Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Design and Implementation of Energy-Efficient Wireless Tire Sensing System with Delay Analysis for Intelligent Vehicles (2405.05757v3)

Published 9 May 2024 in cs.ET, cs.SY, and eess.SY

Abstract: The growing prevalence of Internet of Things (IoT) technologies has led to a rise in the popularity of intelligent vehicles that incorporate a range of sensors to monitor various aspects, such as driving speed, fuel usage, distance proximity and tire anomalies. Nowadays, real-time tire sensing systems play important roles for intelligent vehicles in increasing mileage, reducing fuel consumption, improving driving safety, and reducing the potential for traffic accidents. However, the current tire sensing system drains a significant vehicle' energy and lacks effective collection of sensing data, which may not guarantee the immediacy of driving safety. Thus, this paper designs an energy-efficient wireless tire sensing system (WTSS), which leverages energy-saving techniques to significantly reduce power consumption while ensuring data retrieval delays during real-time monitoring. Additionally, we mathematically analyze the worst-case transmission delay of the system to ensure the immediacy based on the collision probabilities of sensor transmissions. This system has been implemented and verified by the simulation and field trial experiments. These results show that the proposed scheme provides enhanced performance in energy efficiency and accurately identifies the worst transmission delay.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (54)
  1. S. Mishra and J.-M. Liang, “Design and Analysis for Wireless Tire Pressure Sensing System,” in International Computer Symposium (ICS), Taoyuan, Taiwan, 2022, pp. 599–610.
  2. M. Germer, U. Marschner, and A. Richter, “Energy Harvesting for Tire Pressure Monitoring Systems From a Mechanical Energy Point of View,” IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7700–7714, 2022.
  3. X. Wang, Z. Chen, W. Cao, G. Xu, L. Liu, S. Liu, H. Li, X. Shi, Q. Song, Z. Xiao, and C. Sun, “Artificial Neural Network-Based Method for Identifying Under-Inflated Tire in Indirect TPMS,” IEEE Access, vol. 8, pp. 213 799–213 805, 2020.
  4. National Highway Traffic Safety Administration (NHTSA), “Federal Motor Vehicle Safety Standard (FMVSS): Light Vehicle Brake Systems,” https://www.govinfo.gov/app/details/CFR-2022-title49-vol6/CFR-2022-title49-vol6-sec571-135/summary, accessed on March 9, 2024.
  5. Economic Commission for Europe of the United Nations (UN/ECE), “Regulation No 141 — Uniform provisions concerning the approval of vehicles with regard to their Tyre Pressure Monitoring Systems (TPMS) [2018/1593],” http://data.europa.eu/eli/reg/2018/1593/oj, accessed on March 9, 2024.
  6. The Ministry of Land, Transport and Maritime Affairs, “Automobile ESC/TPMS becomes mandatory,” http://www.puntofocal.gov.ar/notific_otros_miembros/kor286_t.pdf, 2010, accessed on March 9, 2024.
  7. N. Dasanayaka and Y. Feng, “Analysis of Vehicle Location Prediction Errors for Safety Applications in Cooperative-Intelligent Transportation Systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 15 512–15 521, 2022.
  8. Z. Ye, Y. Wei, W. Zhang, and L. Wang, “An efficient real-time vehicle monitoring method,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 22 073–22 083, 2022.
  9. L. Wei, X. Wang, L. Li, L. Yu, and Z. Liu, “A Low-Cost Tire Pressure Loss Detection Framework Using Machine Learning,” IEEE Transactions on Industrial Electronics, vol. 68, no. 12, pp. 12 730–12 738, 2021.
  10. A. A. Khan, C. Wechtaisong, F. A. Khan, and N. Ahmad, “A cost-efficient environment monitoring robotic vehicle for smart industries,” Computers, Materials & Continua, vol. 71, no. 1, pp. 473–487, 2022.
  11. N. Sharma and R. D. Garg, “Real-Time IoT-Based Connected Vehicle Infrastructure for Intelligent Transportation Safety,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8339–8347, 2023.
  12. Y. Qin, H. Xu, S. Li, D. Xu, W. Zheng, W. Wang, and L. Gao, “Dual-mode flexible capacitive sensor for proximity-tactile interface and wireless perception,” IEEE Sensors Journal, vol. 22, no. 11, pp. 10 446–10 453, 2022.
  13. F. Tong, D. Wolfson, A. Jenn, C. D. Scown, and M. Auffhammer, “Energy consumption and charging load profiles from long-haul truck electrification in the United States,” Environmental Research: Infrastructure and Sustainability, vol. 1, no. 2, p. 025007, 2021.
  14. S. Madichetty, A. J. Neroth, S. Mishra, and B. C. Babu, “Route Towards Road Freight Electrification in India: Examining Battery Electric Truck Powertrain and Energy Consumption,” Chinese Journal of Electrical Engineering, vol. 8, no. 3, pp. 57–75, 2022.
