- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
International Journal of Distributed Sensor Networks
Volume 2013 (2013), Article ID 101878, 11 pages
http://dx.doi.org/10.1155/2013/101878
Traffic-Aware Data Delivery Scheme for Urban Vehicular Sensor Networks
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
Received 9 November 2012; Accepted 18 December 2012
Academic Editor: Ming Liu
Copyright © 2013 Chunmei Ma and Nianbo Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Vehicular sensor network (VSN) is a promising technology which could be widely applied to monitor the physical world in urban areas. In such a scenario, the efficient data delivery plays a central role. Existing schemes, however, cannot choose an optimal route, since they either ignore the impact of vehicular distribution on connectivity, or make some unreasonable assumptions on vehicular distribution. In this paper, we propose a traffic-aware data delivery scheme (TADS). The basic idea of TADS is to choose intersections to forward packets dynamically as the route from a source to destination based on link quality and remaining Euclidean distance to destination. Specifically, we first present an optimal utility function as the criteria of intersection selection. Besides the packet forwarding through intersections, we also propose an improved geographically greedy routing algorithm for packet forwarding in straightway mode. Moreover, in order to decrease the routing overhead brought by the traffic information gathering, we build a traffic condition prediction model to estimate the link quality. The simulation results show that our TADS outperforms existing works on packet delivery ratio, end-to-end delay, and routing overhead.