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Cross-Layer Optimization for Visual Sensor Networks (CLOViSeN)
Start date: 16 Mar 2009, End date: 15 Mar 2013 PROJECT  FINISHED 

This project is concerned with the cross-layer optimization of wireless visual sensor networks that are based on Direct Sequence Code Division Multiple Access (DS-CDMA). Sensor networks are comprised of typically low-weight distributed sensor nodes that can communicate with each other and/or with a centralized control unit. In this proposal, we are interested in visual sensor networks, where each node is equipped with a camera and transmits video information. Applications of visual sensor networks include surveillance, automatic tracking and signalling of intruders within a physical area, command and control of unmanned vehicles, and environmental monitoring. Most of the previous research on sensor networks has focused on networks that transmit scalar information such as temperature, pressure, acoustic data, etc. Visual sensor networks are much more challenging due to the high bit rates and delay constraints required for video transmission.The OSI (Open Systems Interconnection) Reference Model for layered networks consists of seven layers: Physical, Data Link, Network, Transport, Session, Presentation, and Application. These layers were originally designed so that the information flow between them could be minimal. Thus, each layer could be optimized independently. However, recent research has shown that the joint optimization of the network layers can significantly improve performance, especially for the case of wireless networks. This is the concept of cross-layer optimization. Most of the previous research only considers a subset of the OSI layers in the optimization. In this project, we propose cross-layer optimization for visual sensor networks, which will consider the whole range of layers, from the physical layer to the application layer. The cross-layer optimization will be based on Game Theory Principles.
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