  15. B. Al-Hanahi, I. Ahmad, D. Habibi, and M. A. S. Masoum, “Charging Infrastructure for Commercial Electric Vehicles: Challenges and Future Works,” IEEE Access, vol. 9, pp. 121 476–121 492, 2021.
  16. Z. Yi, B. Yang, W. Zhang, Y. Wu, and J. Liu, “Batteryless Tire Pressure Real-Time Monitoring System Driven by an Ultralow Frequency Piezoelectric Rotational Energy Harvester,” IEEE Transactions on Industrial Electronics, vol. 68, no. 4, pp. 3192–3201, 2021.
  17. Z. Meng, Y. Liu, N. Gao, Z. Zhang, Z. Wu, and J. Gray, “Radio Frequency Identification and Sensing: Integration of Wireless Powering, Sensing, and Communication for IIoT Innovations,” IEEE Communications Magazine, vol. 59, no. 3, pp. 38–44, 2021.
  18. M. Won, “Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey,” IEEE Access, vol. 8, pp. 73 340–73 358, 2020.
  19. A. Gharamohammadi, A. Khajepour, and G. Shaker, “In-Vehicle Monitoring by Radar: A Review,” IEEE Sensors Journal, vol. 23, no. 21, pp. 25 650–25 672, 2023.
  20. L. Manjakkal, S. Mitra, Y. R. Petillot, J. Shutler, E. M. Scott, M. Willander, and R. Dahiya, “Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring,” IEEE Internet of Things Journal, vol. 8, no. 18, pp. 13 805–13 824, 2021.
  21. C. Chen, K. Ota, M. Dong, C. Yu, and H. Jin, “WITM: Intelligent Traffic Monitoring Using Fine-Grained Wireless Signal,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 3, pp. 206–215, 2020.
  22. C. Zhang, J. He, X. Yan, Z. Liu, Y. Chen, and H. Zhang, “Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types,” Journal of safety research, vol. 78, pp. 80–95, 2021.
  23. K. Drozd, S. Tarkowski, J. Caban, A. Nieoczym, J. Vrábel, and Z. Krzysiak, “Analysis of Truck Tractor Tire Damage in the Context of the Study of Road Accident Causes,” Applied Sciences, vol. 12, no. 23, p. 12333, 2022.
  24. S. M. Karim, Y. Rahman, M. A. Hai, and R. Mahfuza, “Tire Wear Detection for Accident Avoidance Employing Convolutional Neural Networks,” in 8th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam, 2021, pp. 364–368.
  25. P. Lin and M. Tsukada, “Model predictive path-planning controller with potential function for emergency collision avoidance on highway driving,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4662–4669, 2022.
  26. E. Artetxe, O. Barambones, I. Calvo, P. Fernández-Bustamante, I. Martin, and J. Uralde, “Wireless Technologies for Industry 4.0 Applications,” Energies, vol. 16, no. 3, p. 1349, 2023.
  27. O. Seijo, X. Iturbe, and I. Val, “Tackling the Challenges of the Integration of Wired and Wireless TSN With a Technology Proof-of-Concept,” IEEE Transactions on Industrial Informatics, vol. 18, no. 10, pp. 7361–7372, 2022.
  28. M. I. Tawakal, M. Abdurohman, and A. G. Putrada, “Wireless monitoring system for motorcycle tire air pressure with pressure sensor and voice warning on helmet using fuzzy logic,” in International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), Pahang, Malaysia, 2021, pp. 47–52.
  29. D. Hou, Y. Zhao, Y. Wang, D. Gu, and Z. Xi, “ID Calibration Device Design for Vehicle Tire Pressure Monitoring System Based on Bluetooth Communication,” Procedia Computer Science, vol. 202, pp. 223–227, 2022.
  30. W.-H. Chang, R.-T. Juang, M.-H. Huang, and M.-F. Sung, “Implementation of Tire Status Estimation Using Hall Sensor and G-Sensor,” in IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Taichung, Taiwan, 2021, pp. 102–105.
  31. H. Fechtner, U. Spaeth, and B. Schmuelling, “Smart Tire Pressure Monitoring System with Piezoresistive Pressure Sensors and Bluetooth 5,” in IEEE Conference on Wireless Sensors (ICWiSe), Penang, Malaysia, 2019, pp. 18–23.
  32. J. M. S. Waworundeng, D. Fernando Tiwow, and L. M. Tulangi, “Air Pressure Detection System on Motorized Vehicle Tires Based on IoT Platform,” in 1st International Conference on Cybernetics and Intelligent System (ICORIS), Bali, Indonesia, 2019, pp. 251–256.
  33. P. Ferrari, E. Sisinni, D. F. Carvalho, A. Depari, G. Signoretti, M. Silva, I. Silva, and D. Silva, “On the use of LoRaWAN for the Internet of Intelligent Vehicles in Smart City scenarios,” in IEEE Sensors Applications Symposium (SAS), Kuala Lumpur, Malaysia, 2020, pp. 1–6.
  34. S. Hussain, U. Mahmud, and S. Yang, “Car e-talk: An iot-enabled cloud-assisted smart fleet maintenance system,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9484–9494, 2021.
