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Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems

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Listed:
  • Shahriar Mohammadi

    (Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada)

  • Karim Ismail

    (Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada)

  • Amir H. Ghods

    (Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada)

Abstract

The purpose of the study is to investigate the comparative field performance of Wi-Fi, Bluetooth Classic (Bluetooth) and Bluetooth Low Energy (BLE) signal modes for integration in vehicle–pedestrian collision warning systems. The study compares these wireless signal modes to find out which one is most appropriate to be utilized in these systems and provides better results in terms of accuracy and functionality. Five factors including received signal strength indicator (RSSI)-distance relationship, rainfall effects on the signals, motion effects, non-line of sight effects and signal transmission rates were selected for evaluation. These factors were selected considering the requirements of vehicle–pedestrian collision warning systems and compared with each other based on experimental outcomes. The results of the experiments indicated the overall superiority of BLE mode over Wi-Fi and Bluetooth modes to be utilized in these systems. Application of this mode may provide the possibility of fast collision warnings thanks to low signal transmission intervals and high probability of simultaneous signal detections by multiple signals scanners. Moreover, the capability of this mode to accurately estimate distance and position is higher than Wi-Fi mode and not significantly different from Bluetooth mode.

Suggested Citation

  • Shahriar Mohammadi & Karim Ismail & Amir H. Ghods, 2021. "Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10823-:d:646491
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    References listed on IDEAS

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    1. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    2. An, Kun & Xie, Siyang & Ouyang, Yanfeng, 2018. "Reliable sensor location for object positioning and surveillance via trilateration," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 956-970.
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