Grid-based Coordinated Routing in Wireless Sensor Networks
Date: January 2007
Creator: Akl, Robert G. & Sawant, Uttara
Description: This paper discusses grid-based coordinated routing in wireless sensor networks. Abstract: This work explores grid-based coordinated routing in wireless sensor networks and compares the energy available in the network over time for different grid sizes. A test area is divided into square-shaped grids of certain length. Fully charged battery powered nodes are randomly placed in the area with fixed source and sink nodes. One node per grid is elected as the coordinator which does the actual routing. The source node starts flooding the network with every coordinator joining in the routing. Once the flooding reaches the sink node, information is sent back to the source by finding the back route to the source. This process is continued until a node (coordinator) along that route runs out of energy. New coordinators are elected to replace the depleted ones. The source node refloods the network so that the sink can find a new back route to send information. This entire process continues until the network is partitioned and the connectivity between the source and the sink nodes is lost. We explore the quality of service of wireless sensor networks, how the coordinator nodes are elected, and the size of the grid area ...
Contributing Partner: UNT College of Engineering
Permallink:digital.library.unt.edu/ark:/67531/metadc30839/
Identification of functional information subgraphs in cultured neural networks
Date: July 13, 2009
Creator: Gintautas, Vadas; Bettencourt, Luis & Ham, Michael I.
Description: This paper accompanies an oral presentation on the identification of functional information subgraphs in cultured neural networks. Abstract: We present a general information theoretic approach for identifying functional subgraphs in complex neuronal networks where the spiking dynamics of a subset of nodes (neurons) are observable. We show that the uncertainty in the state of each node can be written as a sum of information quantities involving a growing number of variables at other nodes. We demonstrate that each term in this sum is generated by successively conditioning mutual information on new measured variables, in a way analogous to a discrete differential calculus.
Contributing Partner: UNT College of Arts and Sciences
Permallink:digital.library.unt.edu/ark:/67531/metadc122146/