no code implementations • 15 May 2024 • Hiba Dakdouk, Mohamed Sana, Mattia Merluzzi
In recent years, the integration of communication and control systems has gained significant traction in various domains, ranging from autonomous vehicles to industrial automation and beyond.
no code implementations • 12 Feb 2024 • Emilio Calvanese Strinati, Paolo Di Lorenzo, Vincenzo Sciancalepore, Adnan Aijaz, Marios Kountouris, Deniz Gündüz, Petar Popovski, Mohamed Sana, Photios A. Stavrou, Beatriz Soret, Nicola Cordeschi, Simone Scardapane, Mattia Merluzzi, Lanfranco Zanzi, Mauro Boldi Renato, Tony Quek, Nicola di Pietro, Olivier Forceville, Francesca Costanzo, Peizheng Li
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents.
no code implementations • 6 Dec 2023 • Francesco Binucci, Mattia Merluzzi, Paolo Banelli, Emilio Calvanese Strinati, Paolo Di Lorenzo
In this work, we explore the opportunity of DNN splitting at the edge of 6G wireless networks to enable low energy cooperative inference with target delay and accuracy with a goal-oriented perspective.
no code implementations • 21 Oct 2023 • Paolo Di Lorenzo, Mattia Merluzzi, Francesco Binucci, Claudio Battiloro, Paolo Banelli, Emilio Calvanese Strinati, Sergio Barbarossa
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data.
no code implementations • 25 Aug 2023 • Mattia Merluzzi, Miltiadis C. Filippou, Leonardo Gomes Baltar, Markus D. Muek, Emilio Calvanese Strinati
Specifically, we address the scenario of an eMBB service, i. e., a user uploading a video stream, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task.
no code implementations • 18 May 2023 • Kyriakos Stylianopoulos, Mattia Merluzzi, Paolo Di Lorenzo, George C. Alexandropoulos
In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs).
no code implementations • 10 Nov 2022 • Fatima Ezzahra Airod, Mattia Merluzzi, Antonio Clemente, Emilio Calvanese Strinati
In line with this vision, this paper proposes an online adaptive method to mitigate the EMFE under end-to-end delay constraints of a computation offloading service, in the context of RIS and multi-access edge computing (MEC)-aided wireless networks.
no code implementations • 27 Apr 2022 • Mattia Merluzzi, Serge Bories, Emilio Calvanese Strinati
To the best of our knowledge, this is the first work addressing the problem of energy and exposure aware computation offloading.
no code implementations • 21 Apr 2022 • Mattia Merluzzi, Claudio Battiloro, Paolo Di Lorenzo, Emilio Calvanese Strinati
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e. g. sensors), possibly pre-processed (e. g. data compression), and finally processed remotely to output the result of training and/or inference phases.
no code implementations • 20 Apr 2022 • Mattia Merluzzi, Miltiadis C. Filippou, Leonardo Gomes Baltar, Emilio Calvanese Strinati
Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.
no code implementations • 21 Dec 2021 • Paolo Di Lorenzo, Mattia Merluzzi, Emilio Calvanese Strinati, Sergio Barbarossa
In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs).
no code implementations • 31 Mar 2021 • Mohamed Sana, Mattia Merluzzi, Nicola di Pietro, Emilio Calvanese Strinati
Then, based on Lyapunov stochastic optimization tools, we decouple the formulated problem into a CPU scheduling problem and a radio resource allocation problem to be solved in a per-slot basis.
no code implementations • 8 Aug 2020 • Mattia Merluzzi, Nicola di Pietro, Paolo Di Lorenzo, Emilio Calvanese Strinati, Sergio Barbarossa
We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks.