Real-Time Networked Systems: Goal-Optimal Design and Performance Limits

In real-time networked systems (e.g., cyber-physical systems, Internet of Thigs), interconnected devices and sensors must deal with intermittent, sporadic, and random signal transmissions and receptions and must provide for selective information storage.

Furthermore, the limited resources of these devices imply the need for novel methods for signal reconstruction from incomplete data and highly selective content caching.

In this four-year project we propose a radical departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information for communications and control in networked systems.

We define semantics of information, not as the meaning of the messages, but as their significance, possibly within a real-time constraint, relative to the purpose of the data exchange.

We envision a joint optimization of the entire process of information generation, transmission and usage in unison by developing: 1) semantic metrics designed to capture the specific goal of the application;  2) an optimal sampling theory combining signal sparsity and semantics, for real-time prediction/reconstruction/control under communication constraints and delays; 3) techniques for decision making and inference directly in the “compressed domain”; 4) semantic-aware rules for updating the content stored in caches around the network to reduce the volume of data and the energy consumption, increasing the number of supportable devices.