Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds.

This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same package, being also smaller than the previous generation. This makes it easier to integrate them into the electronics and improves their performance. In this research, the application of the device is focused on the detection of atmospheric pollutants in order to complement the information provided by the reference stations. To validate the system, it has been tested with different concentrations of NOx including some tests specifically developed to study the behavior of the device in different humidity conditions. Finally, a mobile application has been developed to provide classification services. In this regard, a neural network has been developed, trained, and integrated into a smartphone to process the information retrieved from e-nose devices.

The Internet of Things (IoT) is a paradigm that has rapidly evolved in recent years. The ability to connect many devices is driving the development of new types of applications that allow the remote monitoring of a wide variety of devices. One of the great challenges that has been identified in this field is the monitoring of sensors scattered in wide areas or located in environments with poor or even no communications coverage. To deal with this problem, different approaches based on cellular or satellite communications have been considered. However, these alternatives are complex and very expensive. To overcome these drawbacks, we designed a system based on unmanned aerial vehicles and LoRa technology that enables data transfer from sensors to a central system. Furthermore, machine learning techniques were applied to process and classify the data retrieved from the sensors. Finally, a Java-based application was developed, providing services such as data storage, processing, and visualization. To verify the correct operation of the proposed system, manual and autonomous flight tests were carried out, verifying the correct transmission of the data from the sensors to the central system.

This study addresses the development of a miniaturized wireless sensing module (electronic nose) for a personal use in air quality detection.