We study optimal proportional feedback controllers for spatially invariant systems when the controller has access to delayed state measurements received from different spatial locations. We analyze how delays affect the spatial locality of the optimal feedback gain leveraging the problem decoupling in the spatial frequency domain. For the cases of expensive control and small delay, we provide exact expressions of the optimal controllers in the limit for infinite control weight and vanishing delay, respectively. In the expensive control regime, the optimal feedback control law decomposes into a delay-aware filtering of the delayed state and the optimal controller in the delay-free setting. Under small delays, the optimal controller is a perturbation of the delay-free one which depends linearly on the delay. We illustrate our analytical findings with a reaction-diffusion process over the real line and a multi-agent system coupled through circulant matrices, showing that delays reduce the effectiveness of optimal feedback control and may require each subsystem within a distributed implementation to communicate with farther-away locations.

Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. Federated learning is one of the most promising techniques for training global machine learning models while preserving data privacy of vehicles and optimizing communications resource usage. In this article, we propose vehicular radio environment map federated learning (VREM-FL), a computation-scheduling co-design for vehicular federated learning that combines mobility of vehicles with 5G radio environment maps. VREM-FL jointly optimizes learning performance of the global model and wisely allocates communication and computation resources. This is achieved by orchestrating local computations at the vehicles in conjunction with transmission of their local models in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade training time for radio resource usage. Experimental results demonstrate that VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for semantic image segmentation (doubling the number of model updates within the same time window).

In this paper we investigate the design of optimal spatially distributed controllers for a linear and spatially invariant reaction-diffusion process over the real line. The controller receives state measurements from different spatial locations with non-negligible delays. In this set-up and for the class of proportional spatially invariant state feedback controllers, the optimal control synthesis problem is equivalent to a feedback gain optimization for a spatially distributed delay system. We show that the spatial locality of optimal feedback gains is affected not only by diffusion and reaction coefficients, but also by the parameter representing communication time-delay that causes a sharp flattening of the control gains. In the expensive control regime, the optimal controller is solved analytically, yielding some practical design guidelines.

This paper describes the implementation of an FBG sensor to measure water levels in a dike. The sensor is based on a 3D-printed mechanical transducer through which the external pressure is converted into longitudinal strain exerted on the fiber. An additional FBG integrated within the sensor measures temperature and is used to compensate for the temperature effects on the first FBG. By employing an aluminum alloy case, the sensor is suitable for operations in harsh environments and rough installation procedures. Four sensors of this kind have been successfully tested on a real scale dike at the Water Proof Holland facility in The Netherlands.

Wireless power transfer networks (WPTNs) are composed of dedicated energy transmitters (ETs) that charge energy receivers (ERs) via radio frequency waves. A safe-charging WPTN should keep electromagnetic radiation below predetermined limits meanwhile maximizing the transmitted power. In this paper, we consider this requirement as an optimization problem: the maximization of harvested power by ERs subject to the electro-magnetic safety constraints. In order to provide an approximated solution to this problem, we introduce a dual ascent-like distributed charging algorithm that enables ETs to work without global information and satisfy safety constraints asymptotically. We provide an in-depth theoretical analysis of our algorithm which is supported by numerical simulations.

The paper addresses the problem of multi-agent distributed solutions for a class of linear programming (LP) problems which include box constraints on the decision variables and inequality constraints. The major difference with existing literature on distributed solution of LP problems is that each agent is expected to compute only a single or few entries of the global minimizer vector, often referred as a partition-based optimization. This class of LP problems isrelevant in different applications such as optimal power transfer in remotely powered battery-less wireless sensor networks, minimum energy LED luminaries control in smart offices, and optimal temperature control in start buildings. Via a suitable approximation of the originalLP problem, we propose three different primal-dual distributed algorithms based on dual gradient ascent, on the methods of multipliers and on the Alternating Direction Methods of Multipliers.We discuss the computational and communication requirements of these methods and we provide numerical comparisons.