STCS-GAF: Spatio-Temporal Compressive Sensing in Wireless Sensor Networks- A GAF-Based Approach

Routing and data aggregation are two important techniques for reducing communication cost of wireless sensor networks (WSNs). To minimize communication cost, routing methods can be merged with data aggregation techniques. Compressive sensing (CS) is one of the effective techniques for aggregating network data, which can reduce the cost of communication by reducing the amount of routed data to the sink. Spatio-temporal CS (STCS), with the use of spatial and temporal correlation of sensor readings, can increase the compression rate in WSNs, thereby reducing the cost of communication. In this paper, a new method of STCS technique based on the geographic adaptive fidelity (GAF) protocol is proposed which can effectively reduce the communication cost and energy consumption in WSNs. In the proposed method, temporal data is obtained from random selection of temporal readings of cluster head (CH) sensors located in virtual cells in the clustered sensors area and spatial data will be formed from the data readings of CHs located on the routes. Accordingly, a new structure of sensing matrix will be created. The results show that the proposed method as compared to the method proposed in [29], which is the most similar method in the literature, reduces energy consumption in the range of 22% to 43% in various scenarios which were implemented based on the number of required measurements at the sink (M) and the number of measurements in the routes (m_r).