Skip to main content
SpringerLink
Log in

A reliability estimation framework for cognitive radio V2V communications and an ANN-based model for automating estimations

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Vehicular Ad-hoc Networks (VANETs) are an evolving technology in the transportation industry. Reliability is a critical issues in vehicular communications to ensure road safety. An analytical framework is presented in this paper to estimate the reliability of Vehicle-to-Vehicle (V2V) communications in Cognitive Radio (CR)-VANETs. The proposed framework considers nodes’ reliability and different challenges of physical, MAC, and network layers for communications’ reliability, including the probability of channel availability for CR-enabled vehicles, channel fading, contention among the transmitting vehicles, hidden terminal problem, and transmission redundancy. The channel availability for reliability assessments of CR vehicular communications has not been considered in the previous research. Therefore, a Markov model is proposed to estimate the probability of channel availability for CR-enabled vehicular nodes. Also, a dataset is created using the proposed analytical framework, and the reliability estimation of the V2V communications is automated using an Artificial Neural Network (ANN) model. The analytical findings are validated by NS-3 simulations and compared with existing methods, showing the accuracy of the proposed method. Moreover, the correlation coefficient (R) and Root Mean Square Error (RMSE) of the test data for the best case of the ANN model, with one hidden layer, are 0.9948 and 0.0289, respectively. These values indicate the accurate estimation of CR-vehicular communications’ reliability by the analytical framework.

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

Similar content being viewed by others

References

  1. Satheshkumar K, Mangai S (2020) EE-FMDRP: energy efficient-fast message distribution routing protocol for vehicular ad-hoc networks. J Amb Intel Hum Comp 12:3877–3888

    Article  Google Scholar 

  2. Hu L, Dai Z (2020) Performance and reliability analysis of prioritized safety messages broadcasting in DSRC with hidden terminals. IEEE Access 8:177112–177124

    Article  Google Scholar 

  3. Benrhaiem W, Senhaji HA (2020) Bayesian networks-based reliable broadcast in vehicular networks. Veh Commun 21:1–13

    Google Scholar 

  4. Fawaz K, Ghandour A, Olleik M, Artail H (2010) Improving reliability of safety applications in vehicle ad hoc networks through the implementation of a cognitive network. In: International conference on telecommunication, pp 798–805

  5. Singh KD, Rawat P, Bonnin JM (2014) Cognitive radio for vehicular ad-hoc networks (CR-VANETs): approaches and challenges. EURASIP J Wirel Comm 49:1–22

    Google Scholar 

  6. Dharmaraja S, Vinayak R, Trivedi KS (2016) Reliability and survivability of vehicular ad hoc networks: an analytical approach. Reliab Eng Syst Safe 153:28–38

    Article  Google Scholar 

  7. Shahen Shah AFM, Ilhan H, Tureli U (2019) RECV-MAC: a novel reliable and efficient cooperative MAC protocol for VANETs. IET Commun 13(16):2541–2549

    Article  Google Scholar 

  8. Abbasi HI, Voicu RC, Copeland JA, Chang Y (2019) Towards fast and reliable multihop routing in VANETs. IEEE Trans Mob Comput 19(10):2461–2474

    Article  Google Scholar 

  9. Liu L, Chen C, Wang B, Zhou Y, Pei Q (2019) An efficient and reliable QoF routing for urban VANETs with backbone nodes. IEEE Access 7:38273–38286

    Article  Google Scholar 

  10. Goli-Bidgoli S, Movahhedinia N (2020) Towards ensuring reliability of vehicular ad hoc networks using a relay selection techniques and D2D communications in 5G networks. Wirel Pers Commun 114:2755–2767

    Article  Google Scholar 

  11. Shiddharthya R, Gunavathi R (2021) A selective reliable communication to reduce broadcasting for cluster based VANET. Turk J Comput Math Educ 12(3):4450–4457

    Google Scholar 

  12. Inedjaren Y, Maachaoui M, Zeddini B, Barbot JP (2021) Blockchain-based distributed management system for trust in VANET. Veh Commun 30:1–11

    Google Scholar 

  13. Ullah S, Abbas G, Waqas M, Abbas ZH, Tu S, Hameed IA (2021) EEMDS: an effective emergency message dissemination scheme for urban VANETs. Sensors 21(5):1–19

    Article  Google Scholar 

  14. Faisal SM, Zaidi T (2021) Implementation of ACO in VANETs with detection of faulty node. Indian J Sci Technol 14(19):1598–1614

