# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 6 Conclusions and Future Work  
In this paper, we have presented CS-VDH, a new harvesting protocol for VSNs oriented to obtain perishable information provided by vehicles thanks to their equipped sensors. This proposal comprises a geocast protocol to flood the query message issued by the FC into a specific RoI and whose format has been defined, the use of a VANET routing protocol for sending the measurements back to the FC, and finally the use of the CS technology, more specifically, the concept of projection to compress the information without hardly increasing the packet size at it traverses the network.  
This solution outperforms previous proposals like DB-VDG in terms of overhead. Additionally, we have also compared our proposal with a variation without compression so as to check how efficient CS-VDH is in terms of the values that nodes can carry and the overhead they introduce. Although the NoComp variation can carry nearly up to one sensed value per packet more than our CS solution, the total overhead introduced by NoComp is higher than the CS variant. Moreover, the benefits of CS, where it is not necessary to receive all the values to accurately reconstruct them make the difference in terms of the amount of carried values negligible.  
We have made the first analysis over our delay function and also studied the variations of its TMAX parameter, which sets the maximum time for nodes to answer to the FC with their sensed information. Due to the particularities of our scenario, we have seen that a TMAX value higher than 10 s ensures that CS-based schemes outperform the other alternatives in terms of delivered sensed values.  
As future works, we are considering the application of CS in different urban scenarios. Another interesting task is that of changing the location of the Fusion Center, or even evaluate the use of more than one Fusion Center. The simulation of the deployment of gateways with access to  
Figure 18: RMS relative error for CSAccum.  
the infrastructure network in the scenario can be another interesting future work. Finally, although we have used a representation matrix with the purpose of exemplifying the reconstruction process in CS, a study of different representation matrix is also desirable.  
Conceptualization, J.A.M., P.M.R. and A.F.S.; methodology, J.A.M., P.M.R. and A.F.S.; software, J.A.M.; validation, J.A.M.; formal analysis, J.A.M., P.M.R. and A.F.S.; investigation, J.A.M.; resources, J.A.M., P.M.R. and A.F.S.; data curation, J.A.M.; writing--original draft preparation, J.A.M.; writing--review and editing, J.A.M., P.M.R. and A.F.S.; visualization, J.A.M.; supervision, P.M.R. and A.F.S.; project administration, P.M.R. and A.F.S.; funding acquisition, P.M.R. and A.F.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fed4 IoT project (Grant Agreement 814918), and PERSEIDES TIN2017-86885-R cofunded with EDRF fund.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CS & Compressed Sensing \\ CS-VDH & Compressed-Sensing-based Vehicular Data Harvesting \\ DB-VDG & Delay-Bounded Vehicular Data Gathering \\ DTN & Delay Tolerant Network \\ FC & Fusion Center \\ ns2 & network simulator 2 \\ RoI & Region of Interest \\ VITP & Vehicular Information Transport Protocol \\ VANET & Vehicular Ad-hoc Network \\ VSN & Vehicular Sensor Network \\ WSN & Wireless Sensor Network \\ \end{tabular}