Energy conservation in wireless sensor networks

This dissertation presents a system-level approach for minimizing the power expended in achieving communication between a ground-based sensor network and an overhead Unmanned Aerial Vehicle (UAV). A subset of sensor nodes, termed a transmit cluster, aggregates data gathered by the network and forms a distributed antenna array, concentrating the radiated transmission into a beam aimed towards the UAV. We present a method for more uniformly distributing the energy burden across the sensor network, specifying the time that should elapse between reassignments of the transmit cluster and the number of hops that should be placed between successive transmit clusters. We analyze the performance of two strategies for reconfiguring the communication burden between the sensor network and the UAV in order to bring the UAV and the sensor network's beam into alignment quickly, while minimizing the energy expenditure. We analyze the optimal number of nodes that should participate in a beamforming process in order to minimize the energy expended by the network, and we provide a framework to analyze the minimum energy expended in a simple beamforming algorithm. Finally, we analyze the probability that an arbitrarily selected sensor node is connected to a specified number of other nodes and we present an algorithm for the formation of near-linear arrays given random placement of nodes.http://archive.org/details/energyconservati1094510228Approved for public release; distribution is unlimited

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains