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Mobile Information Systems
Volume 2015, Article ID 490191, 16 pages
Research Article

HCBLS: A Hierarchical Cluster-Based Location Service in Urban Environment

1College of Engineering, Qatar University, Al Tarfa, Doha 2713, Qatar
2HANA Research Lab, University of Manouba, 2010 Manouba, Tunisia
3College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
4Qatar Mobility Innovations Center, Qatar Science & Technology Park, Doha 210531, Qatar

Received 28 July 2015; Revised 15 October 2015; Accepted 8 November 2015

Academic Editor: Laurence T. Yang

Copyright © 2015 Raik Aissaoui et al. 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.


Vehicle location information is central to many location-based services and applications in VANETs. Tracking vehicles positions and maintaining an accurate up-to-date view of the entire network are not easy due to the high mobility of vehicles and consequently rapid topology changes. The design of a scalable, accurate, and efficient location service is still a very challenging issue. In this paper, we propose a lightweight hierarchical cluster-based location service in city environments (HCBLS). HCBLS integrates a logical clustering based on the city digital map and consequently does not involve extra signaling overhead. An advanced location update aggregation at different levels of the assumed hierarchy is adopted to maintain up-to-date and accurate location information. Simulation results show that HCBLS achieves much better performances than the Efficient Map-Based Location Service (EMBLS) and any regular (non-cluster-based) updating scheme. HCBLS increases the success rate by around 10%, improves the overview of the network by more than 30%, lowers the location update and query costs by more than 7 times, lowers the message delivery latency by around 3 times, and presents around 4 times better localization accuracy.