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Coding or not: Optimal mobile data offloading in opportunistic vehicular networks

Coding or not: Optimal mobile data offloading in opportunistic vehicular networks
Coding or not: Optimal mobile data offloading in opportunistic vehicular networks
To cope with explosive vehicular traffic and everincreasing application demands in the vehicular cellular network, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication, which offloads significant traffic from the cellular network. In the current opportunistic vehicular data transmission, coding-based schemes are proposed to address the challenge of opportunistic contact. However, whether coding techniques can be beneficial in the context of vehicular mobile data offloading is still an open question. In this paper, we establish a mathematical framework to study the problem of coding-based mobile data offloading under realistic network assumptions, where 1) mobile data items are heterogeneous in terms of size; 2) mobile users have different interests to different data; and 3) the storage of offloading participants is limited. We formulate the problem as a users’ interest satisfaction maximization problem with multiple linear constraints of limited storage. Then, we propose an efficient scheme to solve the problem, by providing a solution that decides when the coding should be used and how to allocate the network resources in terms of contact rate and offloading helpers’ storage. Finally, we show the effectiveness of our algorithm through extensive simulations using two real vehicular traces.
1524-9050
318-333
Li, Yong
ac705db5-b891-4d14-ac43-a87acd05cdd7
Jin, Depeng
d5ef5d7e-82a7-4950-85cf-800fe7794cc5
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Zeng, Lieguang
7c5984b7-b38d-44a1-a1f5-66f2b87e6ff9
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Li, Yong
ac705db5-b891-4d14-ac43-a87acd05cdd7
Jin, Depeng
d5ef5d7e-82a7-4950-85cf-800fe7794cc5
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Zeng, Lieguang
7c5984b7-b38d-44a1-a1f5-66f2b87e6ff9
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Li, Yong, Jin, Depeng, Wang, Zhaocheng, Zeng, Lieguang and Chen, Sheng (2014) Coding or not: Optimal mobile data offloading in opportunistic vehicular networks. IEEE Transactions on Intelligent Transportation Systems, 15 (1), 318-333. (doi:10.1109/TITS.2013.2281104).

Record type: Article

Abstract

To cope with explosive vehicular traffic and everincreasing application demands in the vehicular cellular network, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication, which offloads significant traffic from the cellular network. In the current opportunistic vehicular data transmission, coding-based schemes are proposed to address the challenge of opportunistic contact. However, whether coding techniques can be beneficial in the context of vehicular mobile data offloading is still an open question. In this paper, we establish a mathematical framework to study the problem of coding-based mobile data offloading under realistic network assumptions, where 1) mobile data items are heterogeneous in terms of size; 2) mobile users have different interests to different data; and 3) the storage of offloading participants is limited. We formulate the problem as a users’ interest satisfaction maximization problem with multiple linear constraints of limited storage. Then, we propose an efficient scheme to solve the problem, by providing a solution that decides when the coding should be used and how to allocate the network resources in terms of contact rate and offloading helpers’ storage. Finally, we show the effectiveness of our algorithm through extensive simulations using two real vehicular traces.

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Published date: February 2014
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 361785
URI: http://eprints.soton.ac.uk/id/eprint/361785
ISSN: 1524-9050
PURE UUID: 51078602-7b25-4355-bac6-9fdb1ff0799e

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Date deposited: 03 Feb 2014 16:43
Last modified: 14 Mar 2024 15:56

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Contributors

Author: Yong Li
Author: Depeng Jin
Author: Zhaocheng Wang
Author: Lieguang Zeng
Author: Sheng Chen

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