Skip to main content
SpringerLink
Log in

Research on adaptive beacon message transmission power in VANETs

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In the future vehicular ad hoc networks (VANETs), vehicles communicate by sending beacon messages. However, fixed-period beacon messages cannot adapt to the characteristics of fast vehicle speed and variable network topology, and may contend for channel failure when there are many vehicles, resulting in the relevant information not being able to be known to surrounding vehicles, increasing the possibility of danger. In order to solve this problem, this paper proposes an adaptive beacon transmission power algorithm based on vehicle position prediction error, which increases the beacon transmission power of vehicles with large vehicle position prediction errors and reduces the transmission power of vehicles with small errors. And analyze the relevant factors that may affect the results in the experiment, and formulate relevant solutions to signal fading and channel contention. Finally, the experimental results show that, compared with the fixed transmit power, the proposed adaptive power reduces the CBT by about 16% and improves the packet transmission rate by about 4.5%, ensuring the effective transmission of security information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Ali Shah SA, Ahmed E, Xia F, Karim A, Qureshi MA, Ali J, Noor RM (2018) Coverage differentiation based adaptive Tx-power for congestion and awareness control in VANETs. Mobile Netw Appl 23(5):1194–1205

    Article  Google Scholar 

  • Behrisch M, Bieker L, Erdmann J, Krajzewicz D (2011) SUMO-simulation of urban mobility: an overview. In: SIMUL 2011, The Third International Conference on Advances in System Simulation

  • Bouk SH, Kim G, Ahmed SH, Kim D (2015) Hybrid adaptive beaconing in vehicular ad hoc networks: a survey. Int J Distrib Sens Netw 2015:16

    Google Scholar 

  • Chen L, Wang J, Chen R, Gu X, Wang J (2019) Research on video streaming media cooperative downloading in vehicular ad hoc network. J Commun 40(1):51–63

    Google Scholar 

  • Cheng L, Henty BE, Stancil DD, Bai F (2007) Mobile vehicle-to-vehicle narrow-band channel measurement and characterization of the 5.9 GHz dedicated short range communication (DSRC) frequency band. IEEE J Sel Areas Commun 25(8):1501–1516

    Article  Google Scholar 

  • Dong W, Zhang H, Lin W, Yin Y, Chen H (2017) Performance simulation and comparison of the routing algorithms in VANETs based on real urban map. J Qilu Univ Technol 31(2):56–62

    Google Scholar 

  • Egea-Lopez E, Pavon-Marino P (2016) Fair congestion control in vehicular networks with beaconing rate adaptation at multiple transmit powers. IEEE Trans Veh Technol 65(6):3888–3903

    Article  Google Scholar 

  • Egea-Lopez E, Alcaraz JJ, Vales-Alonso J, Festag A, Garcia-Haro J (2013) Statistical beaconing congestion control for vehicular networks. IEEE Trans Veh Technol 62(9):4162–4181

    Article  Google Scholar 

  • Fallah YP, Huang C, Sengupta R, Krishnan H (2010) Design of cooperative vehicle safety systems based on tight coupling of communication, computing and physical vehicle dynamics. In: ACM/IEEE international conference on cyber-physical systems

  • Fallah Y P, Huang C, Sengupta R (2010) Congestion control based on channel occupancy in vehicular broadcast networks. In: IEEE vehicular technology conference fall

  • Jiang N, Chen J, Zhou R, Wu C, Chen H, Zheng J, Wan T (2020a) PAN: pipeline assisted neural networks model for data-to-text generation in social internet of things. Inf Sci 530:167–179

    Article  Google Scholar 

  • Jiang N, Tian F, Li J, Yuan X, Zheng J (2020b) MAN: mutual attention neural networks model for aspect-level sentiment classification in SIoT. IEEE Internet Things J 7(4):2901–2913

    Article  Google Scholar 

  • Jiang N, Xu D, Zhou J, Yan H, Wan T, Zheng J (2020c) Toward optimal participant decisions with voting-based incentive model for crowd sensing. Inf Sci 512:1–17

    Article  Google Scholar 

  • Kwon YH, Rhee BH (2016) Bayesian game-theoretic approach based on 802.11p MAC protocol to alleviate beacon collision under urban VANETs. Int J Autom Technol 17(1):183–191

    Article  Google Scholar 

  • Li S (2016) Research on adaptive channel congestion control strategy for DSRC/WAVE. Dalian University of Technology, Dalian

    Google Scholar 

  • Li S, Tan G, Zhang F, Ding N (2017) Adaptive power control strategy for VANET. J Chin Comput Syst 38(1):72–76

    Google Scholar 

  • Li Y, Wang Z, Zhang C, Dai H, Xu W (2017) Trajectory prediction algorithm in VANET routing. Comput Res Dev 54(11):2419–2433

    Google Scholar 

  • Mo Y, Yu D, Bao S, Gao S (2017) Beacon transmission power control algorithm based on the preset threshold in VANETs. J Northeast Univ (Nat Sci) 38(3):331–334

