There has been a steady increase in applications that rely on crowdsensing to gather data for analysis purposes. Crowdsensing enables the use of dynamic sensors to collect data on static objects of interest. However, using dynamic sensors in this way causes a problem. The focus of the collected data is on the position of the sensor, not on the object of interest. This results in difficulties in tracking the object of interest in terms of what part of the data from the dynamic sensor describes the object of interest. To shift the focus from the dynamic sensors to a static object, the virtual sensor is introduced. A virtual sensor enables the grouping of data from different dynamic sensors into a single virtual sensor based on measurement positions. The data from the multiple dynamic sensors can be analyzed to provide information per virtual sensor. The data structure of a visual sensor is close to the SensorThings API data structure, which can be expanded to support virtual sensors by adding an additional entity.

Contractors are moving from traditional maintenance to preventive maintenance. To apply preventive maintenance, up-to-date data is required which is unavailable. The goal is to research the applicability of crowdsensing to detect road pavement distresses, and to investigate how the positions of these detections can be used to gain information on a road pavement section.

The construction sector is currently moving from traditional maintenance contracts, where maintenance was a pay per bill business, to contracts focused on the performance and availability of assets. These types of contracts shift the risk during the maintenance phase from the government to the contractor. The contractor receives a fine if either the minimum performance or the availability is not met. This fine in combination with the risk of unforeseen maintenance costs creates a demand for more efficient maintenance methods like preventive maintenance. However, in the field of road asset management, there is a knowledge gap in how road pavement distresses develop over time. To fill this knowledge gap, up-to-date information is required. The up-to-date information cannot be provided using the current high-quality methods due to the limited availability of equipment.

In this research, raw data is gathered on the state of the road through crowdsourcing with smartphones. This data is then preprocessed by signal analysis, positioning analysis and event detection to retain only the information which describes the occurrence of a road pavement distress. Next, a database analysis consisting of clustering, asset linking and averaging is performed to gather information on the measurements of an instance of road pavement distress. The position of the instance of road pavement distress can be seen as the position of a virtual sensor. A virtual sensor groups events together from multiple smartphones. The location of the virtual sensor is linked to the location of the nearest road pavement asset, allowing asset managers, the people who plan maintenance tasks at contractors, to send their maintenance crew to the correct location.

The results from the experiments show that the detection of road pavement distress is possible by using crowdsensing. The position of the virtual sensor increases in accuracy when more data is used. However, it is possible that two different virtual sensors can be merged due to positioning errors.

It is also possible to connect the position of the virtual sensor to a real world asset location by using the Dutch national road sections and the hectometer posts. The resulting information can be used to get an indication of the status of different assets, giving asset managers insight into which areas and assets they need to focus on.