2017 Wireless Days 2017
DOI: 10.1109/wd.2017.7918146
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Content distribution emulation for vehicular networks

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Cited by 15 publications
(13 citation statements)
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“…Besides the low granularity of this dataset, the information is gathered from sensors placed along the highway. In our case, the dataset was gathered from the vehicle itself, enabling analysis of real mobility patterns, taking advantage of V2V and V2I communications in urban and parking lot scenarios [4].…”
Section: Related Workmentioning
confidence: 99%
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“…Besides the low granularity of this dataset, the information is gathered from sensors placed along the highway. In our case, the dataset was gathered from the vehicle itself, enabling analysis of real mobility patterns, taking advantage of V2V and V2I communications in urban and parking lot scenarios [4].…”
Section: Related Workmentioning
confidence: 99%
“…A vehicular network emulator, entitled mOVERS [4], developed in Institute of Telecommunications (IT), Aveiro, was the platform used in the evaluation of the content distribution strategies. This emulator runs a Delay-Tolerant Network (DTN), which allows the creation of multiple processes that run DTN software, and is capable of scaling for larger networks with the same software as the one that is running in the OBUs/RSUs in the real Oporto vehicular network [1].…”
Section: Software Descriptionmentioning
confidence: 99%
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“…There are several simulators for networking, and for robotics. Pessoa et al [ 23 ] presented mOVERS, an emulator capable of recreating scalable vehicular scenarios of data gathering and content distribution in vehicular networks. This emulator works with mobile Opportunistic VEhicular (mOVE), a DTN-based architecture supporting communications using IEEE 802.11p and Wireless Access in Vehicular Environments (WAVE), and IEEE 802.11a/b/g (Wi-Fi technology) developed in the Network Architectures and Protocols research group , where each node is capable of storing a packet and forwards it when a neighbor is available.…”
Section: Related Workmentioning
confidence: 99%