The widespread use of low-cost environmental monitoring systems, together with recent developments in the design of Internet of Things architectures and protocols, has given new impetus to smart city applications. Such progress should, in particular, considerably improve the fine characterization of a wide range of physical quantities within our cities. Indeed, the cost-effectiveness of these emerging sensors combined with their reduced size allows for high density deployments resulting in a higher spatial granularity. In this paper, we briefly present the 3M'Air project that aims to explore the potential of participatory citizen measures using low-cost sensors in order to improve the local knowledge of air quality and temperature and then bridge the gap between individual exposure and regional measurements. We present then the design, implementation and evaluation of our low-cost, small-size WSN-based participatory monitoring system. This system is composed of mobile sensing nodes measuring temperature, humidity and a number of pollutants (NO 2 , PM 1 , PM 2.5 and PM 10 ). The collected data are sent to a server for analysis and building temperature and air quality maps. To validate our platform, we have carried out multiple tests to compare our sensor nodes to reference stations and to each other. We have also evaluated the energy consumption of our nodes under different configurations. The results are satisfactory and show that our nodes can be used in environmental participatory monitoring.

The use of low-cost Wireless Sensor Networks (WSNs) for air quality monitoring has recently attracted a great deal of interest. Indeed, the cost-effectiveness of emerging sensors and their small size allow for dense deployments and hence improve the spatial granularity. However, these sensors offer a low accuracy and their measurement errors may be significant due to the underlying sensing technologies. The main aim of this work is to reconsider and compare some regression approaches to assimilation ones while taking into account the intrinsic characteristics of dense deployment of low cost WSN for air quality monitoring (high density, numerical model errors and sensing errors). For that, we propose a general framework that allows the comparison of different strategies based on numerical simulations and an adequate estimation of the simulation error covariances as well as the sensing errors covariances. While considering the case of Lyon city and a widely used numerical model, we characterize the simulation errors, conduct extensive simulations and compare several regression and assimilation approaches. The results show that from a given sensing error threshold, regression methods present an optimal sensor density from which the mapping quality decreases. Results also show that the Random Forest method is often the best regression approach but still less efficient than the BLUE assimilation approach when using adequate correction parameters.

Participatory sensing leverages population density and involves citizens in the collection of extensive data in multiple fields such as air pollution monitoring, enabling large-scale deployments and improving the knowledge of air quality. This study highlights the potential of low-cost sensors through a data analysis of pollutant concentrations collected during multiple sensing campaigns we co-organized using a participatory sensing platform we designed. We first compare the estimation quality of four statistical models and investigate the impact of sampling frequency on the quality of estimation and energy consumption of the nodes using an energy model based on the sensing duty cycle. In addition, we evaluate the capacity of regression models to recover missing data of one sensor based on the other sensors. Results are satisfactory and reveal that a small decrease in the sampling frequency slightly reduces the estimation quality, but in contrast, allows the nodes to operate on a longer period.