Administering Quality-Energy Trade-Off in IoT Sensing Applications by Means of Adapted Compressed Sensing

A common scheme to let a very large number of low-resources sensing units communicate their readings to a remote concentrator is to deploy intermediate hubs that collect subsets of readings by means of local communication and perform the needed long-range transmission of a compressed version of the data. We here propose to exploit compressed sensing (CS) as an extremely lightweight lossy compression stage for which it is easy to address the trade-off between the quality of the reconstructed signal and the energy needed to complete acquisition. Over the huge set of parameters characterizing the design space (such as the number of intermediate hubs and the sensors transmission range), we analyze such a trade-off when the placements of the hubs are not completely random but aim at promoting diversity between the subsets of readings considered by each hub. With respect to the case of no intermediate data aggregation, numerical evidence suggests that when an appropriate design strategy for the CS stage is adopted and diversity is promoted, an energy savings higher than 60% with high-quality signal reconstruction can be obtained. This operative point corresponds to 20 intermediate hubs deployed to collect reading from 128 sensors.