# Smart Cities Big Data Algorithms for Sensors Location
The proliferation of sensors is increasing, and with this, the applications that perform a scientific analysis on their generated data, along with the use of different variables which, in one way or another, contribute to improving human wellbeing. Such is the case of the mobile systems, in which data comes from sensors installed inside them [9], or the systems that use sensors for environmental monitoring [10]. Also, a paradigm proposed in smart cities is the mobile crowd sensing (MCS), which focuses on using mobile integrated sensors to monitor multiple environmental phenomena, such as noise, air, or electromagnetic fields in the environment [11]. In this same topic, a system of pollution warning services was developed for smart cities [12], to notify the user of the concentration of pollution in the place where they are located, through mobile devices that measure the quality of the air. For example, a system based on crowdsourcing for mobile phones was developed to help car users to find the most appropriate places to park, in order to avoid problems of traffic congestion, air pollution, and social anxiety problems [8]. Another system of pollution warning services in smart cities was proposed to notify the user regarding the concentration of pollution, and about vehicles. In this case, mobile devices measure air quality [10]. In [11], an implementation of smart sensors was presented to monitor air quality where the tracking variables included dust particles (PM\({}_{10}\)), carbon monoxide (CO), carbon dioxide (CO\({}_{2}\)), noise level (dB), and ozone (O\({}_{3}\)), with the aim to keep people informed in real-time through the IoT. In this context, machine learning and deep learning are two successful techniques both used for the classification of data as well as the identification of patterns. Another technology used in the same context is bio-inspired algorithms for the interpretation of information from the sensors. However, since big data faces the challenge of large volumes of information, several techniques are combined with it to provide proper solutions. As mentioned, the constant use of sensors in smart cities led to the field of big data, which deals with processing and data analysis techniques. Within a broad set of techniques and methods for processing big data are the decision trees (DT) based on classifiers applied to large datasets [13]--DTs are also proposed to analyze large data sets for both numerical and mixed-type attributes. By processing all the objects of the training set without prior memory storage, this requires that the user define the parameters. It works by evaluating the training instances one by one incrementally, updating the DT with each revised case. With a small number of instances, the node is expanded faster than the expansion process of other algorithms. Furthermore, the instances used in the expansion of the node will be eliminated once the expansion is made avoiding in this way the storage of the training set in the memory. In this context, in [14] was proposed the use of swarm search with accelerated particle swarm optimization. This is an algorithm capable of selecting the variables for data mining--its search achieves precision in a reasonable processing time. Further, in [15] was presented a system based on the ideas of pattern recognition that converges in the Bayes classifier. This system is scalable in data and can be implemented using structured query language (SQL) over arbitrary database tables--it uses disk storage with classification purposes. In [16], the algorithm C4.5 implemented a DT with the map-reduce model, to allow parallel computing of big data. This algorithm implements map-reduce with the 'divide and conquer' approach in order to discover the most relevant attributes of the data set for decision-making. Importantly, neither of the two techniques using and not using discs for the training of big data ensure that there are defined patterns.