Introduction to neural networks
Introduction to neural networks
A unifying link abstraction for wireless sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Wireless Communications & Mobile Computing - Distributed Systems of Sensors and Actuators
IEEE Transactions on Wireless Communications
Prolonging the lifetime of wireless sensor networks by cross-layer interaction
IEEE Wireless Communications
Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms
IEEE Wireless Communications
Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Classification by nonlinear integral projections
IEEE Transactions on Fuzzy Systems
Cross-layer design: a survey and the road ahead
IEEE Communications Magazine
Adaptive cross-layer protection strategies for robust scalable video transmission over 802.11 WLANs
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Study of an adaptive frame size predictor to enhance energy conservation in wireless sensor networks
IEEE Journal on Selected Areas in Communications
Cross-Layer Optimization for Video Summary Transmission over Wireless Networks
IEEE Journal on Selected Areas in Communications
Virtual lab of connected vehicle technology
Proceedings of the 2012 SpringSim Poster & Work-In-Progress Track
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Future multimedia applications in wireless multimedia sensor networks impose stringent and diverse Quality of Service (QoS) requirements, which demands a cross-layer architecture. However, the cross-layer optimization for multimedia delivery is not easy to be implemented in resource limited Wireless Multimedia Networks, as QoS requirements may be qualitative, or even fuzzy. In practice, QoS requirements can be defined as a set of constraints indicated by parameters including lifetime, latency, accuracy, energy efficiency, reliability, robustness and scalability. There are interactions among these parameters across layers. Previous cross-layer approaches usually search exhaustively for all possible strategies and use all assumed parameters without considering importance. The computational burden makes them difficult to apply in real practice. Thus, it is critical to identify these interactions and determine a single optimized cross-layer control strategy, where the overall system performance is not independently determined by all parameters at each individual layer, but is determined by significant parameters and their interactions of equivalent layers. In this paper, a supervised learning approach is incorporated into cross-layer architecture to reduce the number of optimized cross-layer parameters based on their significance. Our study shows that the proposed intelligent architecture can significantly reduce the associated complexity (e.g. control, information exchange and computation overheads).