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.

无线传感器网络（WSN）在不断发展的领域（例如无处不在的计算和环境智能）中做出了重要贡献。监视环境是WSN的重要应用。同样，固有的能量限制成为WSN应用中的瓶颈。但是，当进行数据传输时，诸如传感器和接收器之类的节点会吸收高功率。同样，在网络中管理大量数据，这同样会消耗更多能量。本文提出了一种新颖的架构，该架构采用块三对角矩阵（BDM）将聚类和压缩感测（CS）集成在一起。BDM是结合了压缩，数据预测和恢复以产生准确性并在使用群集WSN时提供有效数据处理的测量矩阵。理论分析构成了设计众多执行算法的基础。真实世界的数据用于仿真，并且所提出的结果表明，此处描述的框架为用于监视群集WSN中环境的应用程序提供了一种经济高效的解决方案。提议的IHCS可以达到70％的能源效率和93％的预测率。