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An Emergency Adaptive Communication Protocol for Driver Health Monitoring in WSN Based Vehicular Environments
- An Emergency Adaptive Communication Protocol for Driver Health Monitoring in WSN Based Vehicular Environments
- Kim, Young-Duk; Kwon, Soon; Jung, Woo Young; Kim, Dongkyun
- DGIST Authors
- Kim, Young-Duk; Kwon, Soon; Jung, Woo Young
- Issue Date
- International Journal of Distributed Sensor Networks
- Article Type
- Activity Monitoring; Adaptive Communications; Better Performance; Channel Contention; Packet Delivery Ratio; Periodic Calculations; Queue Management Scheme; Sensor Networks; Vehicular Environments; Wireless Sensor Networks (WSNs)
- Driver health and activity monitoring is one of the principal design issues for the safety provision in vehicular environments. Recently, the wireless sensor network technology is widely used to address the concerns in such applications. However, only few conventional protocols have dealt with reliable and prompt delivery of emergency packets considering the vehicular specifications. In this paper, we propose an emergency adaptive communication protocol, which treats the data packet in a discriminatory manner by investigating whether it is emergency or not. Hence, the proposed protocol defines an emergency factor for each data packet and exploits it for both route establishment and channel access procedures. In route establishment, the proposed protocol chooses a route with low delay and high reliability among the candidates by periodic calculation of emergency factor. Then, it dynamically adjusts back-off parameters before participating in the channel contention among the neighbors. In addition, an emergency aware queue management scheme and packet drop policy are proposed to improve the reliability of emergency data traffic during transmission. Our simulation results show that the proposed protocol provides a better performance compared with the existing protocol in terms of packet delivery ratio, end-to-end packet delay, and number of dropped packets. © 2015 Young-Duk Kim et al.
- Hindawi Publishing Corporation
- Related Researcher
Artificial Intelligence, Machine Learning, Autonomous Driving
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- Convergence Research Center for Future Automotive Technology1. Journal Articles
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