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A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers

Abstract

Reliability is one of the main objectives of many systems, which may be part of man-machine systems, consisting of human operators and machines working under the specified conditions. Vehicular networks are systems in which safety information must be delivered reliably and quickly to ensure the driver’s correct and timely reaction. Hence, in this paper, we propose a general framework based on fuzzy arithmetic to perform a reliability assessment of different systems with various kinds of components. Then we apply the framework to a cognitive radio vehicular ad-hoc network (CR-VANET) with drivers (CRVD system). The reliability of a CRVD system depends on different environmental, vehicular, and human-related factors that change over time. The interactions between these factors are so convoluted and subjective. Hence, reliability estimation of the system with a crisp value is difficult. Moreover, there is a lack of consideration given to driver’s reaction in the system’s reliability estimations even though it is one of the most critical safety issues in intelligent transportation systems (ITS). The reliability analysis in the present study is performed for vehicular communications with and without considering the drivers. The analytical results are supported by simulations using NS3 and compared with existing methods. The evaluation results indicate that reliability is assessed more precisely when considering the driver as one of the system’s components.

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References

  1. 1.

    Lin S, Wang Y, Jia L (2018) System reliability assessment based on failure propagation processes. Complex 2018:1–19

  2. 2.

    Jirutitijaroen P, Singh C, Mitra J (2019) Electric power grid reliability evaluation: models and methods. Wiley-IEEE Press, Hoboken, pp 117–164

    Google Scholar 

  3. 3.

    Candra MZC, Truong HL, Dustdar S (2015) Analyzing reliability in hybrid compute units. In: IEEE conference on collaboration and internet computing, pp 150–159

  4. 4.

    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 

  5. 5.

    Satheshkumar K, Mangai S (2020) EE-FMDRP: energy efficient-fast message distribution routing protocol for vehicular ad-hoc networks. J Amb Intel Hum Comput. https://doi.org/10.1007/s12652-020-01730-8

    Article  Google Scholar 

  6. 6.

    Mchergui A, Moulahi T, Nasri S (2020) QoS evaluation model based on intelligent fuzzy system for, vehicular ad hoc networks. Computing 102(5):2501–2520

    MathSciNet  Article  Google Scholar 

  7. 7.

    Hollnagel E (1998) Cognitive reliability and error analysis method (CREAM). Elsevier Science Ltd, Amsterdam

    Google Scholar 

  8. 8.

    Chang JM, Lai CF, Chao HC, Zhu R (2014) An energy-efficient geographic routing protocol design in vehicular ad-hoc network. Computing 96(11):119–131

    Article  Google Scholar 

  9. 9.

    Dang DNM, Hong CS, Lee S, Huh E (2014) An efficient and reliable MAC in VANETs. IEEE Commun Lett 18(4):616–618

    Article  Google Scholar 

  10. 10.

    Shelly S, Babu AV (2015) Link reliability based greedy perimeter stateless routing for vehicular ad hoc networks. Int J Veh Technol 2015:1–16

    Article  Google Scholar 

  11. 11.

    Goli-Bidgoli S, Movahhedinia N (2017) A trust-based framework for increasing MAC layer reliability in cognitive radio VANETs. Wirel Pers Commun 95:2873–2893

    Article  Google Scholar 

  12. 12.

    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

  13. 13.

    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 

  14. 14.

    Shah AFMS, 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 

  15. 15.

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

    Article  Google Scholar 

  16. 16.

    Benrhaiem W, Senhaji Hafid A (2020) Bayesian networks-based reliable broadcast in vehicular networks. Veh Commun 261:1–13

    Google Scholar 

  17. 17.

    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 

  18. 18.

    Rostamzadeh K, Gopalakrishnan S (2013) Analysis of message dissemination in vehicular networks. IEEE Trans Veh Technol 62(8):3974–3982

    Article  Google Scholar 

  19. 19.

    Yao Y, Rao L, Liu X (2013) Performance and reliability analysis of IEEE 802.11p safety communication in a highway environment. IEEE Trans Veh Technol 62(9):4198–4212

    Article  Google Scholar 

  20. 20.

    Yin X, Ma X, Trivedi K, Vinel A (2014) Performance and reliability evaluation of BSM broadcasting in DSRC with multi-channel schemes. IEEE Trans Comput 63(12):3101–3113

    MathSciNet  Article  Google Scholar 

  21. 21.

