Improving VANET Simulation with Calibrated Vehicular Mobility Traces

Celes, Clayson; Silva, Fabricio A.; Boukerche, Azzedine; Andrade, Rossana M. C.; Loureiro, Antônio A. F.

doi:10.1109/tmc.2017.2690636

Cited by 57 publications

(33 citation statements)

References 40 publications

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“…We have used the databases provided by [32] of real taxi traces, containing twenty-four hours of movement of three cities: Rome (Italy), San Francisco (USA), and Shanghai (China). e original databases received an improvement by filling in the existing gaps according to the granularity of the readings, making the database uniform.…”

Section: Datasetmentioning

confidence: 99%

“…e original databases received an improvement by filling in the existing gaps according to the granularity of the readings, making the database uniform. is uniformity was possible to obtain, through a reference system based on clustering with a calibration method proposed by [32]. Celes et al [32] state that applying the calibration method to real vehicle mobility records improves their quality, leading to more reliable movement analysis and simulation results.…”

Section: Datasetmentioning

confidence: 99%

“…Celes et al [32] had provided twenty-four hours of calibrated data as follows. For the city of Rome, the data of February 4, 2014, contain 3,843,043 records and 187 taxis.…”

Section: Datasetmentioning

confidence: 99%

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Transmission Opportunities: A New Approach to Improve Quality in V2V Networks

Guerber

Gomes

Fonseca

et al. 2019

Wireless Communications and Mobile Computing

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In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communication occurs opportunistically due to frequent node mobility and intermittent contact time. In this scenario, the performance evaluation of forwarding protocols by the use of the existing resources in the network is an open challenge, given the different strategies that the routing protocols adopt for choosing the next hop. Through the data analysis of three real taxis’ mobility traces and varying the radio signal range from 50 meters to 1.000 meters, we could contribute to the analysis of the existence of single-radio transmission opportunities and their quantification and classification considering either serial or parallel. Additionally, the inventory analysis of communication resources is done by evaluating the data transmission rate and the beacon overhead impact and by the proposal of a new metric (TOppMi) for the performance evaluation of forwarding protocols. We discuss the impact on forwarding protocols by the appropriate use of this metric, and we show that this metric can be used either for performance evaluation of forwarding protocols or to improve the quality over the consumption of resources in vehicular ad hoc networks. Our metric can obtain the maximum theoretical resource usage independent of the scenario. Either for Rome or San Francisco or Shanghai, as the radio’s signal range increases, the maximum theoretical amount of resources also increases. We could also show that, for the three scenarios, the beacon overhead has an insignificant impact over the total theoretical data inventory available. Furthermore, we classify the vehicles according to the contact time between them.

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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Transmission Opportunities: A New Approach to Improve Quality in V2V Networks

Guerber

Gomes

Fonseca

et al. 2019

Wireless Communications and Mobile Computing

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“…Given the inherent complexity of simulating wireless signal propagation in urban environments, large scale VANETs are often modeled with a simple disc-like radius of guaranteed transmission [5], [11]. Given that the reserved portion of the EM spectrum lies within the 5.850-5.925Ghz range, V2V communication without LOS is possible, but challenging in cities given obstructions due to buildings and radio interference.…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately no data were available regarding vehicle velocity and uncertainty/error in these measurements. Other taxi trace datasets [11] for cities such as Shanghai and Shenzhen were considered for use, but are not truly open at the time of writing. Open pseudo trace datasets were not considered as real taxi trace data effectively takes into account other vehicular traffic in the city, since participating taxis can only move as fast as the surrounding vehicular traffic.…”

Section: A Data Inputsmentioning

confidence: 99%

Large-Scale VANET Simulations and Performance Analysis using Real Taxi Trace and City Map Data

Carnelli

Sooriyabandara

Wilson

2018

2018 IEEE Vehicular Networking Conference (VNC)

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Wireless vehicular ad-hoc networks comprised solely of city taxis are investigated for their ability to deliver data across an urban environment. Openly available taxi trace datasets for Rome (Italy) and San Francisco (USA) are combined with respective building footprint and road network topology data from OpenStreetMap, to generate a realistic systems level model of a taxi V2V network. Analysis of LOS and NOLOS constraints on wireless transmission range suggests a minimum threshold of 50m is applicable to ensure LOS in over 90% of cases. Variations in taxi location sampling frequency and filtering techniques for the taxi trace datasets are also investigated. Overall vehicular network performance is computed for an all-to-one transmission scenario for both cities with varying taxi fleet size. Results suggest a non-linear relationship between increases in taxi fleet sizes and the reduction of end-to-end delay; doubling taxi fleet size (using a randomised data folding technique) reduces end-to-end delay by a factor of 0.6-0.7. However, doubling the fleet does not increase the fraction of delivered source messages, which saturates at 0.67-0.71 in most simulations. Finally it appears that taxi networks for delivering messages across urban environments are limited more by their routing than by the number of possible V2V exchanges. In a simulated one-to-all continuous V2V broadcast scenario, over 90% of the taxis within the fleet receive the source message within one hour of the original taxi passing the source node.

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