Transmission-Efficient Grid-Based Synchronized Model for Routing in Wireless Sensor Networks Using Bayesian Compressive Sensing

The concept of compressed sensing in wireless sensor networks (WSNs) involves the existence of only a small number of compressible signals that possess adequate information for the retrieval of the initial sensed data. While compressed sensing (CS) has been recognized as a valuable framework for enhancing the performance of wireless sensor networks (WSNs), further enhancements are still required to improve the effectiveness of data aggregation at the sink node. This work presents a framework named grid-based synchronized routing using Bayesian compressive sensing (GSR-BCS) for the purpose of enhancing transmission efficiency. The field of grid-based synchronized routing investigates the correlation between various forms of sensed data, such as pressure, fluid, and temperature, within a network. In addition, it explores the impact of different grid sizes on this relationship. The identification of an ideal grid size based on sensed data plays a crucial role in enhancing transmission efficiency in terms of data size or transmission rate. This, in turn, leads to an extension of the lifespan of the network. The application of grid-based synchronized route Bayesian compressive sensing is utilized to achieve efficient data aggregation at the sink. The primary objective is to enhance the data aggregation rate, also known as accuracy, by mitigating ambiguity through the implementation of Bayesian compressed data aggregation. In this work, the Bayesian compressive grid approximation method is introduced as a promising approach for reducing the number of transmissions and minimizing transmission time. Simulations are additionally performed to corroborate the theoretical findings across several scenarios utilizing the MATLAB/SIMULINK software. Based on the simulated results produced, it can be observed that the suggested framework exhibits a noteworthy enhancement in transmission efficiency across diverse scenarios, as evidenced by improvements in data size, accuracy, and transmission time. Upon experimental analysis, it has been determined that the GSR-BCS framework has the capability to enhance data aggregation accuracy by 16.93% and extend network lifetime by 22.9% when compared to existing state-of-the-art methodologies.