Epidemic information diffusion in realistic vehicular network mobility scenarios

Barberis, Claudia; Malnati, Giovanni

doi:10.1109/icumt.2009.5345435

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…We focus on 30 minutes of consistently fluid traffic conditions, such that, at any instant, the scenario includes about one thousand vehicles simultaneously traveling over the area and taking part in the ITS. The vehicular mobility has been synthetically generated using the SUMO simulator [12]. The time granularity of the resulting mobility trace is 1 s, hence we set the granularity of the traffic manager prediction and the periodicity of the execution of the V2V data relaying protocol to 1 s. We remark that we preferred a synthetic trace over real-world ones, e.g., taxi or bus traces, as these only include a small portion of the car traffic and have a time granularity too coarse for the resulting TEG-PW representation to be effective.…”

Section: A Reference Scenariomentioning

confidence: 99%

Offloading cellular networks through ITS content download

Malandrino

Casetti

Chiasserini

et al. 2012

2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)

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Content downloading by mobile users is expected to significantly increase the cellular network load. Vehicular users, in particular, are likely to engage in information retrieval on the move: in this context, Intelligent Transportation Systems (ITS) can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of ITS in this task, considering that roadside units (RSUs) can exploit mobility prediction to decide which data they should fetch from the Internet and schedule transmissions to vehicles, either potential relays or downloaders. Rather than presenting a specific prediction scheme, we propose a model that allows us to express and account for any prediction technique in a simple, yet effective, manner. We then provide a probabilistic graph-based representation of the system that accounts for the prediction uncertainty. We use such a representation to study the network dynamics by efficiently solving a (non-integer) LP problem. Our results show that the above approach to content downloading through ITS can achieve an 80% offload of the cellular network. Also, we investigate the dependency of the system performance on the accuracy of the mobility prediction, and which prediction errors have the largest impact.

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Section: A Reference Scenariomentioning

confidence: 99%

Offloading cellular networks through ITS content download

Malandrino

Casetti

Chiasserini

et al. 2012

2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)

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“…Notwithstanding the high level of detail granted by the use of such simulators, these traces yield simplistic large-scale features. Indeed, the macroscopic traffic data they employ are based on the authors' perception of the road traffic in the simulated area, as in the traces of Porto, Portugal [11], of several areas of Turin, Italy [12], and of downtown Karlsruhe, Germany [13]. As an alternative, simple assumptions are made, as in the vehicular mobility trace of the city of Zurich, Switzerland [14], where larger roads attract more traffic.…”

Section: A Perception and Small-scale Measurementsmentioning

confidence: 99%

Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset

Uppoor

Trullols‐Cruces

Fiore

et al. 2014

IEEE Trans. on Mobile Comput.

279

120

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Abstract-The surge in vehicular network research has led, over the last few years, to the proposal of countless network solutions specifically designed for vehicular environments. A vast majority of such solutions has been evaluated by means of simulation, since experimental and analytical approaches are often impractical and intractable, respectively. The reliability of the simulative evaluation is thus paramount to the performance analysis of vehicular networks, and the first distinctive feature that has to be properly accounted for is the mobility of vehicles, i.e., network nodes. Notwithstanding the improvements that vehicular mobility modeling has undergone over the last decade, no vehicular mobility dataset is publicly available today that captures both the macroscopic and microscopic dynamics of road traffic over a large urban region. In this paper, we present a realistic synthetic dataset, covering 24 hours of car traffic in a 400-km 2 region around the city of Köln, in Germany. We describe the generation process and outline how the dataset improves the traces currently employed for the simulative evaluation of vehicular networks. We also show the potential impact that such a comprehensive mobility dataset has on the network protocol performance analysis, demonstrating how incomplete representations of vehicular mobility may result in over-optimistic network connectivity and protocol performance.

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“…The trace was generated using OpenStreetMap road information, as well as SUMO for the microscopic mobility. The traffic demand was built based on direct observations by the authors [24]. Traffic volumes.…”

Section: Vehicular Network Connectivity Analysismentioning

confidence: 99%

Large-scale urban vehicular mobility for networking research

Uppoor

Fiore

2011

2011 IEEE Vehicular Networking Conference (VNC)

139

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Simulation is the tool of choice for the largescale performance evaluation of upcoming telecommunication networking paradigms that involve users aboard vehicles, such as next-generation cellular networks for vehicular access, pure vehicular ad hoc networks, and opportunistic disruption-tolerant networks. The single most distinguishing feature of vehicular networks simulation lies in the mobility of users, which is the result of the interaction of complex macroscopic and microscopic dynamics. Notwithstanding the improvements that vehicular mobility modeling has undergone during the past few years, no car traffic trace is available today that captures both macroscopic and microscopic behaviors of drivers over a large urban region, and does so with the level of detail required for networking research. In this paper, we present a realistic synthetic dataset of the car traffic over a typical 24 hours in a 400-km 2 region around the city of Köln, in Germany. We outline how our mobility description improves today's existing traces and show the potential impact that a comprehensive representation of vehicular mobility can have one the evaluation of networking technologies. I. INTRODUCTIONVehicular environments have become increasingly attractive to the telecommunication networking research community over the last years. The reason is that cars are envisioned to become real communication hubs in the near future, thanks to the proliferation of smartphones and tablets, whose Internetconnection capabilities appear especially appealing to passengers aboard cars, as well as to the growing presence of radio interfaces on the vehicles themselves.Enhanced infrastructure-based systems, involving, e.g., the WiMAX and LTE-A technologies, and novel communication paradigms, such as, e.g., ad hoc and opportunistic networking, are being studied in order to accommodate the traffic generated and requested by forthcoming communicating vehicles. Most of these solution require large-scale performance evaluations that are not feasible through experimentation directly, due to costs and complexity. Simulation becomes thus the tool of choice to assess the quality such solutions.When simulating a vehicular network, particular attention must be paid to faithfully represent the unique dynamics of car mobility, characterized at a time by high-peak high-variance speeds, road topology-and road rules-constrained movements, and strong movement correlations over time and space. These properties are the result of macroscopic and microscopic car traffic dynamics, that need to be properly modeled in order to perform a simulative campaign whose results are credible.The relevance of mobility modeling in the simulation of vehicular networks is widely acknowledged, a factor that has led to a substantial progress in the quality of car movement traces for vehicular networking research. The simplistic stochastic models employed in early works [1], [2] have been replaced by

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Epidemic information diffusion in realistic vehicular network mobility scenarios

Cited by 10 publications

References 14 publications

Offloading cellular networks through ITS content download

Offloading cellular networks through ITS content download

Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset

Large-scale urban vehicular mobility for networking research

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