A novel approach of hierarchical compressive sensing in wireless sensor network using block tri-diagonal matrix clustering

Abstract

Wireless Sensor Network (WSN) delivers an important contribution in evolving fields for example ubiquitous computing and ambient intelligence. Monitoring environment is the vital applications of WSN’s. Likewise, inherent energy restriction becomes a bottleneck for applications in WSNs. However, the node such as the sensor and receiver ingests high power when proceeding the data transmission. Also, vast data are managed in network which similarly consumes more energy. A novel architecture is being proposed in this paper which integrates clustering and compressive sensing (CS) by employing Block Tri-Diagonal Matrices (BDM). BDMs are measurement matrices which combine compression, data prediction, and recovery to produce accuracy and provide efficient data processing while using clustered WSNs. Theoretical analysis formed the basis to design numerous algorithms for execution. Real world data were used for simulation and the proposed results revealed that the framework described here provides a cost effective solution for applications that used to monitor environment in clustered WSN. The proposed IHCS achieves 70% energy efficiency and 93% prediction rate.