Ricerc@Sapienza

To cope with climate change energy sustainability is considered the key target EU Countries set to achieve by 2020. Existing energy users such as building stock require huge efforts to be aligned to this goal. This is the case of the University of Rome “La Sapienza” campus since it was built even before first regulations on energy saving, i.e. Italian Law 10/91. Current energy classification assigns to them the energy efficiency class G, far from class A, the energy label to be achieved by 2020.

Modern architecture built in historical urban contexts represents a demanding issue when its energy efficiency should be improved. Indeed, the strongest efforts have to be made to maintain the architectural identity and its harmony with the surrounding cultural heritage. This study deals with the main building of the School of Architecture Valle Giulia in Rome, designed by Enrico Del Debbio in the 30’s. Further constraints are related to several interventions of airspace expansion starting from 1958 which involved the building starting from 1958.

In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy.

In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them convenient for energy-constrained applications such as IoT, embedded devices, or Fog computing. However, designing an optimized early-exit strategy is a difficult task, generally requiring a large amount of manual fine-tuning.

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures.

Our goal in this paper is to devise a strategy for finding the optimal trade-off between the transport and caching energy costs associated to the delivery of contents in information networks. The proposed strategy is proactive with respect to the users' requests, as contents are pre-fetched depending on the distribution of their (estimated) popularity. In particular, we propose a k-center dominating set strategy to find the optimal clustering and then locate the best places to store/replicate the most popular contents.

This paper proposes a novel algorithmic solution for dynamic computation offloading, aimed at reducing the energy consumption of a mobile network endowed with multi-access edge computing. The dynamic evolution of the system is modeled through three queues: a local queue at the user side, a computation queue at the edge server, and a queue of results at the network access point. The optimization problem is cast as the minimization of the long-term average energy consumption of the whole system, comprising user devices, servers, and access points.

The incoming era of the fifth-generation fog computing-supported radio access networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of virtual machines (VMs) toward nearby fog data centers. For this purpose, the bandwidths of the multiple wireless network interface cards of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol.

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs).

This paper presents the project and the economic and environmental evaluation of a ground source heat pump system implemented in a sport centre located in Sora (Italy). The first step of the work has been the estimation and the analysis of the energy consumption of the building, using the energy bills to validate the results. In order to improve the energy system efficiency, three different solution have been proposed and analysed. The first option consists of a geothermal plant and a solar panel plant.