# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 3 Related Works
The topic of gathering information has been widely studied in other research fields like WSNs, where the specific restrictions of the sensors regarding processing, memory and battery have led to efficient harvesting approaches in relation with the previous factors which have also considered compression techniques for a reduction of the information to be transmitted. Among the different compressing techniques that have been applied [17; 18; 19; 20], CS emerged with much strength due to its high compression rate and the accurate reconstruction with respect to the original data. [PERSON] et al. [5] provide a review of CS, as well as its application to different fields like WSNs, where the compression of the information can be even applied to the transmitted information directly in the air as proposed [PERSON] et al. [21], or [PERSON] et al. [22]. The former presents a technique called compressive wireless sensing which allows the combination of two signals. The latter proposed another scheme called sparse distributed compression which avoids the energy consumption in combining the sensing information within each node. Although these techniques are out of the scope of our research domain, there are other approaches applied to the networked data by using projection vectors to compress the information that is more suitable for our research area. The underlying idea is to define a RoI of a specific scenario which is divided into \(r\) rows and \(c\) columns. This way we obtain an \(r\times c\) matrix, whose cells contain a value corresponding to the sensor reading inside the corresponding subdivision of the RoI. The matrix is now seeing as a signal where the CS technology can be applied. Works like [PERSON] et al. [23] apply the CS technology in the WSN domain. In this paper, the authors propose an adaptive scheme managed by the FC. In a first step, nodes randomly decide to send their measurements without any compression scheme to the FC. After receiving this information, the FC chooses a projection vector to obtain information about the areas where it has vague or imprecise information. This message, as it is transmitted through the network, is used by the sensor nodes selected by the projection vector to incorporate their sensed information. Eventually, the projection vector is fulfilled and sent back towards the FC. Additionally, [PERSON] et al. [24] present an adaptive compressed sensing algorithm for wireless image sensor networks where higher sampling rates are assigned to blocks that are less compressible. [PERSON] et al. [25] propose a gathering scheme for WSNs based on the CS theory over graphs. Another work from [PERSON] et al. [26] introduces the concept of clusters and CS over WSNs. Other works, such as [PERSON] et al. [27] computes CS projection in networks by means of randomized aggregations of signal values in a neighborhood. Unlike in WSNs, nodes in VSNs move inside an urban scenario. Therefore, although compressing techniques in the network were applied, the FC will not have enough information about where these measurements have been taken. This is the reason why we do not apply compression in the network, but in at data level using the projection vectors already mentioned. On the other hand, CS has been also applied to the VSNs. [PERSON] et al. [28] present a theoretical work where nodes gather information at intersections, and after the acquisition of several data, they apply CS to these samples that are then transmitted to the FC. More recently, [PERSON] et al. [29] proposed a complete framework for acquiring information which can adjust the parameters of the measurement matrix in CS according to the characteristics of sensed target data and the distribution of vehicles, keeping this way the sparsity feature required for a proper application of the CS technology. Moreover, vehicles act as ferries collecting data along their trajectory. Finally, [PERSON] et al. [30] introduced a compressive sensing approach for capturing and recovering data from a VSN.