    Article  Google Scholar 

  15. Javadpour A, Rezaei S, Sangaiah A K, Slowik A, Mahmoodi Khaniabadi S (2021) Enhancement in quality of routing service using metaheuristic PSO algorithm in VANET networks. Soft Comput, pp 1–12

  16. Kazi AK, Mahmood Khan S, Haider NG (2021) Reliable group of vehicles (RGoV) in VANET. IEEE Access 9:111407–111416

    Article  Google Scholar 

  17. Goli-Bidgoli S, Movahhedinia N (2017) Determining vehicles’ radio transmission range for increasing cognitive radio VANET (CR-VANET) reliability using a trust management system. Comput Netw 127:340–351

    Article  Google Scholar 

  18. Akter S, Mansoor N (2020) A spectrum aware mobility pattern based routing protocol for CR-VANETs. In: IEEE wireless communications and networking conference, pp 1–6

  19. Sattar S, Qureshi HK, Saleem M, Mumtaz S, Rodrigue J (2018) Reliability and energy-efficiency analysis of safety message broadcast in VANETs. Comput Commun 119:118–126

    Article  Google Scholar 

  20. Ali GGMN, Noor-A-Rahim M, Chong PHJ, Guan YL (2018) Analysis and improvement of reliability through coding for safety message broadcasting in urban vehicular networks. IEEE Trans Veh Technol 67(8):1–12

    Article  Google Scholar 

  21. Saajid H, DI W, Xin W, Memon S, Bux N K, Aljeroudi Y (2019) Reliability and connectivity analysis of vehicular ad hoc networks under various protocols using a simple heuristic approach. IEEE Access 7:132374–132383

    Article  Google Scholar 

  22. Ali GGMN, Ayalew B, Vahidi A, Noor-A-Rahim M (2019) Analysis of reliabilities under different path loss models in urban/sub-urban vehicular networks. In: IEEE vehicular technology conference, pp 1–6

  23. Hoque MA, Rios-Torres J, Arvin R, Khattak A, Ahmed S (2020) The extent of reliability for vehicle-to-vehicle communication in safety critical applications: an experimental study. J Intell Transp Syst 4(3):264–278

    Article  Google Scholar 

  24. Zhao J, Li Z, Wang Y, Wu Z, Ma X, Zhao Y (2020) An analytical framework for reliability evaluation of d-dimensional IEEE 802.11 broadcast wireless networks. Wirel Netw 26(5):3373–3394

    Article  Google Scholar 

  25. Gupta S, Khaitan V (2021) Reliability and survivability analysis of long-term evolution vehicular ad-hoc networks: an analytical approach. J Netw Syst Manage, 29(11)

  26. Bahramnejad S, Movahhedinia N (2021) A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers. Computing, pp 1–29

  27. Ghaleb FA, Zainal A, Rassam MA, Mohammed F (2017) An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications. In: IEEE conference on application, information and network security, pp 13–18

  28. Bagherlou H, Ghaffari A (2018) A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks. J Supercomput 74:2528–2552

    Article  Google Scholar 

  29. Jindal A, Aujla GS, Kumar N, Chaudhary R, Obaidat MS, You I (2018) SeDaTiVe: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems. IEEE Netw 32(6):66–73

    Article  Google Scholar 

  30. Ye H, Li GY, Juang BH (2018) Deep reinforcement learning for resource allocation in V2V communications. In Proceedings of the IEEE ICC, pp 1–5

  31. Karabulut M A, Shahen S A F M, Ilhan H (2019) Performance optimization by using artificial neural network algorithms in VANETs. In: International conference on telecommunications and signal processing, pp 633–636

  32. Liu T, Shi S, Gu X (2019) Naive bayes classifier based driving habit prediction scheme for VANET stable clustering. In: Han S, Ye L, Meng W (eds) Artificial intelligence for communications and networks. AICON 2019. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, p 286. Springer, Cham

  33. Adhikary K, Bhushan S, Kumar S, Dutta K (2020) Hybrid algorithm to detect DDoS attacks in VANETs. Wirel Pers Commun 114:3613–3634

    Article  Google Scholar 

  34. Schmidt DA, Khan MS, Bennett BT (2020) Spline-based intrusion detection for VANET utilizing knot flow classification. Int Technol Lett 3:e155