  35. S. Formentin, L. Onesto, T. Colombo, A. Pozzato, and S. M. Savaresi, “h-tpms: a hybrid tire pressure monitoring system for road vehicles,” Mechatronics, vol. 74, p. 102492, 2021.
  36. A. Pangestu, I. Sodikin, M. Yusro, R. Sapundani, R. R. Al Hakim, and S. Wilyanti, “IoT- based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor,” in IEEE 8th International Conference on Computing, Engineering and Design (ICCED), Sukabumi, West Java Indonesia, 2022, pp. 1–4.
  37. A. Vasantharaj, N. Nandhagopal, S. A. Karuppusamy, and K. Subramaniam, “An in-tire-pressure monitoring SoC using FBAR resonator-based ZigBee transceiver and deep learning models,” Microprocessors and Microsystems, vol. 95, p. 104709, 2022.
  38. S. Zhou, Y. Du, B. Chen, Y. Li, and X. Luan, “An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML,” IEEE Access, vol. 10, pp. 98 860–98 871, 2022.
  39. M. A. Rahman, M. A. Rahim, M. M. Rahman, N. Moustafa, I. Razzak, T. Ahmad, and M. N. Patwary, “A Secure and Intelligent Framework for Vehicle Health Monitoring Exploiting Big-Data Analytics,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19 727–19 742, 2022.
  40. D. Katare, D. Perino, J. Nurmi, M. Warnier, M. Janssen, and A. Y. Ding, “A survey on approximate edge ai for energy efficient autonomous driving services,” IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2714–2754, 2023.
  41. Z. Márton, I. Szalay, and D. Fodor, “Convolutional Neural Network-Based Tire Pressure Monitoring System,” IEEE Access, vol. 11, pp. 70 317–70 332, 2023.
  42. N. Xu, Y. Huang, H. Askari, and Z. Tang, “Tire Slip Angle Estimation Based on the Intelligent Tire Technology,” IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2239–2249, 2021.
  43. L. Wei, Y. Liu, X. Wang, and L. Li, “Estimation of Wheel Pressure for Hydraulic Braking System by Interacting Multiple Model Filter,” IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 463–472, 2024.
  44. Y. Wang, Y. Zhang, Z. Jiang, L. Zheng, J. Chen, and J. Lu, “Prototype-Based Supervised Contrastive Learning Method for Noisy Label Correction in Tire Defect Detection,” IEEE Sensors Journal, vol. 24, no. 1, pp. 660–670, 2024.
  45. G. Sidorenko, J. Thunberg, K. Sjöberg, A. Fedorov, and A. Vinel, “Safety of Automatic Emergency Braking in Platooning,” IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2319–2332, 2022.
  46. L. Zhang, P. Guo, Z. Wang, and X. Ding, “An Enabling Tire-Road Friction Estimation Method for Four-in-Wheel-Motor-Drive Electric Vehicles,” IEEE Transactions on Transportation Electrification, vol. 9, no. 3, pp. 3697–3710, 2023.
  47. L. Xiong, X. Xia, Y. Lu, W. Liu, L. Gao, S. Song, and Z. Yu, “IMU-Based Automated Vehicle Body Sideslip Angle and Attitude Estimation Aided by GNSS Using Parallel Adaptive Kalman Filters,” IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 10 668–10 680, 2020.
  48. P. Razmjoui, A. Kavousi-Fard, T. Jin, M. Dabbaghjamanesh, M. Karimi, and A. Jolfaei, “A Blockchain-Based Mutual Authentication Method to Secure the Electric Vehicles’ TPMS,” IEEE Transactions on Industrial Informatics, vol. 20, no. 1, pp. 158–168, 2024.
  49. S. Salih and R. Olawoyin, “Fault Injection in Model-Based System Failure Analysis of Highly Automated Vehicles,” IEEE Open Journal of Intelligent Transportation Systems, vol. 2, pp. 417–428, 2021.
  50. J. C. Kim, D. J. Lee, S. W. Hwang, B.-K. Ju, S. K. Yun, H.-W. Park, and J. T. Chung, “Investigation of the Cause of a Malfunction in a Display Package and Its Solution,” IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 1, no. 1, pp. 119–124, 2011.
  51. AVE, “Ave External Sensor Specification,” http://www.avetechnology.com/index.php?unit=products&lang=en&act=view&id=89, [Online; accessed 16-November-2023].
  52. S. McKean, “An Arithmetic Enrichment of Bézout’s Theorem,” Mathematische Annalen, vol. 379, no. 1–2, p. 633–660, Jan. 2021. [Online]. Available: http://dx.doi.org/10.1007/s00208-020-02120-3
  53. D. MacHale, “92.30 The well-ordering principle for ℕℕ\mathbb{N}blackboard_N,” The Mathematical Gazette, vol. 92, no. 524, pp. 257–259, 2008.
  54. C. M. Farmer and A. K. Lund, “Rollover risk of cars and light trucks after accounting for driver and environmental factors,” Accident Analysis & Prevention, vol. 34, no. 2, pp. 0001–4575, 2002.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets