Performance evaluation of rate adaptation algorithms for seamless heterogeneous vehicular communications


VANET is an emerging area of wireless ad-hoc networks to contribute in the success of connected vehicles projects. The extremely changeable number of mobile nodes and high mobility are challenging issues. Furthermore, this particular network has several problems in term of defining suitable schemes and protocols like rate adaptation mechanisms. The overall performance of diverse applications in VANET such as traffic control and multimedia delivery is based on the achievement ratio these networks can offer and the network throughput. Rate adaptation is an essential technique to evade the performance network degradation and to maximize the throughput by using the estimation of the present channel characteristics and determining the optimal bitrate for subsequent transmissions. Despite there are several available rate control algorithms for 802.11 WLANs standards, there are few works devoted to the rate adaptation for the standard of vehicular networks. In this paper, we compare and evaluate the existing data rate adaptation schemes in numerous vehicular environments to recognize their behavior and analyze their performance in diverse scenarios. Six algorithms were chosen for comparison using the NS-3 simulator: AARF, AARF-CD, AMRR, CARA, Onoe, and Minstrel. The simulation outcomes demonstrate that Minstrel algorithm outperforms the remaining five mechanisms in dense and dynamic situations.

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  1. 1.

    Zekri A, Jia W (2018) Heterogeneous vehicular communications: a comprehensive study. Elsevier Ad Hoc Networks journal 75-76:52–79

    Article  Google Scholar 

  2. 2.

    Jiang D, Delgrossi L (2008). IEEE 802.11p: towards an international standard for wireless access in vehicular environments. IEEE vehicular technology conference. 2036-2040

  3. 3.

    Choi N, Choi S, Seokt Y, Kwon T, Choi Y (2007). A solicitation-based IEEE 802.11p MAC protocol for roadside to vehicular networks. Mobile networking for vehicular environments. 91-96

  4. 4.

    IEEE Standard for Information technology – Telecommunications and information exchange between systems - Local and metropolitan area networks - Specific requirements – “Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 6: Wireless Access in Vehicular Environments,” IEEE Std 802.11p, July, 15 (2010), pp.1–51

  5. 5.

    Msadaa I C, Cataldi P, Filali F (2010). A comparative study between 802.11p and Mobile WiMAX-based V2I communication networks. International conference on next generation Mobile applications, services and technologies (NGMAST). 186-191

  6. 6.

    Ilori A, Tang Z, He J, Li Y (2015) Throughput-based rate adaptation algorithm for IEEE 802.11 vehicle networks. IET Networks 4(2):111–118

    Article  Google Scholar 

  7. 7.

    Shankar P, Nadeem T, Rosca J, Iftode L (2008). CARS: context-aware rate selection for vehicular networks. IEEE international conference on network protocols (ICNP)

  8. 8.

    Liu C, Liu S, Hamdi M (2012). GeRA: generic rate adaptation for vehicular networks. IEEE international conference on communications (ICC). 5311-5315

  9. 9.

    Xiong J, Chen C, Guan X, Hua C (2016). LRRA: location-related rate adaptation algorithm in IEEE 802.11p for DSRC technology in VANET. IEEE 84th vehicular technology conference (VTC-fall)

  10. 10.

    Mahendri SN (2015) A survey on vehicular ad-hoc networks (VANETs). International Journal of Advanced Research in Computer Engineering &Technology (IJARCET) 4(5)

  11. 11.

    Jiang D, Delgrossi L (2008). IEEE 802.11p: towards an international standard for wireless access in vehicular environments. Mercedes-Benz Research & Development North America, Inc.

  12. 12.

    FCC allocates spectrum in 5.9 GHz range for intelligent transportation systems uses. Action will improve the efficiency of the nation’s transportation infrastructure, Washington, D.C. 20554. Oct. 21, (1999). Report no. ET 99–5.

  13. 13.

    Islam A, Bhuyan NHM (2015). The effect of radio channel modelling on the network performance in VANET. Master’s Thesis, Department of Electrical and Information Technology, Faculty of Engineering, LTH, Lund University

  14. 14.

    Sharma A, Raj B, Kapoor K, Jyoti D (2016) A novel approach for detection of traffic congestion in NS2. International journal of advance research, ideas and innovations in technology (IJARIIT) 2(4)

  15. 15.

    Van Wijngaarden P (2011). Frame capture in IEEE 802.11p vehicular networks: a simulation-based approach. Master thesis, University of Twente, July 2011

  16. 16.

    Kamerman A, Monteban L (1997) A high-performance wireless LAN for the unlicensed band. Bell Labs Technical Journal 2(3):118–133

    Article  Google Scholar 

  17. 17.

    Lacage M, Manshaei M H, Turletti T (2004). IEEE 802.11 rate adaptation: a practical approach. proceedings of the 7th acm international symposium on modeling, analysis and simulation of wireless and mobile systems (MSWiM), Venice, Italy, Oct. 2004. 126–134

  18. 18.

    Mohamed MA, Bahget WM, Mohamed SS (2014) A performance evaluation for rate adaptation algorithms in IEEE 802.11 wireless networks. Int J Comput Appl 99(4):54–59

    Google Scholar 

  19. 19.

