Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness.
Mangia, M., Pareschi, F., Varma, R., Rovatti, R., Kovačević, J., Setti, G. (2018). Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS. II, EXPRESS BRIEFS, 65(5), 682-686 [10.1109/TCSII.2018.2821241].
Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications
Mangia, Mauro;Rovatti, Riccardo;
2018
Abstract
Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
