# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
###### Abstract  
We propose a new harvesting approach for Vehicular Sensor Networks based on compressed sensing (CS) technology called Compressed Sensing-based Vehicular Data Harvesting (CS-VDH). This compression technology allows for the reduction of the information volume that nodes must send back to the fusion center and also an accurate recovery of the original data, even in absence of several original measurements. Our proposed method, thanks to a proper design of a delay function, orders the transmission of these measurements, being the nodes farther from the fusion center, the ones starting this transmission. This way, intermediate nodes are more likely to introduce their measurements in a packet traversing the network and to apply the CS technology. This way the contribution is twofold, adding different measurements to traversing packets, we reduce the total overload of the network, and also reducing the size of the packets thanks to the applied compression technology. We evaluate our solution by using ns-2 simulations in a realistic vehicular environment generated by SUMO, a well-known traffic simulator tool in the Vehicular Network domain. Our simulations show that CS-VDH outperforms Delay-Bounded Vehicular Data Gathering (DB-VDG), a well-known protocol for data gathering in vehicular sensor networks which considers a specific delay bound. We also evaluated the proper design of our delay function, as well as the accuracy in the reconstruction of the original data. Regarding this latter topic, our experiments proved that our proposed solution can recover sampled data with little error while still reducing the amount of information traveling through the vehicular network. compressed sensing; vehicular sensor networks; gathering data 202020143390/s20051434 2020201434 2020201434 2020201434 2020201434 2020201434 2020202  
the VSN nodes. Whereas in WSNs, sensors are deployed at specific and fixed positions within a region of interest, in VSNs, vehicles are continuously moving with high speed along streets and roads of urban scenarios. Additionally, the presence of traffic signs and traffic lights cause partitions in the network, the creation of platoons of vehicles, with the corresponding high variability of the wireless links among them. The second difference is related to the requirements the nodes have in terms of performance and energy consumption. Nodes in a WSN are usually equipped with short-spanned batteries and constrained processing and storing capabilities. This means that these different scenarios do not impose the same restrictions and conditions when designing communication solutions. Traditionally, data harvesting protocols take advantage of intermediate nodes to append new information into traversing packets as they forward them to their corresponding destinations reducing this way the number of issued messages, but increasing the packet size as new information is included. Nevertheless, a collection of sampling methods in Information Theory known as compressed sensing (CS), compressive sensing, compressive sampling, or sparse recovery has emerged during the last years [1]. Its key aspect is the idea of reconstructing the original information of a signal using only a few samples without losing accuracy in the reconstruction process. Additionally, CS is able to obtain an accurate approximation of the data by using a small number of generalized measurements, which are known as projections. We explain the essential operation of CS in Section 2. Considering the specific requirements and conditions of VSNs, this paper proposes a data gathering solution which comprises two stages: (i) a query dissemination process where the FC broadcasts a message within a determined region of interest, and (ii) the harvesting stage where vehicles send their measurements back to the FC in an efficient way.