# Smart Cities Big Data Algorithms for Sensors Location
#### Related Work  
In this paper, we propose a classification scheme based on machine learning, using big data generated to construct patterns in real-time for the fixed location of sensors. These patterns will provide the surrounding areas with a low quality of life in order to establish a better-fixed location for new sensors, or the relocation of the old sensors with the information generated by them through established hot-zones. Therefore, some related works regarding sensor locations are next discussed.  
In [6] are presented different methodologies for selecting the places where the sensors should be installed to obtain information on the phenomena to be observed, ranging from the preparation of a grid of the study area to the use of complex statistical models that provide the number and optimal distribution of the sensors, but this is based strictly on the amount of information with which the model is generated.  
In [17] is implemented a genetic algorithm (GA) to determine sensor locations and to establish the number of these sensors for proper coverage. As a first step, the GA randomly creates the population on the input map. This algorithm has the main feature of finding the number of optimum sensors based on the input map.  
In [18] is presented an algorithm that simultaneously defines a sensor placement and a sensor scheduling. An approximation algorithm with a finite set of possible locations establishes where sensors can be placed; this algorithm selects a small subset of locations. These works focus on locating the sensors through random functions or by a set of defined positions.  
There are several works related to the location of sensors for smart cities. Furthermore, some of them have a dynamic identification for sensors positions. However, few of them focus on the identification of fixed positions and, to our knowledge, none of them exploit the information acquired by the same sensors with big data techniques in order to identify their optimal location or relocation. In contrast, our proposal establishes the location of sensors in real-time using the concept of hot-zones in order to identify the sensors with significant activity regarding the phenomena observed with high precision.  
We defined the concept of a 'hot-zone' as a perimeter area source of essential data for analysis. In a hot-zone can be found constant activity with substantial data to evaluate the quality of an observed phenomenon. These hot-zones are then used to locate or relocate new sensors. For that, a process with four phases is proposed. The first three phases are based on the data mining process, and the fourth phase constitutes the establishment of algorithms using selected techniques. The final aim of the process is to settle the hot-zones.  
As an example, this process was implemented in the Guadalajara Metropolitan Zone (GMZ), Mexico, for air pollution. In this case, a first algorithm was designed for data training to generate the classification model with dynamic updating. A second algorithm was designed for data labeling, which is triggered after each data input. The two algorithms are independent. The classification is carried out in parallel with the training process--while some data are classified, others are used for training to observe possible changes in the patterns, or to decrease process time by a possible reduction in the number of variables of the model. In this phase, a frequency matrix is generated that works in conjunction with a neighboring sensor matrix in order to identify the hot-zones and present their visualization in a geo-referenced map [19; 20].