IEEE Transactions on Knowledge and Data Engineering
Replication strategies in unstructured peer-to-peer networks
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Energy-Aware Web Caching for Mobile Terminals
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
On Data Management in Pervasive Computing Environments
IEEE Transactions on Knowledge and Data Engineering
An efficient, unifying approach to simulation using virtual machines
An efficient, unifying approach to simulation using virtual machines
V3: A Vehicle-to-Vehicle Live Video Streaming Architecture
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
An integrated mobility and traffic model for vehicular wireless networks
Proceedings of the 2nd ACM international workshop on Vehicular ad hoc networks
Decentralized discovery of free parking places
Proceedings of the 3rd international workshop on Vehicular ad hoc networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Roadcast: A Popularity Aware Content Sharing Scheme in VANETs
ICDCS '09 Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems
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This paper looks at the problem of data prioritization, commonly found in mobile ad-hoc networks. The proposed general solution uses a machine learning approach in order to learn the relevance value of reports, which represent sensed data. The general solution is then applied to a travel time dissemination application. Through the use of offline learning, the paper analyzes the feasibility of the proposed approach and compares the accuracy performance of several common machine learning algorithms. The results show that not all machine learning algorithms may be used for prioritization and that the use of the logistic regression algorithm is particularly suited for the problem. The learned logistic regression model is then used in a simulated VANET environment. The results of the simulations show that it is better at prioritizing reports in terms of their usefulness in aiding vehicles to choose the shortest travel time paths.