seven smart city subcategories defined in the same phase of inclusion on the hypotheses with a chi square independence test [96]. Table 17 shows the contingence table used in the chi-square test. The following results were obtained: Chi 2 equaled 21.8, liberty grades (gl) equaled 12, and \(p\)-value was 0.0119. The hypothesis \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Loose Integration** & **Tight Integration** & **Data** & **Total** \\ \hline Agriculture and Farming & 1 & 0 & 12 & 13 \\ Energy & 2 & 3 & 5 & 10 \\ Environment & 10 & 2 & 11 & 23 \\ Healthcare & 8 & 0 & 5 & 13 \\ Industry & 6 & 1 & 6 & 13 \\ Transportation & 3 & 3 & 5 & 11 \\ Waste Management & 3 & 3 & 7 & 13 \\ Total of paper & 33 & 12 & 51 & 96 \\ \% Papers & 34\% & 13\% & 53\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Classification of papers by hypothesis. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Strengths** & **Weakness** \\ \hline \(\bullet\) & Possibility of deploying private networks \\ \(\bullet\) & Low cost of hardware (less than 5100) \\ \(\bullet\) & Low power consumption in non-transmission mode (less than 1 A) \\ \(\bullet\) & Long battery lifetime (more than 10 years). \\ \(\bullet\) & Large coverage (up to 15 km) \\ \(\bullet\) & More messages may draw other LFWAN technologies \\ \(\bullet\) & Robust to Doppler Effect and multipath fading, due to CSS modulation \\ \(\bullet\) & LoRa uses adaptive data (ADR) \\ \(\bullet\) & LoRa’s sensitivity is on average 127 dBm \\ \(\bullet\) & Easy deployment and configuration. Many examples of configuration are \\ \(\bullet\) & available in Internet websites and scientific databases. \\ \(\bullet\) & End-to-end security is available in LoRa.