    Google Scholar 

  • Shah SAA, Ahmed E, Xia F, Karim A (2016) Adaptive beaconing approaches for vehicular ad hoc networks: a survey. IEEE Syst J 12(2):1263–1277

    Article  Google Scholar 

  • Shah SAA, Ahmed E, Rodrigues J, Ali I, Noor R (2018) Shapely value perspective on adapting transmit power for periodic vehicular communications. IEEE Trans Intell Transp Syst 99:1–10

    Google Scholar 

  • Sulistyo S, Alam S (2018) SINR and throughput improvement for VANET using fuzzy power control. Int J Commun Syst 31(10):e3579

    Article  Google Scholar 

  • Sun J (2016) Research and solution on channel merging collision problem in the internet of vehicles. Dalian University of Technology, Dalian

    Google Scholar 

  • Torrent-Moreno M, Santi P, Hartenstein H (2006) Distributed fair transmit power adjustment for vehicular ad hoc networks. In: IEEE 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks

  • Torrent-Moreno M, Mittag J, Santi P, Hartenstein H (2009) Vehicle-to-vehicle communication: fair transmit power control for safety-critical information. Veh Technol IEEE Trans 58(7):3684–3703

    Article  Google Scholar 

  • Wang W, He W (2018) New research on the algorithm of denoising noise reduction by MATLAB software. J Disaster Prev Mitig 34(4):45–48

    Google Scholar 

  • Wu Q, Qiu B, Jiang W, Li W (2020) Optimal power allocation scheme for multi-vehicle cooperative communication based on SNR threshold. Mod Electron Tech 43(7):10–13

    Google Scholar 

  • Xu Z, Li S, Lin X, Wu Y (2016) Power control mechanism for vehicle status message in VANET. J Comput Appl 36(8):2175–2180

    Google Scholar 

  • Yu X, Tang J, Wang S (2019) Transmission power control algorithm based on channel load forecasting in VANET. Appl Res Comput 36(01):183–185 (202)

    Google Scholar 

  • Zemouri S, Djahel S, Murphy J (2018) An altruistic prediction-based congestion control for strict beaconing requirements in urban VANETs. IEEE Trans Syst Man Cybern Syst 49(12):2582–2597

    Article  Google Scholar 

  • Zhang J (2014) Installation of network simulation software NS2 based on VMware environment. Electron World 16:444–445

    Google Scholar 

  • Zhang Y, Wang M, Wang J, Du F, Hu Y, Yu M, Li G, Zhan A (2020) Research on adaptive beacon message broadcasting cycle based on vehicle driving stability. Int J Network Mgmt 2020:e2091. https://doi.org/10.1002/nem.2091

    Article  Google Scholar 

  • Zhou D, Qiu B, Chen Y, Xiao H, Alam M (2019) Power allocation for multisource, multidestination cooperative vehicular networks under an outage probability constraint. Trans Emerging Tel Tech 2019:e3624. https://doi.org/10.1002/ett.3624

    Article  Google Scholar 

  • Zuo Y, Guo A, Huang B, Wang L (2017) Power control algorithm based on network utility maximization in Internet of vehicles. J Comput Appl 37(12):3345–3350 (3380)

    Google Scholar 

Download references

Acknowledgements

We are grateful for Juan Wang, Guanxiang Yin from East China Jiaotong University for their constructive suggestions on the experiments. We are also grateful for the supports from National Natural Science Foundation of China, Natural Science Foundation of Jiangxi Province, Transportation Department of Jiangxi Province, Education Department of Jiangxi Province.

Funding

This study was funded by National Natural Science Foundation of China (Grant numbers: 11862006, 61862025), Natural Science Foundation of Jiangxi Province (Grant numbers: 2018ACB21032, 20181BAB211016), Research Project of Transportation Department of Jiangxi Province (Grant number: 2018X0016), Education Department of Jiangxi Province (Grant numbers: GJJ170381, GJJ170383) and China Scholarship Council (Grant number: 201808360320).

Author information

Authors and Affiliations

  1. School of Information Engineering, East China Jiaotong University, Nanchang, 330013, China

    Meng Wang, Tong Chen, Fei Du, Juan Wang, Guanxiang Yin & Yuejin Zhang

Authors
  1. Meng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guanxiang Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuejin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed to the concept and design of the study. The project was designed by YZ and JW. The experiment was designed and executed by MW, FD, JW, GY and TC. YZ and MW wrote this manuscript with the help of all other authors. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Yuejin Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Availability of data and material

All data and material generated or analyzed during this study are included in this published article.

Code availability

All code generated or used during the study are available from the corresponding author by request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, M., Chen, T., Du, F. et al. Research on adaptive beacon message transmission power in VANETs. J Ambient Intell Human Comput 13, 1307–1319 (2022). https://doi.org/10.1007/s12652-020-02575-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02575-x

Keywords

Search

Navigation