# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
###### Abstract
The system managed to report CO\({}_{2}\) concentration in real-time, while the information processing framework minimized outliers. In [10], indoor air quality was improved by controlling particulate matter (PM) consisting of components such as house dust and bacteria, using an atmospheric pressure plasma. In this study, two types of electrode were used for particle collection and formaldehyde removal. Both electrodes were designed to shorten the gap distance and to minimize the discharge voltage used for indoor air treatment. Formaldehyde control was performed in a 1 m\({}^{3}\) chamber. A high gas flow was used with the electrode system to simulate the large flow rate in a room. These electrode systems had lower operating voltages than those reported for conventional ESPs. In the field of technology, there are many indoor air pollution monitoring systems based on WSN. The network of those systems was built with stationary sensor nodes and developed using different architectures, modules, networking protocols, and expensive elements such as: IoT platform and Libelium collections [11], AVR ATmega64 micro controller, Raspberry, and LoRaWAN wireless communication [12], XBee module and Arduino Mega system [13], and ZigBee networking protocol [14; 15], GSM modem [16], and ARIMA prediction models [17]. However, the data collected, and the analyzed results, are not accessible to the general public. For those major reasons, in this project we will realize a low-cost system to monitor indoor air pollution and switch from stationary nodes in a specific area to mobile nodes in the whole city. We will achieve this by using the mobile phones of the general public to compose a WSN and then transmit the values of the parameters collected by using WIFI or a hot spot to the base station where the level of air pollution will be evaluated and then communicated to the general public. Deploying sensor nodes is a critical phase that dramatically affects the performance of the network and the quality of service. Additionally, the performance of the WSN is highly influenced by the method used to deploy the sensor nodes. A number of different issues have been raised in regard to the deployment of sensor nodes in a WSN. Several studies based on optimization algorithms have considered different methods to achieve total coverage of the monitoring area such as: Biogeography based optimization and deferential evaluation [18], virtual forces algorithm [19], genetic algorithm with particle swarm optimization [20], particle swarm optimization with Voronoi Diagram (VD) [21], and differentiated deployment algorithm based on image processing and 3D modeling [22]. These studies mainly concern stationary and mobile cases. The main objective of this project is to ensure the best quality of service, and this begins with full coverage of the monitoring area. The process of deploying the sensors has an impact on the performance of the WSN because it affects the network coverage, communication cost, and management resources, so the deployment strategy of a WSN is a major problem. After we had conducted our review of the relevant literature, we then began to choose the algorithms that would suit us in our work to allow us overcome the aforementioned problems. Therefore, for the first part of coverage optimization and deployment strategy, the Particle Swarm Optimization (PSO) will be applied to fulfill the coverage over the region of interest (RoI) in Section 2. The second part of our application will be divided into two sections: A VD to detect the location of the coverage holes holes, and to then use the Point in Polygon (PiP) for coverage hole healing, which will be expanded in Section 3. Finally, we will use MATLAB language to simulate our system in Section 4, and give the conclusion in Section 5.