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Grant: Discovery Grants Program - Individual

University of Ontario Institute of Technology

$20,000.00

Social Network-based Data Dissemination in Vehicular Sensor Networks to Support Distributed Monitoring Services

EF 2016-17

Résumé
The convergence of vehicle and sensor networks offers viable means for data dissemination to distributed services such as traffic monitoring, safety warnings, infotainment, and proactive surveillance of roads and streets. However, data dissemination in vehicular sensor networks (VSNs) is a challenging task due to high mobility of vehicles; frequent network topology changes; high volume of data; and different data dissemination and application requirements. For instance, most safety warning applications will require fast and reliable data delivery to nearby vehicles, which can be achieved by local broadcasts. A monitoring service may require continuous data delivery to a region or command centre, in which the use of broadcasts would not be feasible or efficient. Thus, another challenge lies in adapting on-the-fly to the best data dissemination approach depending on the different vehicle or user's interests. Existing solutions address some of these issues individually and do not adapt to different services or user requirements. The heterogeneity of services, users and data requirements seems to be an opportunity to apply social network-based decision making when selecting forwarding vehicles. It might be possible to map mobility patterns as close as possible to real traffic behavior by exploiting social ties among drivers. Such mapping would be extremely useful in data forwarding decisions. Therefore, this research program will investigate these issues under a unified framework and then develop novel hybrid adaptive techniques that take advantage of the strengths and deal with the weaknesses of existing data dissemination strategies. This program will focus in devising social network-based schemes that exploit ""interactions"" among vehicles (i.e., common visited places, encounters, mobility patterns) as well as drivers' social ties to improve data dissemination in VSNs. It is expected that the proposed program will not only foster advances in VSNs and intelligent transportation systems (ITS), but also prepare students in this demanding research field and place them in a more competitive position in the academia and industry.
Programme
Discovery Grants Program - Individual
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
436077-2013
Name nom
Pazzi, Richard
Department département
Business and Information Technology, Faculty of
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2016
Competition year année de concours
2013
Award amount
20000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2721
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Multimedia systems and networks
Research subject fr
Systèmes et réseaux multimédias
Partie
4
Nb partie
5
Application title
Social Network-based Data Dissemination in Vehicular Sensor Networks to Support Distributed Monitoring Services
Keyword
NSERC
Application summary
The convergence of vehicle and sensor networks offers viable means for data dissemination to distributed services such as traffic monitoring, safety warnings, infotainment, and proactive surveillance of roads and streets. However, data dissemination in vehicular sensor networks (VSNs) is a challenging task due to high mobility of vehicles; frequent network topology changes; high volume of data; and different data dissemination and application requirements. For instance, most safety warning applications will require fast and reliable data delivery to nearby vehicles, which can be achieved by local broadcasts. A monitoring service may require continuous data delivery to a region or command centre, in which the use of broadcasts would not be feasible or efficient. Thus, another challenge lies in adapting on-the-fly to the best data dissemination approach depending on the different vehicle or user's interests. Existing solutions address some of these issues individually and do not adapt to different services or user requirements. The heterogeneity of services, users and data requirements seems to be an opportunity to apply social network-based decision making when selecting forwarding vehicles. It might be possible to map mobility patterns as close as possible to real traffic behavior by exploiting social ties among drivers. Such mapping would be extremely useful in data forwarding decisions. Therefore, this research program will investigate these issues under a unified framework and then develop novel hybrid adaptive techniques that take advantage of the strengths and deal with the weaknesses of existing data dissemination strategies. This program will focus in devising social network-based schemes that exploit ""interactions"" among vehicles (i.e., common visited places, encounters, mobility patterns) as well as drivers' social ties to improve data dissemination in VSNs. It is expected that the proposed program will not only foster advances in VSNs and intelligent transportation systems (ITS), but also prepare students in this demanding research field and place them in a more competitive position in the academia and industry.