  35. Li F, Zhang J, Szczerbicki E, Song J, Li R, Diao R (2020) Deep learning-based intrusion system for vehicular ad hoc networks. Comput Mater Condens 65(1):653–681

  36. Vitalkar RS, Thorat SS, Rojatkar DV (2020) Intrusion detection system for vehicular ad-hoc network using deep learning. Int Res J Eng Technol 7(12):2294–2300

  37. Bangui H, Ge M, Buhnova B (2021) A hybrid data-driven model for intrusion detection in VANET. Proc Comput Sci 184:516–523

  38. Husnain G, Anwar S (2021) An intelligent cluster optimization algorithm based on whale optimization algorithm for VANETs (WOACNET). PLoS ONE 16(4):e0250271

  39. Rehman A, Hassan MF, Hooi YK, Qureshi MA, Chung TD, Akbar R, Safdar S (2021) Context and machine learning based trust management framework for Internet of vehicles. Comput Mater Condens 68(3):4125–4142

  40. Ma X, Zhang J, Wu T (2011) Reliability analysis of one-hop safety-critical broadcast services in VANETs. IEEE Treans Veh Technol 60(8):3933–3946

  41. Mahmood DA, Horvath G (2019) Analysis of the message propagation on the highway in VANET. Arab J Sci Eng 44:3405–3413

    Article  Google Scholar 

  42. Chembe C, Md Noor R, Ahmedy I, Oche M, Kunda D, Liu CH (2017) Spectrum sensing in cognitive vehicular network: state-of-art, challenges and open issues. Comput Commun 97:15–30

    Article  Google Scholar 

  43. Wang S, Wang Y, Coon JP, Doufexi A (2012) Energy-efficient spectrum sensing and access for cognitive radio networks. IEEE Trans Veh Technol 61(2):906–912

  44. Yang Q, Wang L, Xia W, Wu Y, Shen L (2014) Development of on-board unit in vehicular ad-hoc network for highways. In: International conference on connected vehicles and expo, pp 457–462

  45. Goldsmith A (2005) Statistical multipath channel models. Wireless Communications. UK, CUP, Cambridge, pp 58–90

  46. Parvin S, Fujii T (2011) Hidden node aware routing method using high sensitive sensing device for multi-hop wireless mesh network. EURASIP J Wirel Comm 2011:1–17

  47. Bianchi G (2000) Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J Sel Area Commun 18(3):535–547

  48. Medina EA, Paredes JP (2009) Artificial neural network modeling techniques applied to the hydro desulfurization process. Math Comput Model 49(1):207–214

    Article  Google Scholar 

  49. Wang Y (2020) Robot algorithm based on neural network and intelligent predictive control. Amb Intel Hum Comp 11:6155–6166

  50. You Z, Lu C (2018) A heuristic fault diagnosis approach for electro-hydraulic control system based on hybrid particle swarm optimization and Levenberg-Marquardt algorithm. Amb Intel Hum Comp, pp 1–10

  51. Roh Y, Heo G, Whang SE (2021) A survey on data collection for machine learning: a big data-AI integration perspective. IEEE Trans Knowl Data Eng 33(4):1328–1347

  52. Tohidi S, Sharifi Y (2016) Load-carrying capacity of locally corroded steel plate girder ends using artificial neural network. Thin Wall Struct 100:48–61

  53. Boukerche A, Wang J (2020) A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model. Ad Hoc Netw 106:1–10

    Article  Google Scholar 

  54. Al-Ali A, Chowdhury K (2014) Simulating dynamic spectrum access using ns-3 for wireless networks in smart environments. In: IEEE International conference sensing, communication, and networking workshop, pp 28–33

  55. Witten IH, Frank E, Trigg L, Hall M, Holmes G, Cunningham SJ (1999) Weka: practical machine learning tools and techniques with Java implementations. In: Workshop on emerging knowledge engineering and connectionist-based information systems, pp 192–196

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

    Somayeh Bahramnejad & Naser Movahhedinia

Authors
  1. Somayeh Bahramnejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naser Movahhedinia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naser Movahhedinia.

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

Bahramnejad, S., Movahhedinia, N. A reliability estimation framework for cognitive radio V2V communications and an ANN-based model for automating estimations. Computing 104, 1923–1947 (2022). https://doi.org/10.1007/s00607-022-01072-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-022-01072-7

Keywords

Mathematics Subject Classification

Search

Navigation