# Smart Cities Big Data Algorithms for Sensors Location
## 4 Conclusions  
The development of smart cities has led to the increase of sensors in cities--with them, it is possible to identify phenomena to meet one of its main objectives, the development of quality of life and sustainability. Therefore, it is essential to have methods that help to establish a better location for new sensors in existing critical points or hot-zones.  
We propose that the analysis of the data generated by the same sensors, provides information helpful to identify the hot-zones, and to locate or relocate sensors.  
Based on the data mining paradigm, we observed that this is not sufficient for a dynamic system. Thus, our process includes the last phase for the recognition and visualization of hot-zones, a perimeter with significant data related to the observed phenomenon. The information captured by the sensors determines the location of new sensors, or the relocation of those already receiving data.  
The proposed process offers a scheme for data labeling, creating a dynamic classification model. The classification and training algorithms in the cloud manage an independent control. Then, prediction techniques required to be tested to get those that better fit the data.  
In our case study, data regarding air pollution in the Guadalajara Metropolitan Zone in Mexico was analyzed and, for that, the SVM was selected. With the SVM, different parameters were tested, and it was adjusted to different kernels, allowing for more accurate predictions.  
Finally, two algorithms were designed as a result of the application of the process. These algorithms are independent processes on threads using cloud computing. The first one classifies each one of the entries captured in order to generate a matrix of territorial adjacencies. The second one is trained with a classification and regression tree (CART) and SVM. Although the development of the algorithms was performed in the context of environmental variables, this proposal is autonomous with respect to the sensors' features. It can also be scaled to various types, including the quantity and volume of information, because it does not require data storage. It is worth mentioning that it is necessary to continuously verify the local and international standards for the algorithms since new sensors might be incorporated varying in variables and ranges.  
In future work, the combinations of variables obtained from sensors, along with other retrieving variables, such as those coming from questionnaires, social media, or government data, will be included.  
Conceptualizacion, E.E. and M.P.M.V.; Methodology A.P.P.N.; Software, E.E. and J.G.; Validation G.L.L.; Formal analysis E.E. and A.P.P.N.; Investigation, E.E., M.P.M.V., J.G., and R.M.; Data curation, E.E. and J.G., Writing--Original draft preparation E.E.; Writing--Review and editing E.E., A.P.P.N. and M.P.M.V.; Visualizacion M.P.M.V.; Supervision A.P.P.N.  
Figure 8: Georeferenced map the neighbor’s sensor border on a first run of a recursive process.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We would like to thank [PERSON] director of the Electronic and Computer Division of the CUCEI for his support.  
**Conflicts of Interest:** The authors declare no conflict of interest.