In this paper, a sensor network is used to estimate the dynamic states of a system. At each time step, one (or multiple) sensors are available that can send its measured data to a central node, in which all of processing is done. We want to provide an optimal algorithm for scheduling sensor selection at every time step. Our goal is to select the appropriate sensor to reduce computations, optimize the energy consumption and enhance the network lifetime. To achieve this goal, we must reduce the error covariance. Three algorithms are used in this work: sliding window, thresholding and randomly chosen algorithms. Moreover, we will offer a new algorithm based on circular selection. Finally, a novel algorithm for selecting multiple sensors is proposed. Performance of the proposed algorithms is illustrated with numerical examples.

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.