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An analytical framework for reliability evaluation of d-dimensional IEEE 802.11 broadcast wireless networks

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Abstract

In this paper, we validate that the deterministic distance-based analytical model can be used to estimate the reliability of one-dimensional (1-D) 802.11 broadcast wireless networks compared with the interference-based analytical model. Therefore, we propose a deterministic distance-based reliability analytical framework for such networks in d-dimensional (d-D, \(d \ge 1\)) scenarios. This framework takes into account the fading channel and hidden terminal problem and makes three commonly used reliability metrics able to be resolved, including point-to-point packet reception probability (NRP), packet delivery ratio (PDR), and packet reception ratio (PRR). There are two key factors involved in deducing the effect of hidden terminals. One is to measure the hidden terminal transmission probability during the vulnerable period, which can be calculated based on the approximate solution of the semi-Markov process model capturing the channel contention and the back-off behavior. Another is the challenge to determine the size of the area to which the hidden terminals belong. First, we give a general mathematical expression on the size of the hidden terminal coverage affecting NRP which is an important part of the closed-form solution of NRP/PRR. Second, we adopt the Monte-Carlo method to solve the size of general hidden terminal coverage affecting PDR, making it possible to approximate PDR, as well as control the efficiency and accuracy by constraining the relative error. Finally, we adopt a multi-parameter optimization scheme to find the optimum settings for the network to ensure the quality of service and maximize channel utilization. A series of experimental results show that the framework can be used to access the reliability of 802.11 based d-D broadcast wireless network and pave the way for further optimization.

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References

  1. FallahHoseini, M., & Rafeh, R. (2018). Proposing a centralized algorithm to minimize message broadcastingenergy in wireless sensor networks using directional antennas. Applied Soft Computing, 64, 272–281.

    Article  Google Scholar 

  2. Ma, X., Zhang, J., Yin, X., & Trivedi, K. S. (2012). Design and analysis of a robust broadcast scheme for VANET safety-related services. IEEE Transactions on Vehicular Technology, 61(1), 46–61.

    Article  Google Scholar 

  3. He, J., Tang, Z., Fan, Z., & Zhang, J. (2018). Enhanced collision avoidance for distributed LTE vehicle to vehicle broadcast communications. IEEE Communications Letters, 22(3), 630–633.

    Article  Google Scholar 

  4. Gupta, L., Jain, R., & Vaszkun, G. (2016). Survey of important issues in UAV communication networks. IEEE Communications Surveys & Tutorials, 18(2), 1123–1152.

    Article  Google Scholar 

  5. Pan, C., Yin, C., Beaulieu, N. C., & Yu, J. (2019). 3D UAV placement and user association in software-defined cellular networks. Wireless Networks, 25(7), 3883–3897.

    Article  Google Scholar 

  6. Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  7. Doumi, T., Dolan, M. F., Tatesh, S., Casati, A., Tsirtsis, G., Anchan, K., et al. (2013). LTE for public safety networks. IEEE Communications Magazine, 51(2), 106–112.

    Article  Google Scholar 

  8. Feickert, A. (2009). The Army’s future combat system (FCS): Background and issues for congress. Library of congress Washington DC congressional research service.

  9. Ma, X., Yin, X., & Trivedi, K. S. (2012). On the reliability of safety applications in VANETs. International Journal of Performability Engineering, 8(2), 115–130.

    Google Scholar 

  10. Torrent-Moreno, M., Mittag, J., Santi, P., & Hartenstein, H. (2009). Vehicle-to-vehicle communication: Fair transmit power control for safety-critical information. IEEE Transactions on Vehicular Technology, 58(7), 3684–3703.

    Article  Google Scholar 

  11. Ma, X., Zhang, J., & Wu, T. (2011). Reliability analysis of one-hop safety-critical broadcast services in VANETs. IEEE Transactions on Vehicular Technology, 60(8), 3933–3946.

    Article  Google Scholar 

  12. Ye, F., Yim, R., Roy, S., & Zhang, J. (2011). Efficiency and reliability of one-hop broadcasting in vehicular ad hoc networks. IEEE Journal on Selected Areas in Communications, 29(1), 151–160.

    Article  Google Scholar 

  13. Yin, X., Ma, X., & Trivedi, K. S. (2013). An interacting stochastic models approach for the performance evaluation of DSRC vehicular safety communication. IEEE Transactions on Computers, 62(5), 873–885.

