Design of Efficient M2M Communication for Intelligent Transportation System (ITS)

• Main Authors: Kuan-Ho Huang, 黃冠賀
• Other Authors: 周俊廷
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/71087438666819513499

碩士 === 國立臺灣大學 === 電信工程學研究所 === 102 === The Internet of Things (IoT) envisions to connect a huge number of machines together to provide users with various applications. These machines sense the environment, exchange local or global information, and jointly take actions in an autonomous manner. For example, IoT-enabled vehicles can communicate and coordinate with each other to avoid collisions and reduce casualties. These vehicles and interconnected roadside units (RSUs) together will construct a faster, safer, and greener Intelligent Transportation System (ITS) in the near future. The success of IoT-based ITS relies on efficient Machine-to-Machine (M2M) communications to disseminate sensing and control data. This thesis aims to improve the efficiency of M2M communication for ITS. We focus on two ITS applications including vehicle crash avoidance and vehicle telematics. In the case of crash avoidance, vehicles periodically broadcast their speed, direction, and geo-coordinates to each other. In typical scenarios, vehicles only have 1.5 sec to discover each other and avoid accidents. To expedite neighbor discovery, a multi-channel neighbor discovery with cooperative relay is proposed. Our simulation results show that our protocol outperforms the single-channel solution by 22%. The proposed protocol is also implemented in a ZigBee-based testbed to demonstrate its practicality. In the case of telematics, vehicles need to periodically transmit data to RSUs. The RSUs then forward the data to the server. In such a two-hop wireless network (i.e., vehicle-RSU-server), vehicles and RSUs contend channel access for data transmission. To resolve channel contention, a distributed and prioritized channel channel access is proposed. A mathematical model is also developed to analyze the end-to-end delay. Both analytical and simulation results show that the end-to-end delay is about 50 less when compared to the existing schemes.