TAGSCS: Trust aware data gathering technique based slicing and Compressive Sensing for IoT based WSN

Abstract

The proliferation of the Internet of Things (IoT) network presents formidable challenges, primarily centered around data transmission reduction and safeguarding devices against potential adversaries. To address these challenges, a synergistic approach involving Compressive Sensing (CS) and Data Slicing (DS) can be employed. CS offers the dual benefit of lightweight encryption and compression, resulting in enhanced energy efficiency. On the other hand, DS is a well-known means of guaranteeing sensing data privacy. In this work, we propose a technique, called TAGSCS, that integrates CS and DS to enable efficient data gathering while ensuring high security, privacy, and minimal utilization of energy. By leveraging the benefits of DS for privacy preservation and the CS method for encryption and compression, TAGSCS achieves optimal results. Additionally, the data-gathering process is designed to ensure the energy efficiency and trustworthiness of all the involved nodes. The findings from the simulation show the effectiveness of the TAGSCS technique in delivering superior performance over the alternative approaches. When considering a 0.2 probability of eavesdropping on the communication link, TAGSCS exhibited remarkable enhancements in upholding privacy compared to TDSM, HEEPP, EEHA, and SMART. It achieved privacy boosts of 88.9%, 85.9%, 70.4%, and 68.6% respectively. Moreover, TAGSCS demonstrated a significant extension in the network lifetime, surpassing the performance of TDSM, HEEPP, EEHA, and SMART by 80%, 110%, 122%, and 170% respectively.