Cognitive Distributed Networks in Environmental e-Science
FTDCS '08 Proceedings of the 2008 12th IEEE International Workshop on Future Trends of Distributed Computing Systems
How Long is the Lifetime of a Wireless Sensor Network?
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
Performance modeling of cognitive wireless sensor networks applied to environmental protection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Connectivity optimization for wireless sensor networks applied to forest monitoring
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
CogSeNet: a concept of cognitive wireless sensor network
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Selective Routing Protocol for Cognitive Wireless Networks Based on User's Policy
ICDCSW '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems Workshops
The design space of wireless sensor networks
IEEE Wireless Communications
Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety
IEEE Communications Magazine
Cognitive networks: adaptation and learning to achieve end-to-end performance objectives
IEEE Communications Magazine
A cognitive WSN framework for highway safety based on weighted cognitive maps and Q-learning
Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications
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In this paper, we present our perspective on cognition in wireless sensor networks for highway safety applications. Cognition in the context of sensor networks deals with the ability to be aware of the environment and end-user requirements and proactively adapt to them, thus benefiting the network as a whole. An implementation of how cognition can be introduced into sensor networks in order to make it a smart one is illustrated through an experiment in this paper. Cognitive communication, cognitive components and how interaction among the various network elements in a sensor network can be improved to enhance network performance are the driving ideas behind this work.