Abstract
In large urban spaces, like cities, VANETs are formed by vehicles of highly variable speed and uneven geographic node distribution. Due to the ad-hoc nature of such environments, communication systems must seamlessly adapt to abrupt topology changes to keep the vehicular network organized. Maintain connectivity is hard; a possible naive strategy is based upon expensive on-demand reconnections. Another approach relies on controlled message epidemics. Both of them need to adjust communication behavior under different density situations. Thus, infrastructure-free density estimation methods are becoming popular solutions for this problem. Our paper contributes to this area using a unique density estimation method, independent of beaconing and neighbor discovery (which might generate network congestion), free of cooperative orchestration and based on long-term stability metrics. Our method is validated using vehicular mobility traces, showing outstanding group prediction and stability.
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Notes
- 1.
Because in pairwise connectivity two is the minimum number of points to form a group, so there will be no difference between core and border nodes (see [8]).
References
Aliyu, A., et al.: Cloud computing in VANETs: architecture, taxonomy, and challenges. IETE Tech. Rev. 35(5), 523–547 (2018)
Barrachina, J., et al.: I-VDE: a novel approach to estimate vehicular density by using vehicular networks. In: Cichoń, J., Gȩbala, M., Klonowski, M. (eds.) ADHOC-NOW 2013. LNCS, vol. 7960, pp. 63–74. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39247-4_6
Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., Rabuffi, A.: CRAWDAD dataset roma/taxi (v. 2014–07-17), July 2014. https://crawdad.org/roma/taxi/20140717
Chen, L., Bian, K.: Neighbor discovery in mobile sensing applications: a comprehensive survey. Ad Hoc Netw. 48, 38–52 (2016)
Chesterton, A.: How many cars are there in the world? (2018). https://www.carsguide.com.au/car-advice/how-many-cars-are-there-in-the-world-70629
Darwish, T., Bakar, K.A.: Traffic density estimation in vehicular ad hoc networks: a review. Ad Hoc Netw. 24, 337–351 (2015)
Doone, M.G., Cotton, S.L., Matolak, D.W., Oestges, C., Heaney, S.F., Scanlon, W.G.: Pedestrian-to-vehicle communications in an urban environment: channel measurements and modeling. IEEE Trans. Antennas Propag. 67(3), 1790–1803 (2018)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, KDD 1996, pp. 226–231 (1996)
Grzybek, A., Seredynski, M., Danoy, G., Bouvry, P.: Detection of stable mobile communities in vehicular ad hoc networks. In: IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1172–1178 (2014)
He, J., Cai, L., Pan, J., Cheng, P.: Delay analysis and routing for two-dimensional VANETs using carry-and-forward mechanism. IEEE Trans. Mobile Comput. 16(7), 1830–1841 (2017)
Jabbarpour, M.R., Noor, R.M., Khokhar, R.H., Ke, C.H.: Cross-layer congestion control model for urban vehicular environments. J. Netw. Comput. Appl. 44, 1–16 (2014)
Keränen, A., Kärkkäinen, T., Ott, J.: Simulating mobility and DTNs with the one. J. Commun. 5, 92–105 (2010)
Kim, T., Min, H., Choi, E., Jung, J.: Optimal job partitioning and allocation for vehicular cloud computing. Future Gener. Comput. Syst. 108, 82–96 (2020)
Kimura, T., Saito, H., Honda, H.: Optimal transmission range for v2i communications on congested highways. In: IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–7 (2017)
Korn, A., Schubert, A., Telcs, A.: Lobby index in networks. Phys. A 388(11), 2221–2226 (2009)
Lakkakorpi, J., Pitkänen, M., Ott, J.: Adaptive routing in mobile opportunistic networks. In: 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, MSWIM 2010, pp. 101–109. ACM (2010)
Liu, X.: A survey on clustering routing protocols in wireless sensor networks. Sensors 12(8), 11113–11153 (2012)
Pan, J., Cui, J., Wei, L., Xu, Y., Zhong, H.: Secure data sharing scheme for VANETs based on edge computing. EURASIP J. Wireless Comm. Netw. 2019 (2019)
Panichpapiboon, S., Pattara-atikom, W.: Exploiting wireless communication in vehicle density estimation. IEEE Trans. Veh. Technol. 60(6), 2742–2751 (2011)
Park, Y., Sur, C., Noh, S.W., Rhee, K.H.: Self-controllable secure location sharing for trajectory-based message delivery on cloud-assisted VANETs. Sensors 18(7), 2112 (2018)
Sun, Y., Luo, S., Dai, Q., Ji, Y.: An adaptive routing protocol based on QoS and vehicular density in urban VANETs. Int. J. Distrib. Sensor Netw. 2015, 5:5 (2015)
Toghi, B., Saifuddin, M., Mughal, M., Fallah, Y.P.: Spatio-temporal dynamics of cellular v2x communication in dense vehicular networks. In: IEEE Connected and Automated Vehicles Symposium (CAVS), pp. 1–5. IEEE (2019)
Vahdat, A., Becker, D.: Epidemic routing for partially-connected ad hoc networks. Technical report, Duke University (2000)
Vasilakos, A.V., Zhang, Y., Spyropoulos, T.: Delay Tolerant Networks: Protocols and Applications. Wireless Networks and Mobile Communications. CRC Press, Hoboken (2011)
Wang, J., Peeta, S., Lu, L., Li, T.: Multiclass information flow propagation control under vehicle-to-vehicle communication environments. Transp. Res. Part B Methodol. 129, 96–121 (2019)
Zeadally, S., Guerrero, J., Contreras, J.: A tutorial survey on vehicle-to-vehicle communications. Telecommun. Syst. 73(3), 469–489 (2019). https://doi.org/10.1007/s11235-019-00639-8
Acknowledgment
Authors thank Coord. de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. H.S. also thanks Stic-AMSUD (project 20-STIC-09), and FAPESP (contracts 2018/22979-2 and 2015/24461-2) for their support.
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Costa, L.PP., Marcondes, C.A.C., Senger, H. (2021). Non-cooperative Vehicular Density Prediction in VANETs. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12952. Springer, Cham. https://doi.org/10.1007/978-3-030-86973-1_14
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