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.

Rakeness-Based Compressed Sensing of Multiple-graph Signals for IoT Applications / Mangia, Mauro; Pareschi, Fabio; Varma, Rohan; Rovatti, Riccardo; Kovacevic, Jelena; Setti, Gianluca. - In: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS. II, EXPRESS BRIEFS. - ISSN 1549-7747. - STAMPA. - 65:5(2018), pp. 682-686. [10.1109/TCSII.2018.2821241]

Rakeness-Based Compressed Sensing of Multiple-graph Signals for IoT Applications

Pareschi, Fabio;Setti, Gianluca
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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2705280