# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
###### Abstract  
Wireless Sensors Networks (WSNs) are currently receiving much research interest due to their wide-ranging use is a number of different fields. In the current study, a system based on a WSN is proposed that can monitor indoor air pollution in several public spaces, such as subway stations, offices, schools, and hospitals. The proposed system uses integrated sensors in mobile phones, moving from a stationary nodes model to a mobile nodes model. The main objective of building this system is to provide full coverage of the target area. To achieve this goal, the system is simulated by MATLAB and the following algorithms are applied: Particle Swarm Optimization (PSO) to maximize the coverage in the region of interest (RoI), Voronoi Diagram (VD) to detect holes in the coverage, and finally the Point in Polygon (PiP) algorithm to heal the holes in the coverage. The application of the algorithms mentioned above has been very effective as PSO has increased the coverage rate of the monitoring area to 100%. The VD allowed us to define the exact location of coverage holes whilew the Point in Polygon algorithm allowed us to heal the holes and find the remaining sensors in order to improve network coverage. This enabled us to achieve full coverage of the monitoring area. wireless sensor network; region of interest; voronoi diagram; point in polygon; coverage; coverage hole +
Footnote †: journal: Sensors  
0000-0002-4000-0002-9900-0002-9900-0002-9900-0002-9900-0002-9900-0002-9900-0002-9900-0002-9900-0002-99000-0002-99000-0002-9900-0002-99000-0002  
monitoring system that uses a smart sensor in a wireless network where the nodes have a specific location, the embedding of neural network processing blocks distributing the processing charge between the embedded systems (web sensor) and the web browser installed in a personal computer, the development of PC software for sensing node TCP/IP remote control, advanced data processing, data storage, and web publishing software associated with air quality monitoring systems using JavaScript and LabView for a web publisher. The work in reference [7] presented a prototype WSN that could monitor the environment in a conference room in a smart building using stationary sending nodes. This prototype network facilitated the testing of four different architectures tasked with learning the relationhip between CO\({}_{2}\) and occupant state. Four different learning models were compared: Gradient Boosting (GB), k-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Random Forests (RF). The KNN model returned the best performance under preferred settings with an accuracy of 47.8% and RMSE (Root Mean Square Error) of 1.021. The system in reference [8] aimed to detect the level of seven gases in three different settings, ozone (O\({}_{3}\)), particulate matter, carbon monoxide (CO), nitrogen oxides (NO\({}_{2}\)), sulfur dioxide (SO\({}_{2}\)), volatile organic compound, and carbon dioxide (CO\({}_{2}\)), on a real-time basis. In doing so, the system provided an overall air quality alert using one of two sensor nodes. In the sensing node, a smoothing algorithm was introduced to prevent temporary sensor errors, and an aggregation algorithm was used to reduce network traffic and power consumption. In the middleware, all data from the sensor cloud were calibrated with the characteristic tables of the cross sensitivities and temperature/humidity dependence. Experiments were conducted to validate and support the development of the system for real-time monitoring and alerts. The authors in [9] addressed the concept of a real-time WSN capable of monitoring the concentration of indoor carbon dioxide and dangerous situations inside three different areas (two medium size and one by size) using stationary sensor nodes for data aggregation and mobile nodes for data calibration. They followed a cognitive networking technique with an opportunistic routing protocol. An information processing framework was proposed to detect outliers, form data packets, and calibrate the sensors.