| 1. |
- Li, Yuhong, et al.
(författare)
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Context-aware data dissemination for ICN-based vehicular ad hoc networks
- 2018
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 9:11
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Tidskriftsartikel (refereegranskat)abstract
- Information-centric networking (ICN) technology matches many major requirements of vehicular ad hoc networks (VANETs) in terms of its connectionless networking paradigm accordant with the dynamic environments of VANETs and is increasingly being applied to VANETs. However, wireless transmissions of packets in VANETs using ICN mechanisms can lead to broadcast storms and channel contention, severely affecting the performance of data dissemination. At the same time, frequent changes of topology due to driving at high speeds and environmental obstacles can also lead to link interruptions when too few vehicles are involved in data forwarding. Hence, balancing the number of forwarding vehicular nodes and the number of copies of packets that are forwarded is essential for improving the performance of data dissemination in information-centric networking for vehicular ad-hoc networks. In this paper, we propose a context-aware packet-forwarding mechanism for ICN-based VANETs. The relative geographical position of vehicles, the density and relative distribution of vehicles, and the priority of content are considered during the packet forwarding. Simulation results show that the proposed mechanism can improve the performance of data dissemination in ICN-based VANET in terms of a successful data delivery ratio, packet loss rate, bandwidth usage, data response time, and traversed hops.
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| 2. |
- Zhang, Zhiqiang, et al.
(författare)
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Demo Abstract: Frequent Pattern-based Trajectory Completion
- 2018
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Ingår i: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems. - New York, NY, USA : ACM. ; , s. 311-312
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- GPS sensors have been widely used to track people's everyday life trajectories, generating massive trajectory datasets. The trajectory data typically contains sparse GPS points, and completing trajectories is often necessary. State-of-the-art methods [3, 4] essentially complete the entire route by using a single metric, e.g., either the shortest distance or the fastest driving/walking time. Unfortunately, using a single metric may not always work in real life due to the diversity of mobility patterns. In this demo abstract, we propose a frequent pattern (FP)-based trajectory completion approach, and demonstrate a system prototype to showcase the advantages of our approach over four previous works, in terms of accuracy and running time.
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