    Article  MathSciNet  MATH  Google Scholar 

  14. Hassan, M. I., Vu, H. L., & Sakurai, T. (2011). Performance analysis of the IEEE 802.11 MAC protocol for DSRC safety applications. IEEE Transactions on Vehicular Technology, 60(8), 3882–3896.

    Article  Google Scholar 

  15. Hafeez, K. A., Zhao, L., Ma, B., & Mark, J. W. (2013). Performance analysis and enhancement of the DSRC for VANET’s safety applications. IEEE Transactions on Vehicular Technology, 62(7), 3069–3083.

    Article  Google Scholar 

  16. Ma, X., Yin, X., Wilson, M., & Trivedi, K.S. (2013). Mac and application-level broadcast reliability in vanets with channel fading. In 2013 international conference on computing, networking and communications (pp. 756–761).

  17. Tong, Z., Lu, H., Haenggi, M., & Poellabauer, C. (2016). A stochastic geometry approach to the modeling of DSRC for vehicular safety communication. IEEE Transactions Intelligent Transportation Systems, 17(5), 1448–1458.

    Article  Google Scholar 

  18. Ni, M., Pan, J., Cai, L., Yu, J., Wu, H., & Zhong, Z. (2015). Interference-based capacity analysis for vehicular ad hoc networks. IEEE Communications Letters, 19(4), 621–624.

    Article  Google Scholar 

  19. Ma, X., Lu, H., Zhao, J., Wang, Y., Li, J., & Ni, M. (2017). Comments on interference-based capacity analysis of vehicular ad hoc networks. IEEE Communications Letters, 21(10), 2322–2325.

    Article  Google Scholar 

  20. Goldsmith, A. (2005). Wireless communications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  21. Chun, Y. J., Colombo, G. B., Cotton, S. L., Scanlon, W. G., Whitaker, R. M., & Allen, S. M. (2017). Device-to-device communications: A performance analysis in the context of social comparison-based relaying. IEEE Transactions on Wireless Communications, 16(12), 7733–7745.

    Article  Google Scholar 

  22. Dousse, O., Baccelli, F., & Thiran, P. (2005). Impact of interferences on connectivity in ad hoc networks. IEEE/ACM Transactions on Networking, 13(2), 425–436.

    Article  Google Scholar 

  23. Ma, X., & Trivedi, K. S. (2016). Reliability and performance of general two-dimensional broadcast wireless network. Performance Evaluation, 95, 41–59.

    Article  Google Scholar 

  24. Ma, X., & Butron, G. (2015). On the reliability in d-dimensional broadcast wireless networks. In International conference on computing, networking and communications (pp. 957–961).

  25. Chen, X., Oorjitham, J., & Ma, X. (2011). On the two-dimensional coverage area in broadcast ad hoc networks. In 2011 IFIP wireless days (pp. 1–5).

  26. Zhao, J., Wu, Z., Wang, Y., & Ma, X. (2019). Adaptive optimization of qos constraint transmission capacity of vanet. Vehicular Communications, 17, 1–9.

    Article  Google Scholar 

  27. Xu, K., Gerla, M., Bae, S., & et al. (2002). How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks? In GlobeCom (pp. 72–76).

  28. Bianchi, G., Fratta, L., & Oliveri, M. (1996). Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs. In Proceedings of IEEE international symposium on personal, indoor, and mobile communications (pp. 392–396).

  29. Ma, X., Yin, X., Butron, G., Penney, C., & Trivedi, K. S. (2013). Packet delivery ratio in k-dimensional broadcast ad hoc networks. IEEE Communications Letters, 17(12), 2252–2255.

    Article  Google Scholar 

  30. Tong, Z., Lu, H., Haenggi, M., & Poellabauer, C. (2016). A stochastic geometry approach to the modeling of dsrc for vehicular safety communication. IEEE Transactions on Intelligent Transportation Systems, 17(5), 1448–1458.

    Article  Google Scholar 

  31. Yao, Y., Rao, L., & Liu, X. (2013). Performance and reliability analysis of IEEE 802.11p safety communication in a highway environment. IEEE Transactions on Vehicular Technology, 62(9), 4198–4212.

    Article  Google Scholar 

  32. Killat, M., & Hartenstein, H. (2009). An empirical model for probability of packet reception in vehicular ad hoc networks. EURASIP Journal on Wireless Communications and Networking, 2009, 1–12.

    Article  Google Scholar 

  33. Rappaport, T. S., et al. (1996). Wireless communications: Principles and practice (Vol. 2). Upper Saddle River, NJ: Prentice Hall PTR.

