Multi-Hop Dynamic Map Data Propagation Algorithm for Clustered Vehicular Networks
Autor: | Odilbek Urmonov, HyungWon Kim |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
data aggregation
Computer Networks and Communications Computer science Real-time computing lcsh:TK7800-8360 02 engineering and technology broadcast message Hop (networking) communication overhead 0502 economics and business 0202 electrical engineering electronic engineering information engineering Wireless wireless range Electrical and Electronic Engineering Cluster analysis 050210 logistics & transportation Vehicular ad hoc network business.industry 05 social sciences lcsh:Electronics 020206 networking & telecommunications color allocation Hardware and Architecture Control and Systems Engineering channel reuse factor Signal Processing Communications protocol business |
Zdroj: | Electronics, Vol 9, Iss 1728, p 1728 (2020) Electronics Volume 9 Issue 10 |
ISSN: | 2079-9292 |
Popis: | To ensure the driving safety in vehicular network, it is necessary to construct a local dynamic map (LDM) for an extended range. Using the standard vehicular communication protocols, however, vehicles can construct the LDM for only one-hop range. Constructing large-scale LDM is highly challenging because vehicles randomly change their position. This paper proposes a dynamic map propagation (DMP) method, which builds a large aggregated LDM data using a multi-hop communication. To reduce the data overhead, we introduce an efficient clustering method based on a half-circle of the forwarder&rsquo s wireless range. The DMP elects one forwarder per cluster, which constructs LDM and forwards it to a neighbor cluster. The inter-cluster interference is minimized by allocating a different transmit window to each cluster. DMP copes with a dynamic environment by frequently re-electing the forwarders and their associated transmission windows. Simulation results reveal that DMP enhances the forwarders&rsquo reception ratio by 20%, while extending LDM dissemination range by 29% over a previous work. |
Databáze: | OpenAIRE |
Externí odkaz: |