Ricerc@Sapienza

We live in a world where billions of smart objects, such as sensors and smartphones, collect huge amount of data and are connected together over an ubiquitous Internet of things (IoT) to offer better services in different application sectors. Mining information from massive volumes of data, and embedding intelligent networking of such heterogeneous devices in critical infrastructures such as the power grid, the Internet, or transportation networks, will revolutionize the way our systems are managed, enabling proactive sensing and optimization mechanisms aimed at ensuring guaranteed performance. There is no doubt that large economic growth, along with improvements in the quality of our lives, hinge upon an efficient management and design of IoT.
In this project, we plan to empower emerging IoT applications by providing fundamental advancement in the field of distributed online nonconvex optimization. We aim to develop a new class of online network algorithms promoting an adaptive and scalable design of the IoT system, while optimizing the user experience in heterogeneous, nonstationary, dynamic environments. Building on these fundamental tools, we plan to introduce strong advancements in two important IoT emerging tasks: fog-enhanced computation offloading, and distributed online big data analytics. In the former case, we aim at optimizing the joint radio and computational resource allocation of the fog network in order to minimize the average latency needed for offloading, while serving and making stable all the IoT workloads. Finally, in the latter case, our goal is to build a scalable IoT system enabling big data analytics via online deep learning, where fog nodes interact with each other and with the cloud in order to perform distributed training of deep architectures.