    MATH  Google Scholar 

  34. Li, S. (2011). Concise formulas for the area and volume of a hyperspherical cap. Asian Journal of Mathematics and Statistics, 4(1), 66–70.

    Article  MathSciNet  Google Scholar 

  35. Yin, X., Ma, X., & Trivedi, K. S. (2013). Channel fading impact on multi-hop DSRC safety communication. In Proceedings of the 16th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems (pp. 443–446).

  36. Pasupathy, R. (2010). Generating homogeneous Poisson processes. In Wiley encyclopedia of operations research and management science (pp. 1–6). Hoboken, NJ, USA: Wiley.

    Google Scholar 

  37. Dubi, A. (2000). Monte Carlo applications in systems engineering. New York: Wiley.

    Google Scholar 

  38. Lu, N., & Shen, X. S. (2014). Capacity analysis of vehicular communication networks. Berlin: Springer.

    Book  Google Scholar 

  39. Sander Frigau, M. (2013). Cross-layer transmit power and beacon rate adaptation for vanets. In Proceedings of the third ACM international symposium on design and analysis of intelligent vehicular networks and applications (pp. 129–136).

  40. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of international conference on neural networks (pp. 1942–1948).

  41. Henderson, D., Jacobson, S. H., & Johnson, A. W. (2003). The theory and practice of simulated annealing. In F. Glover, G. A. Kochenberger (Eds.), Handbook of metaheuristics (pp. 287–19). Boston: Springer.

    Chapter  Google Scholar 

  42. Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In F. Glover, G. A. Kochenberger (Eds.), Handbook of metaheuristics (pp. 250–285). Boston: Springer.

    Chapter  Google Scholar 

  43. Kennedy, J. (2003). Bare bones particle swarms. In Proceedings of the 2003 IEEE swarm intelligence symposium (pp. 80–87).

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Acknowledgements

We thank anonymous reviewers for their invaluable comments and suggestions on improving this work. This work is supported by the National Natural Science Foundation of China (NSFC) (Grant No. 61572150), and Central Fund of Dalian University of Technology (No. DUT17RC(3)097).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China

    Jing Zhao, Zhijuan Li & Zhuofei Wu

  2. School of Software Technology, Dalian University of Technology, Dalian, 116024, China

    Jing Zhao

  3. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, 518052, China

    Jing Zhao

  4. Department of Industrial Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Yanbin Wang

  5. College of Science and Engineering, Oral Roberts University, Tulsa, OK, 74171, USA

    Xiaomin Ma

  6. Science and Technology on Communication Security Laboratory, Chengdu, 610041, China

    Yue Zhao

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  1. Jing Zhao
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Correspondence to Jing Zhao, Yanbin Wang or Xiaomin Ma.

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Appendix A PDR/PRR results of the interference-based model and the deterministic distance-based model

Appendix A PDR/PRR results of the interference-based model and the deterministic distance-based model

Figures 23, 24, 25, 26, 27, 28, 29 and 30 present PDR/PRR results of the interference-based model and the deterministic distance-based model with the data rate of 3 Mbps, 6 Mbps, 12 Mbps, and 24 Mbps. PRR results show the same behavior with NRP in Sect. 2.4. We witness that PDRs are almost identical in the low-to-medium density at each data rate. When the density increases, the deterministic distance model obtains better PDRs. This is because that the hidden terminals beyond the interference range in the deterministic distance-based analytical model do not be considered. But in the interference-based model, they can also lead to reception failure. At the same time, the definition of PDR shows that it is the most stringent evaluation metric which requires that a packet is received successfully by all neighbors. Therefore, the phenomenon happens.

Fig. 23
figure 23

PRR comparisons of the two models with the data rate of 3 Mbps

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Fig. 24
figure 24

PRR comparisons of the two models with the data rate of 6 Mbps

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Fig. 25
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PRR comparisons of the two models with the data rate of 12 Mbps

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Fig. 26
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PRR comparisons of the two models with the data rate of 24 Mbps

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Fig. 27
figure 27

PDR comparisons of the two models with the data rate of 3 Mbps

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Fig. 28
figure 28

PDR comparisons of the two models with the data rate of 6 Mbps

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Fig. 29
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PDR comparisons of the two models with the data rate of 12 Mbps

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Fig. 30
figure 30

PDR comparisons of the two models with the data rate of 24 Mbps

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Zhao, J., Li, Z., Wang, Y. et al. An analytical framework for reliability evaluation of d-dimensional IEEE 802.11 broadcast wireless networks. Wireless Netw 26, 3373–3394 (2020). https://doi.org/10.1007/s11276-020-02268-5

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