# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 2 Related Works
Extensive documentation on manufacturers' websites and the provision of vehicle manual details were the main causes of their popularization [44]. It is now possible to find a reasonable number of academic papers and commercial solutions that use the OBD-II self-diagnostic system to create services or devices that benefit the driver [45]. Another interesting work was developed by researchers from the Institute of Electronic Technology of South Korea [46], who investigated the estimation of CO\({}_{2}\) issued in the environment by vehicles equipped with the OBD-II interface. That project has some characteristics that are similar to the ones discussed herein; however, there are certain disparities. The first difference is related to the fact that the application of [46] does not monitor real-time emission of CO\({}_{2}\). In order to estimate it, they need to drive a few kilometers in a car in order to collect some data through the vehicle interface. Using the measured data, such as the information of the distance covered and the amount of fuel consumed, they are able to measure the average CO\({}_{2}\) emissions for that route. The second difference is that the application proposed in this article can communicate with distant servers via the internet, sending real-time information on the measurement of the CO\({}_{2}\) emissions by the vehicle. Vehicular emissions were also investigated in [47]. In order to obtain the desired data about pollution, the authors used a sensor coupled to the exhaust of the vehicle, directly sensing the emission of toxic gases. However, although promising, that approach lacks scalability, and it is hard to deploy in a urban context. Finally, in Table 1, a comparative summary between the works discussed in this section is presented. It is possible to visualize their main similarities and differences. The \"work\" column refers to the works shown previously, and the second column presents a summary about the characteristics explored by the authors. Besides these works, two other works can be highlighted in which the authors made use of virtual sensors to obtain information related to vehicles, showing the potential of this type of tool. The first work was developed by a group from the University of West Virginia. The authors developed a neural network capable of estimating how much nitrogen oxide was generated at the moment of combustion from a diesel engine, using information of the fuel pressure as the input to the neural network [48]. In the end, they could see that, in a comparison between a real sensor and the developed virtual sensor, they obtained similar answers. The second work, performed by a Brazilian group, showed the development of a virtual sensor used for the estimation of the oil temperature of the car engines [49]. For that, the authors obtained a physical model of the engine lubrication system, along with the existing effects of heat exchanges. At the end of the work, it was shown that the obtained model was feasible to for use as an estimator of the temperature of the engine motor oil in operation. From the above discussion, it is clear that there are gaps to be solved in this area. Within this context, the platform proposed in this paper should be capable of performing the following tasks:  
* Monitoring of the amount of carbon dioxide generated in the combustion;
* Integration of data from various sources;
* Enable statistical inference to be performed from collected data;