大眾捷運系統上輔以加速度計之802.11傳輸速率調整設計

由於802.11 無線存取點近年來普及的佈建，提供使用者在大眾運輸系統上可能的Wi-Fi 存取機會。然而，這種車用網路面臨的最主要的挑戰是車上的行動裝置和車站內的基地台之間只有有限的連結時間。因此，如何利用停車的這段幾十秒的時間最大化傳輸吞吐量就變成非常重要的議題。為了達成這個目的，我們提出了輔以加速度計之802.11 傳輸速率調整設計，利用列車加速度的頻帶外資訊來提升傳統的傳輸速率調整機制的效能。我們的機制包含了兩個構成元件：第一個部份是，把列車的移動劃分成四個移動階段並且即時的預測目前列車在哪一個移動階段。第二個部份是，利用預測的結果在各個不同的移動階段分別採用不同的策略來提升速率選擇機制的效能。我們在台北捷運的兩種捷運系統上(高運量捷運及中運量捷運)實測我們的系統。實驗結果顯示，在多種不同的情境下我們提出的機制的平均傳輸吞吐量都優於傳統的速率調整機制。同時我們也考量了以加速度計輔助的傳輸省電機制。

關鍵字

無線網路；車用網路；加速度計；動作預測； 802.11傳輸速率調整；大眾捷運系統； MadWifi

並列摘要

The expansion of 802.11 APs deployment provides opportunistic Wi-Fi access in underground Mass Rapid Transit (MRT) system. However, such vehicular network faces the challenge of limited time for the MS on the train to connect the BS at the station. Therefore, to maximize the throughput within these several tens of seconds becomes crucial to the network. To achieve this goal, we propose Accelerometer-Assisted Rate Adaptation (AARA), a mechanism utilizes the out-of-band information of a train’s acceleration to improve the conventional rate adaptation scheme. AARA consists of two parts: First, AARA divides a train’s movement into four phases and performs real-time estimation on the train’s current movement phase. Second, AARA employs the estimation results to enhance the bit-rate selection during each phase. We conduct experiments on two different Taipei MRT systems: High-Capacity MRT and Medium-Capacity MRT. The experimental results show that the average throughput of AARA outperforms that of the conventional scheme in different scenarios. In addition, we also design a mechanism of power saving with the aid of the movement estimation.

並列關鍵字

Wireless Network ； Vehicular Network ； Accelerometer ； Motion Estimation ； 802.11 Rate Adaptation ； Mass Rapid Transit System (MRT) ； MadWifi

參考文獻

[1] A. Kamerman and L. Monteban, “WaveLAN-II: a high-performance wireless LAN for the unlicensed band,” Bell Labs technical journal, vol. 2, pp. 118–133, 1997.

[12] Y. Zheng, Q. Li, Y. Chen, X. Xie, and W.-Y. Ma, “Understanding mobility based on GPS data,” in Proceedings of the 10th International Conference on Ubiquitous computing (UbiComp), 2008, pp. 312–321.

[13] D. Patterson, L. Liao, D. Fox, and H. Kautz, “Inferring high-level behavior from low-level sensors,” in Proceedings of the Fifth International Conference on Ubiquitous Computing (UbiComp), 2003, pp. 73–89.

[14] S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava, “Using mobile phones to determine transportation modes,” ACM Transactions on Sensor Networks (TOSN), vol. 6, no. 2, pp. 1–27, 2010.

[18] J. Camp and E. Knightly, “Modulation rate adaptation in urban and vehicular environments: cross-layer implementation and experimental evaluation,” in Proceedings of the 14th ACM international conference on Mobile computing and networking (MobiCom), 2008, pp. 315–326.

國際替代計量

大眾捷運系統上輔以加速度計之802.11傳輸速率調整設計

全文下載

主題瀏覽