Design of an adaptive framework with compressive sensing for spatial data in wireless sensor networks

Wireless Sensor Networks (WSNs) gather active sensor data within a specified period to the sink node. The data transmission in restricted resource utilization in wireless surroundings is a primary issue. Compressive sensing enables resource utilization based on spatial data by exploiting the transfer of limited measurements according to the original signals. In this paper, an Adaptive Adjacent based Compressive Sensing (AACS) methodology is proposed for effective data construction in spatial-related wireless sensor networks. A sparse Matrix is constructed with the coordinates of location and position for data transmission. The fuzzy logic is used to find the best forwarder among the sensor nodes in the network using the parameters of Mobility, Energy, and Fuzzy cost. Within a sensing time, the sensor node forwards the data around the time to the adjacent node according to a correlation. The communication time provides proficient enhancement with compressed data with AACS and sparse index. Therefore, AACS gives a reduced amount of communication and maximizes accuracy. AACS is compared with the related techniques and the results prove that the proposed methodology performed well in the performance metrics. The performance analysis shows that the proposed technique has produced 54.7% of network throughput than the relevant technique, 76.9% lesser routing overhead, and 44% of minimized relative error.