Synthetic Mobility Traces for Vehicular Networking

Uppoor, Sandesh; Fiore, Marco; Härri, Jérôme

doi:10.1002/9781118648759.ch6

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…This notwithstanding, there is still a noticeable scarcity of road traffic datasets featuring the level of realism and of spatiotemporal granularity required for network simulation [10]. The result of the lack of a set of realistic, publicly shared, heterogeneous scenarios is that simulations of vehicular networks are often unreliable and non-reproducible [11].…”

Section: Introductionmentioning

confidence: 99%

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Vehicular networks on two Madrid highways

Gramaglia

Trullols‐Cruces

Naboulsi

et al. 2014

2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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Abstract-There is a growing need for vehicular mobility datasets that can be employed in the simulative evaluation of protocols and architectures designed for upcoming vehicular networks. Such datasets should be realistic, publicly available, and heterogeneous, i.e., they should capture varied traffic conditions. In this paper, we contribute to the ongoing effort to define such mobility scenarios by introducing a novel set of traces for vehicular network simulation. Our traces are derived from high-resolution real-world traffic counts, and describe the road traffic on two highways around Madrid, Spain, at several hours of different working days. We provide a thorough discussion of the real-world data underlying our study, and of the synthetic trace generation process. Finally, we assess the potential impact of our dataset on networking studies, by characterizing the connectivity of vehicular networks built on the different traces. Our results underscore the dramatic impact that relatively small communication range variations have on the network. Also, they unveil previously unknown temporal dynamics of the topology of highway vehicular networks, and identify their causes.

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Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the traces of this kind that are currently available yield a partial description of the road traffic, as they only report the positions of, e.g., buses [2], [3] or taxis [4]. Also, they are typically affected by limited temporal accuracy, with positions logged with periodicities in the order of tens of seconds [10].…”

Section: Introductionmentioning

confidence: 99%

Vehicular networks on two Madrid highways

Gramaglia

Trullols‐Cruces

Naboulsi

et al. 2014

2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

Self Cite

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“…The simulation environment incorporates a realistic mobility dataset representing vehicular movement in the German city of Cologne. The city's actual cellular infrastructure, comprising of 247 base stations (eN-odeBs) [15], in addition to 25 core forwarding nodes/switches have been deployed in the simulation. To approximate the coverage area of individual base stations in the region, we perform a Voronoi tessellation on the base station locations.…”

Section: B Seamless Handover In Ip-over-icnmentioning

confidence: 99%

“…Although IP-over-ICN handover employs semi-Markov chains, it is generic and transparent to the prediction method. Thus, other prediction algorithms can be applied; • theoretical analysis of the signaling, packet delivery, and latency cost of IP-over-ICN handover and comparison with the corresponding cost of the Proxy MIPv6 solution; • extensive evaluation of IP-over-ICN handover through simulations using a publicly available realistic mobility dataset that describes vehicular mobility within the Cologne metropolitan region in Germany, in addition to the actual deployment of cellular infrastructure for the same region [15]. Evaluation results show that the proposed solution reduces the total traffic delivery cost by 44% compared to its counterpart PFMIPv6, while reducing handover delay by 26% and handover failure to less than 2% of total handovers.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Seamless Handover in Next-Generation Networks

Al-Khalidi,

Al-Zaidi,

Thomos

et al. 2024

IEEE Trans. Consumer Electron.

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Providing high quality of service (QoS) to mobile end-users, and guaranteeing resilient connectivity for healthcare wearables and other mobile devices is a critical component of Industry 5.0. However, one of the biggest difficulties that network operators encounter is the issue of mobility handover, as it can be detrimental to end-users' safety and experience. Although various handover mechanisms have been developed to meet high QoS, achieving optimum handover performance while maintaining sustainable network operation is still an unreached goal. In this paper, random linear codes (RLC) are used to achieve seamless handover, where handover traffic is encoded using RLC and then multicasted to handover destination(s) using a mobility prediction algorithm for destination selection. To overcome the limitations of current IP core networks, we make use of a revolutionary IP-over-Information-Centric Network architecture at the network core that supports highly flexible multicast switching. The combination of the RLC, flexible multicast, and mobility prediction, makes the communication resilient to packet loss and helps to avoid handover failures of existing solutions while reducing overall packet delivery cost, hence offering sustainable mobility support. The performance of the proposed scheme is evaluated using a realistic vehicular mobility dataset and cellular network infrastructure and compared with Fast Handover for Proxy Mobile IPv6 (PFMIPv6). The results show that our scheme efficiently supports seamless session continuity in high mobility environments, reducing the total traffic delivery cost by 44% compared to its counterpart PFMIPv6, while reducing handover delay by 26% and handover failure to less than 2% of total handovers.

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