Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring.

Public air quality monitoring relies on expensive monitoring stations which are highly reliable and accurate but require significant maintenance and cannot be used to form a high spatial resolution measurement grid. Recent technological advances have enabled air quality monitoring that uses low-cost sensors. Being inexpensive and mobile, with wireless transfer support, such devices represent a very promising solution for hybrid sensor networks comprising public monitoring stations supported by many low-cost devices for complementary measurements. However, low-cost sensors can be influenced by weather and degradation, and considering that a spatially dense network would include them in large numbers, logistically adept solutions for low-cost device calibration are essential. In this paper, we investigate the possibility of a data-driven machine learning calibration propagation in a hybrid sensor network consisting of One public monitoring station and ten low-cost devices equipped with NO2, PM10, relative humidity, and temperature sensors. Our proposed solution relies on calibration propagation through a network of low-cost devices where a calibrated low-cost device is used to calibrate an uncalibrated device. This method has shown an improvement of up to 0.35/0.14 for the Pearson correlation coefficient and a reduction of 6.82 µg/m3/20.56 µg/m3 for the RMSE, for NO2 and PM10, respectively, showing promise for efficient and inexpensive hybrid sensor air quality monitoring deployments.

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing.

Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning.

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93-0.97, 0.82-0.94 and 0.73-0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

Designing Laboratory for IoT Communication Infrastructure Environment for Remote Maritime Surveillance in Equatorial Areas Based on the Gulf of Guinea Field Experiences.

The steady increase of the world population and economy leads to an increase in both types and amounts of goods transported over seas, which further inevitably leads to an increase of criminal activities in the maritime arena. In order to stifle criminal activities nations are forced to develop sophisticated sensor networks. The backbone of any sensor network is a communication network which connects all sensors with the command centers, most often located hundreds of kilometers away from the sensors. In developing countries, communication networks are very often poorly developed, leaving only satellite links as somewhat reliable means of communication. Henceforth, in this paper, a laboratory for the Internet of Things (IoT) communication infrastructure environment designed to facilitate maritime sensor network design process in areas where communication network is dependent on data transfer over satellite links is presented. In order to successfully describe and develop a laboratory for IoT communication infrastructure environment, necessary data are collected during the design and deployment of a maritime surveillance network in the Gulf of Guinea. The main advantage of the proposed laboratory environment is the inclusion of satellite link simulation in the IoT laboratory environment. This feature provides an opportunity to cover a much broader scope of IoT solutions compared to other IoT laboratories.