Abstract
Vehicular networks play a pivotal role in intelligent transportation system (ITS) and smart city (SC) construction, especially with the coming of 5G. Mobility models are crucial parts of vehicular network, especially for routing policy evaluation as well as traffic flow management. The big data aided vehicle mobility analysis and design attract researchers a lot with the acceleration of big data technology. Besides, complex network theory reveals the intrinsic temporal and spatial characteristics, considering the dynamic feature of vehicular network. In the following content, a big GPS dataset in Beijing, and its complex features verification are introduced. Some novel vehicle and location collaborative mobility schemes are proposed relying on the GPS dataset. We evaluate their performance in terms of complex features, such as duration distribution, interval time distribution and temporal and spatial characteristics. This paper elaborates upon mobility design and graph analysis of vehicular networks.
Similar content being viewed by others
References
Tan F, Wu J, Xia Y, Chi KT (2014) Traffic congestion in interconnected complex networks. Phys Rev E 89(6):062813
Jiang X, Du DH (2015) Bus-vanet: a bus vehicular network integrated with traffic infrastructure. IEEE Intell Transp Syst Mag 7(2):47–57
Cheng X, Yang L, Shen X (2015) D2D for intelligent transportation systems: A feasibility study. IEEE Trans Intell Transp Syst 16(4):1784–1793
Kim H, Feamster N (2013) Improving network management with software defined networking. IEEE Commun Mag 51(2):114–119
Silva CM, Meira W, Sarubbi JF (2016) Non-intrusive planning the roadside infrastructure for vehicular networks. IEEE Trans Intell Transp Syst 17(4):938–947
Luan TH, Lu R, Shen X, Bai F (2015) Social on the road: enabling secure and efficient social networking on highways. IEEE Wireless Commun 22:44–51
Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong AC, Zhang JC (2014) What will 5g be IEEE J Sel Areas Commun 32(6):1065–1082
Wang J, Jiang C, Han Z, Ren Y, Hanzo L (2016) Network association strategies for an energy harvesting aided super-WiFi network relying on measured solar activity. IEEE J Sel Areas Commun 34(12):3785–3797
Uppoor S, Trullols-Cruces O, Fiore M, Barcelo-Ordinas JM (2014) Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Trans Mob Comput 13(5):1061–1075
Djahel S, Doolan R, Muntean G-M, Murphy J (2015) A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Commun Surv Tutorials 17(1):125– 151
Bansal N, Liu Z (2003) Capacity, delay and mobility in wireless ad-hoc networks. In: INFOCOM
Qiao Y, Cheng Y, Yang J, Liu J, Kato N (2016) A mobility analytical framework for big mobile data in densely populated area. IEEE Transactions on Vehicular Technology
Zheng X, Chen W, Wang P, Shen D, Chen S, Wang X, Zhang Q, Yang L (2016) Big data for social transportation. IEEE Trans Intell Transp Syst 17(3):620–630
Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440–2
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Wang J, Jiang C, Quek TQ, Wang X, Ren Y (2016) The value strength aided information diffusion in socially-aware mobile networks. IEEE Access 4:3907–3919
Wang J, Jiang C, Gao L, Yu S, Han Z, Ren Y (2016) Complex network theoretical analysis on information dissemination over vehicular networks. In: 2016 IEEE International Conference on Communications (ICC), IEEE, May , pp 1–6
Wang J, Jiang C, Bie Z, Quek TQ, Ren Y (2016) Mobile data transactions in device-to-device communication networks: Pricing and auction. IEEE Wireless Commun Lett 5(3):300–303
Ruela AS, Cabral RS, Aquino AL, Guimaraes FG (2009) Evolutionary design of wireless sensor networks based on complex networks. In: 2009 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE, pp 237–242
Tan W, Blake MB, Saleh I, Dustdar S (2013) Social-network-sourced big data analytics. IEEE Internet Comput 17:62–69
Wu X, Zhu X, Wu G-Q, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26:97–107
Härri J, Filali F, Bonnet C (2009) Mobility models for vehicular ad hoc networks: a survey and taxonomy. IEEE Commun Surv Tutorials 11:19–41. FOURTH QUARTER
Bettstetter C, Resta G, Santi P (2003) The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Trans Mob Comput 2(3):257–269
Hsu W-J, Merchant K, Shu H-w, Hsu C-H, Helmy A (2005) Weighted waypoint mobility model and its impact on ad hoc networks. ACM SIGMOBILE Mobile Comput Comm Rev 9(1):59–63
Zheng Q, Hong X, Liu J (2006) An agenda based mobility model21. In: Proceedings of the 39th annual Symposium on Simulation, IEEE Computer Society, pp 188–195
Sommer C, German R, Dressler F (2011) Bidirectionally coupled network and road traffic simulation for improved ivc analysis. IEEE Trans Mob Comput 10(1):3–15
Fellendorf M, Vortisch P (2010) Microscopic traffic flow simulator vissim. In: Fundamentals of traffic simulation. Springer, pp 63– 93
Zhang K, Wang J, Jiang C, Quek TQS, Ren Y (2017) Content aided clustering and cluster head selection algorithms in vehicular networks. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp 1–6
Song C, Koren T, Wang P, Barabási A-L (2010) Modelling the scaling properties of human mobility. Nat Phys 6(10):818– 823
Musolesi M, Mascolo C (2007) Designing mobility models based on social network theory. ACM SIGMOBILE Mobile Comput Commun Rev 11(3):59–70
Sood V, Redner S (2005) Voter model on heterogeneous graphs. Phys Rev Lett 94(17):178701
Saramäki J, Kivelä M, Onnela J-P, Kaski K, Kertesz J (2007) Generalizations of the clustering coefficient to weighted complex networks. Phys Rev E 75(2):027105
Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25(2):163–177
Dorogovtsev SN, Mendes JFF, Samukhin AN (2001) Giant strongly connected component of directed networks. Phys Rev E 64(2):025101
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6(7):e159
Wang J, Jiang C, Quek TQ, Ren Y (2016) The value strength aided information diffusion in online social networks. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA, pp 1–6
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sun, R., Ye, J., Tang, K. et al. Big Data Aided Vehicular Network Feature Analysis and Mobility Models Design. Mobile Netw Appl 23, 1487–1495 (2018). https://doi.org/10.1007/s11036-017-0981-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-017-0981-z