ISCAS OpenIR
On Geocasting over Urban Bus-Based Networks by Mining Trajectories
Zhang, FS; Jin, BH; Wang, ZY; Liu, H; Hu, JF; Zhang, LF
2016
SourceIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
Volume17Issue:6Pages:1734-1747
English AbstractBus networks in cities have distinctive features such as wide coverage and fixed bus routes so that they show the potential of forming the communication backbone in vehicular ad hoc networks (VANETs). This paper focuses on the geocast in bus-based VANETs and presents a geocast routing mechanism named Vela. Specifically, Vela analyzes and mines historical bus trajectories and characterizes spatial-temporal patterns (i. e., bus travel-time patterns and bus spatial encounter patterns) in a moderate granularity of road segments, which makes the mined patterns both accurate and steady. Furthermore, Vela exploits these acquired patterns to build a probabilistic spatial-temporal graph model and provides the available routing paths with the best possible quality-of-service levels for data delivery requests. Moreover, Vela also employs a two-hop aware strategy that utilizes the real-time spatial-temporal relationships between buses to increase the chances of forwarding the data. The results of the experiments on the real and synthetic trajectories show that Vela performs much better in terms of delivery ratio and delay and has stronger scalability than the other solutions.; Bus networks in cities have distinctive features such as wide coverage and fixed bus routes so that they show the potential of forming the communication backbone in vehicular ad hoc networks (VANETs). This paper focuses on the geocast in bus-based VANETs and presents a geocast routing mechanism named Vela. Specifically, Vela analyzes and mines historical bus trajectories and characterizes spatial-temporal patterns (i. e., bus travel-time patterns and bus spatial encounter patterns) in a moderate granularity of road segments, which makes the mined patterns both accurate and steady. Furthermore, Vela exploits these acquired patterns to build a probabilistic spatial-temporal graph model and provides the available routing paths with the best possible quality-of-service levels for data delivery requests. Moreover, Vela also employs a two-hop aware strategy that utilizes the real-time spatial-temporal relationships between buses to increase the chances of forwarding the data. The results of the experiments on the real and synthetic trajectories show that Vela performs much better in terms of delivery ratio and delay and has stronger scalability than the other solutions.
Indexed TypeSCI
KeywordVehicular Ad Hoc Networks Bus-based Routing Trajectory Mining Time Series Analysis
DepartmentChinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China. Univ Chinese Acad Sci, Beijing 101408, Peoples R China. Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China. Univ Hong Kong, Dept Comp Sci, Pokfulam, Hong Kong, Peoples R China. Ludong Univ, Sch Informat & Elect Engn, Yantai 264025, Peoples R China.
Language英语
WOS IDWOS:000377457200022
Citation statistics
Cited Times:49[WOS]   [WOS Record]     [Related Records in WOS]
Content Type期刊论文
URIhttp://ir.iscas.ac.cn/handle/311060/17332
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Zhang, FS,Jin, BH,Wang, ZY,et al. On Geocasting over Urban Bus-Based Networks by Mining Trajectories[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2016,17(6):1734-1747.
APA Zhang, FS,Jin, BH,Wang, ZY,Liu, H,Hu, JF,&Zhang, LF.(2016).On Geocasting over Urban Bus-Based Networks by Mining Trajectories.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,17(6),1734-1747.
MLA Zhang, FS,et al."On Geocasting over Urban Bus-Based Networks by Mining Trajectories".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 17.6(2016):1734-1747.
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