@proceedings {2273,
	title = {Secure Mobile Technologies for Proactive Critical Infrastructure Situational Awareness},
	year = {2016},
	publisher = {IEEE},
	address = {Waltham, MA},
	author = {G. Salles-Loustau and V. Sadhu and D. Pompili and S. Zonouz and V. Sritapan}
}
@article {2123,
	title = {Laboratory Validation of Inertial Body Sensors to Detect Cigarette Smoking Arm Movements},
	journal = {Electronics},
	volume = {3},
	year = {2014},
	month = {2014},
	pages = {87-110},
	author = {B. R. Raiff and C. Karata{\c s} and E. A. McClure and D. Pompili and T. A. Walls}
}
@article {2109,
	title = {Region of Feasibility of Interference Alignment in Underwater Sensor Networks},
	journal = {IEEE Journal of Oceanic Engineering},
	volume = {39},
	year = {2014},
	month = {01/2014},
	pages = {189 - 202},
	publisher = {IEEE},
	chapter = {189 - 202},
	author = {P. Pandey and M. Hajimirsadeghi and D. Pompili}
}
@proceedings {2106,
	title = {Enabling Real-time In-Situ Processing of Ubiquitous Mobile-Application Workflows},
	year = {2013},
	month = {Oct./2013},
	address = {Hangzhou, China},
	author = {H. Viswanathan and E. K. Lee and D. Pompili}
}
@proceedings {2098,
	title = {Learning-based Framework for Policy-aware Cognitive Radio Emergency Networking},
	year = {2013},
	month = {Dec/2013},
	publisher = {Proc. of IEEE Global Telecommunications Conference (GLOBECOM)},
	address = {Atlanta, GA},
	author = {E. K. Lee and H. Viswanathan and D. Pompili}
}
@article {2110,
	title = {Minimizing Position Uncertainty for Under-Ice Autonomous Underwater Vehicles},
	journal = {to appear in Computer Networks (Elsevier)},
	year = {2013},
	author = {B. Chen and D. Pompili}
}
@proceedings {2099,
	title = {Real-time Tracking of Stress Propagation using Distributed Granger Causality},
	year = {2013},
	month = {Sept/2013},
	publisher = {Proc. of ACM International Conference on Body Area Networks (BodyNets)},
	address = {Boston, MA},
	author = {E. K. Lee and P. Pandey and D. Pompili}
}
@article {2096,
	title = {Thermal Camera Networks for Large Datacenters using Real-Time Thermal Monitoring Mechanism},
	journal = {Journal of Supercomputing (Springer)},
	volume = {64},
	year = {2013},
	month = {2013},
	pages = {383-408},
	chapter = {383},
	abstract = {Thermal cameras provide fine-grained thermal information that enables
monitoring and autonomic thermal management in large datacenters. The real-time
thermal monitor network employing thermal cameras is proposed to cooperatively
localize hotspots and extract their characteristics (i.e., temperature, size, and shape).
These characteristics are adopted to classify the causes of hotspots and make energy efficient
thermal management decisions such as job migration. Specifically, a sculpturing
algorithm for extracting and reconstructing shape characteristics of hotspots is
proposed to minimize the network overhead. Experimental results show the validity
of all the algorithms proposed in this paper.},
	author = {H. Liu and E. K. Lee and D. Pompili and X. Kong}
}
