Wireless Sensor network (WSN) is one of the most important components of the Internet of Things (IoT). IoT on the WSN layer, measures different parameters by different Sensors embedded in the Multi-Sensor nodes. The limitation of energy sources in the Sensor nodes is one of the most important challenges in exploiting WSNs. Routing and data aggregation are two basic methods to reduce the energy consumption in the WSNs. Compressive sensing (CS) is one of the most effective techniques for data aggregation in WSNs. The most studies related to the use of CS techniques to reduce communication cost in the network are based on the single-Sensor node WSNs. So, in this paper, we show that how CS techniques can be applied to the Multi-Sensor IoT-based WSNs. Given that the sparsity of the environmental data read by Multi-Sensor nodes is an important parameter for using the CS techniques in WSNs, various different scenarios have been analyzed from the point of view of data sparsity in this study, as well as transmission methods, and data aggregation techniques in a Multi-Sensor WSN. To evaluate the performance of the CS techniques in a Multi-Sensor WSN, all investigated scenarios are evaluated for two important techniques of CS named compressive data gathering (CDG) and hybrid compressive sensing (HCS), and in order to show the efficiency of the system in using of the CS, these techniques have been compared to the conventional Non-CS method. We show that the use of HCS technique has a considerable effect on reducing communication costs in a Multi-Sensor IoT-based WSN.