Research on distributed dynamic traffic data mining based on vehicular ad hoc network. (10th September 2019)
- Record Type:
- Journal Article
- Title:
- Research on distributed dynamic traffic data mining based on vehicular ad hoc network. (10th September 2019)
- Main Title:
- Research on distributed dynamic traffic data mining based on vehicular ad hoc network
- Authors:
- Jian, Gao
- Abstract:
- There are some problems in current traffic data mining methods, such as poor accuracy, high energy consumption, high packet loss rate and lengthy times. A distributed dynamic traffic data mining method based on vehicular ad hoc network is proposed. Before mining, congestion, routing changes, data channel errors and connection interruptions in vehicular ad hoc networks are detected respectively. Particle Swarm Optimisation (PSO) is introduced to realise traffic data classification mining based on vehicular ad hoc networks under network coding and clustering fusion. The simulations results show that the method has high accuracy and efficiency, low-energy consumption and packet loss rate, and high feasibility.
- Is Part Of:
- International journal of vehicle information and communication systems. Volume 4:Number 3(2019)
- Journal:
- International journal of vehicle information and communication systems
- Issue:
- Volume 4:Number 3(2019)
- Issue Display:
- Volume 4, Issue 3 (2019)
- Year:
- 2019
- Volume:
- 4
- Issue:
- 3
- Issue Sort Value:
- 2019-0004-0003-0000
- Page Start:
- 264
- Page End:
- 278
- Publication Date:
- 2019-09-10
- Subjects:
- vehicular ad hoc network -- distributed -- traffic data -- mining
Automobiles -- Electronic equipment -- Periodicals
629.27 - Journal URLs:
- http://www.inderscience.com/browse/index.php?journalCODE=ijvics ↗
http://www.inderscience.com/ ↗ - Languages:
- English
- ISSNs:
- 1471-0242
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 11165.xml