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Summarizing sensors data in vehicular ad hoc networks

Published online by Cambridge University Press:  11 January 2011

Dorsaf Zekri ,
Bruno Defude  and
Thierry Delot
Dorsaf Zekri
Affiliation:
Institut TELECOM, TELECOM SudParis, UMR CNRS SAMOVAR, 9 rue Charles Fourier, 91011 Evry Cedex, France. [email protected]; [email protected]
Bruno Defude
Affiliation:
Institut TELECOM, TELECOM SudParis, UMR CNRS SAMOVAR, 9 rue Charles Fourier, 91011 Evry Cedex, France. [email protected]; [email protected]
Thierry Delot
Affiliation:
University Lille North of France, UVHC/LAMIH CNRS, Le Mont Houy, 59313 Valenciennes Cedex 9, France. [email protected]
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Abstract

This article focuses on data aggregation in vehicular ad hoc networks. In such networks, sensor data are usually produced and exchanged between vehicles in order to warn or inform the drivers when an event is detected (e.g., accident, emergency braking, parking space released, vehicle with non-functioning brake lights, etc.).In the following, we present a solution to aggregate and store these data in order to have a history of past events. We therefore use Flajolet-Martin sketches. Our goal is to generate additional knowledge to assist drivers by providing them useful information even if no event is transmitted by vehicles in the vicinity.

Keywords

Vehicular ad hoc networks (VANET)event streamssensor dataspatio-temporal datadata aggregation.Réseaux ad hoc inter-véhiculesévénnementscapteursdonnées spatio-tenporellesagrégation de données
Type
Research Article
Information
RAIRO - Operations Research , Volume 44 , Issue 4: COGIS'09 , October 2010 , pp. 345 - 364
Copyright
© EDP Sciences, ROADEF, SMAI, 2011