    Shelly S, Babu A (2014) A probabilistic model for communication link reliability in vehicular ad hoc networks. In: IEEE international conference on vehicular electronics and safety, pp 123–128

  22. 22.

    Wang Y, Hu J, Zhang Y, Xu C (2015) Reliability evaluation of IEEE 802.11p-based vehicle-to-vehicle communication in an urban expressway. Tsinghua Sci Technol 20(4):417–428

    Article  Google Scholar 

  23. 23.

    Saajidi H, Di W, Wang X, Memon S, Bux NK, 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 

  24. 24.

    Nawaz Ali GGM, 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):6774–6787

    Article  Google Scholar 

  25. 25.

    Gholibeigi M, Heijenk G (2016) Analysis of multi-hop broadcast in vehicular ad hoc networks: a reliability perspective. In: Wireless days (WD 2016), pp 1–8

  26. 26.

    Sattar S, Khaliq H, Muhammad Q, Shahid Mumtaz S, Rodriguez J (2018) Reliability and energy-efficiency analysis of safety message broadcast in VANETs. Comput Commun 119:118–126

    Article  Google Scholar 

  27. 27.

    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 (VTC2019-Fall), pp 1–6

  28. 28.

    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 Transport Syst 24:264–278

    Article  Google Scholar 

  29. 29.

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

    Article  Google Scholar 

  30. 30.

    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 26:3373–3394

    Article  Google Scholar 

  31. 31.

    Johannsen G (1982) Man-machine systems-introduction and background. In: IFAC proceedings volumes, vol 15, No 6, pp xiii–xvii

  32. 32.

    Bector CR, Chandra S (2005) Fuzzy mathematical programming and fuzzy matrix games. Studies in fuzziness and soft computing. Springer, Berlin, pp 39–56

    MATH  Google Scholar 

  33. 33.

    Gargama H, Chaturvedi SK (2011) Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Trans Reliab 60(1):102–110

    Article  Google Scholar 

  34. 34.

    Agus W, Abulwafa M (2017) Comparison of wighted sum model and multi attribute decision making weighted product methods in selecting the best elementary school in Indonesia. Int J Softw Eng Appl 11(4):69–90

    Google Scholar 

  35. 35.

    Stillwell WG, Seaver DA, Edwards W (1981) A comparison of weight approximation techniques in multi-attribute utility decision making. Organ Behav Hum Perform 28(1):62–77

    Article  Google Scholar 

  36. 36.

    Barron FH, Barret BE (1996) Decision quality using ranked attribute weights. Manag Sci 42(11):1515–1523

    Article  Google Scholar 

  37. 37.

    Bai Y, Wang D (2006) Fundamentals of fuzzy logic control-fuzzy sets, fuzzy rules and defuzzifications. In: Bai Y, Zhuang H, Wang D (eds) Advanced fuzzy logic technologies in industrial applications. Springer, London, pp 17–36

    Chapter  Google Scholar 

  38. 38.

    Dehghan SMM, Moradi H (2014) Aerial obstacle estimation using RSSI observations based on OHLOSS diffraction model. In: Proceeding of RSI/ISM international conference on robotics and mechatronics, pp 564–569

  39. 39.

    Umar R, Sulan SS, AzlanIbrahim AW, Mokhtar WZAW, Sabri NH (2015) Radio frequency interference: the study of rain effect on radio signal attenuation. Malays J Anal Sci 19(5):1093–1098

    Google Scholar 

  40. 40.

    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 

  41. 41.

    Baldwin JF, Karale SB (2003) Asymmetric triangular fuzzy sets for classification models. In: Palade V, Howlett RJ, Jain L (eds) Knowledge-based intelligent information and engineering systems, vol 2773. Lecture notes in computer science. Springer, Berlin

    Chapter  Google Scholar 

  42. 42.

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

  43. 43.

    Yu H, Zhao Y, Mo L (2020) Fuzzy reliability assessment of safety instrumented systems accounting for common cause failure. IEEE Access 8:135371–135382

    Article  Google Scholar 

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Correspondence to Naser Movahhedinia.

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Bahramnejad, S., Movahhedinia, N. A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers. Computing (2021). https://doi.org/10.1007/s00607-021-00980-4

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Keywords

  • Reliability assessment
  • CR-VANETs
  • Drivers
  • Fuzzy logic
  • Intelligent transportation systems (ITS)

Mathematics Subject Classification

  • 03B52
  • 68M15