A Trustworthy Data Aggregation Model Based on Context and Data Density Correlation Degree

Abstract

Data aggregation is widely used in wireless sensor networks (WSNs) due to the resource constraints of computational capability, energy and bandwidth. Because WSNs are often deployed in an unattended hostile environment, WSNs are prone to various attacks. The traditional security technologies such as privacy protection and encryption technology can not address the attacks from the internal nodes of network. Therefore, the trust management mechanism for data aggregation has become a hot research topic, and an efficient trust management mechanism plays an important role in data aggregation. In this paper, we propose an efficient trust model based on context and data density correlation degree. Our proposed trust model consists of three major contexts, sensing trust, link trust, node trust. We take into full account data aggregating characteristic and different impacts of node trust, link trust and sensing trust on the secure of data aggregation. We also take into account data correlation degree in computing sensing trust, which leads to more accurate trust result. The experiment results show that compared to the existing trust models our proposed trust model provides more accurate sensing trust and improves the throughput and robustness against malicious attacks. Our proposed trust model is more suitable for data aggregation than conventional trust models.