@conference {
	title = {Sensor Scheduling using Smart Sensors},
	author = {Hovareshti, Pedram., Gupta, Vijay., Baras, John S.},
	year = {2007},
	month = {12/2007},
	abstract = {The sensor selection problem arises when multiple sensors are jointly trying to estimate a process but only a subset of them can take and/or use measurements at any time step. In a networked estimation situation, sensors are typically equipped with some memory and processing capabilities. We illustrate that utilization of these capabilities can lead to significant performance gains in the sensor selection problem for improved estimation performance. Further, it also leads to significant pruning of the search tree that yields the optimum sensor schedule. We also present a periodicity result for the case where the decision is whether the sensor should transmit or not.},
	address = {New Orleans, Louisiana},
	booktitle = {46th IEEE Conference on Decision and Control},
	pages = {pp. 494-499}
}
@conference {
	title = {Probabilistic Switching and Convergence Rate in Consensus Problems},
	author = {Hovareshti, Pedram., Baras, John S., Gupta, Vijay.},
	year = {2007},
	month = {09/2007},
	abstract = {We consider the convergence problem of consensus seeking agents and the effect of probabilistic switching on the convergence rate. Using a probabilistic setting, we develop a framework to study the convergence rate for different classes of graph topologies, including Small World networks. We show that by making the effective diameter of the underlying graph small, probabilistic switching can be helpful in the design of fast consensus algorithms.},
	address = {Chicago, Illinois},
	booktitle = {Allerton Conference on Communication, Control and Computing},
	pages = {pp. 423-430},
	organization = {Allerton House, UIUC}
}
@conference {
	title = {Effects of Graph Topology on Performance of Distributed Algorithms for Networked Control and Sensing},
	author = {Baras, John S., Hovareshti, Pedram.},
	year = {2007},
	month = {06/2007},
	abstract = {We consider distributed collaborative control andsensing as they frequently arise in networked control systems.The algorithms are constrained to use local information. Weshow by experiments that the performance of such distributed,local information based algorithms, can depend dramaticallyon the structure of the underlying topology (connectivitypattern) of the agents. We investigate the speed of convergence,accuracy, robustness and resiliency of such algorithms includingconsensus problems. We consider several graphs that can beused to represent collaborative control and communicationpatterns. We first show that small world topologies offer severaladvantages from a perspective of a favorable tradeoff betweenperformance of collaborative behaviors vs costs of collaborativebehaviors (or equivalently constraints for collaboration). Second,we show that a two level hierarchy consisting of carefullylocated and controlled leaders at the higher level and therest of the agents at the lower level, can provide a veryefficient communication pattern with substantial improvementof performance. We close with a description of the possibletopologies for this two tier structure and their performance.},
	address = {Kalamata, Greece},
	booktitle = {Proceedings of the 2007 Kalamata Workshop on Networked Distributed Systems for Intelligent Sensing and Control},
	pages = {pp. 1-8},
	organization = {Filoxenia}
}
