State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Distributed control in virtualized networks

The increasing number of the Internet connected devices requires novel solutions to control the next generation network resources. The cooperation between the Software Defined Network (SDN) and the Network Function Virtualization (NFV) seems to be a promising technology paradigm. The bottleneck of current SDN/NFV implementations is the use of a centralized controller. In this paper, different scenarios to identify the pro and cons of a distributed control-plane were investigated. We implemented a prototypal framework to benchmark different centralized and distributed approaches. The test results have been critically analyzed and related considerations and recommendations have been reported. The outcome of our research influenced the control plane design of the following European R&D projects: PLATINO, FI-WARE and T-NOVA

Ancient Cartographies as a Basis for Geolocation Models in Public Space: The Case of Giambattista Nolli and its Heritage Application

In 1748, the architect and surveyor Giambattista Nolli mapped an abstract reality of the city of Rome. As a challenge to the inherited projections, it represented the city mixing streets, halls, corridors, churches, baths and markets as part of a unique public space network. A new way to design public space and rethink the whole urban system was opened by the possibility of containing in these representations a single layer with all kinds of public space (including the interior of public buildings). Despite this, Nolli's plan remained as a useless instrument since the hegemony of automobile mobility appeared as a pre-eminent system. This research tries to understand how the application of the ancient cartographies' methodology can improve the pedestrian mobility of historic cities by means of enhancing the graphic value of the system of Giambattista Nolli. Nowadays, free public space is represented as empty and built ones, as solid. This proposal would revert this reified conception of the city, understanding this baroque representation as an instrument of identification and assessment of the transitional heritage. The clues unveiled by Nolli seem to be able to integrate the plans of public buildings within the urban tissue, which would result in a step towards the full integration of cartography and mobility. The success of the comprehensive tools offered by large servers such as Alphabet inc. (Google) or Bing Maps confirm the suitability of the combination of new technologies and Big Data with urban planning, reaching the synchronisation of Smart Cities. Nowadays, open public space can be 'walked in' from any electronic device, consequently, the application of the "Nolli methodology" would implement the model of urban geolocation with the assimilation of inner public spaces. In the creation of a great global map of the public space, a chimaera could be intuited. This would be discussed within a tangible reality: every open public space is already housed in the Big Data and it is accessible through geolocation tools. The inclusion of the of the public buildings' interiors would contribute to develop a greater permeability between city and citizens. Furthermore, this representation would optimize pedestrian travel times and would be able to expand the geolocation system network as a documentary repository