DocumentCode :
3156259
Title :
Learning-based routing approach for direct interactions between wireless sensor network and moving vehicles
Author :
Jun Yang ; Hesheng Zhang ; Cheng Pan ; Wei Sun
Author_Institution :
Sch. of Electr. Eng., Beijing Jiaotong Univ., Beijing, China
fYear :
2013
fDate :
6-9 Oct. 2013
Firstpage :
1971
Lastpage :
1976
Abstract :
Wireless sensor network (WSN) has been applied to traffic information collection in many researches. Moving vehicles in the road need to acquire real time traffic information directly from WSN to make proper decisions and avoid potential accidents. Due to the large scale deployment of WSN, routing is necessary to deliver real time information to vehicles in a multi-hop way. Taking the moving vehicle as a mobile sink, a reinforcement learning-based routing approach is proposed to support sink mobility and enable direct interactions between WSN and vehicles. Multiple metrics including time delay, network lifetime and reliability are ensured by designing a comprehensive reward function for learning. The convergence speed of learning is improved with sink announcement. Simulation results show the feasibility of the proposed approach for direct interactions between WSN and moving vehicles. Comparisons are also carried out to show the superiority of the proposed approach over other approaches.
Keywords :
intelligent transportation systems; learning (artificial intelligence); road traffic; telecommunication network routing; wireless sensor networks; WSN; comprehensive reward function; direct interactions; intelligent transportation systems; learning-based routing approach; mobile sink; moving vehicles; network lifetime; real time traffic information; reinforcement learning-based routing approach; sink announcement; time delay; traffic information collection; wireless sensor network; Real-time systems; Reliability; Roads; Routing; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems - (ITSC), 2013 16th International IEEE Conference on
Conference_Location :
The Hague
Type :
conf
DOI :
10.1109/ITSC.2013.6728518
Filename :
6728518
Link To Document :
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