Decentralized discovery of free parking places
Proceedings of the 3rd international workshop on Vehicular ad hoc networks
Opportunistic spatio-temporal dissemination system for vehicular networks
Proceedings of the 1st international MobiSys workshop on Mobile opportunistic networking
Cars communicating over publish/subscribe in a peer-to-peer vehicular network
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Machine learning in disruption-tolerant MANETs
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A cooperative reservation protocol for parking spaces in vehicular ad hoc networks
Mobility '09 Proceedings of the 6th International Conference on Mobile Technology, Application & Systems
Resource discovery using spatio-temporal information in mobile ad-hoc networks
W2GIS'05 Proceedings of the 5th international conference on Web and Wireless Geographical Information Systems
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The use of Vehicular Ad-Hoc Network (VANET) has been applied to many applications involving information dissemination. Many of such applications are limited by the communication limitations of a VANET, such as limited transmission range and bandwidth. This imposes a necessity for evaluating the relevance of information. This paper proposes the use of machine learning for finding relevance of information for a parking information dissemination system. The proposed method uses the learned relevance for aiding vehicles in decision making by finding the probability that a given parking location will be available at the time of arrival. The method was evaluated through simulations and the results show that the proposed method is successful at learning the relevance of parking reports, which resulted in lower parking discovery times for vehicles.