Design and Implementation of Energy-Efficient Wireless Tire Sensing System with Delay Analysis for Intelligent Vehicles
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| Yayımlandı: | arXiv.org (May 12, 2024), p. n/a |
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Cornell University Library, arXiv.org
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| Online Erişim: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3054665972 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3054665972 | ||
| 045 | 0 | |b d20240512 | |
| 100 | 1 | |a Mishra, Shashank | |
| 245 | 1 | |a Design and Implementation of Energy-Efficient Wireless Tire Sensing System with Delay Analysis for Intelligent Vehicles | |
| 260 | |b Cornell University Library, arXiv.org |c May 12, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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 and sensor reception ratio 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 train experiments. These results show that the proposed scheme provides enhanced performance in energy efficiency up to 76.5% in average and identifies the worst transmission delay accurately. | |
| 653 | |a Traffic accidents | ||
| 653 | |a Internet of Things | ||
| 653 | |a Intelligent vehicles | ||
| 653 | |a Traffic safety | ||
| 653 | |a Vehicle safety | ||
| 653 | |a Delay | ||
| 653 | |a Traffic accidents & safety | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Tires | ||
| 653 | |a Collision dynamics | ||
| 653 | |a Real time | ||
| 653 | |a Power consumption | ||
| 653 | |a Fuel consumption | ||
| 653 | |a Energy consumption | ||
| 653 | |a Data retrieval | ||
| 700 | 1 | |a Jia-Ming, Liang | |
| 773 | 0 | |t arXiv.org |g (May 12, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3054665972/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2405.05757 |