Oscar Alvear

An Architecture Offering Mobile Pollution Sensing with High Spatial Resolution

Mobile sensing is becoming the best option to monitor our environment due to its ease of use, high flexibility, and low price. In this paper, we present a mobile sensing architecture able to monitor different pollutants using low-end sensors. Although the proposed solution can be deployed everywhere, it becomes especially meaningful in crowded cities where pollution values are often high, being of great concern to both population and authorities. Our architecture is composed of three different modules: a mobile sensor for monitoring environment pollutants, an Android-based device for transferring the gathered data to a central server, and a central processing server for analyzing the pollution distribution. Moreover, we analyze different issues related to the monitoring process: (i) filtering captured data to reduce the variability of consecutive measurements; (ii) converting the sensor output to actual pollution levels; (iii) reducing the temporal variations produced by mobile sensing process; and (iv) applying interpolation techniques for creating detailed pollution maps. In addition, we study the best strategy to use mobile sensors by first determining the influence of sensor orientation on the captured values and then analyzing the influence of time and space sampling in the interpolation process.

Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.

Mobile Pollution Data Sensing Using UAVs

Nowadays, the impact of global warming is causing societies to become more aware and responsive to environmental problems. As a result, pollution sensing is gaining more relevance. In order to have a strict control over air quality, the use of mobile sensors is becoming a promising alternative to traditional air quality stations. Mobile sensors allow to easily perform measurements in many different places, thereby offering substantial improvements in terms of the spatial granularity of the data gathered. Pollution monitoring near large industrial areas or in rural areas where transportation facilities are poor or inexistent can complicate the mobile sensing approach. To address this problem, in this paper we propose endowing Unmanned Aerial Vehicles (UAVs) with pollution sensors, allowing them to become autonomous air monitoring stations. The proposed solution has the potential to quickly cover a target region at a low cost, and providing great flexibility.

Validation of a vehicle emulation platform supporting OBD-II communications

In the next few years, important developments are expected in the Intelligent Transportation Systems (ITS) area. One of the key issues enabling future solutions is achieving an effective integration between mobile apps and vehicles. Such integration can be efficiently achieved on all existing vehicles by relying on the On Board Diagnostic (OBD-II) interface. This allows obtaining critical information such as speed, fuel consumption, gas emissions and system failures. In this paper we propose a vehicle emulation platform, called VEWE, that allows developing and testing OBD-II aware applications. The advantages of this approach include: avoiding the need for a real vehicle, allowing to easily generate realistic vehicle parameter patterns, and supporting emulated GPS functionality. We evaluate our platform by conducting a performance analysis in terms of OBD-II response times and channel capacity when relying on a Bluetooth adapter. We compare our results with respect to those obtained in real vehicles, and demonstrate that our VEWE platform behaves similarly to realistic on board devices, thereby providing a complete and reliable platform for smartphone application development.

Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues

Evidence shows that Smart Cities are starting to materialise in our lives through the gradual introduction of the Internet of Things (IoT) paradigm. In this scope, crowdsensing emerges as a powerful solution to address environmental monitoring, allowing to control air pollution levels in crowded urban areas in a distributed, collaborative, inexpensive and accurate manner. However, even though technology is already available, such environmental sensing devices have not yet reached consumers. In this paper, we present an analysis of candidate technologies for crowdsensing architectures, along with the requirements for empowering users with air monitoring capabilities. Specifically, we start by providing an overview of the most relevant IoT architectures and protocols. Then, we present the general design of an off-the-shelf mobile environmental sensor able to cope with air quality monitoring requirements; we explore different hardware options to develop the desired sensing unit using readily available devices, discussing the main technical issues associated with each option, thereby opening new opportunities in terms of environmental monitoring programs.

EcoSensor: Monitoring environmental pollution using mobile sensors

Air pollution monitoring has become an essential requirement for cities worldwide. Currently, the most extended way to monitor air pollution is via fixed monitoring stations, which are expensive and hard to install. To solve this problem, we have developed EcoSensor, a solution to monitor air pollution through mobile sensors. It is deployed with off-the-shelf hardware such as Waspmote (based on the Arduino platform), low-end sensors, and Raspberry Pi devices.

Calibrating Low-End Sensors for Ozone Monitoring

Performing pollution measurements is a difficult and costly process. On the one hand, specialized laboratories are needed to calibrate sensors and adjust their readings to units that indicate the level of contaminants in the environment, and, on the other hand, measurements depend on the type of sensor. High-end sensors are very accurate but quite expensive, while low-end sensors are more affordable but have less precision and introduce considerable oscillations between readings. This paper presents a methodology to measure ozone pollution data with low-end mobile sensors, focusing on sensor calibration through historical data and the existing environmental monitoring infrastructure. The proposed methodology is developed in three phases: (i) reduction of data measurements variability, (ii) calculation of calibration equations, (iii) and analysis of the spatial-temporal behavior to reduce variations in time produced when data are captured using mobile sensors.

A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing

Recently, Unmanned Aerial Vehicles (UAVs) have become a cheap alternative to sense pollution values in a certain area due to their flexibility and ability to carry small sensing units. In a previous work, we proposed a solution, called Pollution-driven UAV Control (PdUC), to allow UAVs to autonomously trace pollutant sources, and monitor air quality in the surrounding area. However, despite operational, we found that the proposed solution consumed excessive time, especially when considering the battery lifetime of current multi-rotor UAVs. In this paper, we have improved our previously proposed solution by adopting a space discretization technique. Discretization is one of the most efficient mathematical approaches to optimize a system by transforming a continuous domain into its discrete counterpart. The improvement proposed in this paper, called PdUC-Discretized (PdUC-D), consists of an optimization whereby UAVs only move between the central tile positions of a discretized space, avoiding monitoring locations separated by small distances, and whose actual differences in terms of air quality are barely noticeable. We also analyze the impact of varying the tile size on the overall process, showing that smaller tile sizes offer high accuracy at the cost of an increased flight time. Taking into account the obtained results, we consider that a tile size of 100 × 100 meters offers an adequate trade-off between flight time and monitoring accuracy. Experimental results show that PdUC-D drastically reduces the convergence time compared to the original PdUC proposal without loss of accuracy, and it also increases the performance gap with standard mobility patterns such as Spiral and Billiard.

PdUC-D: A Discretized UAV Guidance System for Air Pollution Monitoring Tasks.

Discretization is one of the most efficient mathematical approaches to simplify (optimize) a system by transforming a continuous domain into its discrete counterpart. In this paper, by adopting space discretization, we have modified the previously proposed solution called PdUC (Pollution-driven UAV Control), which is a protocol designed to guide UAVs that monitor air quality in a specific area by focusing on the most polluted areas. The improvement proposed in this paper, called PdUC-D, consists of an optimization whereby UAVs only move between the central tile positions of a discretized space, avoiding to monitor locations separated by small distances, and whose actual differences in terms of air quality are barely noticeable. Experimental results show that PdUC-D drastically reduces convergence time compared to the original PdUC proposal without loss of accuracy.

Estimating rainfall intensity by using vehicles as sensors

Vehicles are key elements in the envisioned Smart Cities, not only providing more efficient mobility, but also becoming mobile network elements able to perform many useful tasks. Environment sensing is a good example where the combination of data coming from vehicles allows achieving insight only comparable to the deployment of hundreds or thousands of sensors in a city. Obtaining rainfall estimations with a high spatial granularity is an example of a task where relying on traditional methods would become too expensive due to the high number of data sources required. Vehicular networking has a great potential to address such challenge by converting every vehicle in a rain sensor. In this paper we carry out a simulation study to estimate the rainfall intensity in a specific area using a vehicular network as data source. To this purpose, we model a rainfall pattern taking real values as reference, and we devise a simulation scenario where the rainfall pattern is deployed. Experimental results using the OMNeT++ simulator show that, even with a low density of vehicles contributing to the proposed monitoring system, rainfall intensity can still be predicted with a high accuracy and granularity, thereby validating the proposed approach.