Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution  
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1 Department of Informatics and Telecommunications, University of Ioannina Arta, 47150 Arta, Greece; [EMAIL_ADDRESS] (E.D.S. ); [EMAIL_ADDRESS] (I.T. )1 Department of Informatics and Telecommunications, University of Ioannina Arta, 47150 Arta, Greece; e.spyrou@uoi. air pollution monitoring stations are used to assess air quality. These monitoring stations have a high level of data accuracy and can measure a wide range of contaminants [3]. Here, environmental stations are encapsulated that monitor specific environmental parameters, and the CO in the air is among them. Monitoring can be undertaken using Internet of Things (IoT) devices and wireless sensor networks (WSN)s. There are different sensors and architectures that can be employed to perform the task [4]. Environmental monitoring can be performed using a number of technologies, including Zigbee [5], Wi-Fi [6], GSM [7], and other telecommunications [8]. The significance of IoT and WSNs lies in the fact that they can be distributed in nature and measure local microclimate as opposed to other solutions that are centralized and based on an entire area. In such a way, the locality of the readings can be compared and an investigation of potential patterns may be identified. In this work, two environmental stations reside in the greater area of the Port and the Town Hall of Igoumenitsa city in Greece, respectively. The wireless devices that comprise the environmental monitoring stations communicate with a central server at the University of Ioannina campus in Arta, Greece, using GSM, as can be seen in Figure 1. The stations provide a number of environmental parameters, and in this work, CO is classified using this method. Artificial intelligence and machine learning are often encapsulated in order to identify patterns and classify them. An appropriate and good method constructs classification programs in a human-readable format using the technique of grammatical evolution [9]. Grammatical evolution is an evolutionary process that has been applied with success in many areas, such as music composition [10], economics [11], symbolic regression [12], robot control [13], and combinatorial optimization [14]. The method used here was initially described in [15] and the code is also described in [16]. In this paper, we describe the classification technique of grammatical evolution to perform this task using real data obtained from wireless sensor environmental stations. The proposed technique uses grammatical evolution to generate classification rules and is available free from the relevant website. Of course, any other genetic programming technique could be used in place of grammatical evolution. However, grammatical evolution has been established as a technique for creating programs using grammar in BNF. Grammatical evolution has an advantage over other genetic programming techniques in that it does not use trees or other complex data structures to express programs, but strings of integers that represent production rules. This means that the technique is simpler to code and clearly faster compared to other genetic programming techniques. The datasets  
Figure 1: Environmental monitoring wireless communication. obtained from the Port and Town Hall are processed accordingly, namely, normalized, and the k-means algorithm is applied in order to find the clusters of the data. The k-means algorithm is used in order to perform an initial classification of the data into clusters since domain expert knowledge of the patterns is outside of the scope of this paper. The k-means is used as another feature of the data since domain-expert knowledge is absent. The GenClass method has been presented in several related publications and has very good results in classification problems. Moreover, the code of the method is freely accessible to all and can be used in any classification problem.