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References

C. Aggarwal, J. Han, J. Wang and P. Yu, A framework for clustering evolving data streams, in Proc. of the 29th VLDB Conf., Berlin, Germany (2003).
A. Bezenchek, M. Rafanelli and L. Tininini, A data structure for representing aggregate data, in Proc. of the 8th Int. Conf. on Scientific and Statistical Database Management (1996), pp. 22–31.
B.H. Bloom, Space/time trade-offs in hash coding with allowable errors, in Commun. ACM 13 (7) (1970) 422–426.
N. Cenerario, T. Delot and S. Ilarri, Dissemination of information in inter-vehicle ad hoc networks, in Proc. of the Intelligent Vehicles Symposium (IV'08), IEEE Comp. Soc. (2008) 763–768.
C. Chen, Location-based data aggregation in mobile ad hoc networks. Master's thesis, Institute fur Parallele und Verteilte Systeme, Stuttgart (2003).
B. Csernel, F. Clerot and G. Hébrail, Summarizing a 3 way relational data stream, caserta (italie), in Proc. of Workshops on Data Stream Analysis (2007).
Dasarathy, B.V., Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proc. IEEE 85 (1997) 24–38. CrossRef
B. Defude, T. Delot, S. Ilarri, J.L. Zechinelli Martini and N. Cenerario, Data aggregation in VANETs: the VESPA approach, in Proc. of the 1st Int. Workshop on Computational Transportation Science (IWCTS'08), in conjunction with MOBIQUITOUS'08, Dublin (Ireland), ICST (2008).
Delot, T., Cenerario, N. and Ilarri, S., Vehicular Event Sharing with a mobile Peer-to-peer Architecture. Transportation Research – Part C (Emerging Technologies) 18 (2010) 584–598. CrossRef
S. Eichler, C. Merkle and M. Strassberger, Data aggregation system for distributing inter-vehicle warning messages, in Proc. of the 31st IEEE Conf. on Local Computer Networks, Tampa, FL (2006).
Faouzi, N.E., Leung, H. and Kurian, A., Data fusion in intelligent transportation systems: Progress and challenges – a survey. Inform. Fusion 12 (2011) 4–10. CrossRef
P. Flajolet and G.N. Martin, Probabilistic counting algorithms for data base applications, J. Comput. Syst. Sci. 31 (1985) 182–209.
D.L. Hall and J. Llinas, An introduction to multisensor data fusion, Proc. IEEE 85 (1997) 6–23.
W.R. Heinzelman, J. Kulik and H. Balakrishnan, Adaptive protocols for information dissemination in wireless sensor networks, in Proc. of the 5th Annual ACM/IEEE Int. Conf. on Mobile Computing and Networking (MobiCom'99), Seattle, Washington, United States, ACM (1999), pp. 174–185.
G.J.M. Kruijff, J.D. Kelleher and N. Hawes, Information fusion for visual reference resolution in dynamic situated dialogue, in Perception and Interactive Technologies (PIT 2006), edited by E. André, L. Dybkjaer, W. Minker, H. Neumann and M. Weber, Spring Verlag (2006).
Kulik, J., Heinzelman, W. and Balakrishnan, H., Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless Netw. 8 (2002) 169–185. CrossRef
C. Lochert, B. Scheuermann and M. Mauve, Probabilistic aggregation for data dissemination in vanets, in Proc. of the 4th Int. Workshop on Vehicular Ad Hoc Networks (VANET'07), Montreal, Quebec, Canada. ACM (2007), pp. 1–7.
Lopez, I.F.V., Snodgrass, R. and Moon, B., Spatiotemporal aggregate computation: a survey. IEEE Trans. Knowledge Data Eng. 17 (2005) 271–286. CrossRef
J. Luo and J.-P. Hubaux, A survey of research in inter-vehicle communications, in Embedded security in cars – securing current and future automotive IT applications (2005), pp. 111–122.
P. Morsink, R. Hallouzi, I. Dagli, L. Cseh, C. Schafers, M. Nelisse and D. de Bruin, Cartalk 2000: Development of a cooperative adas based on vehicle to vehicle communication, in Proc. of the 10thWorld Congress and Exhibition in intelligent Transport Systems and Services, Saint-Malo, France (2003).
T. Nadeem, S. Dashtinezhad, C. Liao and L. Iftode, TrafficView: Traffic data dissemination using car-to-car communication. ACM SIGMOBILE Mobile Computing and Communications Review, Special Issue on Mobile Data Management 8 (2004) 6–19.
T. Nadeem, P. Shankar and L. Iftode, A comparative study of data dissemination models for VANETs, in Proc. of the 3rd Int. Conf. on Mobile and Ubiquitous Systems (MOBIQUITOUS'06), San Jose, CA, IEEE Comp. Soc. (2006), pp. 1–10.
Nakamura, E.F., Loureiro, A.F. and Frery, A.C., Information fusion for wireless sensor networks: Methods, models and classifications. ACM Computer Survey 39 (2007) 9. CrossRef
F. Picconi, N. Ravi, M. Gruteser and L. Iftode, Probabilistic validation of aggregated data in vehicular ad hoc networks, in Proc. of the 3rd Int. Workshop on Vehicular Ad Hoc Networks, Los Angeles, CA, USA (2006), pp. 76–85.
R. Rajagopalan and P. Varshney, Data aggregation techniques in sensor networks: a survey, IEEE Commun. Surv. Tutorials 8 (2006) 48–63.
R. Ramakrishnan, T. Zhang and M. Livny, Birch: an efficient data clustering method for very large databases, in Proc. of the ACM Int. Conf. on Management of Data (SIGMOD'96), Montreal, Canada (1996).
H. Saleet and O. Basir, Location based message aggregation in vehicular ad hoc networks. in Proc. of the IEEE Global Communications Conference Workshops, Washington, DC (2007), pp. 1–7.
Y. Tao, G. Kollios, J. Considine, F. Li and D. Papadias, Spatio-temporal aggregation using sketches, in Proc. of the 20th Int. Conf. on Data Engineering (ICDE'04), Boston, USA (2004), pp. 214–225.
B. Xu, A.M. Ouksel and O. Wolfson, Opportunistic resource exchange in inter-vehicle ad-hoc networks, in Proc. of the 5th Int. Conf. on Mobile Data Management (MDM'04), IEEE Comp. Soc., Berkeley, California (2004), pp. 4–12.