Abstract
In Vehicle-to-Vehicle and Vehicle-to-Infrastructure (V2X) communication, a large amount of data and information is transmitted over the air by the vehicles. If this data is captured, e.g., by a network of roadside units (RSUs) deployed at strategic locations, cleaned and processed, it may generate an interesting value. The process of cleaning the data involves the removal of data duplicates, as two or more RSUs may capture the same information from the same vehicle. Indeed, a vehicle can be located inside the communication range of multiple RSUs at the same time. The data cleaning process can be achieved through a centralized platform in the backend, where all the deployed RSUs connect and upload their collected data. To avoid overloading the backend, we propose to involve the RSUs in the cleaning process. Ideally, the RSU should be able to detect if any received information from a passing vehicle has not been also received by another nearby RSU. To achieve that, we use an adaptive probability-based splitting of the sensing range. Such a continuous process allows each RSU to adjust the probability distribution of the communication reliability after a sensing time window and to check parameters of neighbor nodes. Simulation results show the efficiency of our solution and demonstrate its ability to adapt with the network dynamicity, by adjusting the algorithm parameters, until reaching a good level of data cleaning compared to static and random approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kenny, J.: Dedicated short-range communications (DSRC) standards in the United States. Proc. IEEE 99(7), 1162–1182 (2011)
Raza, S., Wang, S., Ahmed, M., Anwar, M.R.: A survey on vehicular edge computing: architecture, applications, technical issues, and future directions. Wirel. Commun. Mob. Comput. J. 2019, 1–19 (2019)
Brahim, M.B., Menouar, H.: Distributed upstream data cleaning in VANET. In: The Thirteenth International Conference on Wireless and Mobile Communications, pp. 122–124, July 2017. Nice, France
Zhao, K., Tarkoma, S., Liu, S., Vo, H.: Urban human mobility data mining: an overview. IEEE International Conference on Big Data (Big Data), pp. 1–10, Dec 2016
Shiyale, K.V., Saraf, P.D.: Efficient technique for network lifetime enhancement by cleaning dirty data. Int. J. Sci. Res. (IJSR) 4(4), 2525–2528 (2015)
Javed, N., Wolf, T.: Automated sensor verification using outlier detection in the internet of things. In: 32nd International Conference on Distributed Computing Systems Workshops, pp. 1–6, June 2012
Sha, K., Wang, S., Shi, W.: \(RD^{4}\): role-differentiated cooperative deceptive data detection and filtering in VANETs. IEEE Trans. Veh. Technol. 59(3), 1183–1190 (2010)
Jeffery, S.R., Franklin, M.J., Garofalakis, M.: An adaptive RFID middleware for supporting metaphysical data independence. VLDB J. 17(2), 265–289 (2008)
ETSI EN 302 637-2 V1.3.2 (2014-11) - Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 2: Specification of Cooperative Awareness Basic Service, Nov 2014
SAE J 2735 (2016-03) - Dedicated short range communications (DSRC) message set dictionary, Mar 2016
Brahim, M.B., Drira, W., Filali, F.: Roadside units placement within city-scaled area in vehicular ad-hoc networks. In: International conference on connected vehicles and expo (ICCVE), Nov 2014. Austria, Vienna
Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5(3&4), 128–138 (2012)
Sommer, C., German, R., Dressler, F.: Bidirectionally coupled network and road traffic simulation for improved IVC analysis. IEEE Trans. Mob. Comput. 10(1), 3–15 (2011)
Acknowledgements
This publication was made possible by NPRP grants #NPRP8-2459-1-482 and #NPRP9-257-1-056 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ben Brahim, M., Menouar, H. (2020). AVEC: A Statistical Framework for Adaptive Vehicular Edge Data Cleaning. In: Laouiti, A., Qayyum, A., Mohamad Saad, M. (eds) Vehicular Ad-hoc Networks for Smart Cities. Advances in Intelligent Systems and Computing, vol 1144. Springer, Singapore. https://doi.org/10.1007/978-981-15-3750-9_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-3750-9_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3749-3
Online ISBN: 978-981-15-3750-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)