Utilizing the sparsity of observations, compressive sensing (CS) is a promising technique to observe physical quantities in the Internet of Things (IoT). The existing CS-based IoT networks require the distributed nodes to realize the CS measurements before transmission, which leads to the heavy computation burden at nodes and the low network performance, especially in large-scale IoT networks. To tackle this problem, in this article, we propose a multicluster cooperative CS (CCS) scheme for large-scale IoT networks to observe physical quantities efficiently, which utilizes the cooperative observation and coherent transmission to realize CS measurement. Specifically, CCS-based IoT first utilizes the intracluster cooperation to observe physical quantities and then exploits the intercluster coherent transmission to obtain CS measurements. To evaluate the performance of CCS-based IoT, the upper bound of average recovery error is derived, which is closely associated with the node clustering and the power allocation. Therefore, under the total power constraint, we jointly optimize the node clustering and power allocation to improve recovery performance. With the optimized result, the cooperation gain of the CCS scheme relative to others is derived, which indicates that the CCS scheme is superior as long as total power exceeds a certain threshold. Furthermore, the exact and asymptotic outage probability and average throughput of CCS-based IoT are derived. Finally, simulation results illustrate the correctness of the theoretical analysis.

Energy-efficient designs play an important role in the Internet of Things (IoT) that monitors multiple phenomena, due to the limited power supply and complicated observation. In this paper, taking into account the power consumptions of observation, coding, and communication, we propose a joint sparse observation and coding scheme for energy-efficient monitoring of multiple phenomena using IoT. Through the analysis of outage performance, we find that the sparse observation and coding scheme can achieve the performance of the full observation scheme in which all nodes observe all phenomena with lower power consumption due to the dynamic and selective observation and coding. With the derived achievable rates and network power consumption, we study the trade-off between achievable rates and network power consumption that is determined by both the observation matrix and the coding matrix. For given rate constraints, we propose an optimization problem to minimize the network power consumption by jointly designing the observation and coding matrices. To solve this NP-hard problem efficiently, we propose a low-complexity algorithm with the convex-concave procedure. Moreover, to improve performance in high noise environment, we adopt collaboration among nodes to suppress observation noises and equalize bad observations by utilizing observation diversity. Finally, simulation results illustrate the superior performance of the proposed schemes.