A Parallel Support Vector Machine algorithm to identify patterns of pollution in Smart Cities for Metropolitan Zone of Guadalajara

Abstract

Pollution in dense populations such as the Metropolitan Area of Guadalajara grows exponentially, affecting the health of citizens and reducing their quality of life. One of the main research challenges pursued with the study of Smart Cities is environmental pollution to improve the well-being of citizens and to protect natural areas. Therefore, it is urgent the development of information technologies that allow to reduce this problem by the scientific analysis of data, to classify the zones with greater contamination. Currently, these data are captured by constant monitoring stations, generating a large volume of information representing a challenge for the classification process. This work proposes a model for the automatic execution of a classification algorithm using Support Vector Machine implementing libraries in python for parallel processing. As a result, we obtain two main subsystems: one of the Parameters of Configuration for the storage and cleaning of data and the design of the algorithm parallelized in the cloud importing modules mpi4py, numpy and sklearn.svm.

Keywords

• Data mining
• Support vector machines
• parallel support vector machine libraries
• Internet of Things
• Smart Cities.