Abstract: In the future internet of things, heterogeneous and ubiquitous devices with sensing, processing and communication capabilities will be connected to the internet, enabling applications such as environmental and climate monitoring, wireless body area networks and surveillance. The optimal operation of these large networks is challenged by high-complexity, due to the scarcity of resources and the requirement for distributed operation, thus requiring a cross-layer approach in the design of schemes for joint communication, network control, sensing and estimation. In the first part, we consider a feedback-based scheme for distributed sensing and estimation of a dynamical process in a wireless sensor network (WSN) in which the sensor nodes (SNs) communicate their measurements to a fusion center (FC), based on their local observation quality and the estimation quality feedback from the FC. The sensing-transmission policy is optimized, with the overall objective to minimize the mean squared estimation error (MSE) at the FC, under cost constraints for each SN. We derive structural properties of the optimal policy based on the statistical symmetry and large network approximation of the WSN, proving that a dense WSN provides sensing diversity, i.e., only a few SNs with the best local observation quality suffice to sense-transmit accurately, with no degradation in the MSE, despite the fluctuations in the observation quality experienced across the WSN. In the second part, we present a cross-layer framework for joint control and distributed sensing-estimation in agile wireless networks. We apply it to a spectrum sensing application, where a network of secondary users (SUs) attempt to opportunistically access portions of the spectrum left unused by a licensed network of primary users (PUs). A central controller (CC) schedules the spectrum bands detected as idle for opportunistic access by the SUs, based on compressed measurements acquired by the SUs. We exploit the time-correlation in the spectrum occupancy process: leveraging the spectrum occupancy estimate in the previous slot, the CC needs to estimate only a sparse residual uncertainty vector via sparse recovery techniques, so that only few measurements suffice. Spectrum estimation and scheduling are jointly adapted over time by the CC, based on the current spectrum occupancy belief, and jointly optimized to maximize the SU throughput, under a constraint on the throughput degradation incurred to the PU network, and sensing cost constraints for the SUs. We tackle the high-dimensionality of the optimization problem by proposing a compact state space representation, and we prove the optimality of a two-stage decomposition, which enables to decouple the optimization of spectrum scheduling and spectrum sensing-estimation: the estimator provides a maximum-a-posteriori estimate of the spectrum state, based on which the CC updates the belief state and schedules the available spectrum bands. Bio: Dr. Nicolo Michelusi received the B.Sc. degree with honors, M.Sc. degree with honors and Ph.D. degree in Electrical Engineering from University of Padova, Italy, in 2006 and 2009, and 2013 respectively. Additionally, he received a second M.Sc. degree in Telecommunication Engineering from Technical University of Denmark in 2009, under the T.I.M.E. double degree program (www.time-association.org). In 2011, he was a visiting research scholar at University of Southern California in Urbashi Mitra's group and in Fall 2012, he was a visiting research scholar at Aalborgh University, Denmark, where he worked with Prof. Petar Popovski. He is currently a postdoctoral research fellow at the Ming Hsieh Department of Electrical Engineering, University of Southern California, USA, working with Prof. Urbashi Mitra. His research interests are in the areas of wireless communications, cognitive networks, energy harvesting, distributed estimation and adaptive control for wireless networks. He is the recipient of a scholarship from the Fondazione Ing. Aldo Gini (2010) and he was awarded the Toni Mian scholarship for the best Master's Thesis in Information Engineering from University of Padova, Italy in March 2010. In 2013, he was awarded the grant “Isabella Sassi Bonadonna” from AEIT (Italian association of electrical engineering).
