# Smart Cities Big Data Algorithms for Sensors Location
## 2 Process to Locate and Relocate Sensors  
Our proposed process comprises four phases:  
1. The preparation of sample data;
2. The inquiry or exploration of data analysis methods for big data treatment;
3. Exploring prediction techniques; and
4. Algorithms design and updating of the map to locate and relocate sensors. The process is based on the data mining process for knowledge discovery in data bases (KDD) [21] and which, according to [22], should be guided by the seven phases: data integration, data selection, data cleaning, data transformation, data mining, pattern evaluation/presentation, and knowledge discovery. Data mining is the step of selection through the application of machine learning techniques, in this case, aiming to find classification patterns for hot-zone identification. Similarly, in our process, the first phase corresponds to data integration, data selection, data cleaning, and data transformation. In our case, it consists of reviewing the quality of life in smart city models in order to select variables according to the standards, and then extracting data from open data public domains to conform the data set. Afterward, the sample is prepared by applying a filter, cleaning, and when required labeling, which constitutes data cleaning and data transformation. Figure 2 depicts the four phases. The first phase is the preparation of the data sample, which begins with the revision of the smart city models and the compilation of standards related to the object of study selected. These activities will indicate the variables that the sensors must capture but, at the same time, will guide the search of data in public domains to form a sample data set. Here, preprocessing for filtering and cleaning is also applied. The result to be obtained is a sample of data validated by the metrics of the smart city models, and by models established by international organizations. Once the data is transformed, it goes to Phase 2 for big data analysis focused on data mining, optimizing the performance of the search of the classification patterns. In the second phase, the exploration of data analysis is performed using supervised learning techniques for classification in order to arrive at a prediction model, using as input the sample of the data set of the previous phase. It is suggested to explore techniques with parallelization capacity  
Figure 2: Phases of the proposed process that determine a scheme to design algorithms to place sensors in specific positions. in order to optimize the processing time of patterns from large volumes of data. Also, independent variables must be clearly distinguished from their dependent counterparts. The output of phase two is a candidate list of techniques to be tested in the Phase 3. In the third phase, the selected techniques are tested with the prepared data sample. Here, a plan has to be made to test and apply the candidate techniques. Then, those whose convergence obtained is equal to or greater than the desired limit are chosen. Another factor that should influence this selection is the technique's capacity to reduce variables. The convergence results are sent to the Phase 4, as the basis for the algorithm's design. In the fourth phase are designed and implemented the algorithms. Two types of algorithm are required for the identification of the hot-zones. The first type of algorithm is those that apply the chosen techniques in Phase 3, that is, those with better convergence results. These algorithms serve for the extraction of data patterns from the model. The second algorithm type includes those that classify and update the frequency matrix with the neighboring sensor matrix shapes of the hot-zones. The application of the algorithms implies the existence of a set of sensors in the network, where every installed sensor has a pair of values related to its geographical localization: latitude and longitude. Further, it also emits the value of variables.