    Maguolo F, Lacage M, Turletti T (2008). Efficient collision detection for auto rate fallback algorithm. IEEE symposium on computers and communications. 25-30

  20. 20.

    Xia, Qiuyan, Hamdi M, Chan TH (2006). Practical rate adaptation for IEEE 802.11 WLANs. IEEE GLOBECOM

  21. 21.

    Xia Q, Hamdi M (2008) Smart sender: a practical rate adaptation algorithm for multirate IEEE802.11 WLANs. IEEE Trans Wirel Commun 7(5):1764–1755

    Article  Google Scholar 

  22. 22.

    Website: http://madwifi ath_rate/onoe

  23. 23.

    Kim J, Kim S, Choi S, Qiao D (2006). CARA: collision-aware rate adaptation for IEEE 802.11 WLANs. IEEE 25th international conference on computer communications INFOCOM

  24. 24.

    Description of the Minstrel algorithm, Website: http://madwifi minstrel.txt

  25. 25.

    Zekri A, Jia W (2018). Performance evaluation of rate adaptation algorithms in IEEE802.11p heterogeneous vehicular networks. IEEE 15th international conference on Mobile ad hoc and sensor systems (MASS). 107-115

  26. 26.

    Xia D, Hart J, Fu Q (2013). Evaluation of the minstrel rate adaptation algorithm in IEEE 802.11g WLANs. IEEE international conference on communications (ICC). 2223-2228

  27. 27.

    Nunes N, Sargento S (2014). Data rate adaptation mechanisms in vehicular networks. 16th international telecommunications network strategy and planning symposium (networks)

  28. 28.

    Guo L, Dong M, Ota K, Li Q, Ye T, Wu J, Li J (2017) A secure mechanism for big data collection in large scale internet of vehicle. IEEE Internet Things J 4(2):601–610

    Article  Google Scholar 

  29. 29.

    Dong M, Ota K, Yang LT, Liu A, Guo M (2016) LSCD: a low-storage clone detection protocol for cyber-physical systems. IEEE Trans on CAD of Integrated Circuits and Systems 35(5):712–723

    Article  Google Scholar 

  30. 30.

    Li L, Ota K, Dong M (2018) Humanlike driving: empirical decision-making system for autonomous vehicles. IEEE Trans Vehicular Technology 67(8):6814–6823

    Article  Google Scholar 

  31. 31.

    Yin W, Bialkowski K, Indulska J, Hu P (2010). Evaluations of madwifi mac layer rate control mechanisms. 18th international workshop on quality of service (IWQoS). Pp. 1-9

  32. 32.

    Judd G, Wang X, Steenkiste P (2008). Efficient channel-aware rate adaptation in dynamic environments. Proceedings of the 6th international conference on Mobile systems, applications, and services, ser. MobiSys ‘08. New York, NY, USA: ACM. Pp. 118-131.[online].Available:

  33. 33.

    Lee K, Navarro J, Chong T, Lee U, Gerla M (2010). Trace-based evaluation of rate adaptation schemes in vehicular environments. Vehicular technology conference (VTC 2010-spring). Pp. 1-5

  34. 34.

    Biaz S, Wu S (2008). Rate adaptation algorithms for IEEE 802.11 networks: a survey and comparison. IEEE symposium on computers and communications (ISCC). Pp. 130-136

  35. 35.

    Chen X, Gangwal P, Qiao D (2009). Practical rate adaptation in mobile environments. IEEE international conference on pervasive computing and communications PerCom. Pp. 1-10

  36. 36.

    Zhang J, Tan K, Zhao J, Wu H, Zhang Y (2008). A practical snr-guided rate adaptation. IEEE 27th conference on computer communications INFOCOM. Pp. 2083–2091

  37. 37.

    Ozturk S, Misic J and Misic V (2011). Reaching spatial or networking saturation in VANET. EURASIP Journal of Wireless Communications and Networking special issue devoted to Recent Advances in Vehicular Networks

  38. 38.

    Misic J, Badawy G, Misic V (2011) Performance characterization for IEEE 802.11p network with single channel devices. IEEE TVT 60(4):1775–1787

    Google Scholar 

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This work is supported by FDCT/0007/2018/A1; DCT-MoST Joint-project No. (025/2015 / AMJ); University of Macau funds Nos: CPG2018-00032-FST & SRG2018-00111-FST of SAR Macau, China; Chinese National Research Fund (NSFC) Key Project No. 61532013; National China 973 Project No. 2015CB352401; Shanghai Scientific Innovation Act of STCSM No. 15JC1402400 and 985 Project of Shanghai Jiao Tong University: WF220103001.

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Correspondence to Abdennour Zekri.

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Zekri, A., Jia, W. Performance evaluation of rate adaptation algorithms for seamless heterogeneous vehicular communications. Peer-to-Peer Netw. Appl. 14, 1–17 (2021).

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  • Rate adaptation algorithm
  • Heterogeneous vehicular networks
  • IEEE 802.11p
  • Minstrel
  • CARA