Green compressive sampling reconstruction in IoT networks

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks

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