Internet of things (IoT) devices are typically resource-constrained, characterized by low power, small bandwidth, and limited processing capabilities. Spatial modulation (SM) offers a promising solution to the demands of up-and-coming wireless networks, which can be beneficial for IoT devices. Detection of SM signals can be considered a process of sparse reconstruction, due to their inherent property of sparsity. Recently, compressive sensing (CS) has received considerable attention as a signal processing technique for efficient reconstruction of sparse signals. We present the generalized orthogonal matching pursuit (gOMP) detection as a low-complexity alternative for conventional CS-based detection in SM-MIMO IoT systems. We investigate the recovery and BER performance of the gOMP algorithm-based detector extensively for SM-MIMO systems with different configurations and compare with those of the conventional CS-based detector algorithms. Furthermore, we briefly review the recent work on applications of CS-based detection algorithms in SM-MIMO systems. Our simulation results demonstrate that the gOMP detector outperforms the conventional CS-based detection schemes considered, making it a promising choice for signal detection in SM-MIMO IoT systems.