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Proceedings ArticleDOI

GeoSPIN: An Approach for Geocast Routing Based on SPatial INformation in VANETs

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TLDR
The results show the efficiency of the new method to perform opportunistic routing based on location data, which discovers the best routes to forward packets through the network.
Abstract
Vehicular Ad hoc NETworks (VANETs) have been widely used to provide data exchange services in Intelligent Transport Systems (ITS), which should facilitate user's routines. At the same time that user's routines are improved, they can also be used as an important source of data for helping the decision-making process of VANET services. These routines can be acquired from vehicle's trajectories through the use of Global Positioning Systems (GPS). In this context, the combination of location-based data and data forwarding methods brings interesting challenges since these topics are not directly related. Therefore, the key idea of this work is to provide a geocast routing mechanism in VANETs based on daily movements of users in their vehicles. The main contribution is related to the delivery rate, which increases in partitioned and sparsely connected networks. The results show the efficiency of our new method to perform opportunistic routing based on location data, which discovers the best routes to forward packets through the network.

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Citations
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Journal ArticleDOI

Routing in Internet of Vehicles: A Review

TL;DR: This work aims to provide a review of the routing protocols in the Internet of Vehicles from routing algorithms to their evaluation approaches, and provides five different taxonomies of routing protocols.
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A Street-Centric Routing Protocol Based on Microtopology in Vehicular Ad Hoc Networks

TL;DR: This paper abstracts the MT model reflecting the dynamic routing-related characteristics in practical urban scenarios along streets, including the effect of mobility of vehicles, signal fading, wireless channel contention, and existing data traffic, and proposes a novel street-centric routing protocol based on MT (SRPMT) along the streets for VANETs.
Journal ArticleDOI

Improving VANET Simulation with Calibrated Vehicular Mobility Traces

TL;DR: This work proposes and evaluates a data-based solution using clustering algorithms to fill the gaps of real-world traces, and makes the calibrated traces publicly available, so that other researchers may adopt them to improve their evaluation results.
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On location-privacy in opportunistic mobile networks, a survey

TL;DR: A survey of privacy-protection solutions proposed for Opportunistic Networks and a unified framework that addresses design space elements required to successfully offer better location-privacy while enabling opportunistic communications between users is proposed.
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Mobility Trace Analysis for Intelligent Vehicular Networks: Methods, Models, and Applications

TL;DR: Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities.
References
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Proceedings Article

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
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A density-based algorithm for discovering clusters in large spatial Databases with Noise

TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
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Epidemic routing for partially-connected ad hoc networks

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OPTICS: ordering points to identify the clustering structure

TL;DR: A new algorithm is introduced for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure.
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