NSF-funded research on vehicular social networking

["Dave Farber" <farber () gmail com>]

Begin forwarded message:

> From: Ross Stapleton-Gray <ross.stapletongray () gmail com>
> Date: December 13, 2017 at 12:45:17 PM EST
> To: DAVID FARBER <dave () farber net>
> Subject: NSF-funded research on vehicular social networking
>
> So, yet another issue to give us angst: how to take it when your car
> becomes more popular than you are?
>
> As I read this, the researchers are proposing that it would be helpful
> if your car were socially networked, i.e., more readily communicated
> with cars where past history and interests suggested common concerns,
> value of informational leads, etc. Lots of exercises left to the
> reader, e.g., a ton of privacy implications, opportunities for
> marketing (think cars whose owners are being paid to "push" specific
> routing/destinations as better than others... in the olden days, when
> Jeb suggests the best route into town is to pass the Kroger, and not
> the K-Mart...), etc.
>
> Ross
>
> **********
>
> https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761641
>
> Award Abstract #1761641
> NeTS: EAGER: Intelligent Information Dissemination in Vehicular
> Networks based on Social Computing
>
> NSF Org:CNS
> Division Of Computer and Network Systems
> Initial Amendment Date:December 12, 2017
> Latest Amendment Date:December 12, 2017
> Award Number:1761641
> Award Instrument:Standard Grant
> Program Manager:Thyagarajan Nandagopal
> CNS Division Of Computer and Network Systems
> CSE Direct For Computer & Info Scie & Enginr
> Start Date:July 31, 2017
> End Date:September 30, 2018 (Estimated)
> Awarded Amount to Date:$135,645.00
> Investigator(s):Qing Yang qing.yang () cs montana edu (Principal Investigator)
> Sponsor:University of North Texas
> 1155 Union Circle #305250
> DENTON, TX 76203-5017 (940)565-3940
> NSF Program(s):RES IN NETWORKING TECH & SYS
> Program Reference Code(s):7916, 9150
> Program Element Code(s):7363
>
> ABSTRACT
>
> Vehicular networks are becoming increasingly popular. To make them
> truly useful, irrelevant information exchanges among vehicles should
> to be eliminated to avoid unnecessary driver distraction. This project
> aims to tackle this fundamental problem, wherein what information is
> delivered to which vehicle(s) is intelligently determined. The project
> will study the closeness between vehicles based their interactions, in
> the form of information exchange, so a driver can determine whether a
> received message is relevant based on the closeness information.
> Because information is filtered by a vehicle's close 'friends', the
> amount of irrelevant information it receives will be reduced, and thus
> efficient information dissemination is achieved. The research will
> produce an efficient information dissemination system that complements
> and enhances existing intelligent transportation systems, connected
> vehicles, and vehicular telematics. The project will also include
> efforts to deploy the system to offer a better information provision
> service to drivers. Two PhD students and several undergraduate
> students will be trained in this project.
>
> The researchers propose to use interactions between vehicles to
> estimate their closeness, and most importantly, to determine what data
> should be delivered to which vehicle(s) based on the closeness
> information. The key to their approach is constructing a vehicular
> social network (VSN) that enables drivers to integrate their social
> network with vehicular network. The list of points of interest (POIs)
> that a vehicle visited is considered its genome, and vehicles with
> similar genetic features are considered initially connected in a VSN.
> These connections are then cultivated by the interactions among
> vehicles. With positive, negative, and uncertain interactions, the
> closeness between two vehicles having direct interactions is modeled
> as a Dirichlet distribution. For vehicles that have no direct
> interactions, their closeness is inferred from the social network
> between them. The PIs will design a polynomial-time solution to
> addressing the massive closeness assessment problem, i.e., computing
> the closeness from a driver to all others in a VSN. The researchers
> also propose an efficient algorithm for the all-pair closeness
> assessment problem, i.e., computing the closeness of any pair of
> vehicles in a VSN. A cloud-hosted service is proposed to coordinate
> social connection construction, VSN maintenance, closeness assessment,
> and